Episode Details

On this episode of Innovators, I spoke with Jason Ambrose of People.ai about what “agentic AI” actually means, why sales data is messier than most people think, and why blindly trusting large language models is a mistake.

People.ai has been around long enough to see multiple waves of enterprise software come and go. Now it’s repositioning itself squarely in the agent era.

Most CRM systems tell you what was entered. They don’t tell you what’s actually happening.

People.ai takes a different approach. Instead of relying on manual updates, their AI analyzes the communications that define modern sales, emails, Slack messages, meetings, chat transcripts. The system maps that activity to accounts, contacts, and opportunities.

That sounds straightforward until you scale it up.

If you’re a startup selling to a small business, maybe one salesperson is talking to one buyer about one product. That’s simple. But when Microsoft sells to Verizon, you might have dozens of people on both sides, across legal, technical, procurement, and executive roles. Conversations happen everywhere. Mapping that complexity into a clean CRM record is hard.

That’s where People.ai claims it shines. It uses its own AI models, trained on billions of transactions, to reconstruct what’s really going on inside a sales organization.

What Is an Agent, Really?

We talked about the shift from chatbots to agents.

A chatbot answers a question. An agent has an objective.

Jason framed it in terms of business process automation. Old-school automation works when the logic is predictable. If this, then that. Stay inside one system, follow a defined workflow.

Agents step in when reasoning is required. They cut across systems. They pursue a goal. They have to decide what to do next.

But that only works if they’re plugged into real expertise.

Jason made a useful distinction. Public LLMs are trained on public data. Enterprise expertise lives in private systems. If you want an agent to act intelligently inside a company, it needs access to proprietary data. That’s a big trust ask. You’re effectively saying, “Let our AI read your emails.”

That’s not a small decision.

Avoiding “Build Trust With Stakeholders”

Anyone who has used a generic LLM for business advice has seen the problem. You ask for guidance and you get vague platitudes. “Build trust.” “Accelerate the deal.” “Engage the customer.”

That’s not actionable.

Jason argues that this is where expert agents come in. Instead of spitting out generalized advice, they ground recommendations in specific deal data. Who hasn’t responded in three weeks? Which technical blocker hasn’t been addressed? Where did the last conversation stall?

Without that grounding, AI defaults to corporate fortune-cookie language.

The Capital Markets Reality

We also touched on fundraising.

SaaS is being repriced. Public markets adjusted first, and private markets followed. Companies that once enjoyed premium multiples are now being reevaluated in light of AI disruption.

Capital is flowing into AI-native plays. If you look like “just another SaaS company,” you need a credible AI story. If you genuinely sit at the center of AI transformation, you’re in a stronger position.

People.ai is not currently raising, but Jason sees the shift clearly. The market is asking who is being disrupted by AI and who is using it to build something new.

Is AI Replacing Jobs?

It’s the obvious question.

Jason’s take was pragmatic. Technology changes work. It always has. He remembers the early days of the web and the anxiety that came with it. Some jobs disappear. Most jobs change.

His line stuck with me: people should work with people, and let AI do the rest.

Sales, at its c