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PH1 core capbility
YEARS EXPERIENCE
6 to 8 years
TYPICAL CLIENT
CTO, Chief Transformation Officer, CAIO, VP Product
NECESSARY TIMELINE
2 to 3 months
BUDGET NECESSARY
Up to $100,000
Our POV
Most AI strategies are built around what to buy, what to deploy, and what the vendor said the technology can do. None of that determines whether the investment will produce returns. The variable that decides is adoption — whether the AI product you ship gets used inside customer organizations, whether the AI tools you deployed get used by your own teams, or whether the AI capability you are about to launch is the right one to bet on at all.
PH1 leads adoption-first AI strategies grounded in evidence. For organizations with AI products in market, we define the strategy that drives adoption inside customer organizations — the workflow integration, trust dynamics, and behavioural change that produce repeat use and account expansion, not just sales conversion. For organizations adopting AI internally, we define the strategy that gets weekly active use past the threshold and keeps it climbing. For organizations evaluating first AI capabilities, we define which audience, requirements, and KPIs to commit to before engineering investment goes in.
The difference between a vendor-led AI strategy and an adoption-first AI strategy is the difference between activity and outcome.
For mid-market organizations across all three profiles, the cost of a wrong AI strategy is structural — there is no enterprise innovation budget to absorb the loss, and no specialist team to course-correct after deployment. PH1's strategy reduces decision risk by grounding every recommendation in primary behavioural research, surfacing the audience and governance exposures the board needs to underwrite, and producing a strategy leadership can fund with evidence rather than conviction.
What We Do
Behavioural research with the people who must adopt. Before any roadmap, we conduct primary research with the operators, employees, or customers your AI is for. We observe their workflows, surface the friction that would block adoption, and identify which AI capabilities would meaningfully change how the work gets done.
Audience and requirements definition. We define who needs to adopt for value to materialize, what they need from the AI, and the trust, evidence, and incentive conditions that will get them there. This is the strategic foundation that determines whether the technical roadmap will actually pay off.
Service design and operating model. We map the workflows around the AI capability, define what AI owns and what humans own, and design the governance, escalation, and measurement layers that turn deployment into something operationally sustainable.
KPI architecture aligned to business value. We define the metrics the board needs to see — adoption thresholds, behavioural indicators, business-value proof points — and the measurement infrastructure that will produce them release over release.
AI investment confidence and capability prioritization. For organizations evaluating whether AI is the right intervention for a specific business problem — and for organizations validating their first AI capability before commitment — we run primary research, competitive benchmarking, and behavioural validation that determines whether AI is the right bet, which capability to prioritize first, and what success looks like in measurable terms. The output is decision-grade evidence that supports an investment commitment, not a hypothesis.
Decision risk and audience exposure analysis. For mid-market buyers, we document the confidence level behind every recommendation and surface the audience, regulatory, and governance risks that determine whether a strategic direction is safe to commit to. The strategy is defensible to the board because the risks have been named and the reversibility analysis is documented.
What We'll Deliver
Adoption-first AI strategy document with audience, requirements, service design, and KPIs the board recognizes
Behavioural research findings from primary research with target operators, employees, or customers
Service design and operating model defining what AI owns and what humans own
Sequenced 6–12 month roadmap with adoption milestones tied to business outcomes
KPI architecture with measurement scaffolding the team can implement immediately
AI investment confidence report with go/no-go recommendation, capability prioritization, and audience evidence — for organizations evaluating whether to leverage AI at all
Leadership alignment artifact: a decision-grade document the executive team can act on by Monday
When This is Essential
When an AI product is in market but adoption inside customer organizations is below the business case
When the team is unclear which capabilities to prioritize for the next product release to drive deeper customer adoption
When a board or executive team requires evidence-grade strategy for the AI product roadmap
When AI tools are deployed internally but weekly active use is stalled below 30% and previous rollouts have failed
Before committing engineering resources to a new AI capability and the team is split on direction
When a mid-market organization cannot afford a strategic misstep on AI direction and needs decision-grade evidence to defend the choice
Frequently Asked Questions
How is this different from traditional AI strategy consulting? Traditional AI strategy starts with the technology and works backward to use cases. PH1 starts with the people who must adopt — customers using your AI product, employees using your AI tools, or future users of the AI you are about to build — and works forward to the right technology choices. The difference is often the difference between a strategy that ships and a strategy that stalls.
We have an AI product in market but customer adoption is below the business case. Where do we start? We start with primary research inside the customer organizations using your product. Where is adoption breaking? Which workflows is the AI not fitting into? What is the competitive workflow alternative your customers are quietly using? The strategy is built around what the research surfaces, not around what the team assumes.
We have deployed AI tools internally but adoption is stalled. Can you help reset the strategy? Yes. Most internal adoption stalls because the strategy was vendor-led and skipped the behavioural layer. PH1 redefines the strategy around what your people actually do, what would make them adopt, and what would stop them — then sequences the moves to move weekly active use past 30% and keep it climbing.
We have not shipped any AI yet — is this strategy work right for us? Yes. The pre-launch version of this engagement defines which AI capability to bet on, who your AI is for, what they need, and how you will measure success — grounded in primary research with the future users before engineering invests. Most of the cost of a failed first AI product is created at the strategy stage, not the build stage.
What if our team disagrees with the recommendations? The recommendations are grounded in primary research. Disagreement usually surfaces during the engagement, not after, because findings are shared as they emerge. The final strategy is the team's strategy, not PH1's.
We are mid-market and concerned about making the wrong call. How does this reduce decision risk? Every recommendation comes with explicit confidence levels, the behavioural evidence supporting it, the audience and governance risks involved, and a reversibility analysis — what would have to be true for the recommendation to be wrong, and what the team would do about it. Mid-market buyers need a strategy they can defend to a board, not a bet they have to hope works.
Combine With These Services
AI Trust & Adoption Benchmarking — Run benchmarking first to establish a baseline, then build the strategy around the gaps the benchmark surfaces
Service Design of AI-Powered Workflows — After the strategy is set, the service design engagement operationalizes it inside the workflows
AI Adoption Enablement Program — Run in parallel during rollout to make sure the strategy translates into behaviour change at the team level
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