Agentic AI: from productivity promise to profit reality

Converting AI into tangible financial returns

The current disconnect between AI’s promised efficiency gains and its impact on corporate balance sheets represents the most significant challenge facing business leaders today. While 94% of chief executives expect AI to be embedded in their workflows within three years, fewer than a quarter can yet demonstrate  meaningful profitability improvements from their AI investments. This gap demands attention as organisations move beyond experimentation. With nearly a third of Irish CEOs believing their organisations won’t exist in current form within a decade, the pressure to turn AI potential into financial performance has never been greater. Agentic AI — technology capable of autonomous decision-making and action — offers the bridge between personal productivity improvements and enterprise-wide transformation.

The state of AI adoption

Boardroom sentiment around AI presents a striking paradox. Despite operating in unparalleled macroeconomic conditions, 93% of Irish chief executives maintain a remarkably positive outlook regarding revenue growth according to PwC Ireland’s latest CEO Survey. This optimism exists alongside a profound recognition of the need for internal transformation.

Nearly three in ten Irish CEOs do not believe their organisations will exist in their current form within a decade — a sobering if slightly more sanguine view than their global counterparts, where the figure exceeds 40%. This creates the perfect environment for AI investment as business leaders race to reinvent their organisations.

Six-month trends reveal an acceleration in structured AI implementation, with the proportion of Irish organisations having formal plans and active projects jumping from 50% to 70%. The investment case appears straightforward: 40% of organisations can already demonstrate efficiency gains from their AI initiatives.

Yet herein lies the central challenge. While efficiency improvements are widely evidenced, only a quarter of these organisations have translated such gains into measurable impacts on profitability. This value leakage — from potential to profit — demands explanation.

Five critical obstacles to scaled AI adoption

The translation of AI-driven potential into corporate profitability is not merely a function of technological maturity. Five systemic barriers require attention from executives seeking to move beyond isolated use cases.

1. The business case conundrum represents the most immediate challenge. Fewer than half of business leaders express confidence in their ability to articulate the return on investment from AI initiatives. This hesitancy is not unfounded — less than 40% can demonstrate realised financial benefits from deployed AI systems.
2. Organisational capability gaps present a second, more persistent obstacle. Unlike traditional enterprise technology deployments — which follow predictable training cycles — AI capabilities evolve at a pace that renders conventional upskilling approaches obsolete. The ‘one and done’ implementation paradigm has given way to continuous capability building across both business and technology functions.
3. Governance mechanisms show encouraging improvement but remain insufficient. The proportion of organisations expressing confidence in their AI governance structures has doubled from 28% to 56% in six months. Yet this leaves a substantial 44% acknowledging that they lack adequate frameworks.
4. Stakeholder trust represents perhaps the most intangible yet consequential barrier. Organisations have invested decades building trust with employees, customers, shareholders and regulators. Few executives are willing to risk these relationships through hasty AI implementation. The prudent approach to innovation must incorporate stakeholder concerns rather than dismiss them.
5. Cybersecurity considerations complete the picture, with Irish organisations consistently registering higher levels of concern than global averages. Far from being merely cautious, these concerns reflect sophisticated risk assessment in a market where digital trust has become a competitive differentiator.

The role of Agentic AI in bridging the value gap

If conventional AI implementations have delivered incremental benefits without proportional financial returns, Agentic AI offers a more compelling proposition. The distinction is not merely technical but fundamental to how value is created and captured.

Agentic AI — systems capable of autonomous decision-making, action-taking and process optimisation — represents a shift from what might be termed intelligent data manipulation to intelligent workflow execution. This transition is the difference between personal productivity and enterprise productivity, between automating discrete tasks and reimagining entire processes.

The financial implications become clear when examining how value is created. Consider the distinction between delivering more with the same inputs versus delivering the same with fewer inputs. Both create value, but organisations are discovering that the former — expanding capacity rather than reducing headcount — often represents the more accessible business case in the short-term. This is particularly evident in capacity-constrained functions such as IT, where backlogs are the norm rather than the exception.

The most astute executives are not limiting their search for value to internal operations. External service providers, from business process outsourcers to managed service partners, represent a significant opportunity for value capture. Organisations that scrutinise their supplier ecosystem with an Agentic AI lens often discover more immediate return opportunities than those focused exclusively on internal transformation.

The implications for specific business functions are profound. In human resources, the traditional employee onboarding process requiring coordination across finance, IT and department-specific functions becomes a seamless workflow orchestrated by AI agents. In IT operations, the mundane yet essential tasks of password resets, software licence optimisation, access management and security monitoring can be delegated to agentic systems, freeing human expertise for higher-value activities.

Even in functions that have already embraced AI, such as fraud detection in financial services, agentic systems offer a step-change in capability — moving from identifying suspicious patterns to orchestrating the end-to-end investigation and resolution process. Similarly, in customer service, organisations are reporting not only reduced servicing costs but increased cross-selling through AI-augmented interactions.

The industry-specific applications are equally compelling. Healthcare providers can optimise appointment scheduling and diagnostic workflows, reducing wait times while improving resource utilisation in capacity-constrained settings. In consumer goods, content creation and campaign optimisation can be both accelerated and personalised at unprecedented scale.

What unites these diverse applications is their focus on end-to-end processes rather than isolated tasks — precisely the shift needed to bridge the gap between efficiency and profitability.

Real-world impact cases

Abstract discussions of value realisation ultimately require empirical validation. Three exemplar organisations demonstrate how Agentic AI bridges the efficiency-profitability gap in distinctly different contexts.

A global financial services provider has transcended the limitations of conventional customer service automation by deploying Agentic AI across its contact centre operations. The results challenge the prevailing efficiency-or-quality dichotomy. Customer servicing costs have decreased substantially, while simultaneously cross-selling performance has improved. The mechanism is straightforward: by delegating routine query resolution to AI agents, human specialists focus exclusively on complex problem-solving and relationship development. The resultant financial impact appears on both sides of the profit equation — reduced costs and increased revenue.

In the fast-moving consumer goods sector, a market leader has reimagined its marketing content creation process through agentic systems. Traditional approaches required lengthy cycles of concept development, creative production and market testing — a process ill-suited to capturing ephemeral consumer trends. Their Agentic AI solution not only reduced content creation time but fundamentally altered the campaign personalisation model. By analysing consumer sentiment in near real-time, the system generates highly targeted creative content, resulting in a 20% revenue increase from campaigns. Notably, this required significant reconfiguration of agency relationships, emphasising the ecosystem impacts of agentic implementation.

Perhaps most compelling is the case of a private healthcare provider that deployed Agentic AI across its diagnostic radiology workflow, covering patient consultation notes, lab results and image-based data. The 30% reduction in diagnostic time, which shortened the anxious wait period between consultation and diagnosis, represents only the most visible benefit. It also reduced the number of unnecessary procedures — a chronic inefficiency in healthcare delivery models worldwide. For capacity-constrained health systems, the implications extend beyond financial metrics to questions of access and equity.

These cases illustrate a common pattern. Organisations achieving demonstrable financial returns have moved beyond isolated AI applications to orchestrated workflows spanning multiple systems and functions. The value captured is not merely the sum of individual task efficiencies but the product of reimagined end-to-end processes.

Strategic implementation framework

Translating Agentic AI’s potential into sustainable financial returns requires a deliberate approach that balances innovation with pragmatism. The following framework offers a pathway.

The progression from conventional to Agentic AI implementation is evolutionary rather than revolutionary. The most successful organisations establish proof points through targeted deployments before attempting wholesale business model reinvention. This approach creates the reference experiences necessary to build internal confidence and stakeholder support.

Successful and sustained AI adoption must also simultaneously address the five obstacles previously identified. A sequential approach — solving technical challenges before addressing governance concerns, for example — invariably creates impediments to scale. The most effective organisations pursue parallel workstreams addressing technology implementation, organisational capability building, governance development, stakeholder engagement, cybersecurity and security enhancement.

Particular attention must be paid to the behavioural change requirements. The adoption curve for AI follows predictable patterns — early enthusiasts, the pragmatic majority and reluctant laggards. Effective adoption strategies account for these different constituencies rather than designing exclusively for the enthusiasts. The behavioural shifts required for Agentic AI extend beyond initial adoption to continuous learning as capabilities evolve, a challenge unlike traditional technology implementations with their “train once” deployment models.

Implementation must also proceed at a pace that maintains trust across all stakeholder groups. Trust, once compromised, requires disproportionate effort to restore — a calculation that justifies measured progress over hasty deployment.

Conclusion

The value gap between AI’s promised benefits and its profit delivery represents the central challenge for business leaders navigating the current wave of technological disruption. With nearly a third of Irish chief executives questioning their organisations’ future in their current form, the imperative to bridge this gap has never been more acute.

Agentic AI offers a pathway from incremental improvement to fundamental transformation by shifting focus from isolated task automation to orchestrated process reimagination. The organisations demonstrating measurable financial returns have moved beyond the “faster horses” mindset to rethink how work itself should be structured and executed.

Yet technology alone cannot close the value gap. Successful implementation requires simultaneous attention to business case development, organisational capability building, governance structures, stakeholder trust and security considerations. The most effective approaches balance innovation ambition with implementation pragmatism, building reference experiences before attempting wholesale business model reinvention.

The most valuable lesson from early adopters is perhaps counterintuitive: the strongest financial returns often come not from cost reduction through displacement, but from capacity expansion through augmentation. In capacity-constrained functions and industries — from IT operations to healthcare delivery — the ability to do more with existing resources represents the most accessible path to measurable value creation.

As organisations progress from experimentation to enterprise adoption, they would do well to remember that AI represents not merely a new tool but a fundamental shift in how work is conceived and executed. Those who approach it as merely a means to do existing things more efficiently will find themselves with faster horses in an age that demands flying vehicles.

How we can help

At PwC Ireland, we partner with organisations at every stage of their agentic AI journey. Our GenAI Business Centre, established in collaboration with Microsoft, provides both the technical expertise and business acumen needed to bridge the efficiency-profitability gap.

We offer a comprehensive approach that addresses all five critical implementation challenges — from business case development and ROI articulation to governance frameworks and stakeholder engagement strategies. Having guided more than 100 organisations through this transformation, we combine global insights with deep understanding of the local market.

Whether you are seeking to validate your AI strategy, accelerate your implementation roadmap or reimagine your business model entirely, our team stands ready to help you convert AI’s promise into tangible financial returns. Contact us to arrange a consultation or demonstration at our Dublin-based GenAI Business Centre.