That shiny new AI feature? Your customers won’t use it

Software companies building AI capabilities into existing products are coming up against a pretty big problem: Customers just aren’t that interested.

Fifty-one percent of software makers that have added AI to existing products report that less than a quarter of customers actually use the new features, according to a recent survey conducted by software vendor acquisition firm Banyan Software.

This “deployment gap” comes from a lack of planning and measurement to target what AI features customers would use, the company claims in a report on the survey.

“Plenty of operators moved fast and blind, and that is exactly how you end up with AI no one uses,” the report says. “The ones who pulled ahead moved sooner than felt comfortable, and watched what happened closely enough to know what was working.”

According to Banyan, software makers should put one person in charge of expanding AI features in their products and measure the uptake from customers.

“Wire AI to results you can show, so your board, your team, and a future buyer can see it without you in the room explaining,” the report says.

The same goes for CIOs looking to thread new AI functionality into customer- and employee-facing apps and services.

Features the customers don’t need

Daniel Wilson Kemp, co-founder and CEO of payments and point-of-sale software vendor Lifted Holdings, agrees that many software vendors seem to be building AI capabilities that customers either don’t value or don’t understand.

“Users do not wake up wanting to use AI,” he says. “They want to close the books, answer a customer, resolve an exception, or finish a shift faster. If the AI is a separate destination, requires a new prompt habit, or returns an answer without the context and permissions of the system of record, usage will stay low.”

The problem is more of a workflow issue than an adoption one, Kemp suggests.

“The most important adoption test is whether the AI owns a useful step in an existing workflow,” he says. “AI adoption rises when the feature disappears into the job. If users have to leave the workflow to go use AI, the deployment is already asking too much.”

Kemp doesn’t see the low adoption rate as resistance to AI itself, but related to concerns about unclear value, bad UX, weak domain context, potential errors, and pricing that crop up before customers see measurable benefits.

“Vendors can charge more when the capability reliably saves labor or reduces risk, but an AI surcharge for a generic chat box is difficult to defend,” Kemp says.

Adding AI to a software product doesn’t guarantee it’s useful, adds Darren Kimura, CEO and president of agentic AI infrastructure provider AISquared.

“The reality is that the software industry has become very good at putting an AI button into a product and calling that product AI-native,” he says. “That is not the same as building AI into a product and using that in production.”

Enterprise AI has what Kimura calls a “last-mile” problem. Adoption breaks down when the AI reaches the real operating environment, and companies need to consider cybersecurity, usability, and end-user adoption, he says.

Customers generally aren’t reluctant to use AI, he says, but they’re reluctant to deploy tools they can’t control or understand.

“In my experience, organizations rarely need more AI features,” Kimura says. “They need the controls, integrations, and operating model required to use the features they already have.”

Some AI add-on tools also have integration problems, he adds. “AI that sits next to the workflow becomes a demonstration,” he says. “AI embedded inside the workflow becomes infrastructure.”

Kimura sees a major disconnect between what vendors build and what employees actually need. Employees want to process a claim faster, detect fraud earlier, resolve a supply-chain issue, or eliminate hours of repetitive analysis.

“Employees generally do not wake up asking for another chatbot,” he says. “They adopt it because it removes friction from work they already perform.”

The same warnings and caveats pertain to in-house AI development efforts or attempts by IT leaders to force-feed software makers’ new AI features into company workflows.

Confused customers

Customer confusion plays a big role in slow AI adoption, adds Sofia Barbosa, chief customer officer at BMC Software.

“Customers aren’t reluctant; they’re often overwhelmed,” she says. “They want to use it, but they’re being inundated with vendor messaging about new AI capabilities and how to use them, and it’s hard to know what to prioritize.”

Customers must decide whether to build or buy new AI tools, or use the vendor tools they already have, all while training their teams and anticipating future pricing changes, Barbosa suggests. “That’s a lot to sort through, and it slows adoption down even when the capability itself is ready,” she adds.

Software vendors need to aim to embed AI into their products in a way that feels effortless, is part of the operating model that customers already use, and intuitive enough that early adoption doesn’t require a big lift, Barbosa says.

But that’s only half the battle. Vendors also need to back their AI tools with structured, scaled adoption models that tie directly back to value realization and ROI, she adds. “Capability without that structure is where the gap shows up,” she says.