Nearly 80% of organisations report that their AI agent investments are already delivering measurable economic impact. Whilst the end goal is to generate real return on investment (ROI) from AI, getting the formula right is anything but simple. It requires access to AI-ready talent that remains a challenge for many organisations. 94% of leaders report critical shortages in AI skills.
FDM Head of Product, Eoin Doyle, shares, “The pace of change is actually good news. AI is moving so fast that starting now doesn’t put you massively behind – it’s new for everyone. Nobody has a decade-long head start.
What organisations need to build is simpler than it sounds: give people the tools and the permission to work alongside them. Then be disciplined about where you point them. The failure mode I see most is over-engineering, trying to force AI into every process because it’s there. That kills adoption. People try it, it doesn’t fit, they stop.
Focus instead on the use cases where it genuinely lands. Get those right, and adoption follows naturally. Momentum comes from people seeing the tools work, not from a mandate telling them to use them.”
Building internal capabilities that AI cannot replace
51% of organisations use agents today, with 35% having active plans to implement agents soon. Successful companies will be those that infuse agentic AI throughout their organisations to create a true hybrid human–agent workforce. Equipping teams with the technology skills they need to thrive in an agentic world and deploying effective change management to ensure that people embrace this shift are critical for long-term success. People will need support to step into new roles in which they manage agents alongside colleagues.
The most effective CIOs are looking at the skills of their workforce. They identify the capabilities that differentiate each team, assess current proficiency levels, and invest systematically in development. This strategy shows that constraints are not headcount but about finding out where untapped skills lie.
Eoin adds: “The real shift for organisations is helping people get sharper at defining the problem, then selecting the right tools, AI and non-AI, to reach the best outcome. That’s a human judgement call, and it stays a human judgement call. Keeping people in the loop throughout isn’t a safety net bolted on at the end; it’s what makes the workflow work in the first place.”
What CIOs need to do now to prepare
There are four critical starting points for leaders.
Implement workforce strategy into the competitive strategy
AI will enable new business models. Companies that respond by accelerating innovation, redesigning their offerings, or reconfiguring how they deliver value can change the nature of work within their organisations.
Build smarter workflows with AI
Instead of using AI to reduce headcount, CIOs should use it to redesign how work gets done. The best approach is to identify which tasks can be automated and where human expertise continues to add most value. This shift allows AI to add business value by improving productivity, quality and speed.
Eoin explains:
“The temptation is to think AI changes what we’re trying to achieve. It doesn’t. The outcome stays the same – solve the business problem properly. What changes is the workflow, not the destination.
Organisations that treat AI as a replacement for thinking will automate their way to the wrong answer faster. The ones that treat it as an augmentation of how the work gets done will get to better outcomes, quicker.”
In these situations, leaders must redesign workflows and rethink how performance is measured.
Put upskilling and reskilling at the centre of workforce strategy
Upskilling and reskilling must become central to workforce strategy. Leaders should prepare their workforce to how roles are evolving and create clear pathways and have the following considerations in mind,
- Manage change: As productivity expectations rise, leaders should remain mindful of cognitive overload and actively manage workloads. Reinforcing the narrative that AI enhances long-term value will be critical to sustaining engagement and performance.
- Rebalanced roles: Leaders should identify components that can be automated and reinvest the time savings in higher-value activities that need critical human oversight like strategic problem-solving and decision-making.
- Develop workflows: Leaders should design end-to-end workflows which will define how AI agents and employees work together, including where human oversight, decision-making, and accountability remain essential to ensure that people, processes, and technology evolve together.
- When employees associate automation with displacement, engagement declines and so does the motivation to upskill. Leaders must make it clear that if workers upskill, AI in most roles will be about value creation rather than displacement.
AI creates a massive opportunity for business leaders but also significant uncertainty in terms of how to unlock it. For CEOs, the focus should be achieving the right balance of automation and upskilling to deliver enterprise ROI at scale, and helping their employees develop the skills they need to thrive.
Conclusion
Agentic AI is reshaping how companies operate. CIOs need to review how roles will evolve and invest in internal capabilities in order to anchor long-term advantage.
AI agents will take on more work, so company leaders should explore how the transformation will be managed and can start by asking themselves which capabilities they should help their employees build. Organisations should be transparent about where automation should be implemented and where costs will be cut.
The question for leaders now is not whether agentic AI will reshape their organisation. It is whether their people will be ready to implement it.
At FDM, we’re already putting this thinking to work.
How FDM is implementing AI agents
At FDM, we recognise that successful AI adoption depends on combining technology with human expertise.
Eoin explains, “We empower individuals to build their own AI agents around their own workflows deliberately as people understand their own work better than anyone, so they’re best placed to prototype what’s actually useful. From there, we look at which of those agents generalise, whether a workflow one person solved for themselves is common enough to become enterprise-ready. The good ideas then feed into the enterprise-grade build.
At FDM we’re embedding AI directly across our training, so there’s a unified, common-language approach to using AI in everyday work. That shared language matters more than people expect – it’s what stops AI adoption from fragmenting into a hundred private experiments.
It’s bottom-up prototyping meeting top-down engineering. And it’s the thesis in practice: people staying in the loop, using AI on the work they understand best. Technology plus people is how organisations succeed.”
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