Moving Beyond the GenAI Ping-Pong Match

In many organizations, GenAI has become a kind of ping-pong match: we serve prompts, the model returns replies, and the rally goes on. It’s impressive to watch — quick, dynamic, full of promise. It’s even pretty fun! But the question is, when does the game end and the real work begin?

That’s the pattern I see across enterprises. People tend to equate GenAI with chat interfaces like ChatGPT, Gemini, and Copilot — consumer tools that help them work with AI. There’s value in that. It’s made the technology accessible, even ubiquitous, and proved its potential. These tools are brilliant at one-off tasks — draft a quick email, summarize a document you upload, help to brainstorm a new idea, or even search for information on the web. 

But for an enterprise, this can create a productivity paradox: on one hand, we do tasks faster, yet these tasks stay manual and siloed, never integrating into or automating the core workflows that actually move the business.

Helpful? Absolutely. Transformational? Not yet.

Passing the paddle 

The distinction matters. Doing work with AI means prompting for summaries, answers, or drafts — still relying on a human to take the next step. We’re so busy returning the ball that we forget automation was meant to end the match altogether.

Putting AI to work looks different. It’s time to put the paddle down.

This isn’t about a smarter answer box. It’s about deploying autonomous, intelligent agents that can think, plan, and execute complex, multistep business processes — end to end and with proper governance. Give an agent a specific task or goal; it perceives context, selects tools, calls APIs, coordinates across systems, retries intelligently, and escalates to a human when risk or policy thresholds are crossed.

A real-world example: demand letters

When an insurance carrier receives a demand letter —  a formal communication, typically from an attorney, initiating settlement negotiations — the clock starts ticking. Insurers must respond within a specified timeframe; yet, the current process remains remarkably time-consuming and labor-intensive. Claims specialists still sift through disparate systems to collect critical information: policy details, medical records, photographs, prior correspondence, and notes. They assemble everything manually, rely on experience to judge comparable cases, and draft responses from scratch. It’s slow, inconsistent, and costly. 

Agentic AI transforms this entire workflow. Agents can gather and analyze all relevant information autonomously, compare it to similar historical claims, and automatically draft a response that includes a supporting rationale and recommended settlement range. Humans remain in the loop — reviewing, refining, and approving — but they start from a well-researched foundation, making strategic edits and ensuring the final response aligns with the company’s priorities and risk tolerance. The result: response times shrink dramatically. And perhaps more importantly, insurers achieve better outcomes — resolving claims faster and often at lower cost, while maintaining fairness and reducing litigation risk.

This is the shift from generic to agentic AI. In practice, it behaves less like a calculator and more like a capable colleague that remembers, learns, and adjusts as conditions change. This is what actually moves the needle — saving enterprises time and money, unlocking new efficiencies, and delivering the transformation that AI has long promised.

Agents don’t replace people — they refocus them

Let’s clear up one of the biggest misconceptions: agentic AI isn’t about replacing people. It’s about removing the drag on human potential.

Every organization has many talented people who are spending a disproportionate amount of time collecting, checking, or moving information from one place to another. They’re reformatting spreadsheets, reconciling data across systems, or scanning contracts for the same five clauses. These are the digital equivalents of shuffling papers—and they exhaust people and focus that could be used for more valuable creative, strategic, or customer-facing work.

Agentic AI changes that equation. It eliminates tedium, not talent.

An agent can handle the data gathering, cross-referencing, and first-level decisions that most people never really enjoyed doing in the first place. It becomes a digital assistant that prepares the ground — surfacing insights, tracking exceptions, and intelligently routing work that truly needs human attention. The work that remains is of higher value by design: judgment, relationship-building, creativity, problem-solving.

That’s the real opportunity. This isn’t a cost-cutting story — it’s a capacity-building story. When agents handle the repetitive work, people get to focus on what makes the company different: service quality, speed of innovation, and the ability to act on insights faster than competitors. And, in many cases, the financial impact of AI is on top-line revenue and growth, not bottom-line cost savings. 

The road(map) ahead 

Traditional automation had its moment, but it hit a wall. Most legacy systems relied on carefully drawn process maps and brittle integrations—flows that worked only as long as every step stayed predictable. When real life intervened — an exception, a missing field, an unexpected data format — the automation stopped. The burden fell back on humans to patch, reroute, or re-document the process before work could continue.

Agentic AI breaks that pattern. These systems map and adapt on their own. They learn from the data they encounter, discover shortcuts humans might not have seen, and discard steps that no longer add value. Old processes won’t just be optimized; many will be reimagined entirely. Workflows may look nothing like the ones we’ve spent years diagramming — and that’s a good thing.

In short: be prepared to do things differently. The goal isn’t to bolt agents onto the past, but to let them reshape what productivity looks like in the future.

Educating for the next chapter

That’s really the heart of it — helping people see what this technology can actually do.

Gartner put agentic AI at the top of its 2025 tech trends. Still, when people hear “AI,” they continue to picture a chat window: ask a question, get an answer, move on. But that’s the visible tip of something much bigger. 

The familiarity of chat interfaces is both the opportunity and the obstacle. GenAI’s success with consumer tools (ChatGPT, Gemini, Copilot) has created awareness, but it’s also created a ceiling. People understand how to use AI with them, not how to put AI to work for them. They see it as a personal productivity tool, not an enterprise capability that can reason, act, and coordinate across systems.

That’s the uphill battle right now — education, not experimentation. The technology foundation is already here. Modern agentic platforms now exist to do what early adopters spent years trying to build: connect models, data, and governance into a single, secure operating layer. They manage orchestration, context, and compliance automatically, freeing teams to focus on outcomes, not plumbing.

We used that foundation to build an AI assistant. It retains the familiar chat experience that people already trust, but also exposes the full power of the platform underneath. Instead of answering one-off questions, it can orchestrate multistep workflows, summarizing documents, launching reviews, generating reports, even spawning and coordinating subagents across systems, all from the same conversational interface. It’s the usability of GenAI paired with the muscle of an enterprise platform.

With the right tools and guardrails in place, teams can start experimenting safely, learn quickly, and scale what works. Once that mindset shift happens, the noise fades. People stop asking what AI can say, and start realizing what it can actually do.