When it comes to AI, Irish organizations are investing in pilots and experiments. While that’s important, these efforts still don’t always translate into meaningful business value because technology hasn’t yet translated into redesigning processes, roles, and operating models. The 2026 CEO Survey found that just 17% of Irish CEOs reported an increase in revenue from AI in the past 12 months, and 23% reported a decrease in costs. The majority of respondents said they did not see any benefits in terms of profits or costs.
This gap is important because the first wave of AI has already proven to be important. In other words, AI can help people work faster. Across Irish organizations, AI is accelerating drafting, summarizing, analyzing and supporting day-to-day decision-making. Our AI Agents research shows that 53% of Irish organizations see measurable productivity gains from AI agents, but only 38% say those gains translate into measurable cost savings.
The question for leaders is no longer whether AI can create some of the efficiency. can. An even more important question is why these benefits are often not reflected in the outcomes executives care about most: lower costs, increased throughput, improved service, enhanced conversion, and new revenue.
In most cases, the missing link is not another use case. This is a necessary business change that re-engineers how work flows, how decisions are made and where accountability lies. Companies are still applying AI to workflows built for a pre-AI world. They are improving their tasks, but they have not yet redesigned how they create value throughout the process. That broader pattern is also reflected in MIT Project NANDA’s 2025 State of AI in Business report, which finds that while adoption is ahead of transformation, many organizations still struggle to turn pilots into measurable business impact.
Adoption is not change.
This is a distinction that many organizations must now face. Adoption means that people use the technology. Transformation means that processes, roles, decision-making rights, and performance evaluations change accordingly.
Too many AI programs still build on existing ways of working without being used to redesign the processes, surrounding controls, and supporting operating models. Teams draft faster. Functions summarize information more quickly. Some repetitive tasks are automated. But the broader workflow often remains the same: the same fragmented systems, the same handoffs, the same approvals, the same accountability.
That is one reason why commercial benefits remain limited. Task-level productivity does not automatically translate into enterprise-level value. Tactical AI projects can build trust and competency, but by themselves they rarely deliver measurable value to the enterprise as a whole. Our 2026 CEO Survey shows that Irish organizations continue to lag behind global organizations in the extent to which AI is applied across multiple business areas, from support functions to strategic decision-making.
This is a trap. Applying AI to fragmented processes often accelerates the symptoms rather than solving the causes. Technology will struggle to move from experimentation to measurable impact when data is inconsistent, exceptions are poorly managed, roles are unclear, and employees don’t trust the output. Our AI agent research makes that point clear. Data issues, mindset, responsiveness to change, and employee adoption remain the biggest barriers to realizing value.
Organizations moving forward will do more than just deploy AI. They are redesigning business processes enabled by emerging technologies.
Start by redesigning your end-to-end process
We need to move the conversation forward here. The right starting point is not simply “Where can I use AI?” The question is: Which end-to-end business processes are most important and how should they work differently in an AI-enabled model? This shift sounds subtle, but it shifts the focus from individual use cases to end-to-end performance. This requires leaders to focus on workflows that can significantly improve cycle time, cost of service, service quality, speed to insight, or customer conversion by redesigning them without being constrained by existing organizational structures.
In practice, this means choosing a small number of preferred processes and redesigning the way those workflows work. This may include simplifying handoffs, clarifying decision-making rights, updating controls, improving the data needed to run processes, redefining where human judgment remains essential, and restructuring roles in new workflows. It often also requires changes to the incentives, ownership, and operating models behind the process.
Organizations that are making progress don’t separate AI adoption from business change. They are connecting technology to process redesign, employee onboarding, and governance from the beginning.
Many organizations need to work on that. Our AI Agent Survey found that 70% of Irish organizations plan to increase their AI budget in the coming year due to interest in agent AI. However, only 9% report widespread adoption, and the study notes that few companies are using AI agents to fundamentally rethink their operating models and the way work gets done.
That’s a lost opportunity. The goal is not simply to streamline existing work. It’s about rethinking how work should be done in the first place.
What determines whether value scales?
Trust remains one of the most obvious constraints, as even a well-chosen use case will falter if the foundation is weak. Our research on AI agents found that just 7% of organizations in Ireland report high trust in AI agents across multiple departments. Trust was particularly low in high-stakes areas such as financial transactions and autonomous customer interactions. This is important because AI will not scale to critical parts of the business unless leaders trust the deliverables, understand the controls, and are clear on who is responsible.
Employee trust is equally important. People are more likely to consistently adopt AI if they understand how it is being used, where oversight is placed, and what it means for their role. While the 2025 Workforce Hopes and Fears survey found that Irish workers are more likely to be curious than worried about the impact of AI, adoption remains uneven and access to learning is still far from universal. 67% of Irish workers who used AI in the last year said their productivity had improved, but only 43% said they had used AI at all in the past 12 months, and just 10% used AI tools at work every day. Only 57% say they have access to the learning and development resources they need at work.
The meaning is clear. The value of AI depends on more than tools. To succeed, organizations must redesign processes, build employee trust, and manage data responsibly. The pattern is consistent across surveys of CEOs, AI agents, and employees. While organizations are beginning to realize productivity gains, the financial impact will remain limited unless they address the operational conditions that allow AI to scale.
