2026 AI business predictions

From incremental gains to game-changing transformation

Only a few companies are realising extraordinary value from AI, such as surging growth and valuation premiums. Many others see return on investment, but results are modest: efficiency gains, capacity growth, and general productivity boosts. These pay for themselves but don’t add up to transformation.

That’s starting to change. Driving transformative value remains hard, and AI evolves fast. But success is now becoming visible. We can see what it looks like to use AI to build leading-edge operating models. From mature systems to emerging tools like AI agents, examples of impact are multiplying across strategy, operations, workforce, trust, and tech stacks.

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Companies now have evidence to build benchmarks, measure performance, and accelerate value creation in business and functions like finance and tax. In Ireland, businesses are moving from experimentation to action, but progress varies. Productivity gains are clear, yet cost savings, trust, and governance remain challenging.

Why is success still concentrated in so few? Too often, organisations spread efforts thin. Early wins can mask deeper issues. Real results require precision: picking a few spots for wholesale transformation and executing with discipline, starting at the top. After success in priority areas, the rest can follow.

We’ve seen that discipline pay off. Across industries, deliberate efforts turn AI experiments into engines of growth. In Ireland, success in 2026 will hinge on strategy, benchmarks, workforce reimagination, and Responsible AI. Our own AI transformation and nearly a decade of research give us a clear view of what drives success—and what holds it back.

Our forecasts are grounded in experience and focused on practical impact, so you can turn AI ambition into transformative business value in 2026 and beyond.

1. AI success will come from focused bets

Irish businesses are stepping beyond the trial phase, yet progress varies. Our AI Agent Survey reveals that over half (53%) of Irish participants see clear productivity boosts from AI agents, but only 38% experience real cost reductions—lagging behind their US counterparts. The challenges are significant: 40% point to data issues as the main hurdle, and 36% find it tough to connect AI agents with existing systems. These obstacles highlight why scattered, grassroots efforts seldom lead to transformation.

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.

Crowdsourcing AI efforts can create impressive adoption numbers, but it seldom produces meaningful business outcomes. Senior leadership picks the spots for focused AI investments, looking for a few key workflows or business processes where pay-offs from AI can be big. Leadership then applies the right “enterprise muscle”—talent, technical resources, and change management. 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.

What to do now

Have leadership pick the spots. Top leadership picks a few areas for focused AI investments, often where business priorities, evidence of AI’s value, and availability of talent and data align. Then, leadership focuses on execution.
Rethink the workflow. After you identify the right high-value workflow, aim for wholesale transformation. Instead of cutting a few steps, rethink the workflow, which an AI-first approach may turn into a single step. That often starts by asking not how AI can fit into a workflow but how it can create a new one.
Assign top talent to AI focus areas. Assign top talent to those areas where you’ve decided to focus on AI. These business leads can both work with company leadership to define target outcomes and drive progress with process owners and AI specialists

2. Benchmarks will separate hype from real agentic AI impact

Irish companies are keen to expand agentic AI, with 70% planning to boost AI budgets by 2026 according to our AI Agent Survey. But adoption is slow—only 9% report widespread AI agent use, compared to 52% in the US, while 83% remain in the exploration or pilot phase. This gap underscores the need for clear evidence and standards to support investment.

Agentic deployments saw significant interest last year, but many didn’t fully meet expectations in terms of realised value. In several instances, implementations focused on experimentation rather than high-impact applications, which limited tangible outcomes. When organisations were asked to demonstrate agents actively delivering measurable benefits, it was often difficult to showcase clear examples—underscoring the gap between early adoption and proven results.

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.

Built-in monitoring also includes different agents checking each other’s work, and for higher-risk scenarios, these agents come from different model providers.

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.

What to do now

Create metrics that drive outcomes. For AI that delivers the value your business wants, set concrete outcomes for it to deliver, select suitable “hard” metrics, and stand up a capability (with a mix of tech and people) that can help make those metrics timely and reliable.

Follow the 80/20 rule. Technology delivers only about 20% of an initiative’s value. The other 80% comes from redesigning work—so agents can handle routine tasks and people can focus on what truly drives impact.

Map the workflow. As you design a new agentic workflow, map it step by step, specifying where agents own the work, where people do, where people and agents collaborate, and how oversight can take place for each step.

3. AI generalists will redefine roles as specialisation fades

Irish workplaces are already experiencing a shift towards AI-driven roles. Our Workforce Hopes and Fears Survey shows that, over the past year, 43% of Irish workers have engaged with AI, with 67% reporting increased productivity. Encouragingly, 55% feel they will maintain control over how technology influences their work—an important foundation for building trust and adoption as roles evolve.

But 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.

Across functions, demand may grow for generalists who understand a wide range of tasks well enough to oversee agents and align their work with business goals.

What to do now

Evolve recruitment. Look not just for people who are leading but also ones who are AI-forward and open-minded enough to be generalists and agent orchestrators.

Start on workforce redesign. As agents spread, your workforce may need new skills (like agent orchestration), new incentives (aligned to business outcomes, as agents do intermediate steps), and new roles (often related to oversight and strategy). And don’t underestimate the importance of having a culture that encourages change, evolution, and adoption of the future of work.

Measure what matters. With agents, iterations move quickly, but you may need more to allow for the back and forth required. Still, if an outcome that once took five days and two iterations now takes fifteen iterations but only two days, you’re ahead.

4. Responsible AI will shift from principles to practice

Irish organisations see the potential of Responsible AI but grapple with governance and trust hurdles. According to our Digital Trust Insights Survey, over half (52%) identify an unclear risk appetite as the main obstacle to using AI for cyber defence—surpassing global averages. Leadership hesitation (42%) and lack of clarity on practical application further slow progress. These findings highlight why embedding governance early and clearly defining responsibilities is critical for Irish businesses.

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 Responsible AI principles into operational processes has been a challenge.

2026 could be the year when companies overcome this challenge and roll out repeatable, rigorous Responsible AI practices. 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) Responsible AI also depends on suitable upskilling and user expectations, risk tiering (with protocols for human intervention), and clarified documentation requirements and tools. Responsible AI can then deliver the value you want—performance, innovation, and a reduction in the costs and delays that come with governance models built for another time.

What to do now

Integrate early. The faster and deeper you can align IT, risk, and AI specialists—with clear responsibilities and expectations—the easier it can be to operationalise an RAI framework that can help grow business value and stakeholder trust.

Explore testing and monitoring solutions. New tech capabilities can help operationalise AI testing and monitoring. Experiment with them now to understand challenges and adapt processes to be ready for when your AI adoption takes off.

Add assurance. Unless you have unlimited data science resources, independent assessments may be needed to fill gaps. For higher-risk and higher-value systems, an independent opinion can be critical for performance and risk management.

5. Orchestration will turn AI pilots into scaled business value

Irish adoption of AI agents remains limited, but momentum is building. Our AI Agent Survey showed that less than one in ten (9%) Irish respondents reported broad adoption of AI agents, significantly below US counterparts (52%), with a further 83% reporting limited adoption or exploration (US: 42%). Compared to earlier PwC Ireland data, there is a positive—albeit small—shift. PwC Ireland’s Gen AI Business Leaders survey, published in January 2025, reported that just 6% of Irish firms had widespread AI adoption, while 67% were at testing or partial implementation stages. This underscores the importance of orchestration to accelerate impact and move beyond pilots.

You need tech expertise 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.

You can spot valuable ideas and operationalise them quickly, manage risks, and keep everything aligned with your enterprise priorities.

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.

What to do now

Create orchestrators. As orchestrating agents becomes a part of people’s workdays, you’ll want your employees to know how to spot and correct agents’ mistakes, connect them into teams, and find new tasks for them to do.

Help IT help you. To help run your orchestration layer and execute your AI agenda, IT likely needs new resources and skills. Agentic AI for IT can help create new capacity by automating or assisting in many common IT tasks.
Stay practical. Durable, scaled, industrial-strength deployments depend on practical actions which your orchestration layer can enable—things like testing before release, constant monitoring, and protocols for patches and quick rollbacks if needed.