The news
IBM's Think blog published a breakdown of agentic AI vs. generative AI, laying out the conceptual difference between AI that generates outputs on request and AI that pursues multi-step goals autonomously. The piece highlights emerging agentic use cases in customer service, workflow management, and financial risk — framing the technology as still experimental but directionally significant.
Our take
The generative AI vs. agentic AI distinction matters more than most GTM teams realize — and not for the reasons IBM leads with.
Here's the practical gap: generative AI is a tool you prompt. Agentic AI is a system you deploy. When you ask ChatGPT to draft a follow-up sequence, you're still the operator making every decision. When you deploy an agent to qualify inbound leads, enrich the record, update Salesforce, and trigger a sequence — unsupervised — the system is making decisions based on the process logic you gave it. If that logic is vague, undocumented, or inconsistent, the agent doesn't slow down and ask for clarification. It just runs the wrong play, at scale, automatically.
This is the agentic AI problem that IBM's framing glosses over: the bottleneck isn't the technology. It's the process debt sitting underneath it. Most GTM teams have never written down exactly what "qualified" means, exactly when a rep should follow up, or exactly what triggers a handoff to CS. Generative AI lets you muddle through that ambiguity because a human is still in the loop. Agentic AI makes that ambiguity catastrophic.
The teams who will actually benefit from agentic systems aren't the ones with the biggest AI budgets. They're the ones who have already done the unglamorous work of documenting their GTM processes clearly enough that a new hire — or a well-configured agent — could follow them without improvising.
Customer service, workflow management, and demand gen are all legitimate agentic use cases. But "emerging" and "experimental" are the honest words here. Before you greenlight an agentic pilot, audit whether your process is agent-ready — not just whether the technology exists.
So now what?
- Map one workflow end-to-end before you automate it. Pick something narrow — lead routing, MQL handoff, renewal alerts — and write down every decision point and every "it depends." That document is your agent's instruction set.
- Ask the agentic question: "If this ran without anyone watching, what's the worst decision it could make?" If you can't answer that, the process isn't ready.
- Start with supervised agents. Tools like HubSpot's AI workflows or Clay let you run agentic-style automations with human review gates. Use them to stress-test your process logic before you remove yourself from the loop entirely.
Agentic AI isn't coming for GTM teams — it's already arriving. The question is whether your processes are documented well enough to hand over the wheel.