In the last article in this series, we explored how IT professionals and leaders can cut through the hype surrounding agentic AI and gain a deeper understanding of what the technology actually offers. Now, we turn to the practical side: how to integrate it effectively. Let’s explore the challenges and outline strategies that organizations of all sizes can use to adopt agentic AI with confidence.
When Should You Integrate Agentic AI?
With traditional software projects, you typically start by identifying a concrete business problem and then work backward to see how technology can address it. With agentic AI, that order often flips. The technology itself opens up so many possibilities that teams rush to apply it everywhere, even before asking whether it’s the right fit. This creates a grey zone. Engineers may try to force-fit business problems into agentic systems because leadership insists that to stay competitive, they need to build and integrate agentic AI systems. Product managers and customers, meanwhile, may not fully grasp what agentic systems can or can’t do. As a result, engineers are left validating ideas born more of hype than of business need.
The disconnect between what the technology can achieve and what the business actually wants is where most failures begin. During the hype cycle, objectives tend to blur into a vague mandate like, “Let’s build something with agentic AI,” rather than the more helpful question, “Why agentic AI for this problem?” Over time, as the hype cools, teams usually refocus on solving specific issues and only then ask whether agentic AI can genuinely help. Without clear business objectives, projects risk either stalling or burning resources on use cases that don’t create real value.
Cost Concerns Around Agentic AI
Where does cost come into play when implementing agentic systems?
- Vendor costs: The upfront cost of acquiring solutions with built-in agentic AI is high because the skill set needed to develop it remains specialized.
- Operational costs once agentic systems are running: Unlike traditional software that follows simple instructions, agentic AI operates within an “action space,” making decisions across multiple possible paths. That flexibility comes at a price. For instance, a software engineer might take an hour to track down a bug, while an agent could finish in 5 minutes—or get stuck in loops that consume 100 hours. Without strict guardrails, costs can spiral out of control as the system continues to try to solve problems in inefficient ways. To control this, teams often impose limits, such as capping retries or halting a task when it exceeds a certain dollar threshold. But guardrails introduce trade-offs: the tighter the controls, the more performance suffers. It’s like trying to run a modern game on outdated hardware—the system works, but nowhere near its full potential. Additionally, the underlying large language models that power these agents require substantial GPU resources, further increasing costs.
- Integration with legacy systems: Organizations running outdated infrastructure may not truly be in a position to adopt agentic AI at all. On-premises systems, for example, generally lack the GPU-intensive computing power required to run agentic models. To make it work, organizations must either upgrade their infrastructure with high-performance hardware or shift workloads to the cloud, where those resources can be provisioned.
Seen and unforeseen costs like these are why adopting agentic AI forces organizations to think carefully about their goals. The technology doesn’t appear overnight. It’s built on years of evolution, and jumping straight to agent-based systems can expose serious gaps elsewhere in the IT stack.
Measuring ROI on Agentic AI Systems
With so many pitfalls and uncertainties, it’s wise to consider how organizations should think about performance metrics and ROI for agentic AI. The challenge is knowing whether your investment is delivering genuine value rather than overpromised hype. For now, the right approach is to measure productivity as you normally would in a given domain, then quantify the added value of the agent. Consider these examples:
- Software engineers already measure productivity through the number of features shipped, bug fixes, and code reviews. If an AI assistant reduces the time required for those tasks by even 5 percent, that’s a meaningful gain because we know the baseline.
- In research, metrics might include the quality of publications or turnaround times.
- In observability, we could measure how long it takes to resolve incidents with and without an investigation agent.
These before-and-after comparisons are straightforward, reliable, and don’t require inventing new metrics just for the sake of agentic AI. Keep it grounded in outcomes that matter—faster delivery, reduced costs, or improved quality—and you’ll soon know whether the system is worth the investment.
Steps for Small- to Medium-Sized Businesses
Adopting agentic AI may seem like a minefield, but there are ways for small to medium-sized businesses to begin laying the groundwork today to adopt this technology effectively and sustainably over the next few years.
- Introduce: Start small by asking whether you truly need agentic AI today, or if your current needs can be handled with non-agentic systems like large language models. In many cases, layering in AI gradually provides value without the cost and complexity of a fully agentic approach. The priority is identifying use cases where AI reduces friction while keeping humans firmly in the loop.
- Expand: Look for opportunities to apply AI in non-intrusive ways that save time and reduce repetitive work. For example, in ITSM, an AI assistant might appear alongside the interface to flag recurring tickets and suggest actions. The human agent still verifies and makes decisions, but the AI handles pattern recognition, allowing teams to focus on higher-value work.
- Integrate: Once confidence builds, begin automating smaller workflows and adding tool integrations. This phased approach enables leaders to control costs, measure ROI incrementally, and mitigate the risks associated with over-investing too early.
- Evolve: Over time, expand further by letting AI make more decisions independently. For many organizations, reaching this stage—where AI leverages LLMs or tool calls to handle routine tasks—is enough. Few will need to transition to fully autonomous systems, but progressing step by step ensures adoption is sustainable, cost-effective, and aligned with business needs.
Financial Resilience and Measured Adoption Are Key
The truth is, early adoption can be bumpy. For wealthy companies with deep pockets, this isn’t a deal-breaker. They’re willing to absorb the costs and the failures for a year or two because they see the long-term potential. These early adopters often become the ones who set the path for everyone else, learning through trial and error what works, what doesn’t, and sharing those lessons with the industry. For smaller or mid-sized companies, though, it’s harder to weather that storm. A big upfront investment that doesn’t pay off quickly can easily trigger panic and retreat—back to older systems or cheaper, less ambitious solutions. However, by asking hard questions about why and where agentic systems can be employed, implementing a measured approach to integration, and measuring ROI carefully, you don’t need to be the biggest fish in the pond to begin yielding real value in a short space of time.
More in the Agentic AI series:
