A new study surveyed 1,217 executives across 25 sectors and found most businesses are using AI in exactly the wrong way. Here is what the top performers do differently.
PwC’s April 2026 study of 1,217 executives found that 74% of AI economic gains flow to just 20% of companies. The gap isn’t about tools or budget. Leaders use AI to create new revenue streams and reinvent their business models. Most companies are stuck using AI to automate existing tasks slightly faster, which leaves almost all the value on the table.
PwC published their 2026 AI Performance Study on April 13th, and the headline number is stark: 74% of AI’s economic value is being captured by just 20% of organisations. [1] The study covered 1,217 senior executives at director level and above, across 25 sectors and multiple regions worldwide.
The research measured AI-driven performance as the revenue and efficiency gains attributable to AI, adjusted against industry medians. So this isn’t about which companies spend the most on AI. It’s about which ones actually see it show up in financial results.
The leaders in this study generate 7.2 times more value from AI than their competitors. They also have profit margins that are 4 percentage points higher. That gap is not explained by industry, company size, or geographic region. It comes down to how they’re using AI.
Here is the uncomfortable part: most companies are not in that top 20%. And based on the research, most aren’t close. But the gap is closeable, and understanding what separates leaders from laggards is the first step.
PwC’s framework splits organisations into AI leaders and AI laggards. The distinction isn’t really about technology. It’s about mindset and strategic intent.
AI laggards are using AI to do existing things slightly faster. They’re automating email responses, speeding up report generation, using AI to cut headcount in specific functions. The gains are real but narrow. Essentially, they’ve given everyone a slightly better set of tools.
AI leaders are using AI to do things they couldn’t do before. New products. New customer segments. New business models. They treat AI as a growth engine, not a cost-reduction programme.
A quick self-check: when your organisation talks about AI internally, what framing comes up most often? “How do we cut costs?” puts you in laggard territory. “What can we now offer that we couldn’t offer before?” puts you in the leader category.
The honest version: Most organisations I see in my training work are firmly in laggard territory, and that’s not because they’re not smart. It’s because “make this task faster” is an obvious and low-risk starting point. The problem is staying there. The real value only shows up when AI enables genuinely new capability.
PwC’s study found that capturing growth opportunities is the single strongest factor influencing AI-driven financial performance, ahead of efficiency gains. [1] This is worth sitting with for a moment.
Leaders are 2-3 times more likely to use AI to identify and pursue growth opportunities and reinvent their business model, compared to laggards. That’s not a subtle difference. It’s a completely different job description for their AI investments.
When you use AI to cut the time spent on existing work by 20%, you’ve improved a process. When you use AI to launch a new type of service, reach a customer segment you couldn’t serve before, or enter an adjacent market, you’ve created value that didn’t exist.
The financial results reflect this. Efficiency gains produce linear improvements. Growth opportunities produce compounding returns. A team that saved 20 hours a week on reporting is doing fine. A company that used AI to launch a data product that generates new revenue has a fundamentally different trajectory.
If you’re working on measuring your AI ROI, this distinction matters a lot. Efficiency ROI is straightforward to calculate. Growth ROI takes longer to appear but is significantly larger.
The PwC study doesn’t just describe the gap. It identifies the specific behaviours that separate leaders. Three patterns stand out.
Laggards have a collection of AI pilots spread across the business. Some have saved time. None have changed the business. Leaders have a coherent strategy: a clear view of where AI creates competitive advantage and an intentional plan to get there.
This doesn’t require a 50-page strategy document. It requires honest answers to three questions: Where do we want to compete differently because of AI? What capabilities do our people need to build? What do we do first?
Leaders spend proportionally more on training and capability-building than on software subscriptions. Laggards do the opposite: buy the tools, give everyone a login, and wonder why adoption is low.
This is directly relevant to what we see in our AI training work at Future Factors. The organisations that get the most out of AI are the ones where people actually understand how to use it for their specific role, not just where they’ve been given access to it.
Leaders use AI to generate insights that inform strategic choices: pricing, product development, market expansion. Laggards use AI for task execution. That might sound abstract, but the practical difference is whether AI output ever reaches the decision-making table or just stays in operational processes.
Here’s the finding from the PwC study that I found most interesting, and most underreported: capturing growth opportunities from industry convergence is the single strongest factor influencing AI-driven financial performance. [1]
Industry convergence sounds technical. It’s not. It means using AI to move into adjacent spaces that would have been impossible or too expensive before. A few practical examples:
In each case, AI is the enabler. It makes it economically viable to expand into a space that previously required a different type of expertise or a much larger team. The organisations doing this aren’t all large enterprises. The PwC study found that mindset and approach matter more than scale.
If you’ve built an AI workflow for your own work, the next question worth asking is: what would become possible for your clients or customers if you offered them something similar?
A quick exercise: List the three most time-consuming things your clients wish they could do faster or better. Now ask: is AI already able to help with any of those? If yes, you have a potential convergence opportunity sitting right in front of you.
Before you can close the gap, you need an honest read on where you are. These five questions will give you a rough position:
Be honest with the answers. There’s no value in overestimating your position. The PwC data is clear that most organisations are in the laggard category, so if you are too, you’re in very large company.
Understanding the gap is useful. Closing it requires action. Based on PwC’s findings, here are three moves that will shift your trajectory:
Don’t abandon your efficiency projects. But commit to one experiment where AI is used to generate something new, not just improve something existing. A new service offering. A new content format. A new way to serve a customer segment you’ve been under-serving. Set a 90-day window and measure it properly.
Access is not the same as capability. Most professionals have ChatGPT or Copilot access. Very few have been shown how to use it for the specific tasks in their job. That’s not their fault. It’s a training gap. Fix it. The ROI on AI training is consistently one of the highest in the study.
Put this on the agenda: “Given what AI can now do, is there an adjacent market or new service type we should be exploring?” You don’t need a budget to ask the question. But you do need to ask it. Leaders have this conversation regularly. Laggards don’t.
The PwC gap isn’t destiny. It’s a decision. Companies that moved from laggard to leader in previous technology cycles (digital transformation, mobile-first, cloud adoption) did so by making different choices earlier than their competitors. The same is happening now with AI. [2]
For a step-by-step guide to building your first AI capability, see our guide on what AI agents actually do for business professionals.
What did the PwC 2026 AI Performance Study find?
PwC surveyed 1,217 senior executives across 25 sectors and found that 74% of AI economic gains go to just 20% of companies. The leading companies generate 7.2 times more value than competitors and have profit margins 4 percentage points higher. The key differentiator is using AI for growth and business reinvention rather than just efficiency.
Why are most companies failing to get value from AI?
Most companies are using AI as a cost-cutting tool rather than a growth engine. They automate existing tasks without rethinking how value is created. Leaders use AI to identify new revenue opportunities and cross industry boundaries, while laggards stay focused on doing existing work slightly faster.
What is industry convergence and why does it matter for AI performance?
Industry convergence is when companies use AI to move into adjacent industries or create new types of value at the intersection of sectors. PwC found it is the single strongest factor influencing AI-driven financial performance. A retailer using AI to offer financial services, or a manufacturer using AI to build a data product, are examples of convergence in practice.
How can smaller businesses compete with large AI-leading companies?
Smaller businesses actually have an advantage in speed of implementation. The PwC study found that leadership is about mindset and approach, not budget size. Start with one high-value workflow where AI could open a new revenue stream or customer touchpoint, not just save time. Use tools available today combined with your existing customer knowledge to identify opportunities larger competitors are too slow to spot.
How long does it take to move from AI laggard to AI leader?
The PwC study doesn’t give a specific timeline, but most organisations that make the shift do so over 12 to 24 months by committing to a clear AI strategy, training their people, and focusing on growth use cases rather than just automation. Starting with one ambitious project typically has more impact than spreading effort across many small efficiency tweaks.
This analysis is based on PwC’s 2026 AI Performance Study, published April 13, 2026, and supplementary research from MIT Technology Review and MIT Sloan Management Review. It is written for non-technical business professionals who want to understand what the AI performance gap means for their organisation and what to do about it.
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