Most companies are now using AI to understand their customers, but how they’re using it, and how much they trust it, is more specific than the adoption headlines suggest.
At Makeable Consulting, we surveyed 130 managers and senior professionals in June 2026 to find out how AI is actually being used to understand customers, where it fits into the toolkit, and where it doesn’t.
ADOPTION IS ALREADY HIGH
When looking at the data, we found that 8 out of 10 respondents (85%) said their company uses AI in some way to understand customers. The rest (15%) mentioned they don’t use it for this at all.
Among those professionals using AI for customer understanding, these are the most common applications:
- 45% Personalizing the customer experience
- 45% Analyzing customer feedback
- 41% Identifying patterns in customer behavior
- 37% Automating customer service interactions
- 29% Generating insights from surveys or reviews
- 23% Predicting churn or retention risk
Personalizing the customer experience and feedback analysis often build on data and workflows that are already in place. Churn prediction typically requires more setup: clean historical data, defined retention metrics, and a model trained on what “at risk” actually looks like for a given business.
THE MANAGERS WHO AREN’T USING IT
Among the 15% of professionals who said they aren’t using AI for customer understanding aren’t all in the same boat, their reasons vary:
- 32% Lack of internal expertise
- 32% Privacy concerns
- 16% Prefer direct research methods
- 16% Not necessary for their business right now
- 5% Cost of tools
That split matters for anyone selling or building AI tools for customer insights: The holdouts aren’t uniformly skeptical of AI itself. Some are blocked by capability gaps (expertise, cost), others by a deliberate preference for human-led research, and others simply haven’t found a need just yet.
TRUST IN AI HASN’T FULLY CAUGHT UP
In addition, for those professionals actively using AI for customer insights, trust in this new technology is mixed. We asked how much they trust AI-generated insights compared to insights from direct customer research (Direct interviews, surveys, and focus groups).
44% of them said they trust AI and direct research equally, 38% said they trust direct research more, and only 18% said they trust AI more.
One respondent put it this way:
There is some nuances and special cases that AI just won’t be able to directly be accountable for. AI isn’t perfect and it tends to loop, so making it customer facing would be detrimental.” (33 year old female)
In other words, even in a population that’s actively using AI for customer insights, direct research hasn’t been displaced. The data suggests AI and direct research are being treated as complementary rather than as substitutes.
WHAT THIS MEANS FOR CUSTOMER INSIGHTS TEAMS
Taken together, these findings describe a tool that’s been adopted widely but unevenly: leaned on heavily for personalization and feedback analysis, used far less for the more complex, higher-stakes applications like churn prediction, and still trusted only as much as, not more than, direct research.
For companies building or refining their customer insights approach, the useful question isn’t whether to adopt AI, most already have. It’s where AI is currently pulling its weight, where it isn’t yet, and where direct research still needs to lead.
This article is based on a survey of 130 managers and senior professionals at Canadian companies, conducted by Makeable Consulting in June 2026. Review here the full report.