The AI Multiplier: How Leadership Choices Shape Long-Term Impact

It's difficult to overestimate AI's impact on businesses. As promising pilots evolve into opportunities to scale, organizations are making major investments in the technology. According to Gartner, worldwide spending on AI will reach $2.52 trillion this year – a 44% increase year on year. (*1).
Yet this rapid accleration is also leaving many organizations reeling. Fujitsu's own research shows that 82% of business leaders say that AI's rapid advances has been a reality check for them. (*2).
As AI becomes part of the plumbing of business, it starts to shape how decisions are made, how supply chains function, how services are delivered, and how risk is managed. At the same time, leaders and practitioners are recognising the added complexity this brings.
This presents a dual challenge. AI offers significant opportunity for productivity, innovation and cost discipline. But it also operates as what might be described as a system-level technology: its effects extend beyond a single function or balance sheet line. Decisions about AI deployment can influence energy consumption, data governance, workforce design, supply chain resilience and public trust.
For business leaders, the question is no longer simply whether to adopt AI. It is how to adopt it in ways that create enduring value.

This is where Paul Polman, co-author of Net Positive, offers a useful perspective. Throughout his tenure as CEO of Unilever, Polman argued that long-term performance and societal contribution are not competing priorities.
In the context of AI, that philosophy becomes newly relevant. Technology amplifies intent, and as technology becomes more powerful, so the quality of leadership intent matters more.
“Companies that serve the world best will ultimately be the most successful.”
— Paul Polman

For many organisations, AI offers attractive short-term gains in the form of increased efficiency and productivity. It is natural to pursue these. However, the rush to deploy can be counter productive if the organisation takes a short-term view.
Organisations drive success from their relevance to their stakeholders – which can include employees and wider society, as well as shareholders and customers. The Net Positive mindset asks: is the world better off with your organisation in it?
This is a useful lens to look through when considering AI, to achieve the greatest returns from the technology. For example,

In practice, these questions emerge in the operational detail. As Sven Jagebro of Fujitsu’s Uvance Wayfinders consultancy, who works closely with global clients, observes, the technology itself is rarely the main barrier. More often, the real friction lies in operating models, accountability structures, and cross-functional alignment.

"Trustworthy scaling of AI is increasingly a board-level topic," he notes. "In digital businesses, trust becomes a core currency — and governance is how organisations protect and build that trust as AI scales."
Many organisations begin their AI journey with contained pilots: automating specific workflows, introducing predictive maintenance, or deploying AI-driven customer support tools. But not all of these initiatives demonstrate measurable gains.
In fact, Gartner reports that just over half - around 54 % - of AI projects progress from pilot into production (*4). And gains when they emerge do not automatically translate into systemic advantage.
But when initiatives are intentionally connected, multiplier effect of AI emerges.
For example:

Business leaders are operating under immediate pressure. Quarterly performance expectations, geopolitical volatility, regulatory change and cost inflation leave limited space for abstract debate.
AI is frequently positioned, correctly, as a lever for efficiency. Cost discipline remains essential. Yet the manner in which savings are achieved matters.
Reducing duplication and friction strengthens competitiveness. Eroding safeguards or overlooking compliance complexity may create deferred risk. In compressed business cycles, deferred risk can surface quickly.

A Net Positive perspective does not ask leaders to choose between short-term performance and long-term resilience. It asks whether short-term decisions are coherent with long-term capability.
As Paul Polman puts it: 'AI provides the power, Net Positive provides the direction'.
AI functions as a multiplier. It will amplify efficiency where processes are strong. But it may also amplify fragility where governance is weak. Leadership determines which dynamic dominates.
In this sense, the defining advantage in the AI era may not be access to algorithms alone, but the clarity of principles guiding their deployment.
AI does not remove the complexity of leadership. It heightens it. By applying Net Positive principles — aligning innovation with system responsibility — organizations can position AI not only as a driver of performance, but as a contributor to durable business value.

For leaders seeking to apply Net Positive principles to AI strategy, here are five practical steps: