Agentic AI in the Enterprise: Cost of Autonomy vs. ROI Reality Check 

Silicon Valley has a new partner. Not content with generating text, images, and code on demand, artificial intelligence has taken a new evolutionary step: agency. These so-called “Agentic AI” systems promise not just to assist but to act, independently, pursuing objectives, interfacing with software systems, and learning along the way. 

The appeal is undeniable. Enterprises envision AI agents optimizing supply chains on the fly, resolving customer complaints before they escalate, or even conducting entire market research analyses without human intervention. This vision is already being sold—with polished demos, aggressive vendor roadmaps, and considerable investor enthusiasm. 

But as with many things in the tech world, what’s being promised and what’s being delivered remain very different. Behind the scenes, many companies are quietly grappling with an uncomfortable truth: Agentic AI may have a return-on-investment problem. 

The disconnect: Why expectations and outcomes are diverging 

Agentic AI, in its current form, often sits in a paradox. It’s both powerful and immature. While technology boasts unprecedented autonomy, many early enterprise deployments have not delivered the transformational gains initially forecasted. According to a recent analysis by SiliconAngle, organizations are increasingly reporting a chasm between vendor promises and realized value. 

It’s not that Agentic AI doesn’t work—it’s that it often works just well enough to disappoint. Sophisticated demos rarely capture the unpredictability of live enterprise data, the brittleness of context-limited reasoning, or the sheer cost of customizing and maintaining these systems. 

In short, many businesses are learning a hard lesson: Agentic AI doesn’t fail because it’s broken—it fails because it’s expensive, complicated, and often solving the wrong problem. 

This article explores the practical insights and strategic frameworks from the book, shedding light on how businesses can realistically adopt and scale agentic AI. It also addresses the challenges of organizational readiness, infrastructure, and ROI.

Agentic AI: the buzz beyond tools, toward autonomy 

Understanding the friction between cost and value helps define what Agentic AI actually is. Unlike traditional machine learning models or even modern generative AI tools that respond to prompts, Agentic AI systems initiate action. These agents operate toward defined goals, interacting with APIs, databases, and sometimes humans, with limited oversight. 

Think of the shift as moving from a GPS that provides directions (reactive) to a self-driving car that decides where to go and how (proactive). That step—from tool to actor—introduces not only capability but also risk, oversight, and unpredictability. 

Enterprises are discovering that giving software autonomy means ceding some control, and that comes with its own cost. 

The ROI mirage: When advanced isn’t always advantageous 

One of the most subtle traps ensnaring enterprises today comes disguised as generosity: cloud credits. Major cloud providers often subsidize initial AI workloads with free credits, masking the true cost of running agentic systems at scale. It’s a clever on-ramp, but one that leads to a financial cliff. 

Once credits expire, organizations face ballooning costs from GPU usage, storage, API calls, and specialized support. Few are fully prepared for the operational expenses lurking just beyond the pilot phase. 

Worse, these credits often promote dependency on proprietary infrastructure, making it costly and technically challenging to migrate later. In essence, many companies buy a lifestyle they can’t afford without realizing it. 

Talent, infrastructure, and the total cost of ownership 

Autonomous AI doesn’t just need data scientists—it requires a full ecosystem: MLOps engineers, prompt engineers, AI ethicists, security specialists, and more. Recruiting or upskilling these roles is neither cheap nor fast. 

Then there’s infrastructure. Agentic systems demand robust computing resources—often GPU clusters with high memory throughput and rapid networking. For many mid-size enterprises, the costs of maintaining such infrastructure (or renting it) rival the costs of deploying the AI itself. 

In addition to that, monitoring, retraining, debugging, version control, compliance auditing, and the total cost of ownership quickly eclipse traditional automation tools. Enterprises looking for quick wins often find themselves locked into complex, costly ecosystems for what amounts to marginal gains. 

The data foundation problem: Garbage in, exponential garbage out 

Agentic AI systems are only as good as the data they ingest. Yet many organizations, particularly those outside the digital-native space, are drowning in what could be charitably called “data debt.” 

Legacy systems, fragmented data silos, duplicate records, outdated taxonomies—all pose existential risks to agentic systems tasked with making autonomous decisions. 

When these systems act on flawed or incomplete data, the stakes are much higher than a simple bad prediction. An agent might send the wrong product, approve an erroneous payment, or deliver a customer experience that does more harm than good. 

In this context, the adage “garbage in, garbage out” transforms into something more urgent and alarming: exponential garbage out, and fast. 

Master data management: The missing link 

Without unified master data—standardized records of key entities like customers, vendors, and SKUs—Agentic AI systems struggle to understand the basic contours of the business they’re meant to serve. Even advanced models can’t intuit what’s not there. 

Implementing data governance, ownership models, lineage tracking, and standardized APIs isn’t just good hygiene; it’s essential for AI readiness. Yet these are long-term projects that rarely receive the attention or investment they deserve in the rush to deploy. 

Agentic AI Operational reality check for CTOs for enterprise-scale adoption 

Even the most advanced agent cannot succeed in an environment that isn’t ready for it. This includes technical infrastructure and the human side of the equation. 

Change management remains one of the most overlooked components in AI adoption. Employees wary of automation, unfamiliar with AI systems, or threatened by job displacement often resist adoption in subtle but impactful ways. Low engagement, poor data input, or outright sabotage are not uncommon. 

Meanwhile, business users need education on how these agents work, what they can (and can’t) do, and how to integrate their insights into decision-making. Few organizations are investing sufficiently in cross-functional training or culture-building initiatives to support this shift. 

Security and regulation: The unseen minefield 

Giving an AI agent access to enterprise systems means giving it power. And with power comes risk. 

Agentic systems can trigger financial transactions, access sensitive data, or interact with external stakeholders. That makes them a potential attack surface, a regulatory liability, and a privacy concern—all in one. 

Yet governance for these systems remains immature. Auditing agent behavior, ensuring explainability, managing access control, and enforcing ethical boundaries are still evolving practices. Until these challenges are resolved, large-scale deployment will remain risky and legally fraught. 

A strategic alternative: How to make agentic AI work 

• Start small, think smart 

The road to ROI does not require abandoning Agentic AI—but it does demand sobriety. Organizations must shift from technology-first to value-first thinking. 

This begins by identifying high-impact, narrow-scope use cases. Not every process benefits from autonomy. Focus on areas where human involvement is expensive, where decisions are repetitive but data-rich, and where the AI’s autonomy offers a clear edge over static automation. 

Examples include dynamic pricing in e-commerce, real-time supply chain routing, or proactive fraud detection—all areas where an agent can add measurable value without replacing human judgment entirely. 

• Embrace human-in-the-loop 

Perhaps the most effective deployment model today is human-in-the-loop (HITL). These systems allow agents to propose actions or perform bounded tasks while keeping humans in control of final decisions. 

This hybrid approach balances efficiency with oversight. It helps teams build trust in the system, train models on real-world feedback, and catch failures before they cause damage. Importantly, it also creates space for human expertise to complement rather than compete with AI capabilities. 

• Establish real KPIs—or don’t deploy 

Any Agentic AI project should begin with a simple question: What will success look like? 

Too many organizations launch initiatives with vague aspirations: “Improve productivity,” “enhance customer experience,” or “reduce errors.” These goals are unmeasurable and set up the project for failure. 

Instead, define concrete, quantifiable, and time-bound metrics: reduce support ticket response time by 30% within six months; lower procurement cycle costs by $500K in Q3; increase first-call resolution by 15%. 

These KPIs form the backbone of ROI assessment and provide a rational basis for continued investment or course correction. 

Agentic AI represents one of the most exciting developments in enterprise technology. But it is not a panacea, nor is it mature enough to be deployed indiscriminately. The technology is ahead of the organizational readiness, and the cost structures too often outweigh the benefits—especially when rushed. 

Enterprise leaders must confront the reality of ROI: autonomy comes at a price. Only by acknowledging that cost—financial, operational, and cultural—can businesses make informed decisions about where, how, and why to deploy these systems. 

The question is no longer, “Can Agentic AI transform your business?” It’s “Is your business ready for what transformation truly demands?” 

Alagan Sathianathan, CIO of Agira Technologies, shared this in one of his LinkedIn posts, “Agentic AI could be a game-changer, but it’s about strategy—start small, build trust, and focus on data.”

Bridging vision and reality: A Pragmatic path forward 

What emerges from all this is not cynicism about agentic AI—it’s clarity. The enterprise sector isn’t failing to adopt AI agents; it’s appropriately calibrated its ambitions to match operational reality.

Hyperscaler CapEx is exploding, and cloud giants are forging ahead with AI-first infrastructure, creating a new backbone for computing in the AI era. But most enterprises, hamstrung by technical debt, talent shortages, and disjointed data ecosystems, are still laying the first bricks of that same road. 

2025 will not be “The Year of the Agent.” It will be the year of foundational investments: modernizing data architectures, standardizing APIs, instituting governance, and piloting narrow use cases with measurable ROI. We’ve seen this movie before. Big data, cloud, and mobile all followed a similar arc, initial hype, followed by years of gritty groundwork, and eventual transformation for those who persevered. 

The organizations that emerge as winners won’t be those chasing the flashiest agent demos. They’ll be the ones building patiently and deliberately treating data as a product, integrating systems with intention, and establishing governance before scaling. These companies understand that success in the age of autonomy isn’t about racing to deploy the most agents, it’s about creating an enterprise environment where smart agents can thrive. 

The yellow brick road isn’t optional, it’s the strategy. And the companies that walk it thoughtfully will be the ones who turn today’s agentic hype into tomorrow’s competitive advantage.

A complementary perspective: Tom Davenport’s practical guide on Agentic AI

Tom Davenport’s recent book on Agentic AI titled “Agentic Artificial Intelligence: Harnessing AI Agents to Reinvent Business, Work and Life” provides a practical roadmap for business leaders navigating this complex landscape.

While the Silicon Valley enthusiasm highlights the promise of autonomous AI agents revolutionizing workflows, Davenport reminds us that success depends on pragmatic implementation, balancing ambition with operational readiness.

At its core, the book argues that agentic AI represents a fundamental shift from tools that assist to agents that collaborate and take initiative, transforming how work is designed and executed. However, it also stresses that this transformation isn’t about flashy demos or idealistic visions. Instead, it requires a clear-eyed understanding of organizational readiness, infrastructure needs, data quality, talent requirements, and the often-hidden costs that come with autonomy.

In a world flooded with AI promises, this book anchors agentic AI firmly in the realities of today’s enterprise environments. It is offering a pragmatic roadmap for turning autonomy from a technology experiment into a sustainable competitive advantage.

In brief

Agentic AI offers enterprises a bold promise: autonomous systems that act, decide, and optimize on their own. But behind the polished demos lies a stark reality: high costs, brittle performance, and immature infrastructure. The winners won’t be those who adopt agents fastest, but those who prepare their enterprise to support them best.