Define value through business goals, not technology

One of the most common AI adoption challenges is a misplaced focus on experimentation for experimentation’s sake. “Companies often struggle to get value out of AI projects… because they start with just implementing AI pilots for the sake of cool technologies,” says Ayesha.

Instead, organisations should begin with one or more of the following:

well-defined objectives
pain points
time lags
cost inefficiencies

then establish a clear baseline for comparison.

“The problem today is that people have never measured the baseline.” Without this, it becomes impossible to track improvements or scale success.

Measuring AI success metrics, such as time saved, cost reductions, customer engagement, or product relevance, allows businesses to link outcomes to their business strategy, not just outputs.

Scaling success starts with solid foundations

Why do so many AI projects stall? According to Ayesha, “95% of AI pilots result in no value, because they can never scale. They’re not built to scale.” The reasons are structural: lack of a data foundation, no enterprise-grade AI platform, and siloed teams that operate in isolation.

“Without data, AI will never generate the results you want, but the secret sauce when it comes to data is knowing why you need it,” she explains. Without a clear understanding of purpose and a robust data infrastructure, even the most promising AI use cases remain trapped in pilot mode.

“What really separates the AI winners and losers, is that they have the data foundation, and they have a very inclusive way of AI adoption across their firm,” says Ayesha.

From single wins to systems change

For Ayesha, how to measure the value of AI requires looking beyond isolated success stories. “In order to see value, you don't just do one AI project for one team. What you have to do is launch 100 such projects.”

This scale mindset transforms AI from a department initiative to a company-wide operating system upgrade, one that reshapes how work gets done, how decisions are made, and how value is generated. “It’s not about AI intervention in one place. It’s changing the entire operating system of how your company works,: she says. AI business value, in this sense, is cumulative and systemic.

“We’re seeing this in healthcare… in retail… in marketing,” she says, pointing to AI’s real-world impact on speed, customer relevance, and business outcomes. “That’s when markets are excited, your shareholders are excited, and your customers are gaining a better experience from it.”

The takeaway: Define value, measure impact, scale deliberately

To fully realise the value of AI, leaders must move beyond hype and experimentation. That means defining clear AI success metrics, building strong data foundations, and aligning every project to a measurable business goal.

Success in AI doesn’t come from luck; it comes from clarity, consistency, and the courage to scale with purpose.

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