In 2024, the UK Public Accounts Committee warned that several government AI pilots were stalling before they reached scale. Councils attempting to deploy predictive finance tools found their legacy systems could not integrate with modern platforms. NHS Trusts introduced AI-assisted diagnostics, only to face resistance from clinicians concerned about how the tools would affect professional judgement.
Further oversight revealed deeper structural problems. Half of Civil Service digital roles were unfilled. Fragmented legacy systems slowed integration. Data quality issues undermined trust. The National Audit Office described the pattern as institutional inertia, where isolated successes failed to scale because capability and culture lagged behind ambition.
Although this example comes from the public sector, similar stories play out quietly in the private sector.
Consider a mid-sized services firm that invested heavily in AI-powered forecasting software. The dashboards were accurate. The analytics were sophisticated. But frontline managers did not trust the outputs. They defaulted back to instinct and spreadsheets. Training had been minimal. No one owned adoption. Within six months, usage had fallen sharply, and the software became an expensive reporting layer rather than a transformation engine.
In both cases, the issue was not AI capability. It was the people around it.
These examples reflect a simple truth. Change management in digital transformation determines outcomes far more than tool selection does. Technology can be bought. Confidence, clarity, and ownership cannot.
As Colette Wyatt, CEO of Evolved Ideas, explains, “Organisations often assume transformation begins with software. In practice, it begins with confidence. If people do not understand, trust, or own the change, the technology will never deliver its full value.”
For SMEs in particular, the stakes are higher. Limited resources mean there is less room for stalled initiatives. Driving digital transformation successfully requires more than implementing systems. It requires building capability, culture, and clarity around how people work alongside AI.
This is why people-first digital transformation is not a softer alternative to technical strategy. It is the foundation that determines whether digital ambition becomes measurable progress.
Why People Matter More Than Technology
Technology enables change, but people make it real.
A survey from McKinsey shows that most transformation programmes fail to achieve their objectives, with human factors such as leadership alignment, capability gaps, and cultural resistance cited as the primary drivers of failure.
When organisations focus only on tools, they overlook culture in digital transformation, employee confidence, and organisational readiness. Digital transformation people dynamics shape whether systems are adopted, ignored, or quietly resisted.
Employees need to understand how change affects their roles. Leaders must model new behaviours. Teams need to trust data and automation outputs. Without that alignment, new systems remain technically functional but operationally underused.
Wyatt puts it plainly: “Technology is rarely the constraint. Confidence, clarity, and capability are. When people feel equipped to use new systems, transformation accelerates.”
Driving digital transformation requires visible leadership, structured communication, and practical digital transformation training. When people feel safe experimenting and learning, adoption improves. When they feel excluded or uncertain, progress slows.
The Human Role in AI Adoption
AI has a way of exposing cracks that were already there. Weak governance becomes visible. Skills gaps surface quickly. Unclear ownership becomes obvious once decisions are automated. The AI skills gap in organisations is rarely theoretical. It becomes tangible the moment deployment begins.
According to the World Economic Forum, 44% of workers’ core skills are expected to change by 2027 due to AI and automation. This workforce transformation AI shift is already happening. Employees need critical thinking, data literacy, and judgement to work effectively alongside AI systems.
Leadership in AI transformation therefore extends well beyond procurement. It requires digital transformation leadership that sets guardrails, clarifies accountability, and builds psychological safety. Employee adoption of AI improves significantly when teams understand that systems augment rather than replace their work.
Wyatt notes, “AI transformation strategy must begin with clarity about ownership. If no one is accountable for adoption, the tools become optional.”
A human-centered AI strategy recognises that most resistance is practical, not ideological. People worry about looking incompetent. They worry about losing control. They worry about being replaced. Addressing those concerns openly is part of responsible organisational change digital practice.
Closing the AI Skills Gap in SMEs
The AI skills gap in organisations is particularly acute for SMEs.
Smaller firms often lack dedicated transformation teams or internal AI specialists. Government data shows SMEs are significantly less likely to adopt AI due to capability constraints and limited in-house expertise. The barrier is rarely access to software. It is reskilling for AI adoption and building future skills AI capability across everyday roles.
An effective SME digital transformation strategy does not begin with advanced technical hires. It begins with practical steps:
- Upskilling for AI across operational teams
- Identifying internal champions who test and advocate for tools
- Delivering short, focused digital transformation training
- Embedding responsible AI governance early
SME change management becomes critical because resources are constrained. Quick wins matter. Confidence builds incrementally. When AI workforce transformation is treated as gradual capability building rather than a sweeping overhaul, adoption improves.
Wyatt explains: “In our experience working with growth-focused organisations, capability assessments often surface simple blockers long before complex technical issues appear. Once teams understand how AI supports their workflow, momentum follows naturally.”
Change Management for Digital Transformation
Change management in digital transformation is not a project phase. It is the operating principle.
John Kotter’s change framework highlights urgency, coalition building, and visible early wins as essential drivers of sustainable progress. Those principles remain relevant in AI-enabled programmes.
- Create urgency grounded in business value.
- Build cross-functional ownership.
- Deliver early wins to build credibility.
- Embed new behaviours into the digital operating model change.
Without structured change management in digital transformation, organisations encounter predictable patterns. Employees revert to legacy processes. Data goes unused. Systems become expensive add-ons rather than operational upgrades.
The earlier public sector example illustrates this clearly. Tools were deployed, but digital change leadership authority was fragmented. Skills shortages compounded technical debt, and adoption faltered.
In contrast, programmes that embed leadership in AI transformation early, align digital transformation mindset shifts with measurable outcomes, and track adoption alongside performance metrics tend to scale successfully.
Key Roles in Digital and AI Programs
Successful programmes clarify roles in digital transformation from the outset.
Typically, these include:
- An executive sponsor accountable for vision and investment alignment
- A transformation lead coordinating delivery across teams
- Data and AI specialists ensuring governance and integration
- Operational champions driving day-to-day adoption
- Change management SMEs embedding cultural alignment
Digital transformation leadership must be visible. Employees look for behavioural cues from senior teams. If executives do not use new systems themselves, adoption slows.
An effective AI transformation strategy also requires clarity around governance, ethics, and accountability. The AI skills gap in organisations cannot be resolved without defined responsibility structures.
Driving digital transformation becomes sustainable when people strategy for AI is aligned with technical rollout plans, not bolted on afterwards.
Building a People-First Transformation Strategy
A people-first digital transformation strategy recognises that culture change for AI precedes scale.
It begins with assessing digital maturity and data readiness. It maps transformation capability building requirements before committing to system-wide deployments. It designs digital transformation training pathways that reflect how people actually work.
Organisations that treat workforce transformation AI as an ongoing evolution, rather than a one-off project, see stronger long-term outcomes.
The earlier private sector example reinforces this. The forecasting tool failed not because the algorithm was flawed, but because digital transformation culture had not shifted. Workflows remained unchanged. Trust was never built. No one owned adoption.
In contrast, when organisations combine a structured SME digital transformation strategy with a human-centered AI strategy, outcomes improve measurably. Technology becomes embedded into routines rather than layered on top of them.
Wyatt summarises it clearly: “People-first digital transformation is not a softer approach. It is the only approach that scales. Technology enables change, but people deliver it.”
At Evolved Ideas, digital and AI programmes are designed around this principle. Before selecting platforms or building integrations, we focus on ownership, capability, and clarity. Transformation is treated as capability building, not system replacement. That is what allows organisations to move beyond deployment and into sustainable adoption.
