22.06.2026
There is a phenomenon playing out in boardrooms everywhere. A competitor will announce an AI programme, or a chat bot. Another claims to have transformed productivity through automation.
The reaction is often immediate: we need to do something with AI, quickly. It’s panic stations from the top down.
This pressure is understandable because no organisation wants to be accused of standing still while technology reshapes its industry, especially at the current pace of change.
But there is a problem. Many businesses are racing forward without knowing where they are going, what success looks like, or whether their people are ready to come with them. That is not transformation. It’s just motion in an uncharted direction for the sake of it, and should never be confused with progress.
The reality is that AI has created what might be the largest technology adoption rush in modern business history. Organisations are investing significant sums into platforms, tools, pilots and proofs of concept, often before they have answered a more fundamental question – what are we actually deploying into?
Based on insights from our whitepaper, ‘Investing in AI Without Understanding Your People: The Case for Clarity in an Unprecedented Space’ by Luciana Rousseau. Download the whitepaper below
The uncomfortable truth about AI investment
The answer, more often than not, is uncertainty. The technology may be new, but the pattern is familiar. Organisations become so focused on avoiding the risk of being left behind that they overlook the risks sitting directly in front of them.
AI has become a race. The problem is that most organisations do not have a map.
The headlines surrounding AI suggest widespread success. The reality looks very different.
Research cited from organisations including MIT, McKinsey, BCG and Deloitte paints a remarkably consistent picture. Despite billions being invested globally, only a small proportion of organisations are generating measurable value from AI at scale. Many pilots never progress beyond experimentation. Others reach deployment but fail to achieve meaningful adoption.
The technology itself is rarely the issue. Most AI platforms function exactly as intended. The breakdown happens when tools are introduced into organisations that have not prepared their workforce for what comes next.
It happens when leaders assume access equals adoption and when vague training is mistaken for readiness.
And it happens when people are treated as an implementation detail rather than the determining factor of success. The result is a growing inventory of stranded investments.
Licences are purchased. Infrastructure is built. Integrations are completed. But the anticipated productivity gains never arrive, or at least not in the way many foresee. This isn’t because the technology itself has failed, but because organisations never fully understood their people.
Why speed can become a liability
There is a common assumption that moving faster increases competitive advantage. Sometimes this is true, but AI presents a different challenge. Unlike previous waves of enterprise technology like the internet or even computing itself, AI introduces a level of uncertainty that many organisations have never encountered before. Decisions are probabilistic and accountability is often unclear.
For employees, particularly those operating in regulated, safety-critical or high-consequence environments, that uncertainty matters. When people do not understand how a system reaches conclusions, they become reluctant to rely on it. Some disengage while others create workarounds. Many continue using existing processes while AI tools sit largely untouched in the background. This creates one of the biggest hidden costs in modern transformation programmes, and the gap between business aim and employee usage becomes the gap between investment and value.s strong connection adoption and attitude towards technology. Visibility is attainable through Morson’s propriety measures.
The training map
To their credit, many organisations recognise the importance of people. Their response is usually training. Training is necessary, but even relatively thorough programmes are rarely sufficient.
Most AI learning initiatives focus on awareness and basic capability. Employees attend workshops, complete online modules and learn the fundamentals of prompt writing or tool usage. That is the easy part. The harder challenge is helping people develop confidence, judgement and new habits. Understanding how AI works is not the same as incorporating it into daily decision-making. Knowing a tool exists is not the same as trusting it, and completing training is not the same as changing behaviour.
This is why many organisations report high levels of executive optimism alongside low levels of workforce adoption. Leaders believe the organisation is progressing because programmes have been launched. Employees often feel uncertain about when, where and how AI should be used. The result is a capability gap disguised as a technology programme.
The rise of shadow AI
When sanctioned tools fail to meet expectations, people find alternatives. This has given rise to one of the most significant emerging risks in enterprise AI: shadow AI. Employees begin using consumer tools, personal accounts or unauthorised applications to solve immediate problems. This isn’t because they are trying to undermine policy – they are simply trying to get their work done using the tools that are at their disposal. Shadow AI is often interpreted as a governance issue. In reality, it is frequently a signal that all is not well with your organisations AI implementation.
If approved tools are confusing, restrictive or poorly integrated into workflows, employees will naturally seek alternatives. The technology challenge quickly becomes a compliance challenge. And the compliance challenge is really a people challenge.
Where true AI value comes from
The organisations seeing genuine returns from AI tend to share a common characteristic. They treat AI as a workforce transformation initiative rather than a technology deployment.
They invest heavily in understanding behaviours, motivations and readiness. They identify champions early and they address concerns before they become resistance.
They build confidence among their employees before demanding adoption. Most importantly, they recognise that productivity gains are created by people using technology, not by technology existing. That distinction matters – research consistently shows that organisations taking a people-centred approach to transformation achieve stronger adoption and higher returns than those focused primarily on technology implementation. Technology creates the potential. People create the outcomes.
Before you accelerate, understand the terrain
None of this is an argument against AI. The opportunities are real. The productivity gains are real. The competitive advantages are real, but so is the risk of deploying investment blindly.
Every significant business investment is preceded by due diligence. Companies assess markets before launching products. Developers assess ground conditions before construction. Investors assess risk before committing capital.
Yet many organisations continue to invest in AI without first understanding the workforce expected to adopt it. That is a remarkable contradiction.
In a space that remains new, uncertain and rapidly evolving, clarity is not a luxury.
The organisations that win the AI race will not necessarily be the ones that move first, but the ones that best understand the terrain of their organisational landscape.
Because when everyone else is running without a map, knowing where you are becomes a competitive advantage in its own right.
