AI Doesn't Improve Experience by Default. Here's What Does.

41% of consumers say customer service has gotten worse because of AI.

That number comes from the Qualtrics 2026 Customer Experience Trends Report. Not a fringe survey. A major research firm asking thousands of consumers whether the AI chatbots replacing human agents are actually helping.

For most people, they are not.

At the same time, companies are deploying AI tools internally and watching employee satisfaction dip in the first 30 days. Productivity tools that were supposed to save time create new friction. People spend more time fighting the tool than doing the work it was supposed to help with.

This is not a technology problem. AI works. The models are capable. The problem is how it gets deployed.

The Experience Gap

Most AI deployments follow a pattern: buy tool, configure tool, deploy tool, measure adoption. The experience of the person using it comes last, if it comes at all.

At Marvell, I ran AI adoption across 7,000 employees. We deployed six platforms in under a year and hit 80% activation. The tools that stuck were the ones we designed around how people actually work. The ones that created friction, even temporarily, needed a different approach.

Here is what I learned about getting the experience right.

1. The first 30 days determine everything

When you introduce a new AI tool, people get temporarily worse at their job. They were fast at the old way. The new way requires learning, adaptation, and trust. If you do not account for this transition period, people revert to what they know within two weeks.

At Marvell, we built a 30-day adoption support program for each tool. Not training videos. Not a wiki. Actual humans available to help when someone got stuck. The cost was minimal compared to the license fee. The difference in sustained adoption was massive.

Most companies skip this because it looks like overhead. It is the single highest-ROI investment in any AI deployment.

2. AI that adds steps fails. AI that removes steps wins.

Every AI tool has a workflow cost. It takes time to open it, prompt it, review the output, and integrate the result. If that total time exceeds the time the task took manually, the tool is a net negative.

Customer service chatbots fail when they add steps. The customer has to describe their problem, navigate a decision tree, wait for an irrelevant response, then ask for a human anyway. The total experience is worse than calling a phone number and getting a person.

The AI tools that succeed are invisible. Glean worked at Marvell because people searched the same way they always did, they just got better answers. GitHub Copilot worked because developers typed code the same way, they just typed less of it. No new workflow. No added steps. Just better outcomes inside the existing pattern.

3. The handoff is where experience breaks

The Qualtrics data shows that satisfaction drops hardest when AI provides wrong information or fails to offer a human handoff. This is the most predictable failure mode and the most avoidable.

Every AI system needs a clear escalation path. Not "if the chatbot cannot help, the customer can try calling." An actual, designed transition from AI to human that preserves context. The customer should not have to repeat their problem.

The same applies internally. When an AI tool gives a wrong suggestion, the employee needs to know how to override it and move on. If overriding is harder than doing the task manually, people stop using the tool.

4. Measure experience, not just adoption

Most AI metrics track deployment (how many licenses?) or adoption (how many active users?). Almost none track experience (is the person's workday actually better?).

At Marvell, we tracked qualitative feedback alongside usage numbers. If a team had high adoption but negative sentiment, that was a signal the tool was mandatory but painful. We fixed the experience. If a team had low adoption but positive sentiment from the users who did adopt, that was a signal the rollout needed more support, not a different tool.

The companies in the Goldman report that saw no productivity gains from AI were almost certainly measuring the wrong thing. Adoption numbers can look great while the experience is terrible.

5. Customer and employee experience are the same problem

This is the insight most companies miss. The AI that frustrates your customers is built with the same approach as the AI that frustrates your employees. Deploy first, design later. Measure licenses, not satisfaction. Skip the handoff design. Assume the technology will figure it out.

It will not.

The companies getting results from AI are the ones that start with the experience question: what does this person need, and how does AI make that easier? Not faster. Not cheaper. Easier.

Speed and cost savings follow naturally when the experience is right. They do not follow at all when you bolt AI onto a broken workflow.

What to do instead

If you are deploying AI tools for your team or your customers:

• •Map the current workflow before choosing a tool. Understand where the friction already is.

• •Design the AI to remove steps, not add them. If the AI creates a new action the user did not have before, question whether it is worth it.

• •Build a 30-day support program. Budget for it the same way you budget for the license.

• •Design the handoff explicitly. AI to human should be seamless and context-preserving.

• •Measure experience, not just adoption. Ask people if their work is easier. If the answer is no, fix the deployment before scaling it.

The technology works. The question is whether you are deploying it in a way that actually makes things better for the people who use it.

Take the AI Readiness Scorecard at haagsman.ai/scorecard to see where your organization stands. Or read about why 80% of enterprise AI pilots fail for more on what separates the pilots that stick from the ones that quietly die.