# Competing in the age of AI: PwC

The year 2025 is already proving to be a watershed moment for artificial intelligence (AI). In just the first few weeks, AI dominated conversations at the Consumer Electronics Show, in Las Vegas, took center stage at the World Economic Forum in Davos, and emerged as a key focus for the . These milestones highlight the extraordinary pace at which AI is advancing and its growing influence on every facet of business and society.

As PwC’s  emphasized, the rules of competition are shifting rapidly, and the traditional workforce is on the cusp of transformation. Even those of us leading in AI — transforming our organization and guiding clients through their own AI journeys — can’t fully anticipate what’s coming. The breadth of disruption makes it impossible to have all the answers. What we do know is that success in this transformative era depends on how leaders approach strategy, adapt to the evolving nature of work, and prioritize trust.

The tactics that win today will not necessarily be those that win tomorrow. Critical shifts in strategy will emphasize speed more, scale less, and innovation most of all.

## The need for speed

The speed at which competitive capabilities are changing is accelerating at exponential rates, and these next few years of disruption will likely produce winners that can persist for decades. Periods of discontinuity run fast, and the results have a lasting effect. This has been the case for most major tech-powered industries, such as the energy, automotive, and technology sectors. Given that many businesses are tech-powered, this accelerating innovation cycle is noteworthy. AI can accelerate the business flywheel, including the speed of insights, decision-making, capability building, and organizational change. Creating trust in AI and incentivizing change-ready organizations should be important success factors. Companies that are nimble and fast gain the advantage.

What will that translate to for your business and the economy? Looking back at prior innovation cycles can provide insight. Fueled by next-generation tech such as the internet, the average YoY  doubled to 2.8% during the decade ending 2005, as compared to the previous two decades. This was worth trillions in real GDP. Labor productivity saw a major pullback after 2010, with YoY averages dipping as low as 1.2% up until 2022.

## Strategy in the age of AI

As for the human factor in setting your strategy in the age of AI, consider an analogy from Formula 1 racing. The F1 race car is an engineering marvel, a complex system made of many subsystems. But it’s the driver at the center, in control of performance, supported by a large team of individuals doing many different things behind the scenes. What produces a competitive racing team? An operating model that brings good strategy to life — strategic business partnerships, a well-organized crew, high-performing processes, loads of talent, and, of course, technology — including AI. Lots of AI.

During an F1 race, huge amounts of data on competitors, car performance, and track conditions stream in real time. It’s the driver and team who make AI an intrinsic part of their race, a natural part of every turn and tactic. Between races and seasons, data is continually modeled, studied, and leveraged to make bigger adjustments to strategy that help teams go faster and win more.

## The executive cockpit for your business

Like the racing team, your business is a complex system. To win, you should bring together capabilities in just the right way, and as with racing, your leadership teams should be prepared to react to rapid change. Those who use data and AI to detect and make sense of market events, sector dynamics, and their own company’s performance can create insights that help fuel advantage.

To navigate the disruption, management teams need better instrumentation and more intelligence, and AI is poised to provide just that. AI capabilities are making possible an executive cockpit with near real-time data on market activity, sector benchmarks, tax and regulatory considerations, and corporate metrics. It’s detecting early patterns, opportunities for improvement, and threats requiring strategic countermeasures.

We’re already seeing top teams regularly achieving productivity improvements of 30% from AI solutions, which are being used to unlock cost savings, improve margins, or capture more market share. In , more than half of CEOs (56%) responding tell us that GenAI has resulted in efficiencies in how employees use their time, while a third reported increased revenue (32%) and profitability (34%). The CEOs also tell us they plan to continue systematically integrating AI throughout their businesses, including tech platforms (47%), business processes and workflows (41%), workforce and skills (31%), core business strategy (24%). One-third (30%) are integrating AI in their products and services. GenAI has made it possible to move beyond just predicting customer preferences to personalizing those preferences and differentiating service experiences. Hyper-personalized products and services promise to drive new incremental revenue.

## The future of work means scale matters less

Scale has long been a valuable part of business strategy, serving as a moat and critical capability builder. Deep specialization, big tech budgets, and increased pricing power are privileges of scale. Interestingly, AI may have a muting effect on scale as a differentiating strategy. New disruptors can use AI to mimic the scaled capabilities characteristic of mature businesses. Software developers, for instance, are often attracted to big companies with deep tech budgets. Now, specialized developers aren’t the only ones who can code. Large language models (LLMs) have made possible code generators that non-techies can use to create quality code. This is just one example that illustrates how AI — and in particular AI agents — challenge entrenched operating models for scaling. Today, AI-native businesses or smaller organizations are taking an agentic approach to perform at a level similar to scaled competitors.

Consider a financial services entrant that uses AI to evaluate creditworthiness by analyzing hundreds of variables, outperforming legacy methods. This approach enables the company to approve significantly more borrowers while automating most loan processes. This has led to double-digit revenue growth, achieving scale and efficiency previously reserved for larger incumbents.

For established businesses, AI agents provide a blank-sheet opportunity to reimagine operating models and value chains. It also gives them access to an  that can deliver augmented intelligence to their human workforce. Each employee could practically gain a major multiplier effect with large AI agent teams specialized in many different disciplines. Access to a wide range of specialties was practically out of reach for most smaller businesses but an agentic workforce is changing that. The majors that dominate the business landscape and rely on their scale as a competitive advantage could see scale’s value diminish.

Leadership skills, workforce strategies, business processes, sourcing strategies, and cost profiles will change as the future of work becomes agentic.

The agentic organization will be defined by high-performing human-AI interactions. The more intrinsic AI agents become to how work is done, the more value can be unlocked. Our analysis — informed by our work with clients and our own AI adoption — shows this human-AI collaboration could boost productivity and speed by 50%. What emerges will be a shift of the traditional labor pyramid to a diamond shape.

Just like the F1 racing team, humans will remain at the helm. People will direct AI agents on what to do, correct their mistakes, (with help, of course, from other AI agents), and use AI to turn ideas into new products and better ways of working. But this requires more than new tools and skills. Your people should also know that you intend for AI to increase their value. Only then can they feel confident enough to adopt AI and innovate with it to reinvent their roles.

## Why innovation matters most

During this period of disruption, tech innovation will reign supreme. Management teams capable of creatively disrupting themselves with highly effective, intelligent offerings are positioned to win in increasingly competitive markets. Whole categories of business will be invented. Consider that just two years ago, very few were familiar with LLM providers. Now, as their capabilities continue to advance, their platforms are foundational to modern business — and the LLM providers have achieved extraordinary market valuations as a result.

Many companies we work with are beginning to embrace innovation and starting to rethink their business and operating models. It’s not uncommon for a company to have hundreds of AI enabled use-cases that are creating new savings, insights, and differentiators. From innovative regional banks to tech industry giants, we’re working with many clients to do just that. They are taking a blank-sheet approach and evaluating dozens of use cases across the business — then prioritizing those that can provide the most value initially and longer term.

To keep pace, companies should harvest a breadth of opportunities. In some cases, this could take the shape of smaller improvements. But incremental gains add up. Be intentional about making AI an intrinsic part of your business, a natural part of everything you make and do. One of the most valuable forms of innovation comes from using AI to continuously improve ways of working. Teams that are comfortable using AI in their day-to-day work are more likely to innovate with AI.

Bigger innovations can come from many sources, but there are some steps you can take. First, promote a culture that allows teams to imagine and think differently.

Don’t wait for customers to tell you what they want. Go into the market and see where there’s white space and get creative about more significant change. Second, create an investment pool to fund experimentation and create a tech foundation that includes R&D capacity, a place teams can experiment and, importantly, fail. This is a portfolio approach that will produce hits and misses. Companies that innovate successfully know the importance of placing multiple bets. Third, manage AI-enabled innovation programmatically.

Innovation is a discipline, underpinned by leading practices that teams can adopt and win with. The big ideas should be cultivated, the investments managed, the winners rewarded, the value scaled, and the full spectrum of innovation optimized. Pro-tip: Don’t let the bureaucrats pick the winners. Let the business owners on the front lines of the market be accountable for the AI investments and value realization.

One media company we worked with rethought how it could deliver personalized content for a marquee global event. Using AI, it created a platform to craft personalized playlists, generate text-to-speech content, and automate production and governance. The approach enabled the company to deliver more than 5 million high-quality content variations for its audience.

## The enablers: Tech and engineering

Build modern foundations with next-gen engineering and transform IT. The data is in, and the evidence is overwhelming. PwC’s  found that companies that effectively use next-gen cloud architectures and the latest AI capabilities are more likely than their peers to be improving profitability, productivity, time to market, and so on.

But acquiring the necessary technology at a reasonable cost isn’t easy.

Growing demand for compute capacity and power are straining supply. Your tech team should plan how to meet those needs and how to manage the environmental impact.

How your tech team uses AI is critical too. It should reinvent itself by transforming , enhancing cybersecurity, and accelerating data modernization. The benefits can be especially great for data modernization, which is an imperative for AI and other digital initiatives. GenAI can make sense of unstructured data — making modernization lower cost and faster, but you’ll need the right engineering talent to achieve this.

## Trust can be earned with responsible AI

AI value realization can move as fast as trust in AI is earned. This happens programmatically using . You, your board, and other senior leaders should trust that AI can deliver on its promises with its main risks understood, reported, and managed. Your workforce should trust that AI will make them more valuable — not displace them. Your customers should trust that you are using data responsibly and that the AI you use — or that they interact with in their relationship with you — can be trusted.

An effective responsible AI approach should span the whole organization, engage the entire C-suite, and cover risk management, audit and controls, security, data governance, privacy, bias, and model performance. Furthermore, responsible AI can no longer be a paper exercise – it should be operationalized and digitally enabled. PwC’s  found that only 11% of executives have fully implemented fundamental responsible AI capabilities.

When done right, responsible AI empowers the business. It creates defined guardrails and processes within which your organization can innovate at full velocity.

By growing stakeholder trust, responsible AI can also increase investment flows and encourage workplace adoption.

## Competing to win in the age of AI

Creative destruction is done proactively. While some companies continue to experiment with AI, leading companies integrate it into everything they do. New customer expectations, cost profiles, and clock speeds are redefining the basis for competition. Strategic advantage is being built or rebuilt right now with AI-powered capability systems. As leadership teams work defining their AI agenda and confirming their business strategy, new thinking should guide those efforts.

If speed matters more, teams should get change ready. They should be incentivized to keep pace or outpace the market innovation cycles. Technology architectures should be unburdened from technical debt and reinvented in the cloud with a strong AI tech foundation on which to build.

If scale matters less, small and medium-size businesses should look for ways to achieve scale benefits with AI that traditionally were available only through hiring, acquiring, and contracting. Large enterprise competitors should rethink their moats; scale is no longer a sufficient differentiator. Creating an executive cockpit outfitted with market, sector, and corporate performance analytics that can serve as a strategic AI thought-partner will become a critical leadership capability.

If innovation matters most, big ideas should have a place to be tested and come to life. Leaders should incentivize risk taking and reward those who manage risk well as they activate next-generation capabilities. While the next big thing is being innovated, leaders should harness the power of small plays too. Continuous improvement, those day-to-day incremental gains happening across the organization, have the potential to add up to something big.

Most importantly, adopt a growth mindset. AI promises to be disruptive, but it also promises significant rewards for companies that can build their futures with AI.

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