2025 AI Business Predictions
If we would make one prediction to sum up all the rest, it would be this: Your company’s AI success will be as much about vision as adoption. That means that your AI choices may be the most crucial decisions not just this year but of your career. It’s now clear that AI can deliver value at scale — and we’re just getting started. Nearly half (49%) of technology leaders in PwC’s October 2024 Pulse Survey said that AI was “fully integrated” into their companies’ core business strategy. A third said AI was fully integrated into products and services.
Making AI intrinsic to the organization is vital, because making “big leaps” (such as new business models) is only one source of game-changing AI value. The other is the cumulative result of incremental value at scale: 20% to 30% gains in productivity, speed to market and revenue, first in one area, then another — until the company is transformed.
To help navigate this transformation, we offer a set of predictions covering the most important areas that demand your attention. These are based on real-world experience in helping our clients reinvent their business with AI, the transformation of our own firm with AI and PwC’s strategic alliances with leading tech companies in the AI ecosystem.
There are pockets of hype around AI. Not every promise will pan out. But AI’s pace of innovation, investment and business buy-in are unprecedented. Even the internet (invented in 1983) didn’t move so fast. Our predictions are designed to indicate what to expect in the next 12 months, what may come after that and what to do right now.
Explore the predictions
- Your AI strategy will put you ahead — or make it hard to ever catch up
- Your workforce could double — thanks to AI agents
- ROI for AI depends on Responsible AI
- AI will be a value play — and a boon for sustainability
- AI will cut product development lifecycles in half
- AI will transform industry-level competitive landscapes
“Top performing companies will move from chasing AI use cases to using AI to fulfill business strategy.”
1. Your AI strategy will put you ahead — or make it hard to ever catch up
AI strategy is about value that starts right now — and this value is not just productivity or efficiency. Some AI systems can now reason independently and “understand” the impact of their decisions. That helps AI perform complex tasks such as designing new services or go-to-market strategies. It also helps AI catch its own mistakes. With AI increasingly powerful and reliable, it's time to embed it in your operational fabric. If you don’t, your competitors who do may establish lasting advantages.
An effective AI strategy, designed to deliver value at scale this year, takes a portfolio approach. One part of the portfolio develops a strong “ground game” to deliver many small wins. It’s a systematic approach that harvests additional value from a growing number of more engaging experiences, higher revenue-generating products and services and more productive workflows. This approach depends on scale, but it also requires carefully setting priorities in a phased approach, with each phase generating value that helps pay for the next. The second part of the portfolio picks some “roofshots,” projects that are attainable but require dedicated attention and resources such as all-new ways of working, interacting with customers or designing products. The third part of the portfolio approach focuses on a few high-reward and highly challenging “moonshots” such as new AI-driven business models. Since the roofshots and moonshots require serious resources — including AI specialists’ time — business owners or the C-suite should choose and lead them.
What won’t matter as much for AI strategy is your choice of large language model (LLM). There will be many good options. Everyone will be using them. A shrewd strategy will instead emphasize what can set you apart — how you leverage AI with your institutional knowledge and proprietary data, with the help of AI-powered cloud architectures.
“AI adoption is progressing at a rapid clip, across PwC and in clients in every sector. 2025 will bring significant advancements in quality, accuracy, capability and automation that will continue to compound on each other, accelerating toward a period of exponential growth.”
Beyond 2025: Very few companies will establish dominance
Several decades ago, a few companies built platforms, e-commerce models and other internet-centered business models, all of which remain dominant to this day. We expect something similar with AI. Because AI offers such transformative potential for new operational and business models, those that pull ahead of the pack — whether AI native companies or established companies that reinvent themselves quickly — will likely stay there. The growing gap between AI leaders and laggards will extend to economies too. Businesses in the US, with its relatively flexible regulatory environment, may outperform those in the EU and China, which have more rigid regulations.
What to do now
- Conduct a formal strategy assessment. To set priorities for both AI at scale and your moonshots, identify what AI can and will do for your company and industry — where it will take out costs, create new value, raise customer expectations, threaten core businesses and support new business models. AI may, for example, squeeze margins in one line of business but help another boom with cost-effective, hyper-personalized offerings.
- Take a less-is-more approach to data. AI will “pay you back” for data modernization, if you do it right. You will need an enterprise-wide approach to data, but you don’t need to make it all perfect at once. Instead, as part of your core strategy, set priorities for which segments of your data architecture you should harvest value from first. Then focus on finding just the right data to modernize — no less but also no more. AI today can often fulfill its mission with a small but high-quality subset of data. It can then create synthetic data to close any gaps. The tax function, with its data-heavy, rules-based processes, is often a good place to start, but your business probably also offers many AI-driven data monetization opportunities.
- Apply an operational, KPI lens. Measure business-relevant metrics for AI such as new revenue, accelerated project delivery, productivity and experience. At the same time, take care that your metrics don’t encourage people to automate too much. Human oversight and leadership of AI will always be needed.
2. Your workforce could double — thanks to AI agents
If you think AI will shrink your workforce, think again. You’re going to welcome a host of new members to the team this year: digital workers known as AI agents. They could easily double your knowledge workforce and those in roles like sales and field support, transforming your speed to market, customer interactions, product design and so on. An AI agent can autonomously perform many tasks, such as handling routine customer inquiries, producing “first drafts” of software code or turning human-provided design ideas into prototypes. Workflows will fundamentally change, but humans will still be instrumental since game-changing value comes from a human-led, tech-powered approach. People instruct and oversee AI agents as they automate simpler tasks. People iterate with agents on more complex challenges, such as innovation and design. And people “orchestrate” teams of agents, assigning tasks and then improving and stitching together the results.
Thinking about agentic workflow as a fundamental part of your workforce strategy may be a big leap for many companies. It will, for example, involve new management roles responsible for integrating digital workers into workforce strategies, then monitoring and governing them. But the sooner you begin thinking this way — and transforming your operating model to plan, train and manage a blended digital and human workforce — the better positioned you are to capitalize on AI. When you have both digital and human workers on the job, for instance, you can plan for greater agility and shift resources more quickly to meet changing demands.
As AI agents rise, they’ll do in-house some of what you currently outsource. Their advantages go beyond cost savings. You will have greater control, greater ability to customize and a greater ability to please end users. For customer service, AI agents may enable you to offer customers both faster, more satisfying self-service, and better equipped human specialists for high-touch, high-value interactions. AI agents can push the right information in front of your people at the right time so they can quickly and effectively address even complex customer needs. With AI agents, long-term plans for your geographic footprint may need an update. At the very least, consider how your current growth curve for outsourced services will change.
“AI agents are set to revolutionize the workforce, blending human creativity with machine efficiency to unlock unprecedented levels of productivity and innovation.”
Beyond 2025: Centers for agents will replace Centers of Excellence
As companies become more skilled in orchestrating and governing AI agents, they may “offshore” by, for example, creating AI-agent based workforces in low-cost geographies. The intellectual property (IP) created in developing agents and where that IP sits geographically could offer tax benefits. Building “centers for agents,” as opposed to renting agents from vendors, might have an upfront cost but produce greater ROI within a few years.
What to do now
- Shift mindsets. People will have to adapt as the way they perform workflows will greatly change. They might interact with AI agents like they do with independent, creative teammates now. For this shift in mindset, leadership will need to model these new ways of working — and provide assurance that AI is meant to enhance people’s value, not displace them.
- Give HR a new playbook. As HR manages a workforce that has both humans and AI agents, it will need different skills of its own and new ways to source, develop and measure human talent. Once AI is doing almost all entry-level work, you will need ways (such as partnerships with universities) to prepare new recruits to enter higher-level roles directly.
- Prepare to manage digital workers. Since AI agents are partly autonomous, they require a human-led management model. You’ll need to balance costs and ROI as you deploy them, develop metrics for human-AI teams and conduct rigorous oversight to prevent agents from conducting unexpected, harmful or noncompliant activity. A holistic Responsible AI strategy can provide the framework for addressing this.
3. ROI for AI depends on Responsible AI
Risk management and Responsible AI practices have been top of mind for executives, as we predicted last year when we said 2024 would be a moment of truth for trust in AI. Yet there has been limited meaningful action. That will change. In 2025, company leaders will no longer have the luxury of addressing AI governance inconsistently or in pockets of the business. As AI becomes intrinsic to operations and market offerings, companies will need systematic, transparent approaches to confirming sustained value from their AI investments. They’ll also need to manage the risks of large-scale deployment. Rigorous assessment and validation of AI risk management practices and controls will become nonnegotiable. Even if the specifics of AI assessment and validation are not mandated, stakeholders will demand it — just as they demand confidence in other decision-critical information (such as financial results) or in cybersecurity or privacy practices.
Business leaders, especially those driving AI transformations, will begin to champion this necessary oversight. They won’t wait for regulatory clarity. AI is moving too quickly and is too business-critical for that. When AI was only in isolated use cases, there was a limit to the damage that disappointing ROI, inaccurate outputs or compliance failures could cause. Now, employees rely on it daily. Customers regularly engage with AI-powered experiences and services. And it will be essential for revenue growth. If AI isn't trusted by stakeholders, if it’s subject to a cyber breach or other risk issue or if initiatives run behind schedule or over budget, your company will take a hit.
To implement AI oversight that unlocks value, you’ll need a second set of eyes. This could come from appropriately upskilled internal audit teams or a third-party specialist conducting an assessment based on leading industry practices and standards. Regardless of how it is achieved, an independent perspective on your AI governance and controls will be critical in 2025 and beyond.
“Successful AI governance will increasingly be defined not just by risk mitigation but by achievement of strategic objectives and strong ROI.”
Beyond 2025: AI regulatory approach will enable continued innovation
The November elections make it likely that federal regulations will continue to be supple, enabling continued rapid advances in AI technology and deployment. But companies will need to pay attention to state rules, which are advancing quickly and can create a hodgepodge of sometimes contradictory regulations, especially regarding privacy. Even so, the overall regulatory environment in the United States should remain among the world’s most favorable for AI innovation.
What to do now
- Assess risk comprehensively. If you haven’t already done so, an AI risk assessment is the starting point for Responsible AI. You’ll also need a standardized, AI-focused risk taxonomy to help make governance decisions consistent and repeatable. The AI risk taxonomy we use covers AI models, data, systems and infrastructure; users; legal and compliance; and process impact. One important area of focus is vendors and service providers — how they use AI in the products and services they provide and whether they can provide validation through reports like SOC-2.
- Pick your oversight. Determine how you’ll add a layer of independent, ongoing validation of AI system and outputs — whether from specially trained internal teams or experienced external providers. Start with the highest-risk areas, those with the broadest exposure or financial impact.
- Focus on industry differences. While AI governance and oversight are needed across the board, industry-specific issues will inform approaches. Financial services, for example, need to consider how to meet existing compliance requirements that were designed with older tech in mind. Aerospace and defense companies and others that work closely with the public sector will need to focus on regulatory developments globally. To see how your organization stacks up with industry peers on critical AI governance foundations, take this short survey for a benchmark report.
4. AI will be a value play — and a boon for sustainability
AI will accelerate the energy transition. It will also help companies meet their sustainability goals – especially those in emissions-intensive sectors like manufacturing, construction and transportation – if they take the right approach. AI requires so much energy that there’s not enough electricity (or computational power) for every company to deploy AI at scale. More chips are coming, models are advancing and the energy supply is expanding. But we won't hit an equilibrium of supply and demand in 2025. That will make it wise to treat AI as a value play, not a volume one. Use it in more and more areas, yes, but also be strategic about how and where you roll AI out. You can, for example, design AI interfaces to encourage users not to waste AI time and tokens.
But these near-term challenges shouldn’t overshadow the big picture. AI will be a driver for sustainability. Globally, it will likely speed up the shift to renewables. In the United States, neither economics nor stakeholder pressure will permit a massive rollout of new fossil fuel plants. Instead, business will encourage more renewable supplies (including nuclear) and a more modern grid that uses energy more efficiently and brings it where it’s needed. Some pressure may come from your company. Even if AI vendors bear most of AI’s carbon footprint, you are the end user, so it should show up on your carbon balance sheet. To reduce the impact, you’ll want AI vendors to be green.
Inside your company, AI can potentially simplify compliance with a new wave of sustainability disclosure regulations in the United States, the European Union and elsewhere. The November election means the SEC’s climate-related disclosure rules will likely remain on hold, which may create a void that is filled by states following California’s lead and developing their own rules and requirements.
AI can automate internal and external data collection needed to meet these regulations, analyze the data and generate reports (which can be refined by the finance function). AI’s capacity for data collection and analysis will also help you optimize sustainability across your supply chain. Thanks to AI, even small suppliers will be able provide granular sustainability data such as their monthly or annual energy consumption. AI can quantify new kinds of value like the benefits of commercializing low-carbon products. As these AI capabilities are embedded into corporate strategy and everyday enterprise applications, everyone, not just ESG specialists, will be able to access and use sustainability data to help make decisions.
“It’s just not true that AI is anti-sustainability. If you use it right, AI makes not just carbon targets, but every sustainability goal more accessible.”
Beyond 2025: Costs will drop to near zero
Over time, new sources of computational power and new, renewable energy supplies will come online — dramatically lowering costs and enabling AI in every aspect of your company and industry.
What to do now
- Be strategic. Even if everyone in your company should have access to basic AI functionalities, carefully choose where to deploy more high-powered solutions. The C-suite will need to make this call, in line with your company’s strengths, source of data and priorities.
- Transform sustainability data. AI can help you collect and analyze data once and then report it many times over to cut compliance costs and help you better measure — and reduce — your carbon footprint and broader sustainability impact. As part of this effort, account for AI’s direct and indirect impact (via AI vendors) on your carbon balance sheet. The more you measure this impact, the more pressure your vendors will feel to reduce their own. You can use this sustainability data to enhance marketing as well. Determine, for instance, which customers would pay more for products with a lower carbon footprint.
- Reap new sustainability benefits. AI-driven efficiencies can slash your energy needs, cutting costs as well as carbon impacts. When you slash R&D time in half (and do the same for other processes too), energy usage falls too. Or when you use AI to help make office buildings and energy management systems smarter and more efficient, sustainability wins too.
5. AI will cut product development lifecycles in half
If your company makes tangible goods and your product development teams aren’t using AI for design, prototyping and testing, now is the time to start. Multimodal AI — capable of processing and generating diverse data types, from CAD files to simulations — is now revolutionizing product design and broader R&D processes. For example, GenAI tools can propose improved configurations for a car chassis, simulate performance under different conditions and even suggest designs that engineers might have overlooked.
AI can help you iterate designs in hours not weeks, test solutions virtually before building prototypes and troubleshoot more problems before you move to production. Based on PwC’s work with clients and our analysis of technology and industry trends, we’re confident that adopting AI in R&D can reduce time-to-market 50% and lower costs 30% in industries like automotive and aerospace. In many pharmaceutical companies, AI has already helped reduce drug discovery timelines by over 50%.
Most companies are unprepared for this revolution in physical product design. AI is ready to deliver — but the skills gap is often a hurdle. Engineers with deep expertise in design and manufacturing often lack even foundational data science skills. Upskilling these teams and recruiting AI-savvy talent must begin now. Those who embrace AI’s potential in product development will enjoy faster speed to market, lower costs and increased personalization — and that can add up to more satisfied end users.
“We’re just starting to feel the impact of how the multimodal vision and generation capabilities of AI will change product design and more.”
Beyond 2025: A new age of innovation begins
As the design and engineering workforce is completely reskilled or replaced to work with AI, companies’ R&D capacity will multiply — leading to an age of increasingly rapid innovation in product design and development.
What to do now
- Deploy next-gen engineering. To leverage AI for things like product design, your company will need up-to-date cloud and data architectures, including ones that can push “edge AI” to your engineering teams.
- Reinvent IT. AI itself can help IT transform to better support your broader AI initiatives, including transforming software development, enhancing cybersecurity, accelerating data modernization and so on.
- Reorganize tech teams. Even more than in other areas, the balance of skills your technologists need will change. Even teams that work purely with physical objects will need computer and data science skills.
6. AI will transform industry-level competitive landscapes
AI will transform every industry, but some will move faster than others — and it may not be the “usual suspects” taking the lead. Here’s how we see several major sectors advancing with AI over the next year.
Consumer markets
Consumer-facing companies will deploy AI across their operations and business. AI will enhance marketing, supply chain management, financial operations and customer service. Many will revamp customer services with a mix of more engaging chatbots and AI agents that provide human staff with the exact information they need to assist customers. Other AI agents will (under close human supervision) help automate interactions with customers, using multiple touchpoints to impress and engage.
Further revenue boosts will come from more sophisticated AI-driven dynamic pricing, designed to adjust instantly to market shifts and competition. More consumer markets companies will use AI’s data analysis and automation capabilities to accelerate due diligence for deals and to navigate the regulatory landscape. Some leading companies will also start with AI-enhanced product design, but most companies in the sector still lack the skills and technology infrastructure to fully seize this R&D opportunity in the near term. These laggards will have to make up for lost time soon.
Financial services
The impact of AI is broad, but we’ve seen measurable impact concentrated with AI native startups and large financial institutions. There’s been a resurgence in the fintech space with AI native businesses focused on solving old problems with new platforms and business models. Similarly, we’ve seen many of the largest financial institutions experimenting with several common use cases. This experimentation has not only helped them build confidence with new tech but also refine their risk and control models in ways that position them to benefit at an accelerated pace. While AI native startups and large financial institutions continue to progress their strategies, there is a risk that firms that continue to evaluate their entry strategy will begin to fall behind noticeably starting in 2025.
Health industries
The use of AI in 2025 should be accelerated by a more flexible regulatory environment. The new administration is likely to shift oversight in this sector toward self-governance, creating more space for innovation. Pharmaceutical and medtech companies will be in the forefront of using AI to revolutionize their value chains, especially for drug and product development. Health payers and providers will deploy more AI applications to optimize revenue and volume and to help fill clinical labor shortages and assist doctors in making diagnoses, contributing to better clinical outcomes.
Top AI priorities in healthcare will include workforce transformation, personalization, tech upgrades, eliminating “process debt” (from pre-AI processes) and, above all, the responsible use of AI — as even with a more favorable regulatory framework, health industries organizations are responsible for sensitive data and for life-and-death outcomes.
Industrial products
In 2025, a smaller group of industry leaders will begin to pull ahead of their peers. Those industrial products companies with higher quality data and more standard processes will use AI to improve efficiency and insights, accelerate R&D and slash go-to-market time. Many other companies will still be focused on upgrading tech infrastructure, data governance and AI skills, but the pace of experimentation will accelerate and create additional questions on operating models, organization structures and talent requirements.
Technology, media, telecommunications
In 2025, AI agents will start to reshape demand for software platforms, as companies use them to fill the gaps of existing systems, such as ERPs. With AI agents customizing and extending the life of software platforms, some companies may choose to invest less in premium upgrades. This shift may prompt a change in software business models from seeking large-scale infrastructure investments to offering tailored AI solutions. Telcos will likely advance with hybrid AI solutions that blend GenAI with other technologies like machine learning and digital twins — boosting their own AI capabilities and reducing their dependence on traditional partners.
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