Why is this happening?  A new MIT report revealed 95% of enterprise generative AI pilots are failing to deliver measurable business value. While startups are reaping dramatic gains, large companies are stalling.

MIT calls it the “learning gap”; the space between what AI learns and what the organisation learns to do with it. Despite vast investment, many enterprises still lack the execution layer that turns intelligence into measurable outcomes. 

All data, no structure

AI systems can, of course, generate powerful insights. But too often insights get lost in dashboards, detached from workflows, and ignored by the teams meant to act on them. Companies have more data at their disposal than ever before, yet they are struggling to move faster or serve customers any better.

Why? The answer is the lack of structure that provides intelligence into how people are actually working. The organisations that succeed don’t just run AI but run on it. 

Journey Management as the bridge between insight and impact

The bridge between AI insight and business execution lies in mapping, managing, and continuously improving every step of a customer or employee journey. Otherwise known as journey management.

When enterprises visualise their operations through journeys, they can see exactly where intelligence should fit. Instead of applying AI in isolation, they apply it in context. For example, anticipating issues during onboarding or identifying friction before it hits satisfaction scores.

This transformative approach embeds AI where it matters inside workflows and assigns clear ownership for decisions. This is the difference between a static insight and tangible way of measuring improvement.

The AI success stories

MIT’s findings mirror what we see in the field. The enterprises that break out of pilot purgatory focus narrowly, partner smartly, and execute relentlessly. They pick one pain point, connect AI directly to it, and measure outcomes related to business objectives. 

These companies design AI around actual workflows with meaning and purpose,  rather than reports. They invest in change management to ensure teams trust and act on recommendations. Plus, they bridge silos, bringing together data science, CX, and operations to make AI a shared capability rather than a specialist function.

One leading professional services firm that we work with illustrates this mindset. The firm is using journey management to align its consulting teams around what matters most to clients, surfacing insights faster and spending less time buried in data. A shift away from analysis to execution is where the 5% difference emerges.

The value of connection

Building an execution layer isn’t about adding more technology or creating more disparate dashboards, instead, it means hardwiring intelligence into decision-making. This begins by mapping critical journeys to identify where the issues currently are, and whether AI can remove friction or create value. To do this, it requires connecting data, feedback, and outcomes so that every recommendation links to a measurable impact.

The key thing is transforming disconnected feedback loops into actionable guidance. When intelligence flows through shared journeys, alignment happens naturally, and teams can prioritise the actions that matter most, track the impact, and refine continuously.

The next wave of AI success

Looking beyond pilot projects, and taking a more measured approach to the impact of AI will ensure AI success continues on the upward trajectory. That’s when the 5% becomes the majority and the AI revolution moves from potential to impact.

To do this, businesses need to prioritise operationalising intelligence in a way that embeds it into the journeys that drive growth, efficiency, and experience. AI’s value doesn’t emerge from the lab or when it’s a satellite project. It emerges when it becomes part of how work gets done.

The companies that win with AI will  turn intelligence into action, at scale.

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