The Art of the Possible – Bringing your Agentic AI Strategy to Life

Our earlier insight ‘From discovery to deployment: unlocking value through an agentic AI strategy’ spotlighted the gap between deploying and scaling AI and the creation of tangible organisational value with upward traction in professionalising and positioning organisations for accessing, scaling, managing and measuring AI capabilities. In our second article in the KPMG AI Agentic series, KPMG’s Shane Garahy, Emma Coogan, and Rory Timlin discuss how to bring your agentic AI strategy to life.

KPMG’s Global AI Quarterly Pulse Survey Q2 2026 found that only 7 percent of respondents report having an established return on investment with respondents citing data security/privacy/risk concerns (33%) and pressures to demonstrate value to investors or the Board (24%) as key factors influencing AI Strategy as well as responsible AI and governance being a key AI priority for organisations (28%).

This evolution is not a technology deployment programme. It is an enterprise transformation that reshapes how work is executed, how decisions are made, how risk is managed and how value is delivered. Organisations that approach agentic AI as a technology initiative may achieve pockets of automation, but organisations that approach it as a business transformation can fundamentally redesign operating models, workforce capability and customer outcomes.

Agentic AI as a transformation programme

The challenge facing many organisations is how to bridge the gap between agentic AI strategy and execution. Agentic AI introduces autonomous entities, evolving decision flows, and real-time risk exposure that traditional static models were never designed to govern. As AI agents scale, the challenge shifts from deployment to orchestration.

There are significant challenges associated with any transformation programme and agentic AI introduces additional layers of complexity and risk. Unlike traditional technology programmes, agentic AI can affect every layer of an organisation simultaneously. Processes, organisational structures, governance models, performance management, risk frameworks and workforce capability may all require redesign. The question is therefore not simply how to deploy AI agents, but how to transform the organisation around them in a controlled and measurable way.

Having an agentic AI strategy is not enough. Failing to operationalise your agentic AI strategy effectively in an era where market, regulatory and technological conditions are continuously in flux can result in your organisation missing the strategic window of opportunity.

The inception of any shift in how your organisation operates is a crucial stage for structuring and shaping a successful future state vision.

Identifying how agentic AI can impact every layer

A Target Operating Model provides the structure through which organisations can translate strategic ambition into an executable transformation roadmap. Rather than viewing agentic AI as a collection of isolated use cases, organisations can redesign how value is delivered across each operating model layer. Failing to adapt TOM’s in an agentic AI context can lead to process ownership breaking down across agent chains.

The Target Operating Model is structured across six design layers (Service Delivery Model, People, Functional Process, Technology, Performance Insights and Data and Governance) and can guide your organisation’s agentic AI journey in envisioning what the future of an agentic AI could look like gaining the maximum benefit from your agentic AI activities, simplifying the challenges faced by your organisation and choosing the most efficient approach to turn your long term ambition into measurable outcomes through tailored, practical delivery. Failing to create a TOM for agentic AI means organisations’ control and performance models are falling behind real-time agent behaviour.

Success depends not only on having a clearly defined, long term agentic AI strategy but also on the design, development and operationalisation of the agentic AI strategy. Below outlines each of the six design layers encompassing KPMG’s Target Operating Model (“TOM”) as the execution bridge for your agentic AI strategy and some of the implications that organisations face where an agentic AI strategy is not designed and/or implemented effectively and/or a Target Operating Model (“TOM”) is not considered or designed and/or implemented appropriately.

The cost of standing still

• Limited or no returns – If the intent, documentation and vision of the agentic AI strategy all exist but have not been translated into actions, processes, accountability or tangible outcomes this could result in strategy investment yielding limited or no returns.
• Inconsistent and fragmented execution – Different areas of the organisation can interpret the agentic AI strategy differently with inconsistent and fragmented execution proliferating.
• Strategy fatigue – If the agentic AI strategy consumes time and budget without any tangible outcomes or return on investment, this can result in loss of credibility and confidence in future strategic initiatives.
• Elevated risk exposure – When day to day operations are misaligned to strategic intent, operational risk increases across controls, escalation paths, decision making and regulatory requirements.

How KPMG can help

As organisations accelerate their adoption of agentic AI and explore designing, developing and implementing agentic AI strategy, they face a common set of challenges, from defining strategic ambition to navigating regulatory complexity, designing scalable, measurable, operating models, and maintaining global consistency.

KPMG supports leadership teams to translate strategic ambition into practical, scalable operating realities that can support your organisation in envisaging a sustainable, trusted and competitive agentic AI strategy.

For more insights, visit kpmg.ie.

Article Authors

Shane Garahy – Partner, Risk Consulting, KPMG

Emma Coogan – Director, KPMG.

Rory Timlin – Partner, Management Consulting, KPMG