How companies can turn generative AI into lasting business value

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As enthusiasm for generative artificial intelligence sweeps across boardrooms, a growing number of executives are confronting a harder question: how to turn early experimentation into sustained business value. Experience advising large organisations suggests that success rarely comes from isolated pilots or ad hoc deployments. Instead, companies that extract meaningful returns tend to build a self-reinforcing cycle that links technology choices, people strategy and long-term foundations.

At the centre of this cycle is discipline in selecting where generative AI is applied. The challenge is no longer identifying possible use cases, but deciding which ones matter most. High-value opportunities tend to fall into three broad areas. The first is customer experience, where generative AI can be used to deliver more personalised and contextual interactions, improving engagement, conversion and loyalty. The second is employee productivity, with tools that automate repetitive tasks such as report writing, meeting summaries or coding, allowing teams to focus on higher-value work. The third lies in entirely new capabilities, where generative AI makes it feasible to address complex or time-consuming challenges that were previously impractical.

Focusing on use cases that scale and deliver tangible impact helps organisations build momentum. Early wins strengthen the business case for further investment, creating the conditions for broader adoption rather than stalled experimentation.

However, choosing the right use cases is only part of the equation. Companies must also navigate trade-offs between speed, accuracy and cost. There is no universal formula. A customer service assistant, for example, may require both rapid responses and high accuracy, while medical diagnosis demands maximum precision even if response times are longer. Other workloads, such as document processing, may not require immediate output and can be scheduled in ways that reduce costs.

Managing these trade-offs thoughtfully allows organisations to deploy generative AI more efficiently and frees up resources to pursue additional high-value opportunities. Poorly aligned decisions, by contrast, risk inflating costs or undermining trust in the technology.

The third element of the cycle is future-proofing. Given the pace at which generative AI is evolving, waiting for clarity or trying to predict the next breakthrough can be counterproductive. Instead, leaders are better served by building a foundation that prioritises flexibility and choice. Proprietary data, enriched with the unique context of a company’s operations and customers, is central to this approach and increasingly a key source of differentiation. Equally important is embedding security, privacy and operational resilience from the outset, rather than attempting to add them later as an afterthought.

Together, these three components reinforce one another. Strong foundations enable faster experimentation; successful use cases justify further investment; and careful optimisation improves returns, creating a flywheel effect that sustains competitive advantage.