To find out, I decided to run an anonymous pulse survey on LinkedIn. I wanted to see if the reality on the ground matched the official corporate narrative, or if we were looking at a massive hidden shift in how work gets done.

I have touched on these themes before in my previous guest article, The Shadow AI Economy, where I explored the hidden financial and operational implications of unsanctioned tech. This new data, however, brings the human element into even sharper focus.

As I explore in my book, Enhanced Leadership, true progress often happens in the quiet spaces where traditional management has not yet reached. We are currently witnessing a grassroots transformation that is both thrilling and, for some, a little bit daunting.

The Core Concepts of the AI Shift

To understand this phenomenon, we must look at three primary frameworks: the Governance Gap, the Transactional Mindset, and the transition from Control to Stewardship.

The Governance Gap represents the distance between employee adoption of new technology and the official policies of the organisation. When this gap grows too wide, ‘Shadow AI’ emerges. This refers to the use of unsanctioned tools to complete professional tasks.

The Transactional Mindset describes how employees prioritise immediate efficiency over abstract corporate risk. Finally, moving from Control to Stewardship involves leaders stopping the attempts to block usage and instead guiding their teams toward safe, ethical, and productive habits.

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A Deep Dive into the Internal Data

I chose an anonymous pulse survey format because it is the most effective way to get honest answers about sensitive topics like unsanctioned software use. I kept it brief, asking only a handful of targeted questions to minimise the time burden on respondents while ensuring their identities remained completely protected. By using a secure link that did not require a sign-in, I was able to gather candid feedback from 43 professionals across different sectors.

The results were eye-opening. The first thing that jumps out is the adoption rate; a staggering 90.7% of respondents said they are currently using AI tools in their roles. However, the gap between usage and governance is where the real story lies. While nearly everyone is using it, only 12.8% of those users could say for certain that the tools they were using were officially approved by their organisation.

The vast majority, around 70%, were not sure if their tools were sanctioned at all. Even more telling was the attitude toward that lack of approval. When asked if they cared whether the tools were officially approved, 59% of users simply said they did not care. In Enhanced Leadership, I write: ‘Leadership is not about the enforcement of rules for their own sake, but about creating an environment where the right path is also the most accessible path.’ Currently, the ‘right’ path for many employees is the one that allows them to finish their work by 5:00 PM, regardless of the software source.

The Global Context and Economic Impact

If you are a leader listening to this and feeling a bit of a cold sweat, you should know that my survey is not an outlier. It is a microcosm of a global shift. The 2024 Microsoft and LinkedIn Work Trend Index found that 78% of AI users are bringing their own tools to work. Their research suggests that employees are often reluctant to admit they use AI for their most important tasks because they fear it makes them appear replaceable.

In the UK specifically, a Microsoft study from late 2025 found that 71% of employees have used unapproved consumer AI tools at work. These workers are saving an average of nearly eight hours a week. To put a number on that, Microsoft estimates this contributes roughly £208 billion in value to the UK economy. This is a massive injection of productivity that is currently unmanaged and unmeasured.

However, there are significant risks to consider. Gartner, the global research and advisory firm, provides data-driven insights to senior business and technology leaders. Their research shows that 69% of organisations already have evidence of employees using prohibited public AI tools. They predict that by 2030, 40% of companies will suffer security or compliance incidents directly linked to this unauthorised usage. We are currently in a crisis of governance where the pace of employee adoption is simply outstripping the pace of leadership.

Practical Advice for Leaders

As leaders, we have a clear corporate responsibility. We are bound to ensure our staff are compliant with data laws and security protocols. We cannot simply look the other way while sensitive company data is fed into public models. If your staff are using unapproved tools, it is usually because you have a gap in your own provisions. They are responding to pressure for speed and quality.

My first piece of advice is to audit the gap. Instead of looking for people to catch, look for the ‘why’. If your team is using an unapproved AI to summarise meetings, it means your current software is failing them. Engage with them rather than indicting them. Start an open dialogue. Ask your team what tools are making their lives easier right now. You need to know what they are using so you can help them use those tools safely.

Next, you should shift the conversation from ‘no’ to ‘how’. Move away from telling people they are not allowed to use these tools and start showing them how to use them responsibly. This means setting clear acceptable use policies and providing approved tools that have privacy built-in. You might consider looking at frameworks like ISO 42001, which helps organisations establish a managed system for AI that emphasises ethical and responsible use.

Finally, create a safe sandbox for experimentation. Provide a list of vetted tools that satisfy corporate security but still offer the flexibility staff need. Treat shadow AI as a systems problem, not a discipline problem. When you provide a secure alternative that is just as easy to use as the public one, the risk of shadow usage drops significantly.

Practical Advice for Coaches

For those in the coaching profession, these findings offer a unique opportunity to support clients through a period of intense technological anxiety. Coaches should focus on the human element of the AI transition. Many employees use these tools in secret because they fear obsolescence. Your role is to help them see AI as an augmentative partner rather than a replacement.

Encourage your clients to be transparent with their leadership about the efficiencies they are finding. Help them frame these discoveries as ‘innovation pilots’ rather than policy violations. By coaching individuals to lead from the middle, you help bridge the gap between the executive suite and the front line. As I mention in Enhanced Leadership, ‘The coach’s primary duty is to shine a light on the shadows, making the invisible visible so that it can be managed with intention.’

Leading the Revolution

True leadership in the AI age is about stewardship. It is about creating a culture where innovation and responsibility live side by side. The AI revolution is here. It is not waiting for your next board meeting. It is happening right now, one prompt at a time.

I encourage you to take this data back to your teams. Use it as a conversation starter. If we can move past the fear of Shadow AI, we can unlock a level of productivity and creativity that was previously unimaginable. The question is: are you going to lead it, or just watch it happen from the sidelines?

To dive deeper into these concepts, I invite you to listen to my latest podcast episode where we break down these survey results in even more detail.

Until next time, keep leading, keep learning, and keep levelling up!

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By Lee Whitmore

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So I did something about it. I put in some hard yards doing proper research. That process started, somewhat inevitably, with AI-assisted research tools, but I was deliberate about how I used them. I specifically prompted for breadth rather than confirmation, asked for counter-views, and made a point of verifying sources against peer-reviewed articles and credible institutional reports. I then loaded the material into Google NotebookLM to build a structured foundation before going deeper. And go deeper I did: several full articles read in their entirety, hours of substantive content consumed. I had not planned to go that far. But each layer I peeled away made me more concerned, and that concern made it feel more important to get this right. Intellectual honesty has a momentum of its own once you let it start moving.

The moment that crystallised everything came from an unexpected direction. I had asked NotebookLM to produce an audio summary of the research, specifically framed for “presenting at a team meeting”, something accessible that would help a non-specialist audience engage with the material without being overwhelmed. The summary did its job. It covered the key findings clearly and progressively. But as it moved toward its conclusion, the tone shifted. The predictions about exponential growth in power and water consumption, if current trajectories continue unchecked, became increasingly stark. And then, at the very end, the AI-generated voice wrapped up with what could only be described as a sarcastic sign-off: ‘Good luck at your next team meeting.’

I laughed. And then I sat with it for a while. Because buried in that throwaway line was something pointed. The research, synthesised and reflected back through an AI tool, had essentially arrived at the same place I had: the scale of what is coming is genuinely difficult to communicate in a setting designed for forty-five-minute agendas and action points. The gap between the evidence and the average organisational conversation about AI is significant.

This article is my attempt to help close that gap, with honesty about the challenges and equal honesty about the reasons for genuine, evidence-based optimism.

I want to be clear about my own position going into this. I am not an AI sceptic. I use these tools daily, I help leaders think through how to adopt them, and I believe the productivity and creative potential they unlock is real. But I am increasingly convinced that enthusiasm without literacy is a liability, and that the most valuable thing I can do, both in my coaching work and in writing like this, is model what it looks like to engage with AI seriously rather than selectively.

What the Evidence Actually Shows

The Ethical Dimension

The most persistent myth in AI adoption is that these systems are neutral because they are mathematical. The reality, documented across sources including the USC Annenberg analysis of AI’s ethical dilemmas, comparative framework research across IEEE, EU, and OECD guidelines, and the Stanford HAI AI Index Report, is that AI systems inherit the biases of their training data, and that data reflects the inequalities of the world that produced it.

The consequences are not theoretical. In hiring, AI screening tools have systematically disadvantaged applications from women in technical roles. In lending, algorithmic credit-scoring has consistently produced worse outcomes for applicants from minority communities. In facial recognition, error rates for darker-skinned individuals run dramatically higher than for lighter-skinned ones, a disparity with serious implications when such systems inform identification decisions. These are documented patterns across multiple sectors and geographies, not edge cases.

This matters to me personally, not just academically. One of the core arguments I make in my work is that leadership is fundamentally about how power is exercised and who it serves. When we uncritically adopt AI systems that replicate and scale existing power imbalances, we are not being neutral. We are making a choice, and we should own that choice rather than hiding behind the word ‘technology’.

Alongside bias sits the problem of transparency. The most capable AI models tend to be the least interpretable. They produce outputs through processes that even their creators cannot fully explain, which creates a fundamental accountability problem when those outputs shape consequential decisions about health, finance, employment, or legal status. The EU’s AI Act and IEEE’s ethical framework both identify explicability as a core requirement for trustworthy AI, yet the tension between interpretability and performance remains live. Organisations are making real decisions in that gap, often without acknowledging that the gap exists.

Privacy sits at the third corner of this ethical triangle. AI’s requirement for data at scale means that personalisation and surveillance are, architecturally, very similar things. The Stanford HAI report notes that as AI capabilities expand, so does the surface area for manipulation and profiling, and that the individuals most affected are typically those with the least power to resist it. The T20 India policy brief on environmental and ethical AI challenges flags accountability as a governance gap of genuine urgency, arguing that liability frameworks need extending to cover AI-mediated harms with the same rigour applied to any other product or service.

What runs through all of these concerns is a structural observation drawn from the broader AI ethics scholarship: bias in AI is not a technical bug waiting for a patch. It is a structural problem rooted in who builds these systems, whose data is used, and whose interests are centred in the design process. Fixing it requires institutional change, not just algorithmic adjustment. That is harder, slower, and less comfortable than a software update. It is also the only approach that will actually work.

There is, however, real progress to note. The AIHub review of top AI ethics and policy issues from 2025 reports that AI safety infrastructure experienced rapid and meaningful growth last year. Safety, once discussed primarily in conceptual terms, has evolved into a structured engineering discipline, with independent third-party auditing processes and evaluation centres emerging as a genuine checks-and-balances layer. Leading AI laboratories including OpenAI, Google, Anthropic, and Alibaba adopted standardised benchmarks for assessing deception, persuasion, and planning. The UK proposed its AI Growth Lab, a sandbox for testing new models under real-world conditions. These are meaningful structural improvements, and they matter.

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The Environmental Dimension

If the ethical landscape is complicated, the environmental picture is in some respects more concrete, and as a result more difficult to sit with comfortably. The research is increasingly robust, even if the data disclosed voluntarily by AI companies remains frustratingly incomplete.

Training a single large AI model can emit hundreds of metric tons of CO2. The Cornell University roadmap on the environmental impact of AI data centres, published in late 2025, projects annual carbon emissions from AI infrastructure reaching 24 to 44 million metric tons by 2030. The Oeko Institut’s detailed study adds important nuance: inference, the ongoing act of running deployed models to generate outputs, now accounts for 80 to 90 per cent of AI’s operational energy use. Training happens once. Inference happens billions of times daily. The energy cost does not front-load and then plateau; it scales continuously with adoption.

Water consumption compounds this picture. Data centres require enormous volumes of water for cooling, and the siting of large AI infrastructure in regions already experiencing water stress creates compounding scarcity problems. The International Science Council’s report and the ArXiv paper on AI’s ecological impact both highlight that the hardware running these systems has a short operational lifespan relative to its manufacturing and materials cost, generating an e-waste problem that is rarely factored into AI’s environmental accounting.

The civil society report from February 2026 challenges one of the most commonly used rhetorical escape hatches for AI companies facing environmental scrutiny: the argument that AI’s footprint is justified, or offset, by its potential to help solve the climate crisis. This is not an entirely false argument. The LSE Grantham Institute’s study found that AI applied at scale in energy and transport could reduce global emissions by 3.2 to 5.4 billion tonnes annually, a figure large enough to take seriously. But potential future benefits do not cancel out present costs, and the civil society report is right to insist on that distinction.

The Journal of Environmental Management’s global assessment ‘The Two Tales of AI’ frames the duality well: in one scenario AI accelerates decarbonisation; in the other, efficiency gains are rapidly consumed by scale and rebound effects. Which story becomes dominant is not determined by the technology. It is determined by choices. That framing resonates with me strongly. In my work with leaders navigating major change programmes, the pattern is consistent: the technology rarely determines the outcome. The decisions made around the technology do.

The Counterbalance: Where Genuine Progress Is Happening

It would be intellectually dishonest to present only the challenges without acknowledging the genuine, evidence-based reasons for optimism, and there are several that deserve serious attention.

Efficiency gains in AI systems have been extraordinary. Nvidia’s research published in late 2025 found that energy efficiency in large language model inference has improved by a factor of 100,000 over the past ten years. That is not a marginal improvement. It means that the AI capabilities available today cost a fraction of what equivalent capabilities would have consumed a decade ago, and the trajectory continues. If this efficiency curve persists, the absolute energy cost of AI’s growth may be significantly lower than projections based on historical consumption patterns suggest.

The energy transition itself is being accelerated, in part, by AI’s own appetite. Renewables accounted for over 90 per cent of new utility-scale generating capacity in 2024, partly because they are faster to deploy and easier to scale, and the pressure from data centre demand is pushing utilities and investors to accelerate clean energy development. AI is already demonstrating real value in grid management: within power networks, AI improves forecasting and fault detection, reducing outages by 30 to 50 per cent and enabling better integration of renewable energy sources. AI-managed lighting, heating, and cooling in buildings could save 300 TWh annually, equivalent to the total electricity usage of Australia and New Zealand combined.

The Cornell roadmap, for all its sobering projections, also identifies a practical path forward: deploying advanced liquid cooling and improved server utilisation could reduce carbon emissions by a further 7 per cent and lower water use by up to 32 per cent at existing data centres. That is achievable with currently available technology and deliberate infrastructure investment, not a distant aspiration.

A study published in March 2026 found that while AI consumes electricity at the scale of a country like Iceland, its overall contribution to global emissions may be far smaller than the headline figures suggest, and that the more localised impacts around specific data centres are the more pressing and tractable concern. That reframing matters: it points the conversation toward targeted, solvable problems rather than undifferentiated alarm.

I find this genuinely encouraging, and not in a naive way. I have been in enough strategic change programmes to know that the problems that kill organisations are rarely the ones nobody saw coming. They are the ones people saw coming but felt too big to address incrementally. AI’s environmental and ethical challenges are large, but they have identifiable entry points, measurable indicators, and growing bodies of practice around mitigation. That is a different kind of problem from an intractable one.

Three Layers of Responsibility

The research points consistently to the same conclusion: the risks are real, the trajectory is not fixed, and the outcomes depend on choices made at multiple levels simultaneously. What follows is not a set of abstract principles but a practical framework, organised by where the levers actually sit.

Layer One: Global AI Infrastructure

The first layer sets the conditions within which everything else operates. AI infrastructure, meaning data centres, energy grids, chip supply chains, and training pipelines, is concentrated in relatively few hands and governed by a patchwork of national and voluntary frameworks that does not match the global scale of the technology.

The regulatory picture is uneven in ways that matter. The EU’s AI Act is the most comprehensive legislative attempt yet to codify requirements around transparency, accountability, and risk management. But regulation is jurisdictionally bounded and AI is not. Without binding international frameworks, countries that establish robust ethical standards risk simply displacing irresponsible AI development to jurisdictions with fewer constraints, rather than raising the global floor. Progress on multilateral coordination has been slow, and that slowness is itself a policy choice with consequences.

The Cornell roadmap and the Oeko Institut research both identify mandatory disclosure of energy and water consumption figures from AI operators as a high-leverage intervention, applying transparency standards already common in other high-impact sectors. Carbon pricing mechanisms that account for AI infrastructure’s footprint, alongside renewable energy procurement requirements that go beyond matching to actual clean operation, are identified across multiple reports as necessary systemic interventions. The OECD’s methodological work on measuring environmental impacts creates the foundation for this; what is lacking is regulatory will to make it compulsory.

The optimistic read at this level is that the direction of travel in several major jurisdictions is broadly correct. The UK’s AI Growth Lab, the EU’s enforcement of the AI Act, and the emergence of independent auditing infrastructure all represent structural improvements that were not in place five years ago. The policy scaffolding is being built. The question is whether it is being built quickly enough.

Layer Two: Organisational Decisions and Deployment

This is where most leaders reading this will find their most direct leverage. Organisations adopting AI are not passive recipients of the technology’s consequences. They are active participants in shaping them. The choices made about which tools to deploy, for what purposes, with what governance structures, and with what accountability to those affected, are consequential choices that aggregate into the overall picture.

The ethical frameworks reviewed across this research, whether IEEE, EU, or OECD, converge on a set of principles that translate directly into practice. Transparency means communicating clearly when AI systems are being used and how they affect decisions. Accountability means establishing clear lines of responsibility for AI-mediated outcomes and ensuring redress mechanisms exist when those outcomes cause harm. Proportionality means matching the capability and opacity of an AI system to the stakes of its application. Human oversight means maintaining genuine human judgement in high-stakes decisions rather than treating AI outputs as final.

I speak to leaders across a range of industries, and the pattern I see most consistently is not malicious intent. It is governance that lags behind adoption. Organisations move fast to implement AI tools because the competitive pressure to do so is real, and the ethical and environmental review structures simply have not kept pace. That is a fixable problem, and the organisations that fix it early build something genuinely durable: the credibility that comes from being able to demonstrate that their AI use is both effective and accountable.

Build ethical review into early stages of AI procurement and development cycles, not as an afterthought audit at the point of deployment. The Stanford HAI report and the USC Annenberg analysis both note that organisations embedding ethical review early produce materially better outcomes than those treating it as a compliance checkbox completed after the fact.

Ask AI vendors directly for energy and water consumption data for the services being purchased. This data exists. Many vendors simply do not publish it unless asked. Factoring it into procurement decisions applies the same supply chain responsibility that organisations increasingly apply to other resource categories.

Include AI energy consumption in existing carbon accounting and sustainability reporting frameworks. This is methodologically feasible today, using the OECD’s measurement guidance as a basis, and it closes the gap between an organisation’s stated sustainability commitments and its actual AI-related footprint.

Invest in AI literacy across leadership teams, not just technical fluency but genuine critical capacity: understanding failure modes, limitations, and the structural contexts in which these systems operate. The AIHub ethics review identifies institutional literacy as increasingly central to ethical deployment, and the responsibility to build it sits with organisations, not individual users.

Smaller, purpose-built models for specific organisational tasks use a fraction of the energy of large generalised systems. Where the use case allows it, choosing the efficient tool rather than the most impressive one is a meaningful and immediately actionable environmental decision.

Layer Three: Individual Personal Responsibility

The third layer is the one most easily underestimated, and it is where this article’s personal opening becomes most relevant. The individual choices made by professionals engaging with AI, about how they use it, how they talk about it, and how critically they engage with its outputs, matter more than is commonly acknowledged.

This is, honestly, where my own journey with this research lands. My discomfort with floundering in anecdotal arguments was not just about personal pride. It was about recognising that the conversations leaders have about AI, the way they frame it for their teams, the questions they do or do not ask, shape organisational culture around the technology. If those conversations are evidence-free, the decisions that follow them will be too.

The most practical starting point is developing genuine AI literacy rather than operational fluency alone. Knowing how to prompt a model is a useful skill. Understanding how training data affects outputs, what the limitations of a given system are, and when a tool is being used outside the domain where it performs reliably is a more important skill. The gap between those two states is largely a matter of deliberate effort.

Choose AI tools from providers who publish credible environmental and ethical accountability data. Where that data is not published, ask for it. That question, asked often enough, becomes a market signal. Be deliberate about when not to use AI. The AIHub review highlights a growing recognition that declining to deploy AI in certain contexts is a legitimate and sometimes correct ethical choice, not a failure to modernise. Building personal judgement about those boundaries is itself a form of literacy.

Bring evidence into team conversations rather than anecdote and assumption. Sharing credible sources, raising inconvenient questions, and being willing to sit with complexity rather than defaulting to comfortable narratives is a contribution any individual can make regardless of their seniority or technical background.

Pay attention to the efficiency of your own AI usage. Running repeated large queries for tasks that a simpler tool could handle has a real, if small, energy cost that accumulates across millions of users. Developing habits of proportionate tool selection is a modest act with aggregate significance.

Quick Wins: Where to Start This Week

The research can feel enormous in scope, but the entry points are practical and immediate. None of the following requires budget approval, a board decision, or a technical background. They are simply the actions of someone choosing to engage with this topic seriously.

For individuals:

Ask your AI tool of choice who made it, what energy it runs on, and whether the provider publishes environmental accountability data. The act of asking matters, even if the answer is incomplete. It builds your own literacy and sends a signal to the market.

Before defaulting to a large, high-powered AI model for a task, spend thirty seconds asking whether a simpler tool would do the job. Proportionate tool selection is one of the most immediate and overlooked ways individuals can reduce their personal AI footprint.

Read one piece of primary research on AI ethics or environmental impact, not a summary, not a social media post, but a full article or institutional report. The gap between what the evidence says and what most people believe it says is significant. Closing that gap personally is the first step to closing it in the rooms you operate in.

For teams and organisations:

Put ‘AI ethics and environmental impact’ on the agenda of your next strategy or leadership team meeting, not as a risk item to be managed but as a genuine conversation about values and governance. The AIHub ethics review from 2026 identifies the absence of structured organisational conversation as one of the primary reasons ethical AI deployment lags behind AI adoption.

Audit your current AI tool stack against two questions: does each tool serve a clear purpose proportionate to its cost, and does the vendor provide any transparency on energy and environmental data? You do not need to act immediately on the answers, but having them changes the quality of your next procurement decision.

Identify one AI use case in your organisation where a smaller, purpose-built model could replace a large generalised one. The energy saving at individual level is modest; the cultural signal of making that choice deliberately is considerable.

For leaders with broader influence:

Raise the question of mandatory environmental disclosure with any AI vendor you have a relationship with. The OECD’s measurement framework already provides the methodology; what is missing is the expectation from buyers that vendors use it.

Support or sponsor AI literacy investment in your organisation that goes beyond tool training. The difference between a workforce that can use AI and one that can think critically about it is the difference between adoption and governance.

The Productive Tension: A Genuinely Optimistic Conclusion

The research surveyed here, spanning environmental impact studies from Cornell, the OECD, and the Oeko Institute, ethical analyses from USC Annenberg and Stanford HAI, policy briefs from T20 India, the civil society report on AI climate claims, and the LSE Grantham Institute’s dual-perspective work, does not resolve into a simple verdict. But the fuller picture, including the evidence on extraordinary efficiency gains, accelerating renewable energy investment, and maturing ethics infrastructure, points somewhere more hopeful than the headlines typically suggest.

The numbers that initially gave me pause, the projections on exponential power and water consumption, are real. But so is this: AI model energy efficiency has improved by a factor of 100,000 over the past decade. Renewables accounted for over 90 per cent of new generating capacity in 2024, partly driven by the urgency that AI’s own energy appetite has created. AI is already reducing grid outages by 30 to 50 per cent and enabling cleaner energy integration at scale. The same technology creating the problem is actively contributing to the solutions, provided the deployment decisions are made consciously.

The sarcastic sign-off from the NotebookLM audio summary deserved its laugh, but it is not the final word. The research, taken in full, suggests that the negative impacts of AI are real, currently underestimated by most organisations, and genuinely mitigable. Not eventually, not hypothetically: now, with currently available tools, frameworks, and decisions. The three layers of responsibility described in this article, global policy, organisational governance, and individual accountability, are all moving. Not fast enough, but in the right direction, and with real momentum building.

The gap between the evidence and the average team meeting conversation about AI remains significant. But it is a gap that closes one conversation at a time: one honest question asked, one piece of primary research read, one procurement decision made with environmental accountability in mind. The scale of the challenge is not a reason for paralysis. It is a reason to start with the quick wins available this week, and to build from there with the seriousness the evidence deserves.

The AI reckoning is real. So is the opportunity to get this right.

If you are new here, I am Lee Whitmore, a leadership coach, author, and host of the LevelUp Leadership podcast. If you want a deeper, practical guide to leading in this landscape, my book ‘Enhanced Leadership’ goes further into the tools, mindsets, and conversations that help you and your organisation adapt. You can find it here: https://mybook.to/EnhancedLeadership

Until next time, keep leading, keep learning, and keep levelling up!

LevelUp

Get the weekly briefing that helps you navigate organisational chaos with clarity. From mastering difficult conversations to adopting AI without losing the human touch, LevelUp is your weekly guide to leading with confidence.

By Lee Whitmore

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References

1. Cornell University - “’Roadmap’ shows environmental impact of AI data center boom” (Nov 2025)
   https://news.cornell.edu/stories/2025/11/roadmap-shows-environmental-impact-ai-data-center-boom​

2. Oeko Institut - “Environmental Impacts of Artificial Intelligence” (Apr 2025)
   https://www.oeko.de/fileadmin/oekodoc/Report_KI_ENG.pdf​

3. International Science Council - “Considerations on environmental impact of AI in science” (Sep 2025)
   https://council.science/wp-content/uploads/2025/09/Considerations-on-the-environmental-impact-of-AI-in-science-FINAL.pdf​

4. ArXiv - “Assessing the Ecological Impact of AI” (Jul 2025)
   https://arxiv.org/pdf/2507.21102.pdf​

5. OECD - “Measuring environmental impacts of artificial intelligence compute and applications” (Nov 2022)
   PDF: https://www.oecd-ilibrary.org/docserver/7ba4ec34-en.pdf

6. T20 India Policy Brief - “Environmental and Ethical Challenges of AI” (Jul 2023)
   https://t20ind.org/wp-content/uploads/2023/07/T20_PolicyBrief_TF7_Environmental-Ethical-AI.pdf​

7. Civil Society Report - “AI climate benefits overstated” (Feb 2026)
   https://dig.watch/updates/ai-climate-benefits-civil-society-report​

8. LSE Grantham Institute - Climate studies (2025)
   https://www.lse.ac.uk/granthaminstitute/

9. Journal of Environmental Management - “The Two Tales of AI” (Sep 2025)
   https://www.sciencedirect.com/science/article/abs/pii/S0301479725027896​

10. Stanford HAI - AI Index Report Chapter 3​
    https://hai.stanford.edu/assets/files/hai_ai-index-report-2025_chapter3_final.pdf

11. Google - Responsible AI Progress Report (Feb 2025)​
    https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf

12. USC Annenberg - “Ethical Dilemmas of AI”​
    https://annenberg.usc.edu/research/center-public-relations/usc-annenberg-relevance-report/ethical-dilemmas-ai

13. Zendata - “AI Ethics 101”​
    https://www.zendata.dev/post/ai-ethics-101

14. AIHub - “Top AI Ethics Issues 2025” (Mar 2026)​
    https://aihub.org/2026/03/04/top-ai-ethics-and-policy-issues-of-2025-and-what-to-expect-in-2026/

15. Nvidia - “AI Energy Innovation”​
    https://blogs.nvidia.com/blog/ai-energy-innovation-climate-research/

16. Optera Climate - “2026 AI Energy Predictions”​
    https://opteraclimate.com/2026-predictions-how-ai-will-impact-energy-use-and-climate-work/

17. ScienceDaily - “AI Energy Study” (Mar 2026)​
    https://www.sciencedaily.com/releases/2026/03/260318033103.htm

Here is a link to that article if you want to take a look:

Before we proceed, I want to clarify that this discussion is not sponsored. I am sharing my personal experience because these specific tools have made a genuine difference to my workflow. As an independent creator, I have always prioritised keeping costs to a minimum. I still firmly recommend that people start their journey with free Google Apps. They are robust, reliable, and more than sufficient for most requirements.

However, as my projects grew in complexity, I began to investigate whether certain paid tools were actually worth the investment. I did not jump straight into subscriptions; I started with free versions to see if they could handle the specific pressures of my work. Today, Todoist and Wispr Flow are amongst the very few applications I am prepared to pay for.

Core Concepts: Branching Complexity and Creative Friction

To understand the value of these tools, we must first identify the two primary hurdles facing any modern writer or creator: Branching Complexity and Creative Friction.

Branching Complexity occurs when a single creative project, such as a podcast episode or a long-form article, explodes into dozens of sub-tasks. It is no longer a simple matter of ‘writing’ or ‘recording’. Each piece of content now requires research, editing, formatting for various social platforms, and administrative scheduling.

Creative Friction is the resistance we feel when trying to move an idea from our mind onto the page. If the process of documentation is slow, or if it requires us to stop our flow to fix typos and formatting, the original spark of the idea often fades.

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Untangling the Creator’s Mountain with Todoist

I used to believe that a simple list was enough to manage my day. However, as my output increased, I realised that a linear list is insufficient for complex creative projects. This is where Todoist became a game-changer through its ability to handle branching actions. It allows you to create a main project and then branch out into dozens of neatly nested sub-tasks.

Use Case: The Multi-Platform Content Creator

If you are a YouTuber or a podcaster, you know that ‘uploading a video’ is actually the final step of a marathon. In Todoist, you can create a template for a single episode. This template branches into specific phases: ‘Pre-production’ (research, scripting, guest outreach), ‘Production’ (recording, lighting setup), and ‘Post-production’ (audio cleanup, colour grading, thumbnail design).

By nesting these tasks, you stop the process from feeling like an overwhelming mountain. You can see exactly where the bottlenecks are. Furthermore, Todoist allows for the tracking of durations. If you know that editing usually takes five hours, you can build in a ninety-minute buffer. This ensures that if a technical glitch occurs, your entire publishing schedule for the week does not collapse.

Use Case: The Long-form Writer and Novelist

For writers working on a book or a long-form series, the ‘to-do’ list can become chaotic. You can use Todoist to branch your manuscript into chapters. Each chapter becomes a sub-project with its own status: ‘Drafting’, ‘First Pass Edit’, ‘Fact Checking’, and ‘Final Polish’.

The real power for writers lies in the recurring deadline feature. You can set a recurring task to ‘Write 500 words’ every weekday morning. If you miss a day, the tool helps you reschedule without the guilt of a mounting pile of ‘overdue’ tasks. It provides a level of structural integrity to your writing habit that simple notes apps often lack.

Capturing Thought with Wispr Flow

The second tool I have integrated is Wispr Flow. Many of you will be familiar with standard voice-to-text features on your phone or computer. While functional, they can be frustrating. They often require you to speak like a robot to achieve any level of accuracy, and you usually spend more time fixing the errors than you would have spent typing.

Wispr Flow uses a layer of artificial intelligence to refine your speech as you talk. It seems to know what you mean, even if you stumble over a word or hesitate. It allows you to capture your thoughts at the speed of your voice without the ‘friction’ of typing.

Use Case: Overcoming the ‘Blank Page’ Syndrome

Every writer knows the dread of the blinking cursor on a white screen. Creative friction is at its highest during the first draft. With Wispr Flow, you can simply start talking. You can describe the scene you are trying to write or the argument you want to make in a newsletter.

Because the tool refines your speech and handles basic punctuation, you end up with a coherent ‘brain dump’ that is already eighty per cent of the way to a first draft. It shifts your work from ‘generating’ to ‘editing’, which is a much easier mental state to maintain. You can produce a thousand words of raw material in a ten-minute walk, which you can then refine at your desk later.

Use Case: Rapid Scripting and Hook Generation

For creators who need to write short-form scripts for TikTok or Instagram Reels, timing and ‘hooks’ are everything. Use Wispr Flow to record several versions of an opening line. Because the tool recognises structure, you can say ‘Option one’, ‘Option two’, and ‘Option three’, and it will produce a neat, bulleted list of your ideas.

This allows you to hear how your words sound out loud, which is essential for scripts. You can capture the natural rhythm of your speech, ensuring your scripts feel authentic and engaging rather than stiff and over-written.

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Practical Takeaways for Writers

If your primary work involves the written word, your goal is to protect your ‘deep work’ time.

• Externalise the admin: Use Todoist to store all the ‘non-writing’ tasks, such as chasing invoices or updating your website. This keeps your mental space clear for the actual narrative work.

• Dictate the ‘scaffolding’: Use Wispr Flow to talk through the outline of your piece before you sit down to write. Having a structured list of points waiting for you reduces the anxiety of starting.

• Build in creative buffers: Always schedule your ‘final edit’ at least twenty-four hours before your deadline. Use Todoist to remind you to step away from the text so you can return with fresh eyes.

Practical Takeaways for Creators

For those producing multimedia content, the challenge is managing the sheer volume of moving parts.

• Template your workflow: Do not reinvent the wheel for every video or podcast. Create a master ‘Project Template’ in Todoist that includes every minor step, from ‘Exporting’ to ‘Tagging’.

• Capture ideas on the move: Creators often get their best ideas when they are away from their desks. Use Wispr Flow to record these flashes of inspiration immediately. The AI cleanup ensures that even a wind-swept voice note becomes a professional note you can use.

• Track your energy, not just your time: Use the duration features in your task manager to identify which tasks drain you the most. If ‘Thumbnail Design’ always takes twice as long as you expect, it might be time to delegate that specific branch of your project.

Conclusion and Call to Action

I decided to keep both Todoist and Wispr Flow because they do not just ‘do tasks’; they give me back time and mental energy. They allow me to focus more on the ‘creation’ and less on the ‘admin’. By untangling complexity and reducing friction, these tools provide the infrastructure for a more sustainable creative life.

I would encourage you to test them for yourself. You do not need to take my word for it; see if they fit your specific way of working. Try them.

https://wisprflow.ai

https://get.todoist.io

https://get.descript.com

Thank you for joining me for this bit of ‘tech talk’. I hope it helps you find a bit more space in your day to create something wonderful.

Until next time, keep leading, keep learning, and keep levelling up!

LevelUp

Get the weekly briefing that helps you navigate organisational chaos with clarity. From mastering difficult conversations to adopting AI without losing the human touch, LevelUp is your weekly guide to leading with confidence.

By Lee Whitmore

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The article describes passive leadership as a pattern of inaction and disengagement. These are the leaders who withdraw from making decisions when decisions are needed. They avoid providing feedback when feedback would help someone grow. They fail to intervene in problems until those situations have already become critical and much harder to resolve.

What really caught my attention was her distinction between passive-avoidant leaders (who deflect responsibility entirely, telling their teams to just deal with it themselves) and passive-aggressive leaders (who handle issues indirectly, blame the messenger, or make decisions behind closed doors without explaining their reasoning). Both styles leave teams confused, unsupported, and increasingly disconnected.

The Scale of the Problem

Here’s where it gets uncomfortable. Research from the British Journal of Management found that between 33.5 and 61 per cent of Norwegian employees reported exposure to destructive leadership behaviour. The really sobering part? Laissez-faire leadership (the passive form) had the highest prevalence of all. More than one in five respondents reported experiencing at least one laissez-faire leadership behaviour quite often or very often during the previous six months.

The researchers conclude that destructive leadership behaviour is not a low base-rate phenomenon. In other words, this isn’t some rare outlier that only happens in dysfunctional organisations. It’s something that most subordinates will probably experience at some point during their working life.

When I read that statistic, it aligned uncomfortably well with what I hear from leaders and coaches around the world. The manager who never replies to requests on time. The executive who is permanently unavailable. The leader who is strategic in name only, because they never engage with the operational reality their team is facing.

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The Real Damage

Castrillon highlights five key patterns of damage that passive leadership creates.

First, confusion and role overload. Without clear direction, employees experience role ambiguity and role conflict. They’re left guessing what’s expected of them, and they often take on responsibilities that should have been clarified or delegated by leadership in the first place. This leads to chronic stress and burnout.

Second, unchecked toxic behaviour. When leaders fail to address incivility, bullying, or poor performance, it sends a message that such behaviour is acceptable. Over time, this absolutely destroys morale and engagement.

Third, erosion of institutional trust. Employees lose faith in leadership when they witness compounding problems that are left unaddressed. The trust that holds an organisation together simply evaporates.

Fourth, suppressed innovation. Passive leaders rarely coach or mentor their teams. Without active guidance and feedback, creative employees feel their autonomy is restricted, and their ability to tackle complex challenges gets suppressed.

Fifth, spillover effects. The damage doesn’t stay inside the organisation. Exhausted, unsupported employees reduce their engagement with customers and external stakeholders as a way to conserve their own dwindling resources.

That list could have been lifted directly from many of the coaching sessions I’ve run with burned-out middle managers and disillusioned high performers.

Why Good Leaders Drift Into Passivity

Castrillon offers a compassionate answer to why passive leadership happens. She writes that it rarely stems from who someone is as a person. Instead, it’s often a symptom of deeper organisational issues.

Bandwidth depletion is a significant factor. Leaders are stretched thin, overwhelmed by operational demands, and they simply don’t have the mental or emotional capacity left to lead effectively. Fear of confrontation is another. Many leaders were promoted because of their technical skills, not because they were trained to navigate difficult emotional conversations. The need for approval plays a role too. Some leaders avoid tough decisions because they want to be liked, and they worry that holding people accountable will damage relationships. Finally, there’s a lack of training. Many organisations promote people into leadership roles without giving them the skills they need to succeed.

In my book Enhanced Leadership, I describe what I call the synthetic impersonator leader. This is someone who appears polished and competent on the surface. They say the right things in meetings and follow the processes. But underneath, they’re essentially running on autopilot, following rules and habits without making conscious, values-aligned choices. Reading the article and diving into the research behind it, I realised that passive leadership is a key pathway into that state. You stop making deliberate decisions. You stop engaging with the discomfort of real leadership. And before you know it, you’re drifting rather than steering.

I’ve been there myself. In my early leadership roles, whenever things weren’t going well, my default reaction was to step in and do the work of my team rather than lead them through the difficulty. It felt like the right thing to do. I was helping. I was getting it done. But what I realised much later is that I was simply retreating to the technical work where I felt competent. My real job was to support the team through their difficulties, to allow performance to dip temporarily so they could learn and grow. That took me a long time to learn, and it was deeply uncomfortable.

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The AI Dimension

Here’s where things get particularly interesting for leaders navigating this era of rapid technological change. Research published in Frontiers in Psychology in 2021 explored passive leadership in digital roles. The authors define it as a style where leaders ignore their responsibilities and do not empower employees, and they do not deal with employee issues and workplace problems until it is too late.

The study found that passive leadership has a significant negative effect on how social media engagement editors interact with fans and followers. At the same time, it increases role overload and reduces employee resilience.

Two themes really jump out here for any modern leader. First, passive leadership directly reduces performance in roles that depend on real-time, emotionally demanding customer interaction. Second, there is a spillover effect beyond organisational boundaries. When employees are exhausted and unsupported, they dial back their engagement with customers to protect themselves. That means passive leadership inside your organisation leads to poorer service, damaged reputation, and lost opportunities outside your organisation.

If you push technology into your teams without attending to resilience and autonomy, and then you absent yourself as a leader, you are creating the perfect conditions for burnout and withdrawal. But if you stay engaged, if you design roles that balance clear outcomes with genuine discretion, if you invest in people’s emotional capacity to cope with change, then technology can be genuinely enabling rather than overwhelming.

From Passive to Present

So how do we move from passive to present?

Create space for leadership by delegating or eliminating low-value tasks. You cannot lead effectively if you are drowning in operational minutiae. In Enhanced Leadership, I write about automating the noise so we can focus on the work that only humans can do. AI offers immediate relief here. The 80/20 rule remains as relevant as ever. AI is the perfect tool to attack the 80 per cent of low-value, high-volume, repetitive work that consumes our time and drains our strategic energy.

Develop confrontation skills deliberately. This is not about becoming aggressive. It is about learning to have honest, respectful conversations about difficult topics. Many leaders were promoted because of their technical skills, not because they were trained to navigate difficult emotional conversations.

Establish clear feedback rhythms so that feedback becomes a regular part of how you work rather than a rare and uncomfortable event.

Reframe support as active engagement. This means not just being available but actively reaching out, checking in, and coaching.

Make decisions visible so that teams understand not just what was decided but why.

Address problems early before they escalate into crises.

For Leaders

The leaders I speak to who navigate AI, disruption, and uncertainty most effectively are not the ones with the most sophisticated tech stack. They are the ones who have consciously stepped away from passivity. They have made a deliberate choice to show up. They have the uncomfortable conversation. They make the contested decision. They stand between their people and the chaos rather than hoping someone else will handle it.

Reading Castrillon’s article and diving into the research by Aasland and the Frontiers team has only reinforced that conviction for me. Passive leadership is more common than we like to admit, and it is more harmful than we often see. The antidote is not more dashboards, more tools, or more slogans about empowerment. The antidote is the oldest work of leadership in the world: being present, being clear, and being willing to act.

If you’re a leader reading this, ask yourself honestly: where am I actively engaging? Where am I setting direction, coaching, giving feedback, making decisions visible? And where am I absent?

The patterns that research identifies suggest that even occasional, repeated avoidance is enough to place you into a destructive category from your team’s perspective. Addressing issues early prevents them from escalating into the kind of chronic patterns that destroy trust and well-being. This doesn’t mean being harsh or aggressive. It means being clear, honest, and timely. It means caring enough about people to tell them the truth.

For Coaches

If you’re a coach working with leaders, this research gives you powerful language and evidence to help your clients recognise passive patterns. One framework I use is to help leaders audit their presence, not just their diary. It is not enough to be in meetings all day. The question is: where are you actively making a difference?

I also encourage coaches to help leaders design for resilience and autonomy. If passive leadership depletes resources, then the job is to reverse that by design. The Frontiers findings on resilience and job autonomy are a powerful reminder that how you structure roles, decision rights, and support has a measurable impact on performance and well-being. This is especially true in digital work, where the boundaries between roles can blur and the pace of change can feel relentless.

Help your clients understand that confrontation and feedback are not one-off events but everyday habits. Castrillon’s focus on developing confrontation skills and establishing clear feedback rhythms is strongly supported by the evidence. In Enhanced Leadership, I emphasise that authentic leadership is the alignment between what you believe, what you say, and what you do. When leaders stop making deliberate decisions and stop engaging with discomfort, they begin to drift. And drift is the enemy of intentional leadership.

Until next time, keep leading, keep learning, and keep levelling up!

LevelUp

Get the weekly briefing that helps you navigate organisational chaos with clarity. From mastering difficult conversations to adopting AI without losing the human touch, LevelUp is your weekly guide to leading with confidence.

By Lee Whitmore

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If you know me, you will know that I am a self-professed non-tech end user. I have never written a single line of code in my life. Despite my enthusiasm for technological innovations, the word ‘API’ has always felt like something reserved for developers and software engineers. It was quite daunting to take this test on; however, I was curious to see if a normal human being could make sense of it. I am delighted to say that the experience was eye-opening, even if it came with a few hurdles.

The Power of Automation

The core concept behind an API, or Application Programming Interface, is to allow different pieces of software to talk to one another. In the context of Descript, this means you can perform complex editing tasks without even opening the main application window. It took a little bit of troubleshooting for me to figure out some of the finer details, but that was entirely down to my own learning curve and skill set. Once I had grasped the basics, the process became remarkably straightforward.

There were two primary functions that I tested. First, the API allowed me to upload files remotely. I could send audio and video files into the system, have them named correctly, and placed into specific sequences exactly where I wanted them. Second, through a subsequent prompt, the API handled all the repetitive, ‘heavy lifting’ aspects of editing. It automatically removed filler words, edited for clarity, and applied ‘Studio Sound’ to the audio. It essentially did everything I usually do manually before I begin the fine-tuning process.

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Troubleshooting the Tech

As a non-coder, I hit a few snags that highlighted the difference between being an end user and a tester. I really struggled with getting the right kind of links for the videos to be compatible. I could not find any way to get Google Drive to work, so in the end, I resurrected an old Dropbox account to overcome that obstacle. Once the hosting was settled, the process became much more efficient.

It is important to remember that these challenges were more to do with my own technical experience than the tool itself. Once I moved past the hosting issue, I could see the true potential of the tool. It is a fantastic development that promises to change how we think about production.

The Human Touch versus the Machine

Despite the success of the test, I have realised that I will not be using the API in my regular workflow. My current process is already very streamlined. I record directly into Descript or use SquadCast, which handles my uploads automatically. Therefore, the remote upload feature is somewhat redundant for me.

There is also a question of creative ‘vibe’. When I use ‘Underlord’ within the Descript app, I enjoy the ability to refine the results iteratively. I can tweak things until they feel exactly right. To use the API effectively, you need a perfect, reusable prompt from the start. This is not easy to get right on the first attempt. Because I only work on a small, steady stream of singular projects, I prefer the manual fine control that allows me to find the specific nuances that make a podcast feel personal.

The AI Credit Economy

A significant consideration for any creator is the ‘AI credit economy’. Applying a prompt via the API might burn through AI credits, which then requires replication or adjusting manually if the prompt was not quite right. It feels like a gamble when you cannot see the result in real-time. For a side passion project like mine, the risk of wasting credits on a prompt that does not hit the mark is a concern.

However, if there were a way of simplifying the video upload part and combining the initial routine edits into one single process, it would be a total game-changer. Imagine a ‘single signal’ workflow where you send a file and it returns to you cleaned, levelled, and ready for the final creative polish. That is the kind of innovation that would make even a manual-control enthusiast take notice.

Practical Applications for Content Creators

For those of you producing content on a regular basis, this experiment offers a glimpse into a more efficient future.

• Audit Your Production Workflow: Look for repetitive tasks that drain your creative energy. Currently, those blanket automated edits are the quickest part of my manual workflow, but for someone managing multiple shows, automating them via an API would save hours.

• Scale Without Burnout: If you are an agency managing dozens of creators, this API is a massive step forward. You can standardise naming conventions and apply uniform ‘Studio Sound’ settings across multiple projects simultaneously in the background.

• Bridge the Technical Gap: My experience shows that with a little troubleshooting, even daunting technology can be mastered. Do not let a lack of coding knowledge stop you from exploring tools that could eventually be the foundation for new ‘front-end’ apps that make our lives easier.

Final Thoughts and Recommendations

Descript continues to be one of the most innovative companies in the creative space. This beta phase is surely just the front runner for more to come. While it might not fit my personal, small-scale workflow today, I can see how it will support large-scale content creators and agencies in the near future. It is a massive step forward for the industry.

If you have not tried Descript yet, I cannot recommend it highly enough. It has transformed the way I produce content. You can sign up using my affiliate link here.

get.descript.com

Go ahead and test it for yourself. Whether you are a solo creator or leading a team, there is something in there that will make your life easier.

Until next time, keep leading, keep learning, and keep levelling up!

LevelUp

Get the weekly briefing that helps you navigate organisational chaos with clarity. From mastering difficult conversations to adopting AI without losing the human touch, LevelUp is your weekly guide to leading with confidence.

By Lee Whitmore

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In this latest newsletter, I want to share three connected ideas from that conversation: why mediation matters for leaders, what charity leadership can teach the rest of us about delegation and support, and how early, curious conversations can prevent conflicts from becoming crises.

What mediation really is (and why leaders should care)

Alice described mediation as an impartial, non‑judgemental process where a neutral third party creates a safe structure for people in conflict to hear each other and move towards agreement. The mediator does not solve the problem, they enable the people in the room to find a solution they can own.

That distinction matters. Too many leaders behave like arbitrators, stepping in to decide who is right, who is wrong and what will happen next. In mediation, the power sits with the parties, not with the facilitator. The value comes from:

• One‑to‑one conversations that help each person see their own role in the conflict.

• Carefully structured joint sessions where the focus is understanding, not winning.

• Agreements that are rooted in how people will communicate and behave, not just

In Enhanced Leadership, I talk about the leader’s job evolving “from doing to enabling, from direct action to intentional influence”. Mediation is a practical example of that philosophy. The leader’s role is to design spaces and processes where people can find their own way forward, rather than to act as the hero who fixes everything.

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Charity leadership, complexity and the art of delegation

Mediation Plus is a “small” Sussex charity in budget terms, not in complexity. One full‑time chief executive, nine part‑time staff, around 60 trained volunteers and hundreds of cases a year across neighbourhood, family, intergenerational and workplace disputes. That is a serious leadership challenge.

Alice talked candidly about wearing multiple hats: safeguarding, HR, IT, fundraising, partnership management and case oversight. In that environment, trying to hold everything is not noble, it is a risk to the organisation. Her turning point came when she realised that failing to delegate was starting to hold the charity back.

There were three elements I think every leader can learn from.

• Intentional delegation as a learned skill
  She described delegation as a muscle you have to build, not a personality trait you either have or lack. That resonates strongly with the way I frame development in Enhanced Leadership: continuous improvement requires deliberate practice, not occasional inspiration.

• Trust and honest feedback
  Delegation fails when leaders hand something over, dislike the result and silently take the work back. Alice has worked at naming clearly what is needed, offering feedback on how tasks are done and keeping the conversation open. That is classic “human in the loop” leadership: you stay present in the process without micromanaging every keystroke.

• Purposeful use of governance
  Mediation Plus draws heavily on the skills of its trustees, from safeguarding to data systems and employment law. In the book, I argue that governance should “set the direction and the outcomes, but then get out of the way of the detail”. Their model is a live example of that ethos: trustees as expert allies, not distant auditors.

For leaders in better‑resourced settings, charity leadership can act as a mirror. If a small Sussex charity can integrate volunteers, trustees, funders and multiple service lines, then large organisations have no excuse for clinging to control out of habit.

Early intervention and curious conversations at work

The workplace strand of Mediation Plus’s work felt painfully familiar. Many organisations only pick up the phone when conflict has escalated to sickness absence, formal grievances or the loss of good people. The pattern is similar every time: avoidance, silence, then formal process.

Alice’s challenge to leaders was simple. Use mediation principles much earlier. Bring in support when there is tension, not when there is a tribunal. And even before a formal mediation, line managers can do a huge amount by changing how they approach difficult conversations.

Three practical shifts stood out.

1. Name the discomfort
   Rather than pretending everything is fine, say what you notice: “This feels uncomfortable” or “I can sense there is tension here”. Labelling emotion often lowers the temperature and signals that the topic is safe to discuss.

2. Be genuinely curious
   Go into the conversation with the intention to listen, not to prosecute your case. Ask open questions, check your understanding and resist the urge to jump straight to solutions. In Enhanced Leadership I talk about “helping others to make sense of information, not simply offloading it onto them”. The same applies to relational conflict.

3. Model restorative communication
   Leaders who are calm, transparent and respectful under pressure set the tone for everyone else. That does not mean avoiding tough messages, it means delivering them in a way that keeps dignity intact. Over time, that becomes part of the culture.

When something feels like an awkward chat you would rather avoid, remember that the awkwardness is short‑term. The cost of avoidance is long‑term: entrenched positions, formal processes and broken trust.

Intergenerational mediation, safeguarding and psychological safety

The “Time to Talk” intergenerational project might be the most quietly powerful work Mediation Plus does. It brings together young people aged roughly 11 to 18 and their parents, carers or wider family when relationships are strained or breaking down.

Those conversations sit at the intersection of safeguarding, psychological safety and early intervention. When home does not feel safe, young people are far more vulnerable to risky relationships, antisocial behaviour and disengagement from education. Supporting communication in the family is preventative work across multiple systems.

There are clear lessons for organisational leadership.

• Psychological safety is infrastructure
  For teenagers, a safe, communicative home base is the platform that enables healthy risk‑taking elsewhere. For adults at work, a culture where they can speak up early, admit mistakes and ask for support plays the same role.

• Pace should match the most cautious person
  Intergenerational mediation moves at the pace of the slowest participant, often the young person, so that trust can build. In change programmes, we are often seduced by the enthusiasts and unintentionally leave the sceptics behind. Enhanced leadership requires us to notice who is least comfortable and design our pace accordingly.

• Teach people to “pretend the mediator is in the room”
  When families start to copy the structure and tone of mediated conversations at home, that is when change sticks. In organisations, our equivalent is equipping teams with simple frameworks: how to raise a concern, how to run a restorative meeting, how to separate facts from assumptions.

If your leadership legacy is a culture where people can hold tough conversations without needing an external mediator every time, that is a powerful achievement.

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Takeaways for leaders

If you are leading a team or organisation, some practical steps inspired by Mediation Plus and the principles in Enhanced Leadership:

• Define your role in conflict
  Decide where you are an arbitrator and where you are a facilitator. Then move more of your behaviour into the facilitative space.

• Treat delegation as development
  Identify one area where holding on is now a risk, not a reassurance. Hand it over with clarity, stay available for support and be explicit that you expect some learning bumps along the way.[

• Build mediation principles into your processes
  For example, insert an early “curious conversation” stage into grievance, capability or performance procedures before formal steps kick in.

• Invest in your own reflective practice
  As I write in the book, “authentic leadership is built on reflection … asking ‘When was I at my best today?’ and ‘When did I deviate from my purpose?’”. That habit is particularly vital when you are making judgement calls in emotionally charged situations.

Takeaways for coaches

For coaches working with leaders or teams, there is rich material here.

• Normalise conflict as data
  Help clients see conflict as information about unmet needs, misaligned expectations or unclear roles, not as a personal failure.

• Use mediation‑style contracting
  When two colleagues come to a joint coaching session, borrow from mediation: separate one‑to‑ones, clear ground rules and a structured joint conversation focused on understanding and shared commitments.

• Explore delegation beliefs
  Many coaching conversations circle back to an assumption that value equals personal graft. In Enhanced Leadership, I challenge the idea that “hard graft equals value” and encourage leaders to automate or delegate the “noise” so they can focus on human‑only work. Use that frame when you are exploring delegation resistance.

• Pay attention to support systems
  Encourage leaders in small or pressured organisations to build the kind of peer networks and trustee‑style support that Alice described. Leadership is far less lonely when you do not try to carry everything alone.

  ---

Closing thought and next steps

Mediation Plus demonstrates something profound. With a tiny budget, a web of volunteers and a clear sense of purpose, a local charity can reduce harm, preserve relationships and keep people connected across Sussex. For leaders and coaches, the challenge is to take those same principles and embed them in our daily practice.

If this has sparked ideas for your own context, I would encourage you to:

• Listen to the full Level Up Leadership conversation with Alice from Mediation Plus.

• Reflect on where conflict shows up in your world and what “pretending the mediator is in the room” might look like for you.

• Explore Enhanced Leadership for more on purpose, ethical decision‑making and human‑centred communication in complex, tech‑shaped environments.

You can find more resources, episodes and articles at levelupleadership.uk.

Until next time, keep leading, keep learning, and keep levelling up!

LevelUp

Get the weekly briefing that helps you navigate organisational chaos with clarity. From mastering difficult conversations to adopting AI without losing the human touch, LevelUp is your weekly guide to leading with confidence.

By Lee Whitmore

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Quick download instructions

Click on the imagres below for the free files (9 JPEGs & 9 clear PNGs).

How to use the overlays: step-by-step guide

Each of the nine templates covers a specific platform layout. Use the PNG version (transparent) for clean layering or JPEG (solid background) if you prefer high contrast. Here is how to apply them in Descript or similar editors.

1. Import the overlay: Drag the PNG/JPEG into your project as a new image layer on the topmost track.

2. Align and scale: Match it to your canvas resolution (e.g. 1080x1920 for vertical). Centre it precisely.

3. Lock the layer: Right-click the layer and lock it to avoid accidental moves.

4. Edit with confidence: Position faces, text and graphics outside marked danger zones (red/caption areas, button spots).

5. Export clean: Hide or delete the overlay track before rendering.

Platform-specific tips

• YouTube Shorts/standard vertical: Keep titles above the bottom 20% (captions) and avoid the top 10% (profile/swipes). Centre faces in the middle 60%.

• TikTok: Danger zones intensify at bottom 25% (comments/duet button) and sides (swipe gestures). Safe CTAs go mid-to-upper screen.

• LinkedIn video posts: Bottom 15% often holds reactions; top safe for logos. Frame speakers in the central third.

• Instagram Reels/feed: Captions claim bottom 20%; Stories add top bar. Use upper half for hooks, lower for text if minimal.

• Facebook Reels/Stories: Similar to Instagram but watch for wider comment bars at bottom 25%. Centre content vertically.

• Substack video embeds: Minimal UI but avoid bottom 10% for mobile play controls. Full bleed safe on desktop.

• Lemon8 vertical: Emerging platform; bottom 20% for likes/comments, top for follows. Mirror TikTok framing.

These guides adapt to slight UI changes, but check latest specs for precision.

I have produced short YouTube tutorials for these workflows. Check them out here:

https://www.youtube.com/playlist?list=PLcQ9fu4ilRmSJlPGbI826yaycuL7nuSeX

and subscribe to the YouTube channel for leadership, AI and disruption insights.

Free non-commercial licence for levelupleadership.uk subscribers

Released under Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0). Free for personal, non-monetised use. No client or paid work.

Commercial upgrade

If you intend to use these templates for paid work, client projects or revenue generation, please click the honesty button below and purchase a commercial licence for £9.

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JPEGs

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The Paradox of Choice: The Crockery Experiment

In 1998, the behavioural scientist Christopher Hsee published a study that challenged a fundamental pillar of classical economic theory: the principle of monotonicity. This is the idea that if you have a set of goods, adding any item with positive value should increase the total value of the set. Hsee’s experiment, ‘Less is better: When low-value options are valued more highly than high-value options’, revealed that our brains do not actually work this way.

Hsee presented participants with two different dinnerware sets. Set A, the larger set, contained 40 pieces. This included 24 pieces in good condition, along with 8 cups (2 were broken) and 8 saucers (7 were broken). In total, there were 31 intact pieces. Set B, the smaller set, contained 24 pieces, all of which were in perfect condition.

When participants viewed the sets side-by-side, they acted rationally and valued the larger set more highly. However, when they evaluated the sets in isolation, they were willing to pay significantly more for the smaller set. Even though the larger set had more usable items, the presence of the broken pieces acted as a ‘negative cue’ that dragged down the perceived value of the entire collection.

Evaluability and the Subconscious Average

The reason for this logical slip is the Evaluability Hypothesis. When we look at something in isolation, we find it difficult to assess its absolute value. Instead, we look for attributes that are easy to evaluate. In the crockery study, it is hard to know if 31 pieces of dinnerware is a ‘good’ amount for a specific price, but it is very easy to see if a set is ‘complete’ or ‘broken’.

Rather than adding the value of each piece together, the human brain performs a subconscious averaging of quality. We take the high quality of the intact plates and average it with the zero quality of the broken saucers. This drags the ‘average’ quality of the 40-piece set below that of the ‘perfect’ 24-piece set. For a leader, this serves as a critical warning: if you allow mediocre features, trivial rewards, or poorly articulated risks to enter your professional output, you are not adding value; you are contaminating it.

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Strategic Change and ‘Option Zero’

This psychological bias has a profound impact on how we manage organisational change. One of the most common reasons for the failure of new initiatives is status quo bias. When a leader presents a new strategy in isolation, the audience naturally moves into a state of ‘separate evaluation’. They look at the new plan and immediately spot the ‘broken pieces’, such as the implementation costs, the potential for disruption, or the risk of staff turnover.

Because the current way of doing things is not being evaluated at all, it serves as an invisible, ‘safe’ background. To overcome this, an effective leader must force a joint evaluation. You can do this by framing the current situation as a deliberate choice, which we might call Option Zero.

By making the deficits of the status quo explicit, you bring them into the light. If you are proposing a digital transformation, do not just list the costs of the technology. You must also list the ‘broken pieces’ of the current model: the rising cost of maintenance, the decline in customer engagement, and the increasing speed of competitors. When these two options are placed side-by-side, the brain shifts from averaging flaws to comparing relative value.

The Architecture of Reward and Communication

In the realm of pay and reward, the less-is-better effect manifests as a failure of averaging. Research by Gneezy and Rustichini (2000) suggests that offering small monetary rewards can actually decrease performance compared to offering no money at all. When you offer no reward, people work because of a social contract motivated by professional pride. As soon as you introduce a small monetary reward, you shift the relationship into a market contract.

If the reward is small, it acts like the broken saucers in Hsee’s study. A team that has worked late for a month will feel an ‘insult’ response if rewarded with a ten-pound coffee voucher. They average their immense effort against the trivial sum, and the perceived value of their contribution plummets.

This principle is a core theme in my book, Enhanced Leadership. I argue that the most effective leaders are those who understand that leadership is as much about subtraction as it is about addition. In the book, I discuss the need to ‘curate’ the environment by removing the low-value ‘clutter’ that dilutes our impact. Whether it is a pitch or a reward, it is always better to provide a ‘perfect’ small gesture than a ‘flawed’ large one.

The same logic applies to communication, known as the dilution effect. If you provide four strong arguments for a project and two weak, speculative ones, your audience will fixate on the weak points. Arguments do not add up; they average out. Precision always beats volume.

Actionable Takeaways for Leaders

To develop your leadership abilities, you must stop being a ‘collector’ of tasks and start being a ‘curator’ of value. Consider the following steps:

Audit for Broken Saucers: Look at your current projects or team processes. Are there low-value meetings or outdated reporting requirements that are dragging down the ‘average’ quality of your team’s day? Subtraction is often the fastest way to increase perceived value.

Frame the Cost of Inaction: When pitching a new idea, never let the status quo go unexamined. Create a slide for ‘Option Zero’ that details the specific, ‘broken’ elements of staying the same.

Reward with Quality over Quantity: If your budget for a team reward is small, avoid low-end versions of expensive categories. Do not buy a cheap tablet; buy a luxury version of a lower-cost item, such as a high-end notebook or a premium fountain pen. Excellence in a small category feels ‘whole’ and ‘intact’.

Reflective Questions for Leaders:

If I removed the bottom 20 per cent of my team’s current responsibilities, would the remaining 80 per cent be perceived as more valuable?

In my last major pitch, which two arguments were the weakest, and did they dilute my core message?

Actionable Takeaways for Coaches

In your practice, you can use these frameworks to help clients break through personal or professional plateaus:

Move to Joint Evaluation: If a client is resistant to a career move, they are likely stuck in ‘separate evaluation’ of the new role’s risks. Guide them to list the ‘broken pieces’ of their current role. Make the status quo a visible, competing option.

Identify ‘Insult’ Habits: Help clients identify small, low-value habits that might be devaluing their professional brand. Are they over-promising on minor tasks while under-delivering on major ones?

Practise Argument Subtraction: When a client is preparing for an interview or a board meeting, have them list their arguments. Then, challenge them to remove the bottom two. Help them see that a shorter, more robust case is harder to pick apart.

Reflective Questions for Coaches:

How can I help my client see their ‘status quo’ not as a safety net, but as a deliberate choice with its own set of failures?

Is my client trying to prove their value through volume of work, and how is that averaging down their perceived expertise?

Conclusion and Call to Action

The ‘Less-is-Better’ effect teaches us that human beings are not calculators; we are storytellers who look for consistency and completeness. When a leader allows ‘broken pieces’ to remain in their strategy, rewards, or communication, they risk devaluing their entire enterprise.

To be a truly enhanced leader, you must have the courage to subtract the mediocre to make room for the exceptional. I encourage you to audit your own leadership ‘set’ this week.

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By Lee Whitmore

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Level Up Leadership is a passion project in my spare time. I enjoy doing it, and I intend to keep these articles and podcasts free. However, the software and equipment I use isn’t free! So, if you are enjoying this content and would like to make a donation, you can do so by clicking this button. Thank you.

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Until next time, keep leading, keep learning, and keep levelling up!

The reality of modern leadership is that we are all content creators now. Whether you are leading a global change programme or coaching a startup founder, your ability to scale your insights is your greatest lever. In my book, Enhanced Leadership, I talk extensively about the ‘rewired’ approach to leadership. This means designing our processes from the ground up with technology as a foundation, rather than simply bolting tools onto old, inefficient ways of working. Running a multi-platform show solo is the ultimate test of this philosophy.

The Core Concepts: Text-Based Content Creation

Before we dive into the ‘how’, we must understand the ‘what’. The traditional video editing workflow is linear, technical, and, frankly, exhausting. It usually involves staring at complex waveforms and hunting for the exact millisecond where you tripped over a word. Descript flips this logic on its head by treating video and audio exactly like a Word document.

Whenever I write about a specific product, I always feel compelled to make it very clear but I'm not being sponsored! Just talking about tools that I use myself and I think others would benefit from.

The first core concept is Text-Based Editing. If you can delete a sentence in an email, you can edit a podcast. The software transcribes your recording instantly, and any change you make to the text is reflected in the audio and video. This is the ‘intuition’ I often rave about; it feels like the software finally speaks the language of a writer rather than a technician.

The second concept is AI-Driven Polish. This includes features like Studio Sound, which uses generative AI to make a home recording sound like it was captured in a professional booth, and Filler Word Removal, which identifies every ‘um’, ‘ah’, and ‘you know’ in a single click.

Finally, there is Multi-Platform Repurposing. This is the art of taking one high-quality hour of conversation and atomising it into dozens of assets for LinkedIn, YouTube, and Substack.

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Learning as I Go: The Intuition of the Workflow

When I first started, I was terrified of the ‘technical’ side of podcasting. I spent hours watching tutorials on EQ settings and compression, only to end up with audio that sounded thin and robotic. I felt like a fraud; here I was, an expert in leadership strategy, defeated by a digital slider.

The moment I switched to Descript, everything changed. I remember the first time I used the Remove Filler Words feature. I watched as hundreds of my nervous stammers vanished from the transcript, and the audio stayed perfectly smooth. It was a revelation. It allowed me to stop focusing on the mechanics of ‘fixing’ and start focusing on the ‘strategy’ of the message.

Of course, it has not all been plain sailing. I have had to learn as I go. For instance, I use a teleprompter to keep eye contact with my audience, but as a glasses wearer, I quickly discovered that the glare was making me look like a character from a sci-fi film. This is one area where I have had to be honest with myself. Currently, the software does not have a specific feature to remove lens glare or artificially correct eye contact.

However, because of the sheer pace of innovation I have seen from the team, and their skillful deployment of AI in other areas, I remain incredibly hopeful. I suspect it is only a matter of time before these technical hurdles are solved with a single click. In the meantime, I have learned to tilt my lights and adjust my seating, treating these small imperfections as part of the authentic ‘learning as I go’ process that I often coach others through.

My workflow is now a well-oiled machine. I record a guest interview via a remote recording platform, which feeds directly into Descript. From there, I spend about ninety minutes turning that raw material into a full podcast episode, duplicate versions optimised for the different requirements of various platforms, and several short-form clips. I am not an editor; I am a curator of ideas.

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Detailed Elaboration on the Workflow

To truly understand how this fits into a busy schedule, we can break down the specific tactics that save me over ten hours every single week:

• The Initial Cleanup: Once the file is uploaded, I run the Studio Sound tool. This is essential because, despite having a decent microphone, my home office is not always perfectly quiet. This tool removes the background hum and the distant noise of the street, leaving me with professional-grade audio.

• Highlighting the Magic: As I read through the transcript, I look for ‘Golden Nuggets’. These are the moments where a guest says something truly profound about leadership resilience or AI ethics. I simply highlight these sections and ‘Duplicate to New Composition’. Suddenly, I have the basis for five or six mini-clips or LinkedIn reels.

• The Overdub Safety Net: We all make mistakes. Sometimes a guest mispronounces a name or I stumble over a statistic. In the old days, that would mean a re-record. Now, I use Overdub. I have trained an AI version of my own voice, so I can simply type the correction into the script and the software generates the audio for me. It is indistinguishable from the original recording.

• Batch Exporting: Once the edit is done, I export everything in one go. I get the MP3 for Spotify, the MP4 for YouTube, and a clean markdown transcript for this very Substack.

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For Leaders: Scaling Your Presence

As a leader, your most valuable asset is your time. You might feel that you do not have the ‘bandwidth’ to create content, but I argue that you do not have the bandwidth not to. In Enhanced Leadership, I state that ‘the most effective leaders encourage a sense of agency in others’. By sharing your journey and your ‘learning as I go’ moments, you provide a roadmap for your team to do the same. Use these tools to document your thoughts on strategy or change. It creates a ‘digital twin’ of your leadership style that works for you even when you are off the clock.

For Coaches: Building a Practice with Leverage

For my fellow coaches, this is about visibility. We often live in the ‘referral trap’, where our next client depends entirely on word of mouth. By using a tool like Descript to turn your coaching insights into short-form video clips, you are effectively auditioning for thousands of potential clients at once. It allows you to maintain a ‘high-touch’ brand with a ‘low-friction’ production process. You can spend your evenings coaching rather than cutting video.

Conclusion and Call to Action

The transition from a ‘bolted-on’ approach to a ‘rewired’ approach is what separates modern leaders from those who are merely busy. By embracing intuitive tools like Descript, I have grown my skills, my audience, and my enjoyment!

It has allowed me to lead with my voice, not my technical skills.

I want you to experience this for yourself. Do not wait for a production team that may never come.

Sign Up For Descript

Start by recording a ten-minute leadership tip on your phone, and see how easy it is to transform it into a professional asset.

Your voice deserves to be heard across every platform. It is time to level up!

Until next time, keep leading, keep learning, and keep levelling up!

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By Lee Whitmore

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​The research, which was recently published in the Journal of Applied Psychology, provides a definitive answer. The differentiator is not technical skill, nor is it the quality of the prompts being used. Instead, it is a psychological trait known as metacognition. This is, quite simply, the ability to think about your own thinking. It involves the active planning, monitoring, and evaluation of your cognitive processes as you work. For those interested in the future of work, this is the most important insight of the year.

​A Deep Dive into the Research

​The study, led by Shuhua Sun and Jackson G. Lu, was remarkably rigorous. It utilised a field experiment involving 250 employees at a technology consulting firm, followed by controlled experiments with over 1,000 participants. This methodology is particularly robust because it combines real-world observation with the precision of a laboratory setting.

​The researchers discovered that generative AI only boosts creativity for individuals with high metacognition. These individuals use AI to acquire what the study calls cognitive job resources. Specifically, they use the technology to expand their knowledge base and free up mental capacity for more complex tasks.

​By contrast, employees with low metacognition tend to engage in passive consumption. They treat the AI as an oracle rather than a partner, accepting the first answer they receive without scrutiny. For these individuals, the AI actually does nothing to enhance their creativity. It may help them work faster, but it does not help them work better. This finding challenges the common assumption that AI is a 'rising tide that lifts all boats'. In reality, it appears that AI may actually widen the gap between strategic thinkers and those who merely follow instructions.

​Roles, Industries, and Personalities

​Certain environments and temperaments are naturally more conducive to this metacognitive approach.

​Industries and Roles

We see the greatest impact in knowledge-intensive industries such as software engineering, strategic consulting, and research and development. In these fields, the 'right' answer is rarely obvious. Success requires the synthesis of disparate ideas and the constant questioning of assumptions.

​Roles that involve design thinking or complex problem-solving lend themselves to metacognition because they require a high degree of cognitive flexibility. If you are an architect using AI to generate structural options, your value lies in your ability to evaluate those options against human needs and aesthetic principles. You are not just 'using' the tool; you are managing a complex feedback loop.

​Personality Types

The research suggests that individuals high in Conscientiousness and Openness to Experience are most likely to thrive. Conscientious people have the discipline to monitor their own work and check AI outputs for errors. Meanwhile, those who are open to experience are more willing to experiment with the strange, divergent ideas that AI often produces. Interestingly, a sense of authenticity is also a strong predictor: those who are true to their own values are more likely to use AI as a tool for self-expression rather than a shortcut to conformity.

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​Practical Applications: Actionable Advice

​If we want to close the 'creativity gap' in our organisations, we must move beyond simply providing access to tools. In my book, Enhanced Leadership, I argue that the primary responsibility of a modern leader is to develop the 'internal technology' of their team. We must help people master their own cognitive processes before we ask them to master an external algorithm.

​Here is how we can increase metacognitive capacity at three distinct levels:

​For Individuals

Individual growth starts with a 'strategic mindset'. This involves moving from a reflexive approach to a reflective one.

​The Reflection Loop: After receiving an AI response, ask yourself: 'What is the most obvious flaw in this answer?' and 'What perspective has been completely ignored?'.

​Metacognitive Questioning: Adopt the habit of asking strategy-eliciting questions during a task. 'Is there a way I could do this even better?' or 'What can I do to help myself right now?'.

​The Monitoring Pause: Set a timer for twenty minutes. Every time it rings, stop for sixty seconds to evaluate your progress. Are you getting closer to a creative breakthrough, or are you just refining a mediocre idea?

​Emerald Framework Application: Focus on metacognitive knowledge. This means understanding your own strengths and the specific demands of the task. Do not just start prompting; plan your strategy first.

​For Oversight and Management

Oversight must shift from checking the final output to evaluating the 'human-AI joint activity'.

​Prompt Transparency: Require team members to share not just their final work, but the sequence of prompts that led to it. This allows managers to identify where the thinking process might have stalled.

​Peer Review of Thinking: In project reviews, ask team members to explain how they validated the AI’s suggestions. What did they reject, and why?

​Cognitive Load Audits: Managers should monitor for cognitive offloading. If an employee is consistently producing 'generic' work, they may be relying too heavily on the AI. Re-engage their metacognition by assigning them a task that requires a high degree of personal synthesis.

​For Organisational Culture

Culture is the most powerful lever for sustained innovation. We must build a 'thinking culture' that values the process as much as the product.

​Incentivise Learning Velocity: Instead of rewarding speed, reward those who can show how they used AI to learn a new concept or solve a problem in a novel way.

​Create a Shared Language: Adopt terms like 'metacognitive sensitivity' and 'cognitive resources' across the organisation. When everyone understands what 'thinking about thinking' means, it becomes a collective goal.

​Psychological Safety for Dissent: Encourage a culture where it is safe to challenge 'the computer'. AI can often produce biased or shallow results; employees must feel empowered to call these out without fear of being seen as 'unproductive'.

​Metacognition Training: Invest in training programmes that focus on the science of learning. Teach your employees about metacognitive experiences—those feelings of ease or difficulty that guide our decision-making.

​Conclusion: The Future belongs to the Reflective

​The HBR report is a vital reminder that technology is a multiplier, not a substitute.

Here is a link to the full HBR report: https://hbr.org/2026/01/why-ai-boosts-creativity-for-some-employees-but-not-others

If you multiply a passive mindset by the power of AI, you still have a passive result. But if you multiply a metacognitive, strategic mindset by that same power, the results are truly extraordinary.

​The future of leadership is not about being the smartest person in the room; it is about being the most aware. I encourage you to explore these themes further on our website at levelupleadership.uk, where you can find our latest podcast episodes and more resources on cognitive excellence. Let's stop focusing on what the machines can do and start focusing on what we can do with them.

Until next time, keep leading, keep learning, and keep levelling up!

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By Lee Whitmore

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I found this moment profoundly inspiring. It is a perfect example of design based on genuine empathy. The developer clearly visualised the user’s environment and understood that metre cupboards are almost always poorly lit. By anticipating this specific need, they transformed a minor struggle into a seamless experience.

This level of foresight is exactly what we should strive for in our professional lives. It represents a shift from reactive management to proactive, human-centric leadership. In this edition of the newsletter, I want to explore how we can apply this ‘torch button’ philosophy to coaching and leading teams.

The Core Concepts of Anticipatory Leadership

To lead with the same intentionality as that app developer, we must focus on three core concepts.

The first is Contextual Empathy. This goes beyond simply understanding how someone feels. It involves understanding the physical, mental, and emotional environment in which your team operates. If you know the ‘cupboard’ is dark, you do not wait for them to ask for a light. You provide it as part of the process.

The second concept is Frictionless Support. This is the art of removing obstacles before they cause a slowdown. In my book, Enhanced Leadership, I discuss the importance of being a ‘pathfinder’ for your team. I note: ‘Leadership is the proactive removal of barriers, often before the team even realises those barriers exist.’ By streamlining processes and providing the right tools at the right time, you allow your people to focus on their best work.

The third concept is Implicit Trust. When a leader provides support without being asked, it signals a deep level of awareness and care. It shows the team that their leader is present, attentive, and invested in their success. This builds a foundation of trust that is essential for high performance.

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Applying Design Thinking to Leadership

When we look at the ‘torch button’ example, we see a solution that is integrated and immediate. We can replicate this in our leadership style by auditing the daily experiences of our staff.

Consider the ‘dark corners’ of your organisation. These might be confusing onboarding processes, vague project briefs, or a lack of clarity around decision-making power. As a leader, your role is to identify these areas and install a figurative ‘torch’.

For example, if you know a particular phase of a project is always stressful, you might schedule a brief check-in or provide extra resources before the pressure peaks. This is not micromanagement. It is the thoughtful provision of support based on a deep understanding of the work’s demands.

Practical Applications for Leaders

For those in leadership positions, the goal is to become more observant of the small frustrations your team faces.

Observe the ‘Metre Reading’ Moments: Take time to shadow your team or engage in ‘skip-level’ meetings. Look for the moments where they have to pause, pivot, or struggle with a tool.

Ask the Right Questions: Instead of asking ‘how are you?’, ask ‘what is the one thing slowing you down this week?’. This encourages people to identify the friction points you can help resolve.

Lead by Design, Not Default: Do not rely on ‘how we have always done it’. If a process is cumbersome, redesign it with the user’s ease in mind.

In Enhanced Leadership, I emphasise that: ‘The most effective leaders are those who make the difficult look easy for their followers.’ By providing the ‘torch’ before it is requested, you empower your team to act with confidence.

Practical Applications for Coaches

If you are a coach, these principles can help you deepen your practice and provide more value to your clients.

Anticipate the Resistance: Often, clients face predictable internal barriers when trying to change a habit. Acknowledge these early. Provide the ‘torch’ of awareness so they can navigate their own ‘dark cupboards’ of self-doubt.

Simplify the Framework: Just as the app developer put the button right where the user needed it, coaches should provide tools that are easy to access and apply. Avoid overly complex models that add cognitive load.

Model the Empathy: Show your clients what it looks like to be deeply understood. When a client feels that you truly ‘see’ their environment, the coaching relationship strengthens significantly.

Conclusion and Call to Action

The ‘torch button’ is a reminder that the most impactful leadership often happens in the smallest details. It is about being present enough to see where the light is needed and being proactive enough to provide it. When we design our leadership and coaching around the actual needs of people, we create environments where everyone can thrive.

Let us all look for the dark corners this week and see how we might turn on the light for someone else.

Until next time, keep leading, keep learning, and keep levelling up!

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Get the weekly briefing that helps you navigate organisational chaos with clarity. From mastering difficult conversations to adopting AI without losing the human touch, LevelUp is your weekly guide to leading with confidence.

By Lee Whitmore

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It was an illuminating conversation!

Alex spent decades as an award-winning magician before transitioning into high-level leadership roles at Fortune 500 financial firms. Today, he uses his knowledge of neuroscience and illusion to help leaders navigate change. I found his perspective on how we process information and lead others to be remarkably insightful.

The primary value of Alex’s approach lies in how he uses the mechanics of magic as a metaphor for human connection and cognitive function. In a world where leadership advice can often feel like a rehashing of the same tired models, Alex offers something fresh. He demonstrates how we can ‘light up the brain’ to foster creativity, overcome bias, and manage the constant disruptions of the modern workplace.

Core Concepts of Neuro-Leadership

During our discussion, we delved into several frameworks that challenge the way we typically approach our working day. The most prominent ideas included:

• The Three Stages of an Effect: How information is perceived, remembered, and eventually retold.

• Novelty and Reward: The two primary drivers that hold human attention.

• The Multitasking Myth: The cognitive cost of switching between tasks.

• Parkinson’s Law: The tendency for work to expand to fill the time we allocate to it.

• The Wisdom of Crowds: Using collective intelligence to set realistic and ambitious goals.

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Lighting Up the Brain: Detail and Elaboration

One of the most fascinating parts of our chat was Alex’s explanation of why magic actually works as a learning tool. When we witness something unexpected, our brains move between ‘System One’ thinking (intuitive and fast) and ‘System Two’ thinking (analytical and slow). This creates an ‘aha moment’ or an insight moment. Alex uses magic to trigger the same emotional response that leads to lasting retention of a message.

The Drivers of Attention Alex noted that to hold an audience’s attention, one must provide novelty and reward. Our brains are hardwired to detect new things in our environment and seek out some form of benefit. If a leader stands up and delivers a dry, predictable presentation, the brain switches off. By introducing a story, a visual metaphor, or an unexpected data point, you provide the novelty required to keep your team engaged.

The Cognitive Cost of Multitasking We spent considerable time discussing the dangers of multitasking. Research from the University of London suggests that heavy multitasking can drop a person’s functional IQ by up to 15 points. This is equivalent to the effect of losing a full night of sleep. Alex explained that our prefrontal cortex acts like a battery; it has a limited supply of energy for high-level decision-making. When we ‘flick’ between tasks, we drain this battery rapidly. In Enhanced Leadership, I argue that as AI begins to remove the friction of repetitive tasks, there is a risk that we will fill that gained time with even more frantic multitasking. We must resist this urge to maintain our cognitive clarity.

Parkinson’s Law and the Power of Reward We also explored Parkinson’s Law, which states that a task will grow or shrink to fill the time available for its completion. Alex suggests a practical way to combat this: set a shorter deadline than necessary and promise yourself a reward at the end. For example, if you have an hour between meetings, challenge yourself to finish a report in 50 minutes. The reward could be something as simple as five minutes of quiet time or a fresh coffee. This creates a sense of achievement and prevents the procrastination that usually fills the ‘buffer’ time we give ourselves.

Practical Applications for Leaders

For those of you leading teams, Alex’s insights offer a way to move beyond the ‘command and control’ style of leadership. Here are some ways to apply these concepts:

• Control Your Calendar: Do not let others dictate your entire day. Build in ‘focus blocks’ where you turn off notifications and avoid multitasking. This protects your ‘cognitive battery’.

• The Wisdom of the Crowd: When setting targets, ask your team members for their individual, anonymous estimates of what is possible before discussing it as a group. This prevents the most senior person in the room from ‘anchoring’ everyone else’s opinions. As I mention in my book, the best leaders are often the ones who speak last.

• Create Lasting Impressions: Think about the ‘three stages’ of your communications. What do you want your team to see now? What should they remember tomorrow? Most importantly, what story do you want them to retell to others next week?

Practical Applications for Coaches

If you are a coach working with clients to improve their performance, these neuro-leadership principles are invaluable:

• Highlight the Multitasking IQ Drop: Use the ‘8-year-old child’ analogy to help clients understand that they are not being more productive by answering emails during webinars; they are simply operating at a lower cognitive level.

• Gamify Productivity: Encourage clients to use Parkinson’s Law by setting micro-deadlines. This can help those struggling with procrastination to find a sense of ‘flow’ and immediate reward.

• Use Metaphor and Story: Just as Alex uses magic to make a point stick, coaches should use vivid stories to help clients anchor new habits. An emotional connection to a concept is far more powerful than a simple list of instructions.

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Conclusion and Call to Action

Our brains are remarkable, yet they are easily deceived and easily exhausted. By understanding the neuroscience of attention and the illusions we tell ourselves about productivity, we can become more effective and human-centred leaders. Alex Moffat’s journey from the stage to the boardroom reminds us that communication is the most vital skill we possess.

I highly encourage you to explore this topic further. You can listen to the full conversation with Alex on the Level Up Leadership podcast.

Let us stop multitasking and start leading with focus.

Until next time, keep leading, keep learning, and keep levelling up!

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By Lee Whitmore

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The insights Gabriel shared regarding the evolving role of leadership in the age of Artificial Intelligence (AI) are essential for anyone looking to stay relevant in today’s landscape.

The conversation, coupled with a recent McKinsey report we discussed, sparked some deep reflection for me. It feels as though we are standing at a crossroads where the traditional definitions of ‘business leader’ and ‘tech expert’ are no longer sufficient. Today, I want to explore how we bridge that gap, turning process owners into true AI transformers.

The Missing Piece: The Domain Leader

One of the most striking parts of our discussion focused on the concept of the ‘Domain Leader’. While McKinsey defines these individuals as executives who own major processes or product lines end to end, Gabriel brought a much-needed human perspective to the term.

In the old world, organisations were often divided into two distinct tribes. On one side, you had the tech experts who were deep in code and data. On the other, you had business leaders who almost wore their lack of technical understanding as a badge of honour. This separation used to be manageable, but in the current climate, it has become a significant liability.

A person running a business process who does not grasp the technology shaping it is no longer just a manager: they are a blocker. Gabriel noted that you cannot bypass ‘getting your hands dirty’. This does not mean you must become a computer scientist, but it does mean you must develop technical fluency to maintain credibility and motivate your teams.

The Three Cs Framework

Gabriel introduced a brilliant framework to help leaders navigate this transition. He calls it the Three Cs: Clarity, Capability, and Capture.

1. Clarity

Clarity must always come first. Most AI initiatives fail because teams jump straight to the tools before they have aligned on a purpose. Leaders must ask: What problems are we actually trying to solve? Which decisions are we improving? Who owns these outcomes? Without this foundational alignment, even the most sophisticated technology will fail to deliver results.

2. Capability

This involves more than just technical skills. It encompasses leadership capability, operating models, and trust. As Gabriel pointed out, it is about understanding the intersection of systems and people. You need enough fluency to ask the right questions and spot opportunities where AI can truly add value.

3. Capture

How does the value actually land? Does it manifest as reduced costs, increased speed, or a better customer experience? If you cannot track the value, the initiative is little more than ‘innovation theatre’. Domain leaders must be accountable for capturing the benefits and ensuring they reflect in the bottom line.

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Being Bionic: The Enhanced Leadership Approach

These concepts align perfectly with the philosophies I explore in my book, Enhanced Leadership. I often speak about the necessity of being ‘bionic’, which is the fusion of a human core with technological augmentation.

In the book, I write:

‘The bionic leader does not fear the machine; they use it to amplify their uniquely human qualities of empathy, intuition, and strategic vision. To lead in the future, one must master the art of being both high-tech and high-touch.’

Purpose anchors you, which is the essence of Clarity. Developing your ‘second muscle’ through deliberate upskilling is the path to Capability. When we embrace this bionic mindset, we move from being passive observers of change to active transformers of our domains.

Practical Steps for Leaders

If you are leading a team or a department, the transition to becoming a domain leader starts with small, consistent moves. You do not need to upend your entire schedule to begin building this muscle.

• Audit Your Calendar for Graft: Identify repetitive, manual tasks that AI could potentially handle. Aim to free up two hours a week. Use this reclaimed time to ‘shadow’ your tech team or map out the data friction points in your domain.

• Pick One Tool: Choose a single AI tool and commit to using it daily for a month on real tasks. Pair up with a team member to compare notes. This practice will help you learn how to ask better questions and understand the limitations of the tech.

• Apply the Three Cs to a Project: Take a current project and run it through the framework. Are the conversations clear? Is the capability there? How are you measuring the capture of value?

Insights for Coaches

For those in the coaching profession, your role is shifting as well. You are no longer just coaching for performance; you are coaching for digital literacy and systemic thinking.

• Diagnose Failure Points: Use the Three Cs as a diagnostic tool for your clients. If a leader is frustrated with their ‘digital transformation’, ask them which of the three Cs is missing. Often, they have invested in capability but lack clarity.

• Encourage Technical Curiosity: Help your clients overcome the fear of ‘not being a tech person’. Coach them to see technical fluency as a core leadership competency, much like financial literacy or emotional intelligence.

• Focus on System Ownership: Move the conversation from managing people to owning systems. A coach can help a leader visualise how AI, data, and human talent interlink to create a more efficient and empathetic workplace.

Conclusion and Call to Action

The shift from being a mere process owner to an AI transformer is not an optional upgrade; it is a requirement for modern leadership. By embracing the Three Cs and adopting a bionic mindset, you can ensure that technology serves your purpose rather than complicating it.

I thoroughly enjoyed my time with Gabriel, and I hope these reflections help you on your journey to becoming a more enhanced leader.

Until next time, keep leading, keep learning, and keep levelling up!

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By Lee Whitmore

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But here’s what’s interesting: I never saw the official announcement.

LinkedIn and Descript released a formal press release. They held a coordinated launch. Yet most people I’ve spoken to about this didn’t see that announcement either. We found out through conversations. A colleague mentioned they’d received the badge. I saw it on someone’s profile. That person told someone else. The word-of-mouth spread naturally, organically, because the feature itself was so elegantly designed that it didn’t need marketing. The product sold itself.

This is where the bigger lesson lives. And it has everything to do with how we approach change in our teams and organisations.

The Bolted-On Trap

In Enhanced Leadership, I write about something I call the bolted-on trap. It’s what happens when organisations introduce change without designing it properly from the ground up. You announce a new system, then immediately have to spend weeks training people on it. You send lengthy emails explaining the change. You create thick user guides. You host webinars. You answer questions from confused staff who simply don’t understand why they should care.

All of that effort - the training, the documentation, the marketing - signals something important: the change itself isn’t intuitive enough to stand on its own. It needs help. It needs someone standing behind it, insisting that people pay attention.

But the Descript integration skipped that entire playbook. There was no lengthy onboarding process. No training materials. No “here’s why you need this” campaign. The notification appeared, you read it in the time it takes to drink a sip of coffee, and you understood exactly what it offered and why you might want it. That clarity, that obvious value, made explanation unnecessary.

Think about the difference this represents. When you have to do extensive training, when you have to answer a dozen questions about implementation, when you’re creating documentation that nobody reads, that tells you something crucial: the design could have been better.

This isn’t about people being resistant to change. It’s not about them being change-averse or stuck in old ways. It’s about friction. You’ve created friction where there didn’t need to be any.

Friction as Evidence

I experienced this first-hand years ago when I was part of a retail transformation team. We spent weeks preparing staff for a new operational system. I genuinely believed at the time that the training was necessary. We had presentations, posters, emails, all the usual apparatus of change management. The implication was that people needed to be sold on this, needed to understand why it mattered.

What I didn’t realise then was that we should have spent those weeks on design, not marketing. If the system had been intuitive enough, the training would have been almost unnecessary. People would have figured it out because using it would have been the path of least resistance.

The moment you need to explain a change extensively, you’re usually looking at a design failure, not a communication failure. And we routinely confuse the two.

This is a costly mistake. Because every hour you spend explaining a system is an hour of lost productivity. Every page of documentation you write is evidence that your design could have been clearer. Every question someone asks about how to use something is a sign that the path forward wasn’t obvious.

The Architecture of Trust

What the Descript integration actually reveals is something deeper about autonomy and trust. When you design something to be intuitive, you’re making a statement. You’re saying: “I trust you to figure this out, because I’ve designed it to be obvious.”

Contrast that with the opposite message. Extensive training and hand-holding signal: “I don’t trust you to make sense of this on your own, so I’m going to tell you what to do step by step.”

That matters psychologically. It shapes how people approach the change, and it shapes their sense of autonomy within your organisation. When people feel trusted to navigate something independently, they engage differently. They feel respected. They don’t become dependent on external explanation; they become confident in their own ability to reason through what’s happening.

The Descript example works because it respects user intelligence. It doesn’t presume confusion. It offers value clearly, and then gets out of the way.

When Word-of-Mouth Is the Only Marketing You Need

Here’s what happened next: people started sharing the badge on LinkedIn. Colleagues mentioned it in conversation. It appeared organically across newsfeeds because people wanted to tell others about it. The information spread not because of a sustained marketing campaign, but because the feature was genuinely useful and obviously easy to use.

That’s powerful. Most organisations spend thousands on launch marketing. Paid advertising, influencer partnerships, carefully orchestrated campaigns designed to create buzz. And those can work. But they’re expensive, they’re effortful, and ultimately they’re vulnerable - if people try the thing and it’s actually rubbish, no amount of marketing survives that collision with reality.

Good design survives that collision. It thrives in it.

There’s a principle worth extracting here: when something is designed elegantly, it becomes self-marketing. It doesn’t need you standing behind it, insisting people care. It sells itself because using it is better than not using it, and that truth is immediately obvious.

I think about this in my own work as a coach and content creator. When I produce podcast episodes or write articles, I don’t rely primarily on promotional campaigns. I rely on the quality of the thinking being good enough that people want to share it. The best marketing I have is a listener telling a friend: “You should listen to this episode.” That’s more valuable than any advertisement I could buy.

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What This Teaches Leaders About Change

So if you’re leading change in your organisation - and at some point, most leaders are - here’s what the Descript story teaches us:

First, invest in design before you invest in marketing. Spend your energy on making the change itself so obviously valuable and so clearly easy to navigate that explanation becomes almost unnecessary.

Second, recognise that friction is information. If you’re creating extensive training materials, that’s feedback that your design needs work. If people are confused, that’s not a deficiency in them. That’s a deficiency in the system.

Third, remember that good design respects autonomy. It trusts people to be intelligent enough to figure things out. And that trust, once signalled through clear design, builds the kind of psychological safety where change actually sticks.

Fourth, understand that word-of-mouth is a much better measure of adoption than compliance metrics. If people are choosing to tell others about your change, that’s real adoption. If they’re only participating because you’ve required it and trained them to do so, that’s something else entirely.

The Harder Question

But there’s a harder question underneath all of this, and I want to sit with it honestly.

Not every change can be as simple as a two-click badge integration. Some changes are genuinely complex. Some require coordination across systems, some demand new ways of thinking, some involve real behavioural shifts.

If you’re moving from a command-and-control leadership model to one based on autonomy and trust, that’s complex. If you’re genuinely transforming how people work -not just the tools they use, but the way they collaborate and make decisions - that’s complex. You can’t make that obvious through design alone.

So the insight isn’t that all change should be simple. It’s that as much change as possible should be. And where it must be complex, we need to be honest about that complexity rather than pretending we can market it away.

I’ve seen organisations that tried to dress up genuinely difficult change in positive language and bright posters and town-hall presentations. The underlying change was still hard, and people sensed that disconnect. It undermined trust more than transparency would have.

Better to say: “This is complex. Here’s why it matters. Here’s what I don’t yet know. Let’s figure some of it out together.” That’s honest. That builds trust.

The Design Mindset

What I’m really arguing for is a design mindset in how we approach change. Before you plan the communication, before you think about training programmes, ask yourself: what does this change actually look like from the user’s perspective? Is it obvious why they should care? Is it clear what they need to do? Is the path of least resistance the right path?

In my coaching practice, I use AI tools to help me track themes across conversations, and I’m transparent about doing so. But I designed that use to be nearly invisible. A client doesn’t need to know I’m using AI to help me remember their progress. They just experience me being better prepared, more attuned to their journey. The tool is designed to support the human relationship, not to announce itself.

That’s the model: design the change so well that the tool or the system or the new process just becomes how we work. The announcement takes care of itself through the elegance of the design.

A Question for This Week

Here’s what I want you to sit with: what change are you leading right now where you’re relying on marketing and explanation to drive adoption, when you could be investing that energy into better design instead?

Look at your current projects. Is there an initiative that’s struggling because people don’t understand it? Is there a system that people are resisting? Before you assume you need more training or better communication, pause. Ask whether the real issue is design.

Because the Descript story shows us that good design is the most powerful marketing tool you have. It’s free. It’s authentic. And it works.

Until next time, keep leading, keep learning, and keep levelling up!

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By Lee Whitmore

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I started this piece because I am aware of a growing tension between executive caution and the practical needs of exhausted employees. In my coaching work, I have found that while leadership teams are still drafting ethics policies, their staff are already using unvetted AI tools to manage heavy workloads.

This creates a hidden, two-tier workforce where accountability is lost and data security is compromised. I wanted to highlight that the solution is not to prohibit these tools; instead, we must change how we lead. My goal is to move the conversation toward a more intentional social contract at work, where we prioritise human insight over administrative drudgery.

Working with Victoria to edit and craft this article into the final draft for publishing was an absolute pleasure. Thank you to ‘Corporate Jungle’ for hosting this piece. You can find the full article here: https://open.substack.com/pub/corporatejungle/p/the-shadow-ai-economy

While you are there I encourage you to check out Corporate Jungles’ other articles, and subscribe today!

Corporate Jungle

Survival strategies for the modern workplace

By Victoria

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By Lee Whitmore

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Until next time, keep leading, keep learning, and keep levelling up!

The Digital Fluency Mandate

We began by discussing a recent McKinsey report which suggests that successful AI transformation depends less on the tools themselves and more on the digital fluency of domain leaders. These are the executives and functional heads who do not need to be software developers, but must understand how technology intersects with their specific business processes.

Gabriel noted that we are seeing a shift away from the traditional divide between purely technical leaders and general leaders who manage technical people without understanding the work. In the current landscape, a leader must be willing to get their hands dirty to understand how these technologies interact with their business goals.

The Three Cs Framework

To help leaders navigate this journey, Gabriel introduced a framework he calls the Three Cs: Clarity, Capabilities, and Capture.

• Clarity: This involves understanding the specific business problems you are trying to solve and the decisions you aim to improve. Many AI initiatives fail because teams jump to tools before aligning on purpose.

• Capabilities: This refers to leadership ability, operating models, and the development of domain expertise. Gabriel shared a fascinating example of an organisation where leaders participated in an internal internship with technology experts to learn the basics of prompt engineering and workflows.

• Capture: Leaders must define how the value of the investment will actually land, whether that is through reduced costs, increased speed, or an improved customer experience.

Moving Beyond Deployment to Outcomes

A recurring theme in our discussion was the tendency for organisations to rush into deployment without a clear view of the desired output. Gabriel pointed out that technology does not deserve credit for being impressive; it only deserves credit for delivering results.

In my book, Enhanced Leadership, I refer to this as the ‘bionic’ approach to management. As I state:

‘AI is not going to replace you. You’re not going to be replaced with a robot. But what you’ve got is an opportunity to enhance yourself with AI, still being the human, the human thought, the human empathy, the human strategy, but with AI augmenting everything.’

This perspective aligns with Gabriel’s view that the best AI transformations are human-centred. We must design systems that augment human judgment rather than bypassing it, ensuring that people remain in the loop as sources of accountability and trust.

The 2026 Outlook: Preparing the Body

As we look ahead through 2026, the era of ‘fear of missing out’ (FOMO) is ending. Boards are no longer asking if AI has been deployed; they are asking what measurable value has been captured.

Gabriel used a striking analogy: an AI transformation is like an organ transplant. For the procedure to be successful, the organ must be healthy, but the body receiving it must also be prepared. In many cases, the technology is ready, but the organisation is not. This year is about strengthening the foundations and preparing the ‘body’ of the business to sustain the new technology.

Takeaways for Leaders

For leaders looking to increase their digital fluency, the advice is to start with the business strategy and map backward.

• Focus on Communication: One of the most vital skills a leader can develop is knowing how to ask AI the right questions and how to use it to augment their thinking.

• Set Clear Guardrails: It is the responsibility of senior leadership to communicate appropriate use cases and ethical boundaries, ensuring that internal data is not exposed to public AI systems.

• Encourage Transparency: We must move away from the idea that using AI is ‘cheating’. Encourage your teams to be upfront about their use of these tools and the value they add.

Takeaways for Coaches

Coaches can play a pivotal role in helping leaders build the ‘learning agility’ required for this era.

• Build Psychological Safety: Help leaders create an environment where staff feel safe to experiment and learn without the anxiety of being replaced.

• Focus on Vulnerability: Encourage leaders to ‘learn in public’. As Gabriel noted, nobody knows everything, and the ability to listen and learn from others is a hallmark of wise leadership.

• Challenge the Hierarchy: Use the principle that hierarchy should never silence insight. Support your clients in fostering a culture where every team member feels empowered to bring better thinking to the table regardless of their title.

Conclusion

The journey toward digital fluency is not about becoming a technical expert; it is about developing the leadership muscles to guide your organisation through a period of profound change. By focusing on clarity of purpose and the augmentation of human talent, we can ensure that AI serves as a powerful catalyst for growth rather than a source of friction.

If you would like to explore these concepts further, I highly recommend listening to the full episode with Gabriel on the Level Up Podcast or connecting with him on LinkedIn, where he shares daily insights on human-centred AI: https://www.linkedin.com/in/gabriel-millien

You can also find more about my own framework for the future of management in my book, Enhanced Leadership: https://mybook.to/EnhancedLeadership

Until next time, keep leading, keep learning, and keep levelling up!

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By Lee Whitmore

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On the surface, it sounds like the dream. It promises the ultimate work-life balance. But is it? Or is it actually a wolf in sheep’s clothing? As I read it, I couldn’t help but feel a familiar tug of caution. It reminded me of a concept I explore in depth in several articles and podcast episodes: the fine line between autonomy and the “always-on” trap.

While microshifting offers liberation, it also risks accelerating a phenomenon I have warned about before: the Infinity Workday.

The Core Concepts: Microshifting vs. The Infinity Workday

To understand if microshifting is a solution or a trap, we need to look at two opposing frameworks.

Microshifting (The Trend): As described in The Guardian, this is the intentional fragmentation of the workday. It is not just “working from home”; it is “working from anywhere, anytime, in short bursts”. The goal is to accommodate personal productivity peaks and domestic needs.

The Infinity Workday (The Risk): This is the concept I discuss in Enhanced Leadership and my recent podcast. It is the creeping reality where work has no start or end time. The “neat box” of 9-to-5 dissolves, and without clear boundaries, work seeps into every moment of our lives, leading to chronic fatigue and an illusion of productivity.

Navigating the Shift

Microshifting is essentially the Infinity Workday by design. The question is: who is designing it?

The Case for Microshifting The argument for microshifting is strong. It acknowledges that the industrial 9-to-5 model is a relic. We are not machines that power down at 5 pm. Some of us do our best deep work at 6 am; others are night owls. By allowing people to “shift” their hours, we unlock energy that is usually wasted staring at a clock.

The Danger Zone However, the danger lies in the execution. If you microshift without the right mindset, you simply end up working all the time. That hour you took off for the gym? You pay for it by checking emails during dinner. The school run? You make up for it by answering Slacks in bed.

In Enhanced Leadership, I argue that flexibility without boundaries is just exploitation in disguise. If we are not careful, microshifting validates the toxic culture where being “responsive” at 10 pm is seen as a badge of honour rather than a failure of planning.

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The Solution: To make microshifting work - to turn it into true freedom rather than fatigue - we must apply the three core principles:

1. True Ownership: You must own your calendar. Microshifting only works if you choose the blocks, not if your boss dictates them.

2. Radical Focus on Outcomes: We must stop measuring hours. If I can do my job in four intense, microshifted blocks of 90 minutes, that should be enough. As I often say: “No one asks a freelancer how long it took; they ask if the problem is solved”.

3. Intentional Rest: You cannot just “shift” work; you must also schedule rest. As I have said before, elite performance requires elite recovery. If you microshift, you must block out “do not disturb” time just as rigorously as you block out work.

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Practical Takeaways

How can we apply this? Here is how different groups can use these insights.

For Leaders: Focus on Output, Not Presence If your team wants to microshift, let them. But you must change how you lead.

• Stop clock-watching: Do not worry if someone is offline at 2 pm. Worry if they haven’t delivered their project by the deadline.

• Set clear guardrails: Be explicit about communication. If a team member is working a “late shift” block, ensure they know they don’t need to reply to your emails instantly.

• Model the behaviour: If you microshift, tell your team. Say, “I am offline from 4 pm to 7 pm for family time, and I will be back on for an hour later”. This permission gives them the psychological safety to do the same.

For Coaches: helping Clients Design Boundaries Your clients are likely already microshifting, perhaps without realising it, and feeling guilty about it.

• Validate their rhythm: Help them understand that their energy cycles are unique. Coaching them to work with their energy, not against it, is a key part of Enhanced Leadership.

• Challenge the guilt: Many leaders feel guilty when they are not “visible”. Work with them to reframe their value around impact rather than availability.

• Design the “off” switch: Help them create rituals that signal the end of a work block. Without a commute, the brain needs a new signal to transition from “work mode” to “home mode”, even if that transition happens three times a day.

Conclusion

Microshifting is here, and it is likely the future of knowledge work. It holds the promise of a happier, more balanced life, as The Guardian suggests. But that promise is fragile. Without the solid foundation of ownership and outcome-based thinking, it can easily crumble into the exhausted, always-on nightmare of the Infinity Workday.

The choice is ours. We can let work happen to us, filling every crack in our day, or we can lead with intention, designing a life where work fits us, not the other way around.

If this topic resonates with you, I highly recommend you check out The Infinity Workday. I dive deeper into the strategies for reclaiming your time and leading with sanity in an always-on world.

Until next time, keep leading, keep learning, and keep levelling up!

LevelUp

Get the weekly briefing that helps you navigate organisational chaos with clarity. From mastering difficult conversations to adopting AI without losing the human touch, LevelUp is your weekly guide to leading with confidence.

By Lee Whitmore

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The Core Concepts: Productivity Paranoia and Brain Drain

The root of this issue often stems from what Microsoft’s Work Trend Index calls “productivity paranoia”.

Research shows that while 87% of employees feel productive, only 12% of leaders share that confidence. This gap creates a vicious cycle: anxious leaders ask for more check-ins, and employees join more meetings to prove they are working. Since the start of the pandemic, the number of weekly meetings has soared by over 150%.

However, this hyper-collaboration comes at a physical cost. Brain-scanning technology has proven that back-to-back virtual meetings cause significant stress to accumulate in the brain. Conversely, taking even short breaks allows the brain to reset, dramatically improving focus and engagement. By over-scheduling, we are scientifically decreasing our ability to do our best work.

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The Consequences: Diluted Accountability

The most concerning unintended consequence of this culture is the erosion of accountability. When we default to “collaboration by committee” - inviting 15 people to every decision\ - we aren’t truly collaborating; we are diluting responsibility.

In my book, Enhanced Leadership, I emphasize that human-centric leadership must be anchored by a clear purpose. As I state: “Purpose is the function you fulfil in an organisation, not simply the tasks you perform”. When decisions are made by committee, it becomes unclear who is the single person accountable for the outcome. This stifles the “bias for action” we value in capable people, teaching them to wait for consensus rather than taking initiative.

Practical Takeaways for Leaders

As leaders, we must remember that “the standard you walk past is the standard you set”. Every time we accept a meeting with no agenda or reply-all unnecessarily, we endorse a low standard of productivity. To reset this, consider these actions:

• Model Ruthless Prioritization: Audit your own calendar first. For every meeting, ask: “What is the essential outcome and am I the right person to achieve it?”. If the answer is unclear, decline the invitation or suggest an alternative.

• Actively Audit Team Calendars: Don’t just help team members “find a gap” for more work. Sit down with them and identify what is genuinely essential and what is “rubbish” that can be cleared out. Take the heat for them if they need to decline meetings.

• Empower Decisive Action: Identify one decision this week that you would normally make and delegate it entirely to a trusted team member. Tell them: “I trust your judgment. You do not need to check back with me - just get it done”.

• Redefine Collaboration: Break the link between “collaboration” and “60-minute committee meetings”. Encourage shared documents, quick one-to-one calls, or crisp emails with clear questions instead.

Practical Takeaways for Coaches

For those coaching and mentoring aspiring managers, use these concepts to help them find clarity:

• Challenge “Busyness” as a Metric: Help coachees distinguish between activity and progress. Use the “AI-aware lens” from Enhanced Leadership to ask: “How could technology automate the ‘noise’ of scheduling to free you for deep, strategic work?”.

• The “Human in the Loop” Test: When a coachee is struggling with a committee decision, ask who is the “human in the driving seat”. Reinforce that while AI or groups can provide data, the final call on high-impact human issues must remain with a single accountable leader.

• Focus on the “Why”: As noted in Enhanced Leadership, leaders must most deeply engage with the “why”. Coach your team to communicate the purpose of a project so clearly that the “what” and “how” can be handled autonomously without constant meetings.

Conclusion and Call to Action

Breaking the culture of busy work takes courage. It requires the confidence to protect your time and your team’s focus. My leadership manifesto is clear: have the courage to focus on just the essential.

What one action will you take this week? Will you clear your own calendar as a model, or will you sit with a team member and help them clear theirs?

To dive deeper into these strategies, I invite you to listen to the full “Meeting Culture” episode of the Level Up Leadership podcast. You can also visit our website for more resources on building high-performing, empowered teams.

Until next time, keep leading, keep learning, and keep levelling up!

LevelUp

Get the weekly briefing that helps you navigate organisational chaos with clarity. From mastering difficult conversations to adopting AI without losing the human touch, LevelUp is your weekly guide to leading with confidence.

By Lee Whitmore

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​The primary goal of this newsletter is to help you transform your approach to AI. By looking at how Finland consistently ranks at the top of the European Media Literacy Index, we can find a blueprint for managing the AI disruptions predicted for 2026. This is about building resilience and ensuring our teams remain sharp, ethical, and discerning in an age of deepfakes and automated bias.

​Core Concepts of AI Literacy

​To navigate the future effectively, we must first understand the pillars of the Finnish success. Their strategy is built upon three core concepts that are directly transferable to the corporate world.

​The first concept is the development of transversal competencies. These are skills that cut across different areas of expertise, such as critical thinking and the ability to recognise bias. In Finland, these skills are taught from a very early age. For leaders in the UK, this means moving beyond simple technical training and focusing on the cognitive habits of our staff.

​The second concept involves the human-value surplus. As AI begins to handle more of our routine tasks, we must identify the specific areas where human empathy, context, and ethical judgement are irreplaceable. We must decide which decisions require a human touch to maintain our reputational integrity.

​The third concept is probabilistic protocols. This involves moving away from blind trust in AI outputs. Instead, we must treat AI as a tool that provides suggestions based on probability, requiring human verification through structured confidence scores and escalation chains.

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​Detailed Elaboration: From Classroom to Boardroom

​Finland’s success stems from a progressive approach that starts in early childhood. The Finnish National Curriculum notes that ‘media literacy aims to develop critical thinking, media analysis abilities, and ethical media behaviour’. This foundation has now been extended to include AI deepfake detection. In my book, Enhanced Leadership, I argue that ‘the strength of a leader is found in their capacity to prepare their team for any version of the future’. By adopting the Finnish ethos, we can treat our staff as the first line of defence against AI errors.

​To implement this, you should consider regular AI system testing. You might schedule bi-monthly exercises where cross-functional teams probe AI reports for errors, mirroring the content analysis used in Finnish schools. To make this engaging, you could offer rewards for verified flaws that might affect company strategy. This creates a culture where finding a mistake is celebrated rather than feared.

​Furthermore, we must deconstruct the objective functions of the large language models we use. Through prompt engineering tests, your teams can identify whether a model has a bias towards speed over accuracy. This is similar to the media deconstruction techniques led by KAVI, the Finnish National Audiovisual Institute.

​In terms of productivity, we know that AI can handle roughly 70% of routine tasks. However, this leaves humans with the full load of verification. To manage this, we should redesign our metrics. I suggest tracking Critical Review Hours as a key performance indicator. If AI triples your output, you must mandate proportional verification time. Using human-in-the-loop pipelines ensures that while AI drafts the content, your teams validate it using checklists such as ‘Who benefits from this message?’.

​Strategies for Leaders

​For those of you in senior leadership roles, your focus should be on the structural and cultural shifts required to support these new competencies.

• ​Audit and Reflection: Regularly ask your team: ‘If our AI prioritises cost over ethics by 10%, what reputational impact hits us in 18 months?’. This keeps ethical considerations at the forefront of technological adoption.

• ​Sector Collaboration: Do not work in a vacuum. Partner with libraries or technology firms for joint workshops. This echoes the Finnish model of teamwork with non-governmental organisations to build a media-savvy population.

• ​Normalise Glitches: Create a safe space for staff to report AI errors. Hosting bi-weekly forums where employees can share significant errors, such as a biased hiring algorithm, can provide invaluable insights for the whole company.

​Strategies for Coaches

​If you are a coach working with high-level executives, your role is to facilitate the transition from being a performance expert to becoming a cognitive ally.

• ​Identify Blind Spots: Use exercises to help leaders name five AI blind spots in their leadership. This encourages a healthy level of scepticism and awareness.

• ​Focus on Empathy: Help your clients identify the ‘human-value surplus’ in their decision-making. These are the moments that demand context and empathy that AI simply lacks.

• ​Track Progress: Suggest using systems to track the annual AI literacy progress of their teams. An annual budget of £300 to £600 per person for upskilling is a wise investment in the current climate.

​Conclusion and Call to Action

​The Finnish model demonstrates that resilience is built through education, audit, and constant adaptation. By benchmarking your own literacy gaps against these high standards and investing in your teams annually, you can ensure your organisation is prepared for the synthetic threats of 2026.

​I encourage you to take the first step today. Start by reviewing your current AI verification processes and consider how you can implement a ‘Bias Library’ to curate anonymised failure cases for onboarding.

Until next time, keep leading, keep learning, and keep levelling up!

LevelUp

Get the weekly briefing that helps you navigate organisational chaos with clarity. From mastering difficult conversations to adopting AI without losing the human touch, LevelUp is your weekly guide to leading with confidence.

By Lee Whitmore

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What is a Capsule Decision?

The core concept is simple: a Capsule Decision is a single, focused choice that packs the ‘code’ of your entire leadership philosophy into one action. While we often get bogged down in ‘administrivia’, these decisions punch way above their weight by doing three things simultaneously:

1. Living the Values: They turn abstract concepts like ‘trust’ or ‘wellbeing’ into tangible reality. As I argue in my book, Enhanced Leadership, “Purpose is more than a job title... It is the reason that you, specifically, serve where you do.”. A capsule decision is that purpose in motion.

2. Setting a Precedent: They establish a ‘new normal’, guiding future choices without the need for constant oversight or thick policy manuals.

3. Triggering Ripples: They kick off consequences that spread far beyond the initial move, often simplifying dozens of subsequent choices.

Think of it as the leadership version of a ‘capsule wardrobe’. By investing in a few essential, versatile ‘pieces’ of policy or behaviour, you ensure everything else starts to flow with much less effort.

Decision Fatigue and the White House

The higher you rise in leadership, the more precious your mental bandwidth becomes. In Enhanced Leadership, I discuss ‘automating the noise’ to free up strategic energy. A classic example of this is Barack Obama’s decision to only wear grey or blue suits.

By automating a trivial daily choice, he preserved his ‘willpower battery’ for monumental decisions. This is a strategic discipline. As the bonus chapter of my book, ‘The Sanctuary of the Analogue’, notes: “The most valuable asset a leader possesses today is not their software stack; it is their undivided attention.”. Capsule decisions protect that attention.

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Transforming Culture Through Action

Capsule decisions are the ultimate ‘show, don’t tell’ tool. If your company values are just words on a wall, these decisions make them real. They blast through bureaucracy by replacing complex rules with simple, powerful principles.

For instance, a manager who starts every meeting by asking ‘What went right this week?’ is making a capsule decision. They are not just chasing ‘good vibes’; they are intentionally building a recognition culture, one meeting at a time.

Key Takeaways for Leaders

If you are looking to simplify your leadership and amplify your impact, consider these ‘capsule’ moves:

• Focus on Outcomes, not Outputs: Shift your team’s focus from ‘cases processed’ to ‘actual impact’. This re-energises people by connecting them to real purpose rather than just paperwork.

• Establish ‘No-Email’ Windows: Banning after-hours emails is a massive signal that you are serious about rest. It makes wellbeing non-negotiable and ensures your team returns with sharper focus.

• Radical Transparency: Opening the financials to your team sends a loud message: ‘We are in this together’. It encourages people to start acting like owners rather than just employees.

Key Takeaways for Coaches

For those supporting leaders, these frameworks can help clients move from ‘firefighting’ to ‘gardening’:

• Identify the ‘Willpower Drain’: Help your coachees identify which trivial decisions are exhausting them and look for ways to ‘capsule’ those choices.

• Audit Values vs. Actions: Use coaching sessions to see if the leader’s actions (or inactions) align with their stated purpose. A capsule decision can often bridge a ‘credibility gap’.

• The ‘Blank Slate’ Exercise: If a process is clunky, don’t just ‘bolt on’ a new tool. Ask the client: ‘If you were designing this from scratch today, what one rule would change everything?’.

Conclusion and Call to Action

Leadership does not have to be a grind of endless complexity. Often, the most effective move is to find the small hinge that swings the big door. I challenge you this week to look at your team, your business, or even your personal life. Where is the one small lever or powerful principle that could flip the script and make everything else easier?.

To learn more about these concepts and hear real-world examples from different company stages, listen to the latest episode of the Level Up Leadership podcast. You can also visit my website to explore more articles on ‘bionic’ leadership and the human core of management.

Until next time, keep leading, keep learning, and keep levelling up!

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By Lee Whitmore

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