Why do AI projects fail, and how can enterprises get it right in 2026?

AI promises to transform industries, boost productivity and unlock new business opportunities. Yet, many enterprises don’t experience this. Projects stall, tools and solutions go underutilised, and the anticipated value of AI simply fails to materialise.  

Clearly AI is falling short, but it’s not because of the technology. The hidden bottleneck here is the legacy consulting model that many are, but shouldn’t be, relying on for expertise. We recently found that Britain’s largest businesses now spend over £320 billion annually on AI, with more than £66 billion of that going to external consultants. That’s twice the UK’s budget surplus going towards support for AI projects, all for a success rate that’s a coin flip.  

In essence, they’re spending enormous amounts for AI advice that is often misaligned or disconnected from practical realities. This raises a critical question: how can enterprises avoid sinking costs into the wrong support and truly set themselves up for AI success in 2026 and beyond? 

The legacy consulting problem 

We all know that traditional consultancies operate on billable hours and large teams; this creates incentives that reward activity over outcomes. Organisations pay for effort rather than tangible results and, despite significant investment, AI projects are continuing to fall short. In fact, around 30% of AI project failures occur because organisations prioritise technology itself, rather than the value it is meant to deliver according to our survey of enterprise leaders. Time isn’t on their side either, as a third of projects take more than six months to show any meaningful results at all. And all this comes at a time where many are under pressure to adopt at speed, with impact often being the trade-off.  

As it stands, the traditional consulting model itself is structurally misaligned for enterprises’ AI needs. Advice is never entirely impartial as consultancies often push preferred vendors, licenses or solutions that generate revenue for them rather than meet actual customer needs. Knowledge gaps compound the problem. Many legacy firms lack the deep technical expertise to truly understand AI’s capabilities and potential, meaning organisations end up effectively funding the consultants’ own transformation as they learn on the job. 

The result is predictable, but all too common. Business problems are tackled by bloated armies of consultants who suggest AI tools that go unused, as care hasn’t been taken to train people up to use them. This suggests a more systemic issue, all rooted in a model that prioritises delivery over adoption. 

Rethinking AI adoption  

To unlock real impact, organisations need to rethink how they implement and scale AI with two main pillars in mind: putting people at the heart of technical transformation and staying laser focused on measurable outcomes. When working with external partners, these priorities need to be communicated and agreed upon from the outset. 

People-first 

Harvard recently found that workers using AI complete tasks 25% faster and produce 40% higher-quality results, showing the true impact of amplifying individuals’ expertise. But those results only come when people are able to use AI tools to maximum effect, and that comes through training and upskilling that accounts for the real workflows being augmented. 

In 2026 and beyond, there needs to be greater focus on hands-on guidance, clear explanations and promoting simple ways for people to apply AI in their daily work to boost overall trust. This allows leaders to gain clarity on what good AI use looks like, while teams learn from shared examples, community learning and best practice.  

By embedding practical know-how, celebrating early wins and continually sharing what works across the organisation, a people-centric approach turns AI into a tool people want to use, not something they’re being forced to adopt. The result is meaningful performance improvements, fewer friction points and lasting value for the people who will use AI every day (if they don’t already). 

Value-first 

In parallel, enterprises should adopt a value-first mindset that takes a holistic view of how different processes overlap and how AI can create efficiencies across them. This starts by identifying high-value use cases across the organisation and honing in on where AI can have the most impact.  

From there, proven technologies can be deployed in fast, iterative cycles. Enterprises and their partners can structure data through ontologies, build agentic systems that fit existing stacks or integrate AI into decision-making workflows – whatever is best suited for the problem at hand.  

Organisations must concentrate on ongoing optimisation and knowledge transfer internally to ensure projects aren’t a ‘one and done’. AI is evolving so quickly, what’s valuable today might not be in a few years’ time. By putting clearly defined goals and objectives at the heart of adoption strategies, enterprises and internal consultants can set themselves up for more success in a more sustainable way, prioritising the tools and transformations that will move the needle. 

This will allow teams to further adopt AI confidently, embed it into daily workflows and realise the long-term business value of intelligence. AI should then become a trusted, practical tool that strengthens capabilities, improves processes and really delivers sustained impact across the organisation. 

Looking ahead 

The enterprises that succeed will be those that take both a people-first and value-first approach, prioritising upskilling and meaningful results over investing in the technology without clear goalposts to aim at.  

AI will soon no longer be defined by individual experimentation or siloed pilots, and will be integrated holistically across organisations, powering all that they do. Successful adoption will move beyond the isolated projects we’ve seen fail to date, laying the new foundations for the modern enterprise – think shared agents, collective prompt libraries and integrated workflows. 

Breaking free from the legacy consulting model is central to this transformation. By moving away from outdated processes and priorities, organisations can focus on partnerships, processes and structures that deliver real impact – minimising wasted spend and getting the results they need in the process.