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Methodology for AI Adoption

A repeatable process for measurable outcomes.

Assess Prioritise Model Implement Measure

We don't assume cloud. We don't push one solution. We benchmark cost against ROI, map your use case to the right option (cloud, self-hosted, or hybrid), and justify every recommendation. This methodology is how we do it.

1

Assess

Discovery: understand current state before recommending.

  • Discovery workshop: stakeholder interviews; map pain points, goals, and constraints
  • Current state inventory: existing tools (CRM, Office, accounting), integrations, data flows
  • Data sensitivity review: IP, GDPR, sovereignty, cyber security requirements
  • Risk appetite: tolerance for cloud vs self-hosted; regulatory needs
  • Use-case discovery: candidate use cases (support, sales, admin, etc.)
Output: Assessment report with current state, constraints, and use-case longlist.
2

Prioritise

Use-case scoring: impact vs effort vs risk.

  • Scoring framework: impact (revenue/time/quality), effort (complexity, integration), risk (data sensitivity, failure impact)
  • Score each use case: rank by weighted score
  • Quick wins vs strategic: separate low-effort/high-impact from longer-term plays
  • Shortlist for modelling: select top 3–5 use cases for Phase 3
Output: Prioritised use-case roadmap; shortlist for cost/ROI modelling.
3

Model

Cost (TCO), ROI, and risk for shortlisted use cases.

  • Volume and usage: estimate tokens/queries per month; data volumes; user count
  • Solution options: for each use case: cloud-only, self-hosted, hybrid, platform-native
  • Cost benchmark: token pricing, credit models, infrastructure; compare options
  • ROI model: time saved, revenue uplift, risk reduced; payback period
  • Benchmark cost vs ROI: only recommend solutions where cost justifies return
  • Risk assessment: data sovereignty, vendor lock-in, operational risk per option
Output: Cost/ROI model; justified recommendation per use case; option comparison.
4

Implement

Phased delivery with clear milestones.

  • Implementation plan: phases, milestones, dependencies, timeline
  • Pilot or MVP: start with one use case; validate before scaling
  • Integration setup: connect CRM, Office, accounting; configure workflows
  • Security and governance: access controls, audit trails, data handling
  • Handover and training: user guides; workshop or seminar
  • Go-live: deploy; monitor; iterate
Output: Live solution; documentation; trained users.
5

Measure

Defined KPIs, reporting, and iteration.

  • Define KPIs: time saved, cost per transaction, conversion uplift, etc.
  • Baseline: capture pre-implementation metrics where possible
  • Reporting cadence: weekly/monthly; dashboard or report
  • Review and iterate: compare actual vs modelled; adjust or expand
  • Case study / proof: document outcomes for future sales and narrative
Output: KPI report; proof of ROI; case study for marketing.

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