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Irish organisations are investing heavily in AI, experimenting with new tools, and exploring emerging technologies such as agentic AI. Yet the evidence suggests that a relatively small group of organisations are capturing a disproportionate share of the benefits. PwC's 2026 AI Performance Survey found that roughly 20% of organisations are capturing 75% of the economic value generated by AI. The difference isn't access to technology; it's execution. The leaders are embedding AI into business strategy, redesigning how work gets done, and creating the conditions needed to scale value safely and sustainably. But closing the value gap isn’t only about doing existing work better. It also means asking where AI could change what the business offers and how it grows.
The AI adoption story is entering a new phase
The first stage of enterprise AI was characterised by experimentation. Organisations encouraged employees to explore generative AI tools, launched pilots, and tested use cases across different functions. For many businesses, that phase delivered useful insights and individual productivity gains. The next stage is different.
Boards and executive teams are increasingly asking whether AI is creating measurable value rather than simply generating activity. The questions have shifted from "What can AI do?" to "What is AI doing for our organisation?" and "How do we scale the benefits?" The answers are not always straightforward.
PwC's 2026 AI Performance Survey highlights a significant gap between AI adoption and AI value. While many organisations are deploying pilots and experimenting with new capabilities, only a minority are converting those activities into meaningful financial returns. Roughly one-fifth of organisations capture three-quarters of the economic value being created by AI.
That suggests AI success is becoming less about access to technology and more about organisational capability.
"The organisations creating the most value from AI aren't running more pilots than everyone else. They're redesigning how work gets done, building trust in the technology, and measuring outcomes that matter."
David Lee, Chief Technology Officer, PwC IrelandWhy some organisations create more value
The survey identifies a clear pattern among the highest-performing organisations. Rather than treating AI as a technology initiative, they treat it as a business transformation programme. Their AI strategy is closely linked to broader business priorities; they invest in governance, data quality, and workforce adoption; they support employees in learning how to work effectively with AI; and most importantly, they focus on outcomes rather than activity. These organisations are also more likely to use AI beyond isolated operational use cases
Instead of limiting AI to administrative efficiency programmes, they apply it across value chains, tackle more complex business challenges, and use it to pursue growth opportunities. Many are exploring how AI can help them enter adjacent markets, create new products and services, and redesign business models. That’s a different ambition from improving an existing process: it asks where AI could change how the organisation creates value for customers, and tests whether those opportunities can deliver a measurable return. In short, the leaders view AI as a reinvention engine rather than a productivity tool.
Productivity gains don't automatically create business value
This distinction is becoming particularly important as agentic AI gains momentum. PwC's AI Agent Survey found that 70% of Irish organisations plan to increase AI-related budgets because of growing interest in AI agents. However, only 9% report broad adoption of agentic AI today, while the vast majority remain in the exploration or limited-adoption phase.
The same research found that more than half of Irish respondents reported measurable productivity gains from AI agents. Yet only 38% had translated those gains into tangible cost savings. This disconnect highlights a critical leadership challenge.
Productivity improvements at an individual level don't automatically translate into improved organisational performance. Value emerges when processes, governance structures, and operating models evolve alongside the technology. Simply giving employees AI tools is rarely enough.
Organisations need to rethink how decisions are made, how work flows across teams, and where human expertise adds the most value. Without those changes, many of the benefits remain trapped at an individual level.
“Saving time is useful, but reinvention asks a bigger question: what could we do differently as a business? That means redesigning work across teams and using AI to develop products or services that create measurable value.”
Amy Ball, Reinvention Leader, PwC IrelandMeasuring what matters
One reason many organisations struggle to close the AI value gap is that they focus on the wrong metrics. Usage statistics can demonstrate adoption, prompt volumes can demonstrate engagement, and pilot counts can demonstrate activity. But none of these metrics demonstrate value.
Meaningful measures of AI success include revenue growth, cost reduction, productivity improvement, faster cycle times, improved customer experience, and better risk management outcomes. Organisations need to understand whether AI-generated efficiency gains are producing measurable business benefits.
This is becoming even more important as AI software pricing models shift towards consumption-based charging, increasing the need for organisations to demonstrate clear returns on investment.
The strongest organisations establish value metrics before implementation begins. They define expected outcomes, monitor progress continuously, and make rapid decisions to stop, adjust, or scale initiatives based on evidence.
Governance is becoming a growth enabler
One of the most persistent misconceptions about AI is that governance slows innovation. The evidence suggests the opposite.
According to PwC's 2026 AI Performance Survey, the organisations creating the highest levels of value are significantly more likely to have Responsible AI frameworks and cross-functional AI governance boards in place. They are also twice as likely to report high levels of employee trust in AI outputs.
Effective governance provides clarity around risk, accountability, and decision-making. It creates the confidence needed to move from experimentation to deployment. Strong governance also helps organisations avoid one of the biggest barriers to scaling AI: uncertainty. When employees understand how AI should be used, what safeguards exist, and who is accountable, adoption becomes easier and trust increases.
For leaders, governance should not be viewed primarily as a compliance mechanism. It's increasingly becoming an operational capability that enables organisations to innovate safely and scale responsibly.
The next competitive advantage
The AI value gap likely won't persist forever. More organisations will improve their use of AI, new capabilities will emerge, costs will continue to fall, and adoption will accelerate. The question is: Who will be ready to capture the next wave of value?
The evidence suggests that successful organisations won't necessarily be those investing the most money or running the largest number of pilots. They will be the organisations that combine technology with strategy, governance, workforce transformation, and execution discipline.
Key actions for business leaders
1. Define outcomes before deployments
Begin every AI initiative by identifying the business outcome you want to improve. Focus on revenue, cost, productivity, customer experience, or risk reduction rather than usage metrics.
2. Redesign work and test new opportunities
Look across teams and customer journeys, not just individual tasks. Identify where AI could remove friction from an end-to-end process, then ask whether the same capabilities could support a better product, service, or way of serving customers. Set a business outcome for each opportunity and test it before scaling.
3. Build governance early
Establish Responsible AI principles, ownership structures, and decision-making frameworks before scaling deployment. Governance enables innovation when implemented effectively.
4. Invest in adoption as well as technology
Provide employees with training, support, and clear usage guidance. Organisations that create trust in AI achieve higher levels of value and adoption.
5. Scale what works
Measure outcomes rigorously and make fast stop-or-scale decisions. Focus resources on use cases that demonstrate measurable impact.
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