AI, Trust and the Leadership Challenge we’re not talking enough about

We’re sitting on the edge of a shift as significant as the industrial revolution.

For some organisations, it’s already here.  For others, it’s just the beginning.  But the direction of travel is clear: the future of work will be shaped by AI and by how well we integrate it into our organisations and our society.

History provides a useful lens for us to learn from.  The industrial revolution reshaped how we work, how we structure organisations and how society functions.  It brought progress, but not without friction and pushback.  Resistance, systemic societal changes and regulation all followed.

We’re seeing echoes of that now.

AI adoption is accelerating, but the results are mixed.  Many organisations are investing heavily, yet relatively few are realising a meaningful return at this stage.  The difference isn’t necessarily the technology itself but rather, how the transformation is being led across the organisation.

Digital transformation (DX) is often framed as a systems or infrastructure programme.  In reality it’s the intersection of technology and organisational change.

This is where many efforts fall short.

You can’t layer AI onto legacy cultures and expect transformation, nor can you drive innovation in environments that don’t tolerate risk or accept learning through failure.  You certainly can’t expect adoption where there isn’t trust, particularly from underrepresented groups and those who are marginalised across your organisation.

According to David Rogers, Digital Transformation O.G. in his book, The Digital Transformation Playbook, organisations that are succeeding are doing a few things differently:

• They are clear on their purpose and use-case
• They invest in capability building, not just in the technology itself
• They address legacy talent and processes rather than working around them
• They create psychological safety to enable a culture of experimentation where testing, learning and failure are all part of the process
• And, critically, they take a human-centred approach to AI

 

As Olivia Gambelin outlines in her work on responsible AI, ethics is not a layer added at the end or a tick-box exercise, it’s a practical tool for ensuring AI systems do what they are intended to do.

One example highlighted by Olivia Gambelin in ‘Responsible AI’ illustrates how well-intentioned AI can still produce harmful outcomes.  She describes a tool designed to help small businesses grow by increasing their online visibility and connecting them with new customers.  However, in practice, the system ended up amplifying existing inequalities.

This is because the AI relied on historical data and engagement patterns, it tended to favour businesses that were already more visible or well-established.  As a result, some businesses, particularly those with Black business-owners, experienced reduced visibility rather than growth. This meant fewer customer interactions, less traffic and ultimately a negative impact on their ability to compete and expand.

The example highlights a key risk in AI systems: without careful design and oversight, they can unintentionally reinforce structural biases embedded in the data they are trained on.

A responsible AI strategy, as Gambelin describes, is focused on defining purpose and values upfront and ensuring every decision throughout the creation of AI solutions aligns back to them.  Without this, organisations risk scaling outcomes they never intended or imagined.

The conversations we were involved in at the Anthropy UK National 2026 gathering reinforced something important: AI is revealing existing systemic problems that organisations and society haven’t either identified and/or resolved yet.

Bias in our systems.  Gaps in access.  Differences in who feels able to engage or experiment and adapt to AI.

If we scale AI without addressing these underlying issues we won’t be transforming our organisations and society for the good, instead we actually run the risk of amplifying existing inequities.

Many organisations are currently focusing on percentage rates of adoption and leaders’ roles in facilitating this as a demonstration of Return on Investment. Counting the number of AI licences used does not measure effective use and adoption of a tool. Successful adoption should also assess why we are adopting AI tools and the potential impacts, tying back to our values and purpose.  As we look ahead to 2030 and beyond, the question is clear:

Are leaders ready to lead on the ethical and responsible adoption and scale of AI?

That means building on pilots and proofs of concept and focusing on the harder and more important work: leadership, culture, capability and trust. These are the very conditions that determine whether people adopt AI in practice and ultimately whether organisations see any meaningful return on investment.

For leaders driving transformational programmes today, this is the opportunity as well as the responsibility.  We have the opportunity to build environments where people can adapt, contribute and thrive, and design systems that are inclusive by default.

The question is, do we want to take this opportunity and ensure that the future of work works for everyone?

The organisations that get this right will be the ones that will attract, retain and unlock the talent needed to succeed in a very different world.

To hear more about AI, Leadership and Trust, check out our latest podcast with Iain Preston below

Discover how a human-first approach to AI can still be commercially strong and deliver better business outcomes here.

This work isn’t easy, but it’s important to get it right. If you’re looking to work with a consultancy that will help you navigate these new complexities and implement solutions that are inherently human-centred and free from bias, book a 30-minute call with us today:

This article is a part of our 'Inclusive Leadership 10 Years On' series. See more about the series here.

Co-authored by Charlotte Sweeney OBE & Elizabeth Pollitt

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Sources:

Gambelin, O. (2024) Responsible AI: Implement an ethical approach in your organisation. London: Kogan Page

McKinsey and Co. (2025) The State of AI in 2025 Available at: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

Rogers D.L (2016) The Digital Transformation Playbook: Rethink your business for the digital age.  New York: Columbia Business School Publishing

World Economic Forum (2016) The Fourth Industrial Revolution: what it means and how to respond. Available at: https://www.weforum.org/stories/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/ (Accessed March 2026)