Most organisations are using AI to do the same things faster. A small number are using it to build entirely new business models, such as new revenue streams, new ways of delivering value and new competitive moats. The gap between these two groups is widening fast. AI Ireland’s own 2026 survey of 130 Irish AI leaders confirms this: the dominant goal is cost optimisation, not transformation. If your board hasn’t moved the AI conversation beyond efficiency, you’re already behind.
The Efficiency Trap: Why Doing Things Faster Isn’t Enough
There’s nothing wrong with using AI to cut costs. Automating invoice processing, speeding up customer service, streamlining reporting – these are sensible moves that deliver quick returns. However, the risk is that when every competitor has access to the same AI tools, efficiency gains become table stakes. They keep you in the game, but they don’t help you win it.
We can see this pattern clearly in the Irish market. AI Ireland’s 2026 State of AI in Irish Business report surveyed 130 AI leaders – 32% of them C-suite or VP level – and found that cost optimisation is the number one AI goal for the next 6–12 months, cited by 28% of respondents. Engineer assistance came in at 19%, and predictive maintenance at 15%. These are all efficiency plays. Very valuable, but none of them change how the business makes money.
The good news is that Irish organisations are no longer experimenting. Nearly 74% have moved AI into production – 41% in early production and 33% in broad adoption. Only 2% haven’t started at all. The question is no longer “Should we use AI?” It’s “Are we using AI to protect our current business model – or to build the next one?”
Deloitte’s 2026 State of AI in the Enterprise report puts a global number on this same problem. Only around a third of surveyed organisations worldwide are using AI to deeply transform their operations – creating new products, services, or business models. A third are redesigning key processes. The rest are making surface-level changes. All three groups capture some efficiency value, but only the first group is truly reimagining how their business works.
This is the efficiency trap. It feels like progress. The numbers look good on a quarterly review, but it leaves your business model untouched and that’s where the real risk sits.
What AI Business Model Transformation Actually Looks Like
When we talk about AI-driven business model transformation, we’re not talking about adding a chatbot to your website. We’re talking about fundamental changes to how your organisation creates, delivers and captures value. Here are three patterns emerging across industries in 2026:
From Products to Outcomes
AI enables companies to shift from selling products to selling guaranteed outcomes. A manufacturer doesn’t sell equipment – it sells uptime, monitored and maintained by AI-driven predictive systems. A consulting firm doesn’t sell hours – it sells measurable business results, powered by AI analytics. This shift turns one-off transactions into long-term recurring revenue with higher margins and deeper client relationships.
It’s worth noting that 15% of Irish AI leaders already rank predictive maintenance as a top goal. That’s the seed of an outcome-based model – using AI to guarantee performance rather than just fixing problems after they happen. The leap from “we use AI to predict when equipment will fail” to “we sell guaranteed uptime as a service” is smaller than most boards realise.
From Services to Platforms
Organisations are discovering that the data and AI capabilities they build for internal use can become products in their own right. A logistics company that builds AI-powered route optimisation for its own fleet can license that capability to others. A financial services firm that develops AI fraud detection can offer it as a service. Your proprietary data and AI models aren’t just operational tools, they’re potential new revenue streams.
AI Ireland’s survey found that over a quarter of firms (the 27.7% “Other” category) are already pursuing niche AI innovations tailored to their specific industries. That kind of specialisation – deep, industry-specific AI capability – is exactly what creates platform potential. The organisations building proprietary AI solutions today are building tomorrow’s competitive moats.
From Fixed Pricing to Usage-Based and Outcome-Based Models
AI’s unit economics are different from traditional software. Usage-based pricing – where customers pay in proportion to the value they actually receive – is quickly becoming the dominant model in AI-powered businesses. This lowers the barrier for new customers and scales revenue naturally as adoption grows. Outcome-based pricing takes this further: customers pay only when a result is achieved. These models build trust and align incentives in ways that flat-fee structures never could.
The Irish Readiness Gap: Why It Matters for Business Models
AI Ireland’s survey reveals what we call a “readiness gap.” While 74% of organisations are in production, the biggest challenges aren’t about AI itself; they’re about the infrastructure, skills and governance needed to scale it.
The top three blockers Irish leaders face tell the story. Integration challenges top the list at 25% – the technical difficulty of making new AI tools work with legacy systems. Skills and resource shortages follow at 15%, compounded by a lack of AI literacy at leadership level. Security concerns round out the top three, also at 15%, with leaders focused on data protection and regulatory compliance including ISO 42001.
Here’s the critical insight: these same blockers that slow down efficiency projects will completely prevent business model transformation. You can’t shift to an outcome-based model if your data systems don’t talk to each other. You can’t build a platform play if your leadership team doesn’t understand what AI makes possible. You can’t monetise proprietary data if you haven’t sorted your governance framework.
The 56% of Irish leaders who prefer “controlled use” of generative AI with company data are being sensible about security. However, controlled use must be a bridge to confident, governed deployment – not a permanent holding pattern. Organisations that stay in sandbox mode while competitors move to production-grade AI-powered business models will find themselves playing catch-up on a much bigger stage than cost savings.
Why This Is a Board-Level Conversation
Business model decisions are not operational decisions. They are strategic decisions with long-term implications for revenue, risk and competitive position. That makes them the board’s responsibility.
Recent IBM research found that nearly eight in ten executives expect AI to significantly contribute to their revenue by 2030, up from around four in ten today. However, only about a quarter have a clear view of where that revenue will actually come from. That gap between expectation and clarity is exactly where board oversight is needed most.
PwC’s 2026 AI predictions reinforce this point. The organisations seeing real transformative value from AI aren’t the ones that crowdsourced ideas from the bottom up. They’re the ones where senior leadership picked focused areas for investment – a top-down programme aligned to enterprise priorities. Without board-level direction, AI efforts fragment into dozens of small experiments that rarely add up to transformation.
AI Ireland’s survey data echoes this. The leaders who are successfully moving past MVPs into production are the ones who have built what respondents describe as “demonstrated earned business value” – proof that AI delivers real results, which in turn earns trust across the organisation. That kind of disciplined, value-first approach requires board-level direction.
Mark Kelly, Founder at Ireland, states: “The AI Ireland 2026 survey tells a clear story: Irish businesses have moved past curiosity. Nearly three-quarters are in production. But production doesn’t mean transformation. The boards that will lead in the next phase aren’t the ones asking ‘How do we save money with AI?’ They’re asking ‘How should we make money differently?’ That shift in question changes everything.“
Five Questions Every Board Should Be Asking Right Now
1. Are we using AI to protect our current model or to build the next one?
There’s a place for both, but if 100% of your AI investment is defensive – cutting costs, reducing headcount – you’re leaving the bigger opportunity on the table. The AI Ireland survey shows that cost optimisation and engineer assistance dominate Irish AI goals. That’s a solid foundation.
2. Where does our proprietary data create a competitive moat?
In a world where every company can access the same AI models, your unique advantage is the data and context only you possess. Boards need to understand what data assets the organisation holds and how they could be monetised or turned into new offerings.
3. What new revenue streams could AI unlock?
Think beyond your current products. Could your internal AI capabilities be offered as a service? Could you shift from selling products to selling outcomes? Could you monetise the insights your AI generates?
4. Do we have the right governance model for AI-driven business decisions?
With 56% of Irish leaders preferring controlled use of GenAI with company data, governance is clearly on the agenda. However, governance needs to enable transformation, not just manage risk. Boards need clear oversight frameworks, especially as agentic AI systems take on more autonomous roles in operations and customer interactions.
5. Is our AI strategy led from the top?
The evidence, both globally and from the Irish market, is clear. AI Ireland’s survey found that AI literacy at leadership level remains a significant gap. Organisations that treat AI as a top-down strategic programme are the ones seeing genuine transformation. The board sets the direction and everything else follows from that.
The Cost of Waiting
McKinsey estimates that generative AI alone could add up to $4.4 trillion annually to the global economy. However, that value won’t be distributed evenly. It will flow to the organisations that move from using AI as a cost-reduction tool to using it as the engine for new business models.
In Ireland, the window is narrowing. AI Ireland’s 90-day use case priorities show that predictive alerts (25%) and cost optimisation (25%) dominate the immediate roadmap. These are sensible starting points. But the organisations that will define the next era of Irish business won’t be the ones that stopped at efficiency. They will be the ones that used efficiency as a launchpad for transformation.
The rest will be left optimising a business model that the market has already moved past.
Ready to Move Beyond Efficiency?
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Frequently Asked Questions
Q: What is the difference between AI efficiency and AI business model transformation?
A: AI efficiency means using AI to do existing tasks faster or cheaper, from automating reports, speeding up customer service or reducing manual processing. AI business model transformation means using AI to fundamentally change how your organisation creates and captures value, e.g. new revenue streams, outcome-based pricing, platform models or data monetisation. AI Ireland’s 2026 survey shows that 28% of Irish leaders cite cost optimisation as their top AI goal, which is an efficiency play. The opportunity ahead is using that foundation to rethink how the business makes money.
Q: Why should AI business model strategy be a board-level discussion?
A: Business model decisions affect long-term revenue, competitive positioning, and organisational risk. They are strategic, not operational. Research from PwC and IBM shows that organisations seeing real AI transformation are those where senior leadership drives the AI agenda from the top. AI Ireland’s own data confirms that AI literacy at leadership level remains a significant gap in Irish organisations. Boards have a fiduciary duty to ensure AI investment creates sustainable value, not just short-term cost savings.
Q: How mature is AI adoption in Ireland in 2026?
A: Very mature in terms of deployment, but still early in terms of transformation. AI Ireland’s 2026 survey of 130 leaders found that 74% of organisations have moved AI into production – 41% in early production and 33% in broad adoption. Only 2% haven’t started. However, the dominant goals remain efficiency-focused (cost optimisation, engineer assistance, predictive maintenance) and the biggest blockers – integration challenges (25%), skills shortages (15%), and security concerns (15%) – suggest that the infrastructure for true business model transformation is still being built.
Q: What is outcome-based pricing and why does AI make it possible?
A: Outcome-based pricing means charging customers based on the results they achieve, not the time or resources you spend. AI makes this viable because it can measure, track and verify outcomes in real time. For example, an AI-powered recruitment tool might charge per successful hire rather than per job listing. This model aligns your incentives with your customer’s success, builds trust, and often generates higher lifetime value per client.
Q: What is the biggest risk of only using AI for efficiency gains?
A: The biggest risk is competitive irrelevance. When every company in your sector has access to the same AI tools, efficiency gains become the baseline, not a differentiator. Deloitte’s 2026 report found that only a third of organisations globally are using AI to truly reimagine their businesses. Meanwhile, competitors who are redesigning their business models are creating new forms of value that are harder to replicate. You may be running your current model perfectly, while the market moves to a completely different model around you.
Want to understand how AI is really shaping business in Ireland in 2026?
The AI Ireland 2026: The State of AI in Irish Business report reveals that most Irish organisations have moved beyond experimentation into real-world AI use — improving efficiency, boosting engineering productivity, and shifting from reactive to predictive operations — while also facing challenges around integration, skills and governance.
Download the full report to see how companies are turning AI from curiosity into measurable impact, and get strategic insights to inform your own AI roadmap.
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