The AI Strategy Transformation Gap Nobody Is Talking About

MIT Technology Review Insights has just published a study in association with EY titled “Finding value with AI and Industry 5.0 transformation.” It is a survey of 250 executives across industrial and energy companies globally and maps the gap between where companies focus their AI investments and where the actual value is being created.

Buried in the report is a chart on ‘barriers to adoption’ of emerging technologies. On the surface, it reads like a familiar list: culture, skills, collaboration, data silos, strategic alignment and more, with how respondents rated them. Leaders glance at these numbers and nod, thinking they have seen this before. They move on. Situation normalised.

That is a problem.

When you stop reading the chart as a list of adoption barriers with associated weights and start reading it as an interlinked system, a different picture begins to emerge.

You realise that the barriers are not independent variables. They are clusters with a self-reinforcing loop. As you unravel the underlying connections, the data makes an uncomfortable argument:

Most organisations are not failing at technology. They are failing at the organisational architecture that is required to support it.

The reported data comes from industrial or energy companies specifically, but the insights extend further. The same dynamics apply in retail, consumer goods, financial services, healthcare and beyond, i.e., anywhere leadership teams are investing in AI-driven transformation and wondering why they are failing to scale.

The Paradox

Start with the most striking adoption barrier in the chart.

“Lack of support at the board and C-suite level.” This parameter records the highest “not a barrier” score with 46%. This is a good number and signals that the board is all in.

The real story begins to unravel when you look at the entry “Organisational culture that limits innovation.” This one gets 86% between “somewhat – extreme” barrier! This carries a huge signal.

Leaders are confident they are enabling transformation. The organisation is not ready to implement.

This is not a leadership vision problem. This is a strategy problem and something deeper that we will look at soon.

The ability to craft a transformation agenda in the face of rapid evolution in technology is one thing, but to be able to deliver on it is another. Vision creates goals, but creating the right roadmap requires detailed action.

Most organisations have the first. Most have also created the road map, but very few have effectively evaluated the structure that is needed to successfully execute the roadmap.

This gap in the transformation agenda is where every adoption barrier on this chart originates.

Three Clusters. One System.

When you deconstruct the chart, three distinct clusters emerge. Each is causally linked to the next.

Cluster One: Culture Misalignment

“Organisational culture that limits innovation” → “Lack of interior mindset” → “Lack of strategic alignment.”

These are not three separate problems. They are a cascade.

1. Culture shapes mindset.

2. Mindset determines whether the strategy feels owned or imposed.

3. Strategic misalignment leads to transformation failure.

Most transformations tend to focus on the end point – trying to fix strategic misalignment, but the same cannot be fixed until the culture has shifted.

Off-sites, cross-functional alignment workshops et al. are activities, not progress and fail outside the conference rooms if the culture is not ready to absorb them.

The tools are right. The focus is wrong.

Cluster Two: Capability Vacuum

"Lack of relevant employee skills at the board level" → "Lack of relevant employee skills at the implementation level" → "Insufficient creation and prioritisation of use cases."

Capability initiatives carry a built-in bias; they default to the frontline while leadership pleads busy schedules or seniority. That is the wrong instinct. AI is new for everyone, regardless of title or role. No one gets a pass.

AI-led transformation, like any other, is not a bottom-up task. The gap at the top is more dangerous than the one at the bottom.

When boards and senior leadership lack AI literacy, the consequences are systemic and counterproductive. Capital gets allocated without challenging assumptions. Vendor proposals go unchallenged. Use cases get selected not by reimagining systems and workflows with AI, but for how quickly they can lower costs or drive efficiency of what already exists.

The focus shifts from building the right version for the future to delivering a faster version of the present.

The result: AI investments plateau with scale and miss the opportunity to stay relevant in an evolving marketplace.

The skills gap is across tiers. Until it is addressed at the board, CXO, and frontline levels simultaneously, decisions will be driven by human comfort rather than strategic logic and transformation will remain a dream.

Cluster Three: Execution Fault Lines

“Insufficient cross-site collaboration” → “Poor data integration” → "Alliance partner gaps".

These look like operational problems. They are not. They are the downstream consequences of culture misalignment and capability vacuum. Respondents rated them between 66%-87% as a 'barrier to adoption' of some degree. This is not surprising.

Siloed functioning reflects the culture. When organisations are locally optimised, teams default to protecting their own turf. Poor data integration across the organisation is an expected outcome of this territorial behaviour. Data is the fuel for AI, and when it is inadequate or siloed, the outcomes are suboptimal.

Partner ecosystems follow the same logic. External partners align with what they see and experience. When the internal narrative is unclear or polarised, it shows in how alliances perform. Partnerships flourish or stall based on the undercurrents they navigate.

The Compounding Loop

These three clusters form a compounding loop.

• Culture constrains strategic thinking

• Strategy misaligns use case prioritisation

• Skills are not built across levels

• Collaboration fragments

• Data stays siloed

• ROI disappoints

• Culture hardens further against AI transformation

 Each cycle of disappointment makes the next attempt harder. Each cycle of disappointment reinforces scepticism. Budgets get cut. The narrative that surfaces reads - “We tried AI, it’s over hyped” and gradually it becomes an organisational memory that shapes future risk appetite. The onus of not letting this percolate rests with the leadership team.

Where to Start

Once you look below the surface, adoption barriers become a design input. Every cluster, points to a specific leadership action. Three priorities emerge.

1. Own the Right Scoreboard

Most AI transformation is measured by what was deployed - tools rolled out, processes automated, costs reduced. These are activity metrics, not outcomes.

Strategic value looks different. It shows up in growth unlocked and resilience built for the evolving future. These are not numbers that tick on the standard operating dashboard. They require a measurement framework that connects AI investment to strategic objectives that go beyond operational efficiency and quarterly results.

Without that framework, IT tracks delivery, finance tracks cost, and no one is tracking whether the transformation is moving the business forward in the right direction. This needs a clear future value creation clarity at the top.

2. Close the Literacy Gap at Both Ends

46% of respondents rated board and C-suite support as not a barrier. That may be true. But there is a difference between a board that is supportive and a board that knows what questions to ask. A board that lacks AI fluency will neither be able to provide direction nor have effective oversight over organisational actions. This is not poor governance. This is abdication.

The solution starts with the constitution, not with training. When the next board seat opens, AI literacy needs to be a selection criterion, not a nice-to-have. CXOs need to drive this conversation, not wait for governance to catch up.

Build literacy top to bottom simultaneously. The goal is not technical depth. It is decision-making quality.

3. Rewire the Organisation Around A Culture That Supports Transformation

Culture does not change through communication programs. It changes when the organisation is rewired. How incentives, governance, data ownership, and cross-functional accountability are used determines how people behave, regardless of what the transformation deck says.

Culture is top-down. Leadership needs to get caught doing what it preaches.

AI works across functions; it does not respect org charts or reporting lines. But most organisations are still designed around them. Structures are important, but AI needs fluidity and collaboration to create value. To enable this requires conscious leadership action.

Every major technology transition since the industrial age has produced the same lesson. Technology is not the limiting factor. The organisation’s capacity to absorb, adapt, and operate is.

What is different today is the scale and speed, unlike anything experienced before.

The technology is already ahead. It can generate insights faster than organisations can act on them. It can identify opportunities that existing structures are not designed to capture.

The gap is not in what AI can do. It is in whether the organisation - its people, culture, and governance structure is ready to support.

It is this gap where transformation dies.

The organisations that close that gap deliberately will define their categories. The ones that don’t will keep measuring activity and calling it progress.

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