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

In the Press

·

5 Mins

Tarek discusses with The AI Journal - British enterprises now spend over £320 billion annually on AI, with more than £66 billion going to external consultants. The success rate is roughly a coin flip. Tarek Nseir, co-founder and Senior Value Partner at Valliance, identifies the structural culprit. The legacy consulting model, built on billable hours and large teams, creates incentives that reward activity over outcomes.

Around 30% of AI project failures stem from organisations prioritising the technology itself rather than the value it should deliver. A third of projects take more than six months to show any meaningful results. And consultants pushing preferred vendors or licences, rather than offering impartial advice, mean organisations often fund their consultants' own AI education whilst their own projects stall.

The path forward rests on two principles. The first is people-first adoption. Harvard research cited in the piece shows that workers using AI effectively complete tasks 25% faster and produce 40% higher-quality output, but only when training reflects real workflows rather than generic onboarding.

The second is a value-first mindset, starting from high-impact use cases, deploying in fast iterative cycles, and building ongoing optimisation and knowledge transfer into the programme from day one.

The enterprises that get this right will move beyond isolated pilots into shared agents and integrated workflows, making AI a trusted part of how they operate.

Tarek discusses with The AI Journal - British enterprises now spend over £320 billion annually on AI, with more than £66 billion going to external consultants. The success rate is roughly a coin flip. Tarek Nseir, co-founder and Senior Value Partner at Valliance, identifies the structural culprit. The legacy consulting model, built on billable hours and large teams, creates incentives that reward activity over outcomes.

Around 30% of AI project failures stem from organisations prioritising the technology itself rather than the value it should deliver. A third of projects take more than six months to show any meaningful results. And consultants pushing preferred vendors or licences, rather than offering impartial advice, mean organisations often fund their consultants' own AI education whilst their own projects stall.

The path forward rests on two principles. The first is people-first adoption. Harvard research cited in the piece shows that workers using AI effectively complete tasks 25% faster and produce 40% higher-quality output, but only when training reflects real workflows rather than generic onboarding.

The second is a value-first mindset, starting from high-impact use cases, deploying in fast iterative cycles, and building ongoing optimisation and knowledge transfer into the programme from day one.

The enterprises that get this right will move beyond isolated pilots into shared agents and integrated workflows, making AI a trusted part of how they operate.

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