PSS Chicago 2025 - Emerson

Choosing the right problems to solve with AI: Lessons from Emerson

When it comes to AI in customer support, success isn’t about how advanced the technology is, it’s about picking the right problems to solve.

That was the key takeaway from our recent session with Seth, who leads the global digital customer experience team at Emerson, one of the world’s largest industrial automation companies. From manual valves to connected smart devices, Emerson’s 1000s of products cover an incredibly wide spectrum that makes delivering quick, accurate support a constant challenge.

We sell everything from a valve that can be hand-operated to things that are smart IoT devices. The challenge is getting the right depth of support at scale when you have all these different customer personas.

From a switchboard model to smarter support

For years, Emerson ran a centralized support model designed to cut costs, but the approach came with tradeoffs.

“It turned into more of a switchboard mentality. We couldn’t get the depth of support that was needed.”

Customers struggled to reach the right experts. Feedback like “It’s too hard to find someone to talk to at Emerson” became common. The company knew it needed to rethink its model, but simply adding more people wasn’t sustainable.

“Hiring people is not always easy, especially for this kind of work, when there’s pressure to implement AI out there.”

“We used to take a lot of phone calls, a lot of chats, just asking for, I have this part number. What does it even do? Or does it have this feature?’ Now people can find that out instantly.”
Seth Natala
Director, Digital Customer Experience at Emerson

But the biggest surprise came from unexpected use cases.

“The one that’s used the most today was one we didn’t expect at all. It changed our outlook on where to focus next.”

The power of design and user experience

Seth emphasized that great AI solutions aren’t just about algorithms, they’re about experience.

“A lot of AI projects fail because people leave UX as an afterthought. The first feedback you’ll get after launch is always: ‘This UI is terrible.’”

Emerson found Mavenoid’s out-of-the-box UX intuitive enough to avoid endless design debates and move faster.

“Because the UX was already strong, we had fewer internal arguments and could focus on content and performance instead.”

The results

By grounding its AI initiative in real, solvable problems, Emerson saw measurable impact:

“We started at about 55% self-resolution. After improving our content, we got to 65–70%.”

And while the goal wasn’t to reduce headcount, the team has been able to redirect human effort to higher-value work.

It’s not about cutting people, it’s about letting them focus on the right stuff, not the easy stuff.

Key takeaways from Emerson’s AI journey

  • Start with what you already do well.
    Don’t expect AI to fix broken processes or missing data.
  • Stay focused on the customer.
    Internal efficiency is important, but solving external pain points delivers faster, clearer wins.
  • Measure, learn, and adapt.
    Use real usage data to validate assumptions and guide where to invest next.
  • Don’t overlook UX.
    A poor interface can undermine even the smartest AI system.
  • Get it to production.
“Ideas are great, but the hardest thing is getting things into production – and having the tenacity to push them over the line.”