Agentic AI: Navigating ROI Challenges and Building a Blueprint for Enterprise Success

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Agentic AI—autonomous systems capable of perceiving, reasoning, and acting toward goals with minimal human intervention—is rapidly emerging as a transformative force in enterprise technology strategy. Unlike traditional AI, which typically responds to prompts or executes predefined tasks, AI agents possess memory, adapt to feedback, and orchestrate complex workflows across systems. These agents can plan, decide, and execute actions independently, often coordinating with other agents or external tools to achieve business objectives.

As Southeast Asia accelerates its digital transformation, agentic AI offers unprecedented opportunities to scale operations, enhance decision-making, and unlock new efficiencies. Yet, despite its promise, many enterprises face a sobering reality: the return on investment (ROI) from agentic AI remains elusive.  Gartner predicts that over 40 percent of agentic AI projects will be canceled by 2027 due to inflated expectations, technical complexity, and unclear business value.But this need not be the case.  Real value remains waiting for an unlock via Agentic AI.

This article explores the latest thinking on Agentic AI strategy, clarifies key terminology, proposes a blueprint for overcoming ROI challenges holding enterprises back, and examines this all in the context of Southeast Asian AI investment trends.

1. Defining Agentic AI: Beyond Automation

Agentic AI encompasses autonomous systems endowed with the ability to perceive, reason, act, and learn. By ingesting diverse data inputs—ranging from structured databases and unstructured text to real-time event streams—these agents build an internal model of their environment. They apply advanced reasoning algorithms to identify patterns, evaluate alternatives, and make decisions aligned with defined objectives. Once a course of action is selected, the agent executes tasks across digital ecosystems—triggering workflows, invoking APIs, or coordinating with other sub-agents—to accomplish complex goals with minimal human intervention. Crucially, agentic AI continuously monitors its own performance, measuring outcomes against targets and incorporating feedback to refine its models and strategies over time.

To place this in context, Agentic AI applications already span a wide array of industries, delivering concrete impact:

1. Retail: Autonomous agents drive real-time inventory replenishment, dynamic pricing adjustments, and personalized in-store experiences.

2. Financial institutions: Agentic systems are deployed for continuous fraud detection, automated portfolio rebalancing, and regulatory compliance monitoring.

3. Manufacturing:  Agents orchestrate predictive maintenance schedules, optimize production sequencing, and conduct computer-vision quality inspections with minimal human oversight. 

4. Oil & Gas and broader energy sectors: Agentic AI autonomously analyzes drilling sensor data, forecasts supply-demand imbalances for grid management, and schedules equipment upkeep to prevent costly downtime.

Developing and deploying AI agents typically entails several layers of technical and commercial considerations.  Choosing the right agentic project and then its architecture requires careful consideration of an organization’s unique context.  Enterprise teams must:

1. Define exactly what they seek to accomplish – which KPIs or business levers need to move and how so.

2. Evaluate their risk tolerance and governance maturity to determine how much autonomy agents can safely wield.

3. Incorporate regulatory requirements and industry-specific constraints that shape permissible data usage, decision boundaries, and auditability standards.

4. Review integration complexity with legacy systems, anticipated scale, and performance service-level agreements that inform whether a centralized “conductor” model or a decentralized mesh of collaborating agents is most appropriate.

5. Review vendor qualification and experience, as well as which internal skills will be needed to co-deploy and maintain these intelligent systems.  

6. Agree on and calculate financial considerations—namely, the total cost of ownership versus projected benefits, the “ROI calculation”

The enterprise’s security and trust posture will dictate architecture choices that balance innovation with transparency and safeguard sensitive assets.

2. The ROI Challenge:  How to Get it Right

In this review, we focus on Item 6 – the ROI calculation.  One hurdle slowing the overall adoption of agentic AI and perhaps over-indexing on false-negatives in project review, ie, redlights that could be greenlights, can be described as internal struggles to conceptualize and accurately quantify ROI.

Traditionally, enterprises assess ROI on AI projects almost exclusively through a cost-cutting lens. Finance and operations teams set targets for headcount reduction, cycle-time compression, and unit-cost savings. They establish baseline metrics—FTE hours spent on manual tasks, error rates in processing, and per-transaction labor costs—and then overlay projected efficiency gains from automation. Success is judged by monthly dashboards tracking hours saved, error avoidance, and compute-versus-labor cost ratios. Pilots are spun up in controlled environments and only scaled if they meet or exceed predefined cost-savings thresholds.

Common Pitfalls in Traditional ROI Models

While this methodology is not inaccurate and serves as a necessary baseline, it is far from complete.  The cost-centric focus alone is too narrow and fails to acknowledge critical value elements of the equation:

1. It overlooks strategic or intangible benefits such as faster decision-making, improved customer satisfaction, and new revenue streams.

2. It discourages investment in critical foundations (clean data pipelines, robust governance) that don’t deliver immediate labor savings.

3. It incentivizes “checkbox” projects that automate low-value tasks rather than tackle complex, high-impact workflows.

4. It fails to account for downstream effects—like reduced compliance risk or enhanced brand reputation—that accrue over time.

5. It fails to quantify actual improvements in output and overfocuses on input-cost reduction. Reducing input costs is simple to measure with standard accounting methods, whereas measuring output improvements carries uncertainty and requires upper- and lower-bound analysis due to AI’s evolving nature and improving performance with larger datasets.

   
   

Moreover, to understand why conventional ROI assessments frequently misfire for agentic AI initiatives in particular, consider these four recurring pitfalls noticed by Sertis in Agentic AI project review:

1. Superficial “Agent Washing.”  Vendors and technology providers often mislabel simple automation or chatbot solutions as “Agentic AI,” leading to inflated expectations, misaligned costs that show up later, and wasted spend.

2. Data Readiness and Infrastructure Gaps.  Legacy systems and brittle pipelines starve AI agents of reliable, real-time data, crippling their autonomy and adaptability.

3. Short-Term ROI Metrics. Narrow financial KPIs mask broader value levers—agility, resilience, and decision quality—that agentic AI can unlock.

4. Misuse of Hyperscaler or Cloud Credits. Relying on promotional cloud or hyperscaler credits during pilots or in the case modelling can artificially lower compute costs and distort ROI calculations. While these credits improve cash flow in the short term, they don’t represent sustainable cost structures and should be excluded from core ROI assessments.

   
   

Let’s wrap all of this into a real example, the case of a well-known multinational logistics firm where agentic AI was deployed to autonomously analyze weather patterns, port congestion data, and fuel prices to reroute shipments in real time.Previously, such decisions relied on manual coordination and static rules, and several dedicated staff, and often resulted in delays, suboptimal routes, and cost increases.

Despite going through the sequence above as described in Section 1, the initial case for deploying an Agentic AI fix was not sufficiently motivational.  However, by including the following potential benefits in its initial ROI calculations, the enterprise expanded its framework from simple cost reduction to account for the significant value delivered via decision quality metrics:

1. Reduction in error rates or missed deadlines,

2. Impacts of improved forecast accuracy,

3. Financial impact of better decisions (e.g., avoided penalties, optimized resource allocation).

   
   

Alongside traditional metrics, these potential outcomes were identified and quantified during a POC, allowing leadership to see how agentic AI enhanced strategic responsiveness and operational reliability—benefits that would have been invisible in a purely cost-efficiency model.

Within the agentic framework, the new AI agent system:

1. dynamically adjusted routes in actionable timeframes,

2. reduced delivery times by 18 percent,

3. avoided $3.2 million in annual penalties from missed service-level agreements, and

4. enabled the company to create new capabilities with the time savings, such as more high-touch customer-centric activity from the same team.

   
   

A more robust ROI model was the difference between a false negative and a successful deployment of an Agentic AI application. One last noteworthy point:  in the ROI calculation, an AI application can increase overall enterprise value. This is both a complex and contextual matter, and depends largely on the overall strategy an enterprise has regarding AI adoption, as “AI is an IA (Intangible Asset).” This concept and defining its actual impact have been pioneered by Paul Adams, Managing Director at Andersen Consulting. If a reader has an interest in this area, please contact the author, CCO of Sertis and Andersen Consulting Partner Randy McGraw at jmcgr@sertiscorp.com to arrange a discussion.

3. Southeast Asian Investment Trends and Strategic Imperatives

Southeast Asia is rapidly emerging as a strategic hub for agentic AI and cloud infrastructure. AWS’s launch of its Asia Pacific (Thailand) Region underscores this momentum, with a planned investment of over US$ 5 billion expected to contribute US$ 10 billion to Thailand’s GDP and support 11,000+ jobs annually. This expansion aligns with Thailand’s national “Cloud First” initiative and ambition to become a regional AI powerhouse. Across the region, hyperscalers like AWS, Google, Microsoft, and Alibaba Cloud are accelerating infrastructure deployment, with Southeast Asia’s data center market projected to reach US$ 30.47 billion by 2030, up from US$ 13.71 billion in 2024.

As Prasad Kalyanaraman, AWS VP of Infrastructure Services, noted:  “This new AWS Region in Thailand will help customers across all industries deploy advanced applications... and support Thailand’s digital transformation as a regional hub for artificial intelligence.”

Varoon Raghavan, COO of Princeton Digital Group, added: “Hyperscalers are expanding rapidly within Asia... often working with strategic partners as they drive this expansion.”The following grid shows where ASEAN stands relative to the infrastructure and underpinning of agentic AI:

Hyperscaler Activity in Southeast Asia (2025)

Despite the surge in investment and infrastructure, Southeast Asia faces ecosystem-level challenges that complicate agentic AI adoption:

1. Fragmented Data Ecosystems Many enterprises operate across borders with inconsistent data standards, siloed systems, and limited interoperability. This fragmentation hinders agentic AI’s ability to ingest and act on real-time, cross-functional data.

2. Regulatory Complexity and Enforcement Gaps While most SEA nations have AI and data protection frameworks, enforcement remains uneven. Legal traditions vary widely, and compliance burdens can stall innovation or lead to risk-averse deployments.

3. Environmental and Infrastructure Constraints Deforestation, biodiversity loss, and climate-related disruptions—especially in regions like the Mekong Basin—impact supply chains and operational continuity. These factors introduce volatility that agentic systems must learn to navigate.

4. Talent and Capability Mismatch While hyperscaler investments are creating jobs, there’s a lag in upskilling for agentic AI-specific roles—especially in governance, observability, and multi-agent orchestration.

5. Cultural and Organizational Resistance In some sectors, hierarchical decision-making and low digital trust slow adoption. Agentic AI requires a shift toward decentralized, data-driven operations, often clashing with legacy mindsets.

6. Misinformation and Disinformation Southeast Asia’s high social media penetration makes it especially vulnerable to election-related disinformation, deepfakes, and manipulated narratives.  These distort public trust and complicate agentic AI’s ability to operate in truth-sensitive domains like media, governance, and public health.

7. AI-Powered Scams and Fraud The region has seen a surge in AI-enabled scams, including impersonation fraud and investment schemes using deepfake videos of public figures. These erode consumer trust and raise the stakes for agentic AI systems deployed in financial services and e-commerce.

8. Algorithmic Opacity and Accountability Gaps Many agentic systems operate as black boxes, making decisions that are difficult to audit or explain. Without robust observability and governance tooling, enterprises risk deploying agents whose actions cannot be traced or justified, especially problematic in regulated industries.

These challenges are not insurmountable, but they require ecosystem-wide coordination—from governments, vendors, and enterprises—to build resilient, agent-ready environments.

4. Blueprint for ROI Success with Agentic AI

At Sertis, we partner with clients to establish a rigorously validated, institutionally aligned perspective on every Data & AI initiative before it advances. Drawing on our experience navigating agentic AI’s unique pitfalls and the intertwined technical and organizational complexities, as I have noted throughout, we recommend the following heuristic to guide strategic decision-making and ensure accurate (sustainable) ROI calculations:

1. Clarify Use Cases and Business Objectives.  Focus on high-impact workflows—e.g., onboarding, procurement, or customer engagement—where agents can deliver measurable outcomes.

2. Redesign ROI Frameworks Expand beyond cost savings to include decision speed and quality, improved KPIs and business levers, operational agility, risk mitigation, and employee and customer experience.

3. Invest in Data and Governance Build an agile, clean data infrastructure at all times and start NOW. Implement metadata systems, observability tools, and governance agents to ensure transparency and trust.

4. Start with Human-in-the-Loop Systems Deploy agents in controlled environments with oversight. Gradually scale autonomy as confidence and maturity grow and as the impact of Agentic AI in the organization can be fully absorbed.

5. Leverage Global and Local Insights Ask Sertis and Andersen Consulting to provide both broad, global best practices and nuanced, local market perspectives, enhancing your learning from case studies and de-risking strategic decisions.

Conclusion: Strategic Investment with Eyes Wide Open

Agentic AI is not just another tech trend—it’s a structural evolution in how enterprises operate, and as we predicted in 2024, it is the next step in AI for enterprise. But success hinges on strategic clarity, robust infrastructure, and, importantly, new and nuanced ROI models. 

Southeast Asia’s investment trajectory is promising, and while our business at Sertis is accelerating in 1H25, it must be matched by strategy, governance, and smart vendor selection and interaction models such as we have pioneered in our decade-plus and 400-plus enterprise deployments. 

The path forward does not have to be as Gartner predicts. That path is not to retreat from Agentic AI, but to engage with it deliberately, treating it as a transformative capability to be measured, governed, and aligned with enterprise goals. With the right blueprint, including proper ROI calculations and a comprehensive approach to project initiation and evaluation, agentic AI can deliver not just automation but an enduring strategic advantage.