Denis Hannigan
AI & Data Consulting Lead, UK and Ireland, Accenture
Liam Connolly
AI & Data Consulting Lead, Accenture in Ireland
Agentic AI is reshaping industries. Only prepared organisations will thrive
Agentic artificial intelligence — AI systems that can reason, collaborate, plan, make decisions and execute tasks autonomously — isn’t the far-fetched technology of some distant future. It’s here now and has entered business reality, so organisations must move quickly to prepare — or risk being left behind.
Agentic AI represents the next evolution of AI, advancing from systems that can produce responses and generate outcomes to systems capable of executing tasks and delivering measurable impact — and that distinction is going to transform the workplace, explains Denis Hannigan, AI & Data Consulting Lead, UK and Ireland, at professional services company Accenture.
“At its core, agentic AI brings a higher degree of autonomy to how work is carried out,” he says. “These systems can plan, adapt and interact with tools and data to manage complex activity. Agentic architectures — networks of AI agents that coordinate and execute tasks — mark a shift from basic automation to more sophisticated operational execution. That is where the transformative potential lies.”
Why you should care about agentic now rather than later
Three enablers have simultaneously developed to make agentic AI possible: enterprise-grade AI infrastructure, data governance and advanced model capabilities, making operational, autonomous agents viable — not just theoretical.
“AI, and increasingly, agentic AI, are reshaping industries. But meaningful reinvention requires more than adoption; it demands that organisations embed these capabilities into strategy, core processes and decision-making,” says Liam Connolly, AI & Data Consulting Lead, Accenture Ireland. “Companies that delay will struggle to catch up. As with previous technological revolutions, the advantage compounds quickly — and leaders are separating themselves through capabilities in data, talent, change adoption and C-suite leadership. In the end, it’s likely readiness and ambition will likely be the defining factors.”
Currently, Accenture is noticing two types of AI investments. The first is a broad adoption of the technology (eg. enterprise chatbots, coding assistants) to boost productivity. However, the second focuses on strategic bets to drive real transformation and requires end-to-end process reinvention across value chains.
Examples of where agentic AI is already generating significant returns include banking (‘know your customer’ operations and mortgage processing), insurance (fraud detection and claims optimisation), communications (self-healing automated networks) and utilities (workforce operations optimisation).
The trouble is, fewer than 15% of organisations have the capabilities to scale agentic AI beyond pilot programmes and unleash its full power, based on Accenture’s research.
“Common obstacles include fragmented processes, underdeveloped data and AI foundations, unclear priorities and value measurement, limited change capability and slow adoption,” says Hannigan. “To move beyond experimentation, organisations are establishing AI Centres of Excellence to coordinate effort and build the capabilities required to scale. Our research points to a set of essential data and AI competencies — and five imperatives — that determine whether investment translates into impact.”
How to prepare for and deploy agentic AI
Hannigan and Connolly outline key steps for any business preparing to deploy agentic AI. First, lead with value: identify a small number of high-impact workflows where AI can deliver a clear outcome. Second, ensure the technical foundations are in place — a secure, AI-enabled digital core, modernised data, governed architectures and integration patterns capable of supporting agents. Third, prepare the workforce by equipping people with rapidly evolving skills and encouraging a culture of curiosity. “And from a governance perspective, autonomous agents will require new control frameworks,” Connolly adds.
Other steps include running controlled pilots with built-in safeguards such as failure-recovery mechanisms, observability and human-in-the-loop oversight. “The mantra is: start small, instrument everything and build for scale — and measure it all,” says Connolly.
“This is a defining moment for the workplace,” stresses Hannigan. “Organisations need to treat AI reinvention as a business transformation, not a technology upgrade. That means strengthening data, talent and governance capabilities, embedding the five imperatives into enterprise strategy, and aligning AI systems with human decision boundaries and business outcomes. Without these, progress stalls — with them, the value opportunity compounds.”
