While confidence in AI’s ability to drive future revenue has never been higher, many organizations are still grappling with the practicalities of embedding the technology into the heart of their business.
New research shows 77% of UK and Ireland executives now expect AI to significantly contribute to their revenue by 2030, up sharply from just 37% today.
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Managing Partner ay IBM Consulting, UK & Ireland.
At its best, AI promises to transform how organizations operate, innovate and create value. However, the gulf between aspiration and execution illustrates that technology alone is not enough.
The difference in 2026 will be determined by three priorities: reskilling the workforce, embedding AI innovation across the business and building robust governance to maintain trust and control.
The urgency of reskilling
According to the World Economic Forum, 77% of employers plan to upskill employees due to the impact and utilization of AI tools by 2030 . This is not incremental change but a reshaping of the labor market and organizational capability.
In January the government also announced plans to upskill 10 million people and inject £27 million to connect people to technology jobs in local communities. Reskilling is firmly at the top of the UK government agenda.
Yet, too many organizations treat reskilling as a secondary task, something to be tackled after technology decisions are made. However, evidence suggests that this approach is insufficient.
AI adoption is not about replacing people; it is about empowering them to work differently and to focus on tasks that require judgement, creativity and domain expertise, where machines augment human capability rather than replace it.
Companies that get ahead of the reskilling challenge will be better positioned to capture new revenue streams and drive productivity gains.
Reskilling must be strategic, and ongoing. It should span not only technical roles but also line managers, operational teams and executives. By investing in human capital today, organizations don’t just prepare for AI’s impact; they unlock new growth opportunities.
Integration and business transformation
AI is reshaping leadership and skills and as such transforming businesses, whether organizations are ready or not. By 2030, UK and Ireland executives expect that one in four of enterprise boards will include an AI advisor.
At the same time, 65% of respondents say job roles are becoming shorter lived, with over half (51%) predicting that most of their organizations’ current employee skills will be transformed by AI by 2030. Technical expertise still matters – it just does not last as long as it used to.
Against this backdrop of rapid change, the defining challenge for AI will not be the technology itself, but its implementation. There will be a reckoning for initiatives that aren’t deeply integrated into core business processes.
This isn’t surprising: projects built on isolated use cases deliver small improvements at best. Furthermore, by 2030, 81% of executives expect their capabilities to be multi-model. It’s clear, that to realize the true transformative potential of AI, intelligence must be woven into the fabric of everyday business operations.
The need for a culture shift
Moreover, embedding intelligence into systems and workflows so that data flows seamlessly across functions and insights can be actioned in real time.
It also requires a cultural shift: leaders must stop thinking in terms of “AI projects” and start thinking about an AI-first enterprise where strategy, structure and processes are aligned around data, models and outcomes.
Integration also extends to how organizations measure success. Traditional ROI metrics that focus on cost savings or efficiency gains are important, but they are no longer sufficient on their own. Leaders today must also look at how AI contributes to innovation, customer value, business model evolution and long-term resilience.
Governance as a growth enabler
Finally, governance cannot be an afterthought. In a world where AI touches customer experiences, regulatory decisions and strategic outcomes, confidence in how intelligence is controlled, audited and governed is essential.
It’s now widely understood that trust and transparency are foundational to adoption. Consumers, regulators and stakeholders are increasingly skeptical of opaque systems, and organizations that fail to demonstrate responsible use risk eroding trust and competitive position.
Effective governance is about more than compliance. It’s about creating frameworks that allow organizations to innovate with confidence, establish clear accountability for outcomes, and manage risk proactively.
Governance structures should encompass data quality, ethical considerations, explainability and human oversight. They should be designed not to slow innovation but to enable it safely.
Looking ahead: A strategic, people-centric approach to AI
As we look to the year ahead, the future of AI in enterprises hinges not on incremental enhancements but on strategic, holistic adoption. Leaders must prioritize reskilling, embed AI into the core of their operations and commit to governance frameworks that uphold trust and transparency.
Only then can the promise of AI: greater innovation, productivity and economic growth, be realized.
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