The takeaways
- The most ‘AI-fit’ companies in our research deliver AI-driven revenues and efficiencies that are 7.2x as high as those of other businesses.
- When companies strengthen their AI foundations—strategy, investment, data and technology, workforce, governance, and innovation—they generate far greater performance improvement as they increase their AI use.
- Leading companies treat AI like a reinvention engine, using it to reshape business models and pursue growth opportunities as industries converge.
In [EJ(2.1]conference rooms from New York to Singapore, the same moment occurs again and again. Someone pulls up a slide with a tidy grid of AI pilots—chatbots here, co-pilots there—and the room nods along. Then the questions begin. Which of these pilots are increasing revenue? Which are driving costs down? How many decisions have been made better, faster, safer?
The silence that often follows reflects an uncomfortable reality: for many companies, all that AI activity isn’t producing measurable returns. PwC research finds that value is currently concentrated in a small cohort: 20% of the 1,217 companies we surveyed capture 74% of the AI-driven returns.
What separates these AI leaders from the rest? It’s what we’ve come to define as “AI fitness”: the ability to point artificial intelligence at what matters, build fit-for-purpose foundations, and hardwire AI throughout the enterprise.
This article is written for business leaders who want to stop counting AI pilots and start driving measurable revenue gains and cost savings with AI. It explains what companies that are seeing outsized results do to become AI fit—and their practices are well within reach of all businesses.
The 7.2x performance advantage
To understand why some companies are seeing real returns while most are not, we benchmarked 1,217 companies—spanning regions around the world and 25 sectors—on their AI-driven financial performance, defined as the revenue and efficiency gains derived from AI and adjusted so each company could be compared against its sector’s median.
We also asked senior executives at these companies about their engagement in 60 areas of AI management and investment practice to test those areas’ effects on AI-driven financial performance. We grouped these practices into nine categories related to the ways in which companies use AI and the foundational capabilities that make AI reliable and scalable, such as strategy and governance. These nine categories make up the components of our AI fitness index.
The headline result is clear: the most AI-fit companies in our research deliver AI-driven financial performance that’s 7.2x as high as the other respondents’ performance.
The reason for this dominance? High levels of AI fitness improve a broad set of intermediate performance outcomes that, in turn, influence companies’ financial results. Consider that companies with the strongest AI-driven financial performance (our “AI leaders”) are more likely than other businesses to find that their AI portfolio has increased the speed at which they bring out new products and services, transformed their business and operating models, improved the quality of their decision-making, and enhanced customer experience and trust—an array of metrics that many executives already focus on improving.
A large technology provider improves customer experience
IDEA IN MOTION
It’s clear that companies pulling ahead through AI aren’t simply “doing more AI.” They’re building the capabilities that make AI scalable and reliable and then choosing where to apply that scale for maximum financial leverage.
What do the leading companies point AI at first? Not just incremental efficiency, but also reinvention and growth, particularly where value is moving as industries converge in a “value in motion” world.
Aim AI at growth and reinvention
Plenty of companies use AI to become more efficient at the work they already do. Think of insurance firms, where AI solutions rapidly process claims, or software makers, where programmers direct AI to write a substantial portion of new code. The AI leaders we studied use AI for efficiency, too. But they don’t stop there. These companies treat AI like a top-line-boosting reinvention engine—one that helps them create fresh offerings and reshape their business models to move into promising new markets. Our study shows that leading companies are 2.6 times as likely as others to report that AI has improved their ability to reinvent their business model.
At AI leaders, the technology’s utility spans all the business reinvention activities we studied. It starts with the search for opportunity. Leading companies, we found, are 1.8 times as likely as other companies to use AI to spot emerging value pools—in particular, value pools centred on customer needs that call for innovative, multi-sector combinations of products and services. As industries converge to fulfil these needs, the rewards accruing to companies that reinvent their business models will increase.
In fact, the ability to capture growth opportunities resulting from industry convergence stands out in our research as the single strongest AI fitness factor influencing AI-driven financial performance. AI leaders are two to three times as likely as others to use AI to collaborate with companies in other sectors, to unlock value by working in ecosystems of businesses, and to compete beyond their usual sectors. Consider the possibility of automakers and healthcare providers working together to equip vehicles with high-tech sensors that monitor the driver’s health and feed the data to AI systems that then design personalised prevention programmes.
The leading companies we studied also reinforce their AI-informed growth agendas with disciplined management. They make strategic choices early, and they operationalise those choices with ownership and measurement. Compared with others, leading companies are more likely to have a prioritised AI road map across near- and long-term horizons, to align AI vision with business objectives, to systematically track business impact, and to hold senior leaders directly accountable for AI outcomes.
Your next move: Shift from cost to cash. Treat “growth from industry convergence” as a distinct AI portfolio with senior sponsorship. Use AI to scan for where value is moving, then back that view with decisions: a prioritised road map, explicit owners, and impact metrics that force trade-offs.
John Deere reinvents itself by adding intelligence
IDEA IN MOTION
Build focused AI foundations
Aiming AI at reinvention and industry convergence opportunities is the easy part. The hard part is delivering those outcomes repeatedly—which is why the next differentiator is not ambition, but the six targeted foundations. Rather than treating foundations as an abstract modernisation agenda, AI leaders build only what’s needed to turn AI use that’s aimed at growth and other high-value business objectives into measurable outcomes at scale.
Foundations change the economics of AI. They reduce friction, rework, and ‘one-off’ builds, so each new deployment gets faster, cheaper, and more reliable.
As mentioned earlier, this shows up as a conversion-rate effect: after an underperforming company institutes the right practices, it should see double the payoff from each new AI use case, on average.
Our research shows that the five practices described below are the ones that lead to the greatest performance gains.
Fund and flex the AI portfolio like an investor
The leading companies in our study invest materially more in AI than other companies do: 2.5 times as much of their revenue. Leaders in the software, banking, and media and entertainment sectors report investing the most, about 5% of annual revenue. Ample investment in AI, however, is just part of the leaders’ formula. These companies also endeavour to keep their investments aligned with their business needs. According to our research, they’re 1.3 times as likely as other companies to reallocate financial and human resources towards high-value AI projects as their business priorities shift. That approach is consistent with a large body of research linking dynamic resource allocation to superior financial outcomes.
Foster AI-powered innovation
If funding is the fuel, innovation is the engine. AI leaders create the conditions for high-velocity experimentation. They’re 1.5 times as likely as other companies to provide tech infrastructure specifically to support AI experimentation: think of ‘sandbox’ environments, walled off from enterprise systems, where developers can safely try new AI solutions. These leaders are also more likely to appoint innovation owners who direct AI projects within business units. That combination makes it easier to kick off pilots and run them quickly and safely.
Moreover, AI leaders are more likely than others to carry out structured reviews of AI innovation efforts so they can decide which ones to prioritise, scale, or terminate. The result is a pipeline of experiments that reliably lead to value-generating AI solutions.
To boost adoption, cultivate employees’ trust
AI value materialises when people use AI. That makes employees’ trust of the technology much more than a ‘change management’ line item. Lack of trust is a throughput constraint. Low trust means low usage, which means low impact.
Leaders create the conditions for uptake. Employees at AI-leading organisations are 2.1 times as likely to trust AI-generated insights and act on them in day-to-day work.
What drives trust is rarely a single programme. It is a system composed of the following:
- Involvement. At AI leaders, 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. Leading companies 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 further strengthen trust—and accelerate
Leading companies take governance seriously, but ensure it’s applied in a way that speeds up delivery rather than slowing it down. A governance board sets Responsible AI policies, and teams apply them in their day-to-day work through mechanisms like standard build templates, quick checkpoints, and regular monitoring. This keeps routine use cases moving quickly, as teams tap the board to review only the highest-risk work.
Wyndham scales trusted AI agents
IDEA IN MOTION
Companies that lead in AI are more likely to have this machinery in place: they’re 1.7 times as likely to use a documented Responsible AI framework that applies to processes from use case selection through application monitoring, and 1.5 times as likely to have a cross-functional AI governance board.
Eliminate tech and data friction
In our experience, some of the biggest blockers to scaling AI are data quality and access, tech integration, and the hidden cost of rebuilding the same components (such as data pipelines and integration layers) repeatedly. AI leaders focus on removing those bottlenecks for their high-stakes use cases. They’re 2.4 times as likely to create reusable, centrally catalogued AI components that teams can pull off the shelf instead of reinventing. They’re also 1.7 times as likely to provide the high-quality data needed for prioritised AI applications.
Southwest Airlines uses GenAI to turn legacy code into modernisation-ready requirements
IDEA IN MOTION
Your next move: Build only what your AI strategy demands rather than getting lost in an unending, broad-stroke transformation. That means anchoring foundations to a small set of priority outcomes, funding the portfolio in order to scale winners, modernising only the necessary data and platforms, and providing targeted workforce reskilling and governance. This message applies to both AI laggards and AI leaders—even the outperformers aren’t engaging in some best practices, which means they’re still leaving value on the table. For example, while AI leaders are more disciplined than peers about pruning initiatives, only 28% say they conduct AI portfolio reviews to terminate initiatives to a “large” or “very large” extent.
A leading healthcare company turned data into actionable insight—and revenue potential
IDEA IN MOTION
Hardwire AI into the enterprise
Once executives define the set of business objectives they hope to reach with AI—growth, reinvention, efficiency, or some combination of those—they make sure AI solutions are developed and implemented everywhere in the enterprise that they can make a difference. Hardwiring AI into the enterprise involves working along three dimensions: implementing AI broadly across many parts of the business; embedding AI into core workflows and systems so it can enhance the execution of tasks; and applying AI in sophisticated ways, moving 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 hardwiring AI into processes throughout the value chain, 54% having done so in direction setting (e.g. strategy, planning), 55% in demand generation (e.g. marketing, sales), 35% in support services (e.g. finance, HR), and 41% 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.
Lucid goes finance first, then AI everywhere
IDEA IN MOTION
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 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 15% of AI leaders say their most sophisticated use case is autonomous and self-improving. Plus, although 48% of AI leaders expect head-count reductions of at least 5% due to AI, another 49% 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.
Your next move: Scale selectively. Pick a handful of priority use cases tied to your objectives, then industrialise them. This means redesigning the workflow from end to end to embed AI into processes and then replicating the pattern across teams, regions, functions, and decision points. A practical starting point to increase automated decision-making: begin with a small set of decisions that are high frequency, repeatable, and measurable, and that have low to moderate risk (for example, triage, prioritisation, and routing). Automate within explicit guard rails, instrument decision quality, and expand only when reliability and trust thresholds are met.
A retail giant scales AI agents across the enterprise
IDEA IN MOTION
AI is more than capable of producing quantifiable benefits. However, the opening scenario of this article—rooms full of AI pilots and too little measurable impact—will keep repeating for companies that don’t make targeted efforts to turn pilots into performance. Our research offers a clear and encouraging path to measurable gains. What separates AI leaders is the set of management choices they make: aligning AI uses to critical business outcomes, building fit-for-purpose foundations, and hardwiring AI into the enterprise.
Putting that formula into place requires deliberate, sustained effort. It won’t be easy, not with the myriad priorities calling for executives’ attention. Still, companies that want to catch up to the leaders can’t afford to wait. The advantage that the AI leaders already enjoy will only grow, because these companies are learning fast, redeploying solutions faster, and safely automating decisions.
The time has come to think beyond pilots and aim higher. Executives should look to point AI towards the biggest strategic moves on the table and establish an operating model that turns AI investments into measurable gains. When AI is trusted, aimed at reinvention, supported by targeted foundations, and scaled through repeatable patterns across workflows and decisions, the results go beyond incremental improvement—they add up to a compounding performance premium.
About the author(s)
Download PwC’s AI performance study
Go deeper on AI performance insights and actions.
Artificial intelligence services
We don’t just bring tech. We bring results.
Connect with PwC Belgium
Contact us
Managing partner Advisory, Technology Consulting & Innovation, PwC Belgium
Tel: +32 495 59 08 40
