HubSpot’s AI Problem Isn’t the Technology. It’s Proving the Value

HubSpot’s slowing growth raises the question: What happens when customers stop paying for AI promises and start demanding results?

For years, software companies had a relatively straightforward sales pitch: Buy our platform, give your employees access, and watch productivity improve. But artificial intelligence has complicated that arrangement.

Customers increasingly want proof that AI actually works before committing their budgets. They want measurable outcomes, predictable costs, and fewer promises about what the technology might accomplish someday.

HubSpot is discovering just how difficult that transition can be.

According to Constellation Research’s November 5 analysis, HubSpot’s move toward outcome-based AI pricing is creating longer buying cycles, greater scrutiny from executives, and a less predictable revenue outlook.

But there’s a bigger story for revenue operations leaders.  The next competitive advantage in AI may have less to do with how intelligent the software becomes and more to do with whether companies can turn that intelligence into measurable revenue performance.

That’s where Fullcast has an opportunity to distinguish itself.

Why Is HubSpot Struggling With Outcome-Based AI?

HubSpot isn’t struggling because customers have stopped wanting AI. Its challenge is that customers are changing how they evaluate and purchase it.

During the company’s third-quarter earnings call, CEO Yamini Rangan explained that buyers increasingly expect to test AI in their own environments, using their own data, before committing. That shift is affecting HubSpot’s financial outlook.

HubSpot reported $947.6 million in third-quarter revenue, an increase of 17% year over year. But its fourth-quarter guidance of $945 million to $947 million represents approximately 12% growth, below Wall Street’s expectations. Meanwhile, agent trials jumped 64% sequentially.

That’s the fascinating contradiction: More companies are experimenting with AI, but experimentation isn’t translating into software purchases as quickly as vendors expected. HubSpot’s strategy may prove successful over time. Yet the numbers reveal the difficulty of converting AI enthusiasm into durable commercial value.

Why Doesn’t More AI Adoption Automatically Mean More Revenue?

Because activity and outcomes are different measurements. An AI agent can generate hundreds of prospecting emails, summarize dozens of sales calls, and recommend next steps for every opportunity in the pipeline.

Those are useful capabilities. They aren’t necessarily business results.

Consider a sales organization that deploys an AI prospecting agent. The agent identifies 500 promising accounts and generates personalized outreach.

Impressive.

But what happens when those accounts enter the revenue operation?

Some might be assigned to territories already overloaded with opportunity. Others could be routed to the wrong representatives. The sales capacity plan may not account for the additional workload. Suddenly, the company has purchased a remarkably efficient way to create more operational confusion. This is the distinction experienced RevOps leaders understand: Automating an activity doesn’t guarantee improvement in the system responsible for producing revenue.

What Does HubSpot’s Transition Reveal About the Future of AI Pricing?

The industry is moving toward a world where software vendors must demonstrate value rather than simply charge for access.

In an earlier Constellation Research analysis, HubSpot’s move toward outcome-based pricing was linked to growing customer concerns about unpredictable AI expenses. The company began pricing certain Breeze AI agents around completed assignments rather than traditional software access.

The idea makes sense. Customers want to pay for work accomplished. But completing a task isn’t always the same as improving a business outcome. An AI agent that resolves a support inquiry has completed a measurable task. Whether that resolution improves customer retention or expansion revenue is another question.

For revenue teams, the distinction becomes even more important. Outcome-based AI requires more than a new pricing model. It requires an operating model capable of connecting actions to results.

How Is Fullcast Positioned to Compete in Outcome-Based RevOps?

Fullcast’s advantage is its focus on the operational decisions that determine whether revenue teams can execute effectively.

Rather than treating territory planning, quota management, lead routing, forecasting, and commissions as unrelated administrative functions, Fullcast’s Plan-to-Pay platform connects them across the revenue lifecycle.

That matters because revenue outcomes rarely depend on a single AI action. A more complete approach connects several critical functions:

Fullcast’s combination of planning, execution, intelligence, and compensation creates an opportunity to measure AI’s contribution in a broader business context.

For example, an AI-generated opportunity becomes much more valuable when the organization can route it correctly, evaluate its progression, incorporate it into a reliable forecast, and compensate the resulting sale accurately.

That doesn’t mean Fullcast has already solved every challenge of outcome-based AI. Nor does it mean HubSpot lacks capabilities in sales automation or revenue management. It means Fullcast is positioned around a particularly important part of the problem: connecting revenue decisions that otherwise remain fragmented across teams and systems.

Could the Next AI Winner Be the Company That Makes Everything Work Together?

HubSpot’s challenges offer a useful warning to the broader software industry.

Customers are becoming more demanding about AI investments. Executives are participating earlier in purchasing decisions. And the ability to demonstrate meaningful business results is becoming increasingly important.

For RevOps leaders, that changes the technology conversation. The question is no longer simply whether AI can perform a task.  It’s whether the organization has the data, governance, workflows, and accountability to turn that task into an improvement in revenue performance.

HubSpot is making a significant investment in outcome-based AI, and its growing trial activity suggests customers remain interested.

Fullcast has an opportunity to compete by addressing the operational conditions that help those outcomes materialize.

The AI race won’t necessarily be won by the company with the most agents. It may be won by the company that can prove what happens after those agents go to work.

Four key takeaways

1. AI adoption and revenue growth aren’t the same thing. HubSpot’s 64% increase in agent trials, alongside slower projected revenue growth, illustrates the challenge of converting interest into paid adoption.

2. Outcome-based pricing raises the stakes. Buyers increasingly want to see measurable value before committing, putting greater pressure on vendors to demonstrate results.

3. RevOps infrastructure determines whether AI delivers. Better prospecting, forecasting, and automation require effective territory design, routing, capacity planning, and data governance.

4. Fullcast has a differentiated opportunity. Its connected Plan-to-Pay approach addresses the operational decisions that translate sales activity into measurable business performance.