Implementing technological transformation with heart
Organisations are not abstract entities: they are living things comprising people. Every mindful organisation knows that if you want to make transformational change you need direction from the top down and a symmetrical engagement from the bottom up. Looking at organisations holistically through our Head, Heart and Hands framework, we have seen this lesson learned most explicitly in recent years as organisations have been figuring out how to drive a sustainability agenda. For example, Amazon helps deliver on its sustainability goals via a network of 9,300 Sustainability Ambassadors who amplify company-level ambitions at the local level.
But just as organisations were beginning to get their collective heads around the sustainability agenda, along comes another change that is equally impactful and moving at a much faster pace: Artificial Intelligence. References to AI have become so ubiquitous that it is tempting to think that everyone is doing it. Still, it might be more accurate to say that everyone is talking about it, because while a lot is going on, it is still early days. Many organisations remain at the stage of asking questions rather than implementing solutions.
Putting the task in context
Organisations have a dual task when it comes to AI. First, they must figure out how their operations are to be transformed to meet the challenges and opportunities posed by AI. This is where most of the effort is being directed right now and loosely correlates with the direction from the top down mentioned above. But, as also mentioned, you can’t make transformational change unless you have symmetrical engagement from the bottom up, and this is equally challenging to both understand and address. From the perspective of the bottom-up, AI looks quite fragmented in practice, and its implications can result in a feeling of overwhelm for many employees.
Before diving into the experiences of your own organisation, it’s worth looking at what is currently happening elsewhere. Despite all the hype around AI, a recent study shows that less than a quarter of employees (23%) said they were currently using AI tools such as ChatGPT for work tasks. Just 6% of respondents said they’re actively looking at ways AI could improve processes in their current role. That’s probably not as advanced as you had imagined.
Unlocking employee engagement with AI can provide significant value
Reasons why this usage is surprisingly low are predictable. One study of workforce AI adoption barriers found that lack of training, distrust in AI tools and in their leadership’s approach to AI are among common barriers. And don’t worry too much that your organisation is behind the curve on this issue. A recent McKinsey study found that only 23% of large businesses had created an approach to foster trust among employees in Gen AI’s use. Only 19% have established a compelling story of change around AI adoption, and only 9% have created incentives for AI adoption.
As expected, unlocking employee engagement with AI can provide significant value. According to BCG, organisations that successfully remove AI adoption friction achieve: 50% higher revenue growth; 60% higher total shareholder returns; 40% higher returns on invested capital; 1.9x more patents filed; and 1.4x better employee satisfaction. So, let’s get moving!
Six considerations for a people-centric approach to AI success
The phrase “you manage what you measure” is somewhat overused in management, but that’s because it’s true. Once in receipt of the kind of context discussed above, organisations need to understand how their current capabilities measure up relative both to what’s happening in the broader market and what is required to implement their specific AI transformation ambitions in a way that centres people rather than the technology. Based on our years of experience around employee engagement with sustainability, including via our assessment of employee readiness for sustainability transformation, here are six considerations we believe every organisation should engage to meet the task of implementing their AI transformation.
The technology itself is not the differentiator; the differentiator will be the organisation’s capacity to absorb, adapt and apply it with intent and purpose to ensure AI brings a net benefit for everyone
1. Meet employees where they are at
How are different teams able to apply your strategy and general knowledge about AI to their specific functions and roles? Develop tailored approaches that respond to the levels of knowledge and adoption in functions and job levels. Inform function-specific strategies and discover cross-functional synergies in AI training.
2. Know where to go next
What topics or forms of engagement should you prioritise for future investments? Identify and prioritise topics and concepts where employee knowledge is lower, or where employees show greater interest. Inform design of future training or policies with measures of existing knowledge.
3. Set a baseline from which to measure progress and report against it
What is your starting point as you set out to further engage your employees about AI? Provide a measure of progress that can be used to engage teams across the organisation. Generate quality data that can be used to fulfil reporting obligations on employee engagement.
4. Ensure employees can implement what they learn to contribute to improved outcomes
What activities might you invest in to complement training and new tools, making them more actionable for teams? Establish action plans to address potential issues such as a lack of perceived support from managers or a lack of available resources. Harvest suggestions and ideas shared by employees on how to leverage AI effectively and safely.
The future of work is not about replacing people with AI; it is about re-equipping people so that human and artificial intelligence together create new value
5. Leverage existing champions How might you do more with employees who are already knowledgeable and engaged with AI? Identify where existing champions are in the business, their interests, and build out new engagement strategies and opportunities for this group.
6. Know more about the effectiveness of your investments
What is the impact of your existing AI training, tool deployment and other engagements? Improve reporting on workforce AI investments. Adjust investments in existing assets and programmes according to their effectiveness.
Leading with the heart
In the same way that sustainability demanded both executive commitment and workforce engagement, AI readiness will depend on a balanced architecture of leadership, learning and lived experience across the organisation. Technology itself is not the differentiator; the differentiator will be the organisation’s capacity to absorb, adapt and apply it with intent and purpose to ensure AI brings a net benefit for everyone.
AI readiness will depend on a balanced architecture of leadership, learning and lived experience across the organisation
Leaders must therefore treat AI not as a discrete project but as a long-term capability-building journey. This means investing in enablement, embedding trust and transparency in governance, and creating feedback loops that connect insights from the frontline with strategic intent from the top.
Organisations that move with clarity and empathy, setting measurable baselines, engaging employees in co-creation, and continually iterating their approach, will be both better equipped to navigate disruption and convert it into competitive advantage.
The future of work is not about replacing people with AI; it is about re-equipping people so that human and artificial intelligence can work together to create new value.
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