Unlocking ROI with agents

There’s no shortage of excitement around AI agents right now. The buzz is everywhere—from startup demos to boardroom pitches. But if you’re building or buying AI solutions for the enterprise, there’s really just one question to focus on:

Where’s the return on investment (ROI) coming from?

1. Operational Efficiency: The Entry Point

Let’s start with the most obvious—and popular—use case.

AI agents are fundamentally cost-saving machines. They don’t sleep, quit, or make mistakes because they’re bored or burned out. Compared to human labor, the math is hard to ignore: a few cents per task instead of a few dollars. Instant scalability. 24/7 uptime.

That’s why this layer is so appealing. It’s easy to model, easy to explain:
Replace manual work with AI → lower costs.

But here’s the reality check: efficiency gains don’t always translate to immediate savings.

Making your team 20% faster is good—but it doesn’t necessarily mean you’re spending 20% less. You can’t cut headcount overnight. Most workflows are built on a foundation of tacit knowledge, edge cases, and messy human dependencies. Real transformation takes thoughtful implementation, adoption, and continuous refinement.

So how do you actually get value here?

You let agents handle the repetitive, time-consuming grunt work. This creates space for your people to level up—tactically and strategically.

Benefits emerge in second-order ways:

Faster responses lead to happier customers
Staff focus on higher-leverage activities
Burnout drops, engagement climbs
Errors go down thanks to consistency
Issues get resolved faster with better information

2. Net-New Value Creation: Where It Gets Interesting

Once the low-hanging fruit is handled, a new opportunity appears: capturing value that was previously out of reach.

There are countless revenue opportunities that businesses ignore—not because they’re unimportant, but because they were just too inefficient to pursue manually. AI agents change that.

Suddenly, things like:

Engaging cold leads you’d normally skip
Recovering dropped opportunities in your pipeline
Nudging renewals and cross-sells based on behavior
Auto-generating proposals, quotes, or RFP responses
Proactive onboarding to prevent early churn

…become not only feasible, but scalable.

These aren’t cost-cutting measures—they’re value generators. You’re creating revenue streams that previously didn’t exist. The baseline cost was zero, so the upside is pure ROI.

This is also why we’ve seen early success stories in areas like sales enablement, customer success, and lifecycle marketing. It’s the simplest case to make to a CFO: "This money didn’t exist before. Now it does."

3. System Intelligence: From Workflows to Working Smarter

Most organizations think of AI agents as task-doers. But the real unlock comes when they stop just executing workflows and start understanding and improving them.

Once agents are embedded in your day-to-day operations, they see everything:

What steps take the longest
Where handoffs break down
Which tasks get repeated unnecessarily
Where decisions are delayed or overridden

This is where the third layer of ROI emerges—not from doing more, but from doing things better.

With continuous exposure to workflows, AI agents can start to propose changes, flag inefficiencies, and recommend optimizations. Instead of simply automating what already exists, they become co-pilots in reengineering how work gets done.

Examples include:

Suggesting workflow changes based on real usage patterns
Highlighting bottlenecks or redundant approvals
Learning which types of requests need escalation (and which don’t)
Recommending better routing logic based on historical outcomes
Identifying process gaps that lead to delays or errors

Think of it as turning your organization’s operations into a self-improving system. You're no longer relying solely on people to spot what’s broken. The system itself starts to surface those insights.

This layer compounds over time. Each improvement feeds back into the system, leading to faster cycle times, better outcomes, and fewer surprises.

It's not just automation. It’s automation with insight—and that’s where things start to scale in a completely different way.

Final Thoughts

If you’re thinking about ROI from AI agents, don’t stop at cost savings. That’s just the first layer. The real value shows up when agents help you do things you never could before, and ultimately, when they help you rethink how your systems work altogether.

To summarize:

Efficiency – Do the same work, faster and cheaper
New Revenue – Do more work that wasn’t possible before
Optimization – Redesign how the work should be done in the first place

The winners in this new wave won’t be the ones who simply reduce costs. They’ll be the ones who reinvest those gains into systems that keep improving.