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PH1 core capbility
YEARS EXPERIENCE
10+ years
TYPICAL CLIENT
VP CX, CMO, VP Digital Transformation, Director of Innovation
NECESSARY TIMELINE
2 to 3 months
BUDGET NECESSARY
Up to $50,000
Our POV
Customer attitudes toward AI are not uniform, and they are not what most institutions assume. Some segments welcome AI-assisted interactions — they value the speed, the availability, and the lack of friction. Others are actively resistant, based on past negative experiences, privacy concerns, or the belief that their situation is too complex or too personal for an AI to handle with the care it requires.
The same variation exists inside your organization. Some frontline teams see AI as a tool that will make their work better. Others see it as a threat to their expertise, their role, or the quality of service they take pride in delivering. The ones who feel threatened do not reject AI loudly. They comply at the minimum level required — never using it deeply enough for it to deliver the value it was designed to deliver.
Deploying AI without understanding these perceptions means building adoption metrics on a foundation that will not hold.
What We Do
We conduct behavioral research with your customers and your frontline teams to surface how they actually perceive AI — not what they say in a survey, but what their behavior reveals when AI is part of the experience. We identify which segments are ready, which are resistant, what triggers trust, what triggers abandonment, and where the cultural resistance inside your organization will undermine adoption before it can compound.
The research produces a deployment readiness map that tells you where to launch AI first, where to sequence differently, and where to invest in change management before any AI goes live.
What We'll Deliver
Customer AI perception report segmented by audience type and interaction context
Frontline team AI readiness assessment identifying adoption enablers and resistance patterns
Deployment readiness map: which interactions and segments are ready now, which need preparation
Change management and communication framework for introducing AI to customers and staff
Segment-specific trust triggers and resistance drivers for use in product and UX decisions
Executive summary for leadership alignment before deployment begins
When This is Essential
Before launching or scaling any AI-powered customer-facing experience
When a previous AI deployment produced lower adoption or satisfaction than expected
When the organization serves diverse customer segments with significantly different technology attitudes
When frontline staff resistance is already visible during AI pilot programs
When regulatory, compliance, or reputational sensitivity makes customer trust a non-negotiable prerequisite for deployment
Frequently Asked Questions
How do you measure AI perception without just asking people what they think? Through behavioral observation in realistic interaction scenarios. We put customers in structured sessions where AI is part of the experience and observe what they do — where they engage, where they hesitate, where they abandon, and where they seek human intervention. Self-reported attitudes and observed behavior often diverge significantly. We report on both.
What do you do with frontline staff research? We identify the patterns that determine whether AI adoption inside the organization will be genuine or performative. In most institutions there are two or three specific concerns that drive the majority of staff resistance — and they are almost never the ones leadership assumes. Surfacing them early allows the change management strategy to address the real blockers, not the assumed ones.
How do the findings connect to our deployment decisions? Directly. The readiness map tells you which customer segments and which interaction types are safe to launch AI in now, which need a different rollout approach, and which should wait. It also tells you where to invest in change management before deployment — because adoption that fails publicly is significantly harder to recover from than adoption that is delayed until the organization is ready.
Can this research be done alongside an existing AI pilot? Yes, and it is often most valuable that way. Running perception research in parallel with a pilot allows you to explain early adoption patterns, identify the specific moments that are driving resistance, and adjust the rollout strategy before it scales.
How is this different from standard user research? Standard user research evaluates whether customers can use a product. AI perception research evaluates whether customers will trust it — over time, at scale, in the moments that matter most to your business outcomes. Trust is a more complex variable than usability, and it requires a different research methodology.
Combine With These Services
AI Strategy to Elevate Customer Experience — Use perception findings to inform where AI should and should not be deployed
Benchmark Customer Experience Performance — Baseline the current experience before AI enters it
CX Strategy to Future-Proof Your Organization — Connect perception findings to the broader organizational CX direction
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Submissions