Want returns from AI? Accelerate your growth

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In boardrooms around the world, we see the same pattern. There is plenty of AI activity, but too little measurable impact. Our research shows why: 74 per cent of AI‑driven returns are captured by just twenty per cent of the 1,217 organisations that participated in the study.

What sets these frontrunners apart is what we call AI fitness: the ability to focus AI on what truly matters, build fit‑for‑purpose foundations, and embed AI across the enterprise.

We see the same pattern among Dutch organisations. ‘The strength of organisations with high fitness lies in their consistency. It’s much like top-level sport. In elite sport, you push your limits by training frequently, eating and drinking well, and taking rest at the right times. With AI, consistency comes from focusing on high-value areas for transformation, engaging people effectively, and developing the foundations in parallel to maturity,’ says Edwin Bommel, AI expert at PwC Netherlands.

To understand why some organisations see real returns while most do not, we benchmarked 1,217 companies worldwide across 25 sectors on their AI‑driven financial performance, defined as revenue growth and efficiency gains directly attributable to AI, adjusted for sector differences.

We also asked senior executives at these companies about their engagement across sixty areas of AI management and investment practice, to assess the impact of these practices on AI‑driven financial performance. We grouped these practices into nine categories, covering both how organisations use AI and the foundational capabilities that make AI reliable and scalable, such as strategy and governance. Together, these nine categories form the components of our AI fitness index.  

The headline result is clear: organisations with the highest AI fitness achieve AI‑driven performance that is 7.2 times higher than that of other respondents.

Why? Because AI fitness drives stronger intermediate outcomes that directly translate into financial performance. AI leaders bring new products and services to market faster, improve their operating models, make better decisions, and build greater trust with customers and employees.  

Similar compounding effects occur between AI usage and AI foundations. When organisations with strong foundations increase their use of AI, they see almost double the improvement in AI‑driven performance compared with organisations that have weaker foundations.

Foundations increase the conversion of AI activity into measurable outcomes. Stronger data and platforms reduce deployment time, while workflow redesign and employee trust‑building accelerate adoption. Greater adoption, in turn, generates richer data and feedback – enabling continuous improvement and increasing impact with each deployment.  

It is clear that organisations pulling ahead through AI are not simply “doing more AI”. They are building the capabilities that make AI scalable and reliable, and then deliberately choosing where to apply AI for maximum financial impact.

Many organisations use AI to work more efficiently. That matters. But the real acceleration comes when AI is also applied to reinvention and growth.

Our study shows that AI leaders are 2.6 times more likely to say that AI has improved their ability to reinvent their business model. They deploy AI early in the value chain – to identify opportunities, uncover new value pools, and develop propositions that span industries.  

We also find that capturing growth opportunities driven by industry convergence is the strongest factor influencing AI‑driven financial performance. AI leaders are two to three times more likely to use AI to collaborate with organisations in other sectors, unlock value through business ecosystems, and compete beyond their traditional industries.

Consider, for example, automotive manufacturers working with healthcare providers to equip vehicles with advanced sensors that monitor driver health. AI systems then use this data to design personalised prevention programmes.  

The frontrunners in our research reinforce their AI‑driven growth strategies with disciplined management. They make strategic choices early and operationalise them through clear ownership and performance measurement.

Compared with other organisations, AI leaders are more likely to have a prioritised AI roadmap for both the short and long term, align AI vision with business objectives, systematically track business impact, and hold senior leaders directly accountable for AI outcomes.  

Aiming AI at reinvention and industry convergence is the easy part. The real challenge is delivering those outcomes consistently. The differentiator is not ambition, but focused foundations.

AI leaders build only what is required to scale AI use for growth and other strategic objectives. These foundations change the economics of AI: less friction, less rework, and faster, more reliable deployment.  

That effect shows up as a conversion rate: once organisations adopt the right practices, the payoff from new AI use cases doubles.

Our research shows that the following practices deliver the greatest performance improvements.

Fund and flex the AI portfolio like an investor

Leading companies invest 2.5 times more of their revenue in AI than others. Leaders in software, banking, and media and entertainment invest the most – around 5% of annual revenue.

But investment alone is not enough. These organisations also ensure that funding remains aligned with business priorities. They are 1.3 times more likely to reallocate financial and human resources to high‑value AI initiatives as priorities shift.  

Foster AI‑driven innovation

If funding is the fuel, innovation is the engine. AI leaders create the conditions for rapid experimentation. They are 1.5 times more likely to provide dedicated infrastructure for AI experimentation, such as sandbox environments isolated from core systems.

They also more often appoint innovation owners to steer AI initiatives within business units. This combination enables pilots to start quickly and run safely.

In addition, AI leaders conduct structured reviews of innovation efforts to decide which initiatives to prioritise, scale or stop – creating a pipeline that consistently delivers AI solutions with measurable value.  

Increase adoption by building employees’ trust

AI only creates value when people use it. Employee trust is therefore far more than a change‑management issue. Low trust leads to low usage – and low impact.

At AI‑leading organisations, employees are 2.1 times more likely to trust AI‑generated insights and act on them in daily work.

Trust rarely comes from a single initiative. It is built through a system of:

• Involvement. At leading companies, it’s more likely that teams of business, data, and AI specialists are co-creating AI solutions. Cross-functional collaboration promotes alignment between business needs and solution designs, and it does away with the clunky developer-to-user handoffs that kill adoption. AI leaders also offer employees clear incentives to experiment with AI, which can give rise to ideas for scalable solutions that support the firm’s priorities.
• Skill building. Effective upskilling teaches employees how to apply AI in everyday work situations. Leading companies are more likely to provide employees with this sort of ongoing, role-based AI learning. Their senior executives are also more likely to set good examples, by attending training sessions and visibly using AI in their work.
• Safety. AI leaders further build employee trust with guard rails and protocols. When people understand what AI is allowed to do, which matters require escalation, and who’s accountable, they can use AI with greater confidence. At leading companies, employees are more likely to have role-based controls on data and AI access, along with robust, up-to-date security for data, models, and infrastructure.

Use governance to build trust, and accelerate

AI leaders take governance seriously while applying it in a way that accelerates delivery rather than slowing it down. A governance committee defines Responsible AI policies, while teams apply them through standard templates, fast checkpoints and continuous monitoring.

Routine use cases continue at pace, with the committee focusing only on the highest‑risk work.

AI leaders are 1.7 times more likely to use a documented Responsible AI framework and 1.5 times more likely to have a cross‑functional AI governance committee.  

Eliminate technology and data friction

In practice, the biggest barriers to scaling AI are data quality and access, technology integration, and the hidden cost of repeatedly rebuilding the same components.

AI leaders focus on removing these bottlenecks for their highest‑value use cases. They are 2.4 times more likely to create reusable, centrally catalogued AI components and 1.7 times more likely to provide the high‑quality data required for prioritised applications.  

Once leaders define their AI objectives, they ensure AI solutions are developed and deployed everywhere they can make a difference.

Embedding AI across the enterprise means:

• deploying AI broadly across functions and teams
• integrating AI into core workflows and systems
• applying AI at higher levels of sophistication, from assistance to automation

Go broad

Our research indicates that most companies still concentrate AI in pockets, consisting of a few use cases scattered across a few functions. Leading companies scale proven use cases across teams, regions, functions, value chain activities, and products so that value is not trapped in one isolated area. For example, an insurer that proves AI can cut invoice processing time in finance can reuse the same document intake and workflow model to automate contract review in the legal function and claims processing in operations.

We found that AI leaders are roughly twice as likely as other companies to apply AI across the value chain, in areas as varied as corporate strategy, supply chain operations, and the front and back office.

Some sectors are further along in using AI across the enterprise than others. Media and entertainment companies rate near the top for embedding AI into processes throughout the value chain, with 54 per cent having done so in direction setting (e.g. strategy, planning), 55 per cent in demand generation (e.g. marketing, sales), 35 per cent in support services (e.g. finance, HR), and 41 per cent in demand fulfilment (e.g. production, supply chain planning).

Other sectors rate well in particular parts of the value chain: direction setting for pharmaceuticals, life sciences, and automotive; demand generation for technology services and hospitality and leisure; support services for private equity; and demand fulfilment for insurance.  

Go deep

The top-performing companies in our study don’t just add AI on top of workflows. They fully integrate AI into standard operating processes. That’s essential to improving both task efficiency and output quality. This could look like redesigning customer support so AI runs inside the case management system—pulling the right customer context and knowledge, drafting responses, and routing only complex cases to specialists—rather than bolting on a separate chatbot that agents have to consult and then manually copy back into a support ticket.  

Go autonomous

Across all the operational performance indicators we tested, automating decisions has one of the strongest links to AI-driven performance. The reason is simple: when AI can safely take on a larger share of routine, high-frequency decisions, cycle times shrink, throughput rises, and performance improvements emerge.

Our research shows that AI-driven performance leaders are nearly twice as likely to operate AI at higher sophistication levels, meaning that AI executes multiple tasks within guard rails or even operates autonomously and self-improves. Perhaps it’s no surprise then that AI leaders are 2.8 times as likely to increase the number of decisions made without human intervention. These leaders also report much stronger gains in decision quality, a reminder that automation works best when quality improves alongside speed.  

This doesn’t automatically mean machines are taking everyone’s jobs. Full autonomy is still the exception: only fifteen per cent of AI leaders say their most sophisticated use case is autonomous and self-improving. Plus, although 48 per cent of AI leaders expect head-count reductions of at least five per cent due to AI, another 49 per cent expect either little to no change in head count, or head-count increases. Finally, in many cases, we’ve seen that the immediate shift is not the removal of people, but the removal of delay: AI handles repeatable judgment calls inside guard rails, while humans focus on exceptions, trade-offs, and the steering of decisions towards strategic objectives.

AI can deliver measurable value – but only with AI fitness: clear choices, focused foundations and scalable execution.

Our research offers a clear path forward. What separates AI leaders is the set of management choices they make: aligning AI with critical outcomes, building fit‑for‑purpose foundations and embedding AI across the enterprise.

The advantage these leaders enjoy will only grow. It is time to think beyond pilots, aim higher, and turn AI investment into AI‑driven performance.