The Agentic AI Use Cases That Are Actually Working
This piece reflects Ema's own view. Three passages are quoted verbatim from Gartner, "Emerging Tech: Top Use Cases for Agentic AI," Anushree Verma, Aakanksha Bansal, Alfredo Ramirez IV, Danielle Casey, Akhil Singh, 15 September 2025. View the full report on Gartner.
There is a term for what is happening across the agentic AI market right now, and it is not flattering: agent washing. Vendors relabel existing AI assistants as "agents," enterprises buy in expecting autonomous decision-making, and what arrives is a chatbot that can summarize a document a little faster. The technology has not changed. The marketing has.
The 2026 Gartner CIO and Technology Executive Survey revealed that 17% of the enterprises surveyed had already deployed AI agents and 42% will deploy in the next 12 months.
That's real momentum. But most of what's being deployed today still sits closer to "AI assistant" than genuine agentic capability. Organizations aren't wrong to be excited. They're wrong about what they're buying.
The distinction matters because the cost of getting it wrong compounds. Enterprises that deploy agent-washed tools expecting enterprise-wide productivity gains get, at best, incremental automation on a single task. Real value only shows up when the use case is specific enough for the agent to have deep contextual understanding of a business process, not a generic layer bolted onto an existing product.
Three use cases stand out as places where agentic AI is already producing measurable results, not just pilot enthusiasm.
Customer service: the clearest ROI story right now
Customer service was the top use case in 20% of the cases in order to improve customer experience and engagement, increase automation and drive cost-efficiency.
The reason is straightforward: it's a high-volume, high-friction function where the cost of getting a resolution wrong is immediate and visible. Agentic AI's advantage here isn't answering questions faster — traditional bots already do that. It's handling multistep, multi-system resolutions that used to require a human coordinating between a CRM, a carrier's website, and an internal database, adjusting the response as new context comes in.
Bigblue, a European logistics platform and an Ema customer, is a useful illustration. As the company scaled across e-commerce partners and carriers, ticket volume grew unpredictably and each resolution required pulling context from multiple disparate systems — compounded by a requirement to communicate accurately across several languages. Working with Ema's AI Employees, Bigblue scaled support capacity roughly 10x without adding headcount, cutting average response time from two hours to under 90 seconds, while maintaining service quality through seasonal demand spikes that would typically have required temporary staffing.
What made that work was not automating one step. It was an agent capable of dynamic, multistep conversations that adjusted based on evolving context — retrieving the right SOP, integrating across systems, and taking action to close the ticket, rather than just answering a query and stopping there.
With its agentic AI employees, Ema helped Bigblue tackle this challenge and scale up customer support by 10x reliably without increasing the workforce.
Knowledge management: high potential, mostly unrealized
Knowledge management is one of the most piloted use cases in the market — and one of the least differentiated in practice. Most of what ships under that label is an assistant that searches documents and produces summaries, which is genuinely useful and almost impossible to attach a number to.The result is limited, hard-to-quantify value, which is a big part of why so many knowledge management pilots stall or get quietly discontinued.
Domain-specific models are where the more transformative outcomes show up. A model trained on how a specific organization actually diagnoses and resolves problems behaves differently from a general-purpose one pointed at the same technical library, and in specialized fields that difference is the whole return.
The distinction is instructive. A generic model summarizing documents is convenient. A model trained on a domain's actual troubleshooting logic is the difference between a first-try fix and a repeat ticket. A second-order problem follows from this: as more task-specific agents get deployed, orchestrating them coherently, rather than letting "agent sprawl" accumulate unchecked, becomes its own technical debt problem worth planning for early.
Cybersecurity: from reactive to continuous
Security operations centers face a structural problem no amount of headcount solves: modern enterprise environments generate more telemetry and alerts than any human team can monitor around the clock.
AI SOC agents, used for natural language investigation queries, false-positive reduction, alert enrichment, and next-step advisory, are the most established application, because they let a security team apply threat detection and response at machine speed rather than waiting for a human to triage each alert.
The pattern is consistent wherever alert volume outpaces analyst capacity: agents absorb the triage work that was going undone by default, and human analysts get their attention back for genuine threats rather than clearing noise.
The throughline across all three use cases is the same: agentic AI works when it's deployed against a specific, well-bounded process with clear inputs and outcomes — not layered generically across "productivity" as a whole.
The real test is ROI.
The more useful question isn't how many companies have deployed agents — it's how many can actually calculate the operational ROI of what they deployed, against a real cost baseline, rather than taking a vendor's "agentic" label at face value.
That distinction will only get sharper as more vendors relabel existing tools to ride the category's momentum. The organizations that benefit won't be the ones that adopted earliest. They'll be the ones that asked the harder question before signing: is this actually making a decision, or just retrieving information faster than before?
This piece reflects Ema's own view of the agentic AI market. Three passages are quoted verbatim from Gartner, Emerging Tech: Top Use Cases for Agentic AI, Anushree Verma, Aakanksha Bansal, Alfredo Ramirez IV, Danielle Casey, Akhil Singh, 15 September 2025. All other analysis, commentary, and customer results are Ema's own. Gartner subscribers can view the full report on Gartner.
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