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PH1 core capbility
YEARS EXPERIENCE
10+ years
TYPICAL CLIENT
Founder/CEO, VP Product, VP Customer Success, VP Design
NECESSARY TIMELINE
Less than 2 months
BUDGET NECESSARY
Up to $50,000
Our POV
Standard analytics report seats activated, prompts sent, and tokens consumed. None of it tells you whether trust is collapsing, whether adoption is stalling at a specific segment, or whether the AI is winning or losing against the competitive alternatives your users are quietly testing. The behavioural layer where AI value is actually decided is invisible to the dashboards most teams rely on.
PH1 benchmarks the behavioural layer. For organizations with AI products in market, we benchmark adoption inside customer organizations against the competitive alternatives your buyers are weighing — where trust is being earned, where it is being lost, and which features competitors are winning on inside customer workflows. For organizations adopting AI internally, we benchmark weekly active use, task success, and trust by team, role, and seniority — surfacing where the gaps are draining value. For organizations evaluating first AI capabilities, we benchmark the category to establish a baseline before launch so future progress is measurable from day one.
The output is a decision-grade benchmark that tells you what is working, what is not, and where the highest-leverage interventions sit.
Mid-market organizations cannot absorb a public trust failure the way enterprises sometimes can. PH1's benchmarking produces the evidence governance teams need to defend AI decisions, surfaces audience and trust risks before they materialize as churn, complaints, or regulatory exposure, and structures the output for board and audit defensibility — not just for the product or operations team.
What We Do
Internal adoption benchmarking. We measure weekly active use, task success rates, and behavioural friction by role, team, and seniority — surfacing the gaps your analytics cannot see and the segments where adoption is stalling.
External product adoption benchmarking. For organizations building AI products, we measure customer adoption against the alternatives your users are actually weighing — the trust signals, the use cases, the price points, and the workflow integration competitors are winning on.
Trust dynamics tracking. We benchmark trust release over release: where users are pulling back, where confidence is growing, where silent failures are eroding willingness to depend on the AI for a real task.
AI Product Calibration scoring. We score the AI across Power, Speed, Impact, and Joy — the four dimensions that reveal whether the AI is operationally working in production.
Pre-launch trust benchmarking for new AI products. For organizations launching a new AI product or capability, we benchmark target-user trust signals, adoption willingness, and competitive trust positioning before ship — surfacing what the product must deliver to earn adoption, where trust thresholds sit for the market, and which competitive alternatives the product will be measured against on day one.
AI viability and investment confidence research. For organizations evaluating whether to leverage AI at all for a specific business problem, we benchmark the category, the competitive alternatives, and the audience trust conditions — producing research evidence that tells leadership whether AI is the right bet, where the value would actually come from, and what success would look like in measurable terms before any commitment is made.
Governance-grade evidence packaging. For mid-market and regulated buyers, we structure the benchmark output for board and audit defensibility — explicit methodology, sample sizes, confidence levels, and traceable evidence trails — so risk and audit teams can use the report without having to translate it.
What We'll Deliver
Decision-grade benchmark report with task success, trust, and adoption metrics by segment
Internal adoption diagnostic: which teams, roles, and workflows are driving or blocking value
External competitive adoption analysis: how your AI compares to the alternatives users are considering
AI Product Calibration scorecard across Power, Speed, Impact, Joy
Pre-launch trust benchmark for new AI products: trust thresholds, adoption-willingness signals, and competitive positioning evidence the product must clear to earn adoption
AI viability research report with category benchmarking, competitive context, and investment-confidence recommendation — for organizations evaluating whether to leverage AI at all
Behavioural friction map: the specific moments where users disengage, distrust, or work around the AI
Prioritized intervention list: the 3–5 highest-leverage moves to recover or accelerate value
When This is Essential
When an AI product is in market but the team cannot explain why adoption metrics are not moving — or why a competitor is winning
When customer-segment adoption of an AI product is diverging from the business case and product leadership needs precise diagnosis
When defining a baseline for the next AI product release so future progress is measurable
When an internal AI dashboard reports activity but no one can explain why the business outcome has not moved
Before launching a first AI capability, when the team wants to benchmark the category and define what success will look like in measurable terms
When a mid-market governance, audit, or risk team requires defensible evidence on AI performance, trust dynamics, or audience exposure
Frequently Asked Questions
How is this different from standard product analytics? Standard analytics measure activity — clicks, sessions, prompts. PH1's benchmarking measures behaviour — whether users completed real tasks, whether they trusted the output, whether they came back, and whether they would pay for it. The two often disagree.
Can you benchmark our AI product against specific competitors? Yes. We run benchmarking studies that test users against multiple AI products simultaneously and surface where the competitive advantage is, where it is not, and what your roadmap should respond to.
We are using vendor AI internally but adoption is stalling. Can you benchmark that? Yes. Internal adoption benchmarking measures weekly active use, task success, and trust by team and role — surfacing the friction your analytics cannot see and the highest-leverage interventions to recover adoption.
We are evaluating an AI capability before launch. Is benchmarking valuable now? Yes. Pre-launch benchmarking establishes the baseline you will compare to once the AI is live. Without it, there is no defensible answer to "did this work?" six months after launch.
Do you need access to our usage data? We work with both first-party usage data and primary behavioural research. Many engagements run on primary research alone because internal data is incomplete or trust-eroded.
Will the benchmark satisfy our board or audit team? Yes. The benchmark output is structured for governance use — explicit methodology, sample sizes, confidence levels, and traceable evidence. Many mid-market clients use the benchmark report directly in board materials, audit responses, and regulatory filings.
Combine With These Services
AI Adoption Strategy — Use the benchmark as the evidence foundation for an adoption-first strategy
AI Product Discovery & Validation Research — Extend the benchmark with deeper validation of specific use cases
Golden Data Sets & Failure Modes — Pair the behavioural benchmark with engineering-grade evaluation infrastructure
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