2026 AI Business Predictions

Only a few companies are realising extraordinary value from AI today, things like surging top-line growth and significant valuation premiums. Many others are also experiencing measurable ROI, but their outcomes are often modest—some efficiency gains here, some capacity growth there, and general but unmeasurable productivity boosts. These results can pay for themselves and then some. But they don’t add up to transformation.

The picture’s starting to shift. It’s still hard to use AI to drive transformative value, and the technology continues to evolve at speed. That’s not changing. But what’s new is this: Success is becoming visible. We can now see what it looks like to use AI to build a leading-edge operating or business model. From mature systems to emerging tools like AI agents, examples of impact are multiplying—across strategy, operations, workforce, trust, tech stacks, and sustainability.

Companies now have enough evidence to build benchmarks, measure performance, and identify levers to accelerate value creation in both the business and functions like finance and tax so they can become nimbler, faster-growing organisations.

Why, then, has this kind of success—the kind that drives revenue growth and opens up new markets—been concentrated in so few? Too often, organisations spread their efforts thin, placing small sporadic bets. Because AI feels easy to use, early wins can mask deeper challenges.

But real results take precision in picking a few spots where AI can deliver wholesale transformation in ways that matter for the business, then executing with steady discipline that starts with senior leadership. After success in your priority areas, the rest of the company can follow.

We’ve seen that discipline pay off.

Our own AI transformation at PwC has given us this perspective from the inside too. Combined with nearly a decade of research through our executive surveys and annual AI predictions, we’ve built a clear view of what drives success—and what holds it back.

Our forecasts are grounded in real experience and focused on practical impact—so you can take confident steps to turn AI ambition into transformative business value in 2026 and beyond.

Explore the predictions

1. The disciplined march to value begins
2. Proof points and real-world benchmarks set the pace for agentic AI
3. Rise of the AI generalist: A new workforce emerges
4. Responsible AI moves from talk to traction
5. From vibe to value: Orchestration that accelerates impact
6. The demand for business returns drives AI for sustainability

With AI, many companies make an understandable mistake. Instead of leadership calling the shots with a top-down programme, they take a ground-up approach, crowdsourcing initiatives that they then try to shape into something like a strategy. The result: projects that may not match enterprise priorities, are rarely executed with precision, and almost never lead to transformation.

In 2026, we expect more companies to follow the lead of AI front-runners, adopting an enterprise-wide strategy centred on a top-down programme. Senior leadership picks the spots for focused AI investments, looking for a few key workflows or business processes where payoffs from AI can be big. Leadership then applies the right “enterprise muscle”—talent, technical resources, and change management. Often, this programme is executed through a centralised hub that we call an “AI studio.” It brings together reusable tech components, frameworks for assessing use cases, a sandbox for testing, deployment protocols, and skilled people. This structure links business goals to AI capabilities so you can surface high-ROI opportunities.

Agentic AI looks to play an increasingly important role. AI agents can go beyond analysis and automate parts of complex, high-value workflows. Especially ripe areas for agents include demand sensing and forecasting, hyper-personalisation, product design, and functions like finance, HR, IT, tax, and internal audit.

There is—rightfully—little patience for “exploratory” AI investments. Each dollar spent should fuel measurable outcomes that accelerate business value. But many agentic deployments last year didn’t deliver much value. If you looked under the hood, many weren’t using agents in ways that matter. If you asked for a demo—to see an agent at work delivering value—you often couldn't get it because there wasn’t anything to see.

We expect that to change in 2026. We now know what good agentic AI looks like. It has proof points like benchmarks that track value that matters to the business, whether that’s financial (P&L impact), operational (market differentiation), or related to workforce and trust. Instead of siloed efforts, it has a centralised platform for deployment and oversight that draws on a shared library of agents, templates, and tools. Before each deployment, agents are tested, with flaws corrected and working demos created for future users to try—so they can offer feedback and start to trust what agents can do.

Agents are rolled out as part of all-new workflows, with clearly-articulated steps for human initiative, review, and oversight—and with people who have the training and incentives to work with agents and provide that oversight.

Since agents can automatically document their decisions and actions, continuous monitoring can be highly effective in tracking adoption and performance, fixing errors quickly, and building stakeholder trust.

Agents today are imperfect, but new technologies generally are. Now that companies know how to proceed—with focused, centralised implementation guided by real-world benchmarks—2026 could be the year when agents shine.

AI could soon end a shift that has marked most of the industrial era—the ever-increasing specialisation of work. Agents can increasingly do the specialised tasks that fill the workdays of experienced, mid-tier employees. In IT, for example, you may no longer need coders specialised in specific languages. Instead, you may want engineers who understand both tech architecture and how to manage and oversee the agents that do know these languages. In finance functions, as agents do tasks like invoice processing, purchase order matching, reconciliation, and anomaly detection, people with general finance skills can focus on growing revenue and expanding margins, engaging with vendors on payment terms, working with sales on dynamic pricing models, and conducting more scenario planning.

In knowledge work, many of these roles can be filled by entry-level employees who tend to be AI savvy. As agents—available for anyone to buy or rent—take on more “midlevel” work, differentiation comes from senior professionals who excel at strategy and innovation. With more talent concentrated at the junior and senior levels, and a smaller mid-tier, the knowledge workforce may look like an hourglass. But for front-line employee-based task work, agents could replace entry-level workers with more mid-level people needed to orchestrate and manage these agents—creating a workforce more like a diamond.

Executives know what Responsible AI (RAI) is worth. In our 2025 Responsible AI survey, 60% said that it boosts ROI and efficiency, and 55% reported improved customer experience and innovation. Yet, nearly half of respondents also said that turning RAI principles into operational processes has been a challenge.

The acceleration of adoption leaves companies little choice, and agentic workflows are spreading faster than governance models can address their unique needs. In many cases, agents can do roughly half of the tasks that people now do—but that requires a new kind of governance, both to manage risks and improve outputs.

The good news: The proliferation of new, tech-enabled AI governance approaches brings new techniques to the challenge. Automated red teaming, deepfake detection, AI enabled inventory management, and other advancements can help make continuous assessment and monitoring a reality.

These tools are powerful and nimble, but to support effective (and cost-effective) RAI, also depends on suitable upskilling and user expectations, risk tiering (with protocols for human intervention), and clarified documentation requirements and tools. RAI can then deliver the value you want like performance, innovation, and a reduction in the costs and delays that come with governance models built for another time.

AI agents make possible “vibe” coding—where people write software without technical expertise—and other “vibe” work, where almost anyone can invent and test new ideas. But you usually need tech teams to “industrialise” this innovation, putting ideas into production with continuous monitoring. That’s why an orchestration layer is so important. Its unified “command centre” view helps you catch mistakes and track and fine-tune performance. It can also help end-user innovation enhance your top-down strategy.

A good AI orchestration layer should be easy even for non-techies to use, with intuitive dashboards and commands that let you drag-and-drop agents into new workflows—even for complex, high-value tasks. It should enable you to combine AI tools from different vendors into unified processes. It should integrate real-time data and natural language. And it should be built for centralised governance and security, with integrated code reviews and tools like encrypted credential vaults and sandboxes for prototyping. Most of all, it should put you in charge, enabling you to control AI anywhere in your company.

Whether AI is a boon or a burden for sustainability in 2026 is up for grabs—but we lean toward boon. That’s not to understate the challenges. Even as AI quickly gets more energy efficient, its use is growing even faster. In fact, its fast-rising efficiency—by making AI cheaper—could accelerate its use even more. That could impact emissions, water supplies, and energy prices. But companies can become more efficient in AI use by approving token usage only when it delivers significant value and using methods like carbon scheduling to further cut emissions and costs.

But there’s another reason that AI could boost sustainability—the quest for sales growth and margin expansion. AI agents, by gathering and analysing customer data, can identify which customers would pay what premium for what kind of sustainability in products. Or they can measure and document that sustainability to strengthen your brand and grow your markets. AI can also help manage transport and electricity use to lower travel and power bills. Its simulations can show how to grow resilience against natural disasters. And it can cost-effectively help trace products across your value chain to reduce both environmental impacts and costly product recalls. These and other AI solutions can create financial value for you while making your operations more sustainable.