78% Have Deployed AI. Almost None Have Built Advantage.

The numbers look like progress. They aren't.

According to recent data, 78% of companies now use AI in at least one business function. That's a 55% increase from the previous year. By 2027, 89% of organisations expect to have generative AI embedded in their operations.

The adoption race appears to be over. Everyone won.

So why did confidence in AI as a strategic opportunity drop from 82% to 68% in a single year?

Across every major study, the same pattern emerges: deployment is accelerating while belief is eroding.

Boston Consulting Group surveyed 1,000 executives across 59 countries and found that 74% of companies have yet to show tangible value from their AI investments. Only 26% have developed the capabilities needed to move beyond proofs of concept. Just 4% are generating significant, consistent value.

Meanwhile, spending continues. 63% of businesses now use AI in two or more functions, up from 50% six months prior. Companies aren't slowing down. They're doubling down.

But more AI isn't producing more confidence.

In the UK, 30% of companies state they are not currently considering AI adoption at all. Among those who have adopted, only 28% describe themselves as "fully embracing" the technology. The majority, 40%, are using AI selectively, still evaluating where else it might add value. Another 20% report minimal usage so far.

The gap between having AI and using AI has a name. It's the adoption crisis hiding in plain sight.

Over the next year, Roku predicts that 100% of the streaming audience will see ads. For growth marketers in 2026, CTV will remain an important “safe space” as AI creates widespread disruption in the search and social channels. Plus, easier access to self-serve CTV ad buying tools and targeting options will lead to a surge in locally-targeted streaming campaigns.

Read our guide to find out why growth marketers should make sure CTV is part of their 2026 media mix.

Learn more.

When asked to explain the struggle, 73% of organisations cite data quality as their biggest AI challenge.

It's a convenient answer. Technical, blameless, solvable with budget.

But data quality doesn't explain the confidence collapse. If the barrier were purely technical, more spending would restore optimism. It hasn't. Investment is up. Belief is down.

The actual blockers live in territory that's harder to discuss in boardrooms:

Culture resistance. People protecting workflows AI threatens to change.

Governance fog. Unclear ownership of AI decisions and outcomes.

Middle management freeze. The layer that controls daily work but wasn't consulted on transformation.

Usage theatre. Tools deployed, dashboards built, nobody logging in.

BCG's research confirms this. Around 70% of implementation challenges stem from people and process issues. Only 20% involve technology problems. A mere 10% relate to AI algorithms, despite algorithms consuming a disproportionate amount of organisational time and resources.

Too many companies make the mistake of prioritising technical issues over human ones.

The companies generating actual value from AI follow what BCG calls the 70-20-10 principle: 70% of effort goes into people and processes, 20% into technology and data, 10% into algorithms.

The laggards invert this. They spend months refining models while ignoring the fact that their teams haven't changed how they work.

AI leaders, by contrast, focus on change management, workflow optimisation, talent development, and governance. They invest twice as much in workforce enablement. They scale twice as many AI solutions across their organisations. And they expect 60% higher revenue growth from AI by 2027.

The difference isn't the technology. It's what happens after the technology arrives.

The research reveals something else that challenges common assumptions. The greatest AI value doesn't come from support functions like IT or HR. It comes from core business operations.

Leaders generate 62% of their AI value from core business functions: operations (23%), sales and marketing (20%), and R&D (13%). Support functions contribute 38%, with customer service (12%), IT (7%), and procurement (7%) as top contributors.

This matters because most AI adoption programmes start with support functions. They're safer. They're more contained. They're easier to pilot.

But they're not where the value lives.

Financial services organisations, in particular, are positioned to benefit from AI in core operations. The sectors with the highest concentration of AI leaders, according to BCG, are fintech (49% leaders), software (46%), and banking (35%). These industries experienced digital disruption early, giving them a head start in developing the capabilities AI requires.

But leading in deployment also means leading in consumption gap exposure. The pain financial services feels now is the pain every sector will feel by 2027.

If your two or three AI champions left tomorrow, would adoption survive?

This is the question that separates organisations with durable AI capability from those sitting on fragile foundations.

In most companies, AI adoption depends on a small number of informal champions who figured out how to make the tools work. They share tips in side conversations. They troubleshoot problems others haven't solved. They carry the load.

If they disengage, adoption collapses.

Usage data doesn't capture this fragility. Dashboards show activity, not dependency. They can't tell you whether behaviour has actually changed or whether a handful of power users are generating all the numbers.

This is why 74% of companies struggle to scale AI value. They've deployed tools without building the human infrastructure that makes adoption stick.

The standard AI implementation model invests in two things: tools and training.

Tools are the platforms, models, and infrastructure. Training is the courses, certifications, and lunch-and-learns.

Then organisations expect behaviour change to follow. It doesn't.

What's missing is the connective tissue between tools and transformation. The operating layer where adoption actually happens. This layer isn't technical. It's human. And it requires something most AI programmes never build:

Mission Alignment. Connecting AI use to work that matters, not experiments that impress. People adopt tools that help them do their jobs better. They ignore tools that feel like mandates.

Organic Leadership. Identifying and empowering the informal champions who are already making AI work. These aren't the people on the AI steering committee. They're the ones solving real problems in real workflows. They need visibility and support, not just recognition.

Authentic Engagement. Creating conditions where people choose AI because it helps, not because they're told to. Compliance doesn't produce commitment. And commitment is what makes adoption sustainable.

Tribal Identity. Building communities of practice where adoption becomes shared identity, not individual burden. When learning happens in isolation, it stays in isolation. When learning happens in community, it spreads.

This is the MOAT framework: Mission Alignment, Organic Leadership, Authentic Engagement, Tribal Identity. Four pillars that address what the research keeps surfacing but never names, the human infrastructure of AI adoption.

The 89% expecting generative AI adoption by 2027 creates a narrowing window.

Organisations have 18 to 24 months before AI capability becomes universal. When everyone has the same tools, the advantage shifts entirely to those who learned to use them.

The research confirms financial services is already ahead. Fintech and banking led because they faced digital disruption first. But leading in deployment means leading in consumption gap exposure.

The organisations that solve adoption first don't just perform better. They become the reference points for everyone else.

The research tells you what's happening across the market. It doesn't tell you what's happening inside your organisation.

You have AI deployed. Usage dashboards show activity. But when you ask whether adoption is real or fragile, whether it would survive if your champions moved on, the answer is usually unclear.

This is the gap the data can't close: the distance between deployment metrics and actual behaviour change.

The 30-Day AI Adoption Pilot

BuildMoat is running a hands-on diagnostic for financial services organisations who've implemented AI and want to know the truth about their adoption health.

In 30 days, we help you:

See where AI is actually changing work. Not usage counts, but behaviour patterns at team level.

Identify your organic leaders. The informal champions already making AI stick, often invisible to leadership.

Surface the workflows that spread versus the ones that stall. So you know what to scale and what to fix.

Deploy human-powered engagement. Peer-to-peer learning infrastructure that doesn't depend on hero users or top-down mandates.

This isn't training. It isn't a platform demo. It isn't surveillance.

It is 30 days of visibility into whether your AI investment is building durable capability or sitting on fragile foundations.

At the end, you'll have evidence, not anecdotes, to take to leadership. And you'll know whether adoption is real, fixable, or at risk.

Who this is for:

Who this isn't for:

Apply for the 30-Day AI Adoption Pilot

We're running a limited number of pilots to keep this senior-led and hands-on. If there's a fit, we'll confirm quickly. If not, we'll tell you that too.

[Apply for the Pilot]

The future belongs to organizations that build communities, not those that buy customers.

Which future are you building?

Sources: BCG "Where's the Value in AI?" (October 2024); McKinsey Global Survey on AI (2024); Moneypenny UK Business AI Survey (2025); Hostinger AI Statistics Report (2025)

We appreciate you taking the time to read this. Building community isn't just theory for us, it's a framework we've developed and refined with companies at every stage.

Have questions about adopting The Moat framework for your business? We'd love to help. Book a discovery call to speak directly with one of our consultants. We'll audit your current community engagement process and show you exactly where the opportunities are.

Did someone forward this to you? Subscribe here to get strategic insights on building community as your competitive advantage, delivered directly to your inbox.

Keep building your community until next time.

Build Moat
Where Community Becomes Your Competitive Edge