FIVE PHASES. ONE PROTOCOL.
SERVICES is what you buy. PLAYBOOK is how it lands on the calendar: the free scan converts into a paid audit, the audit into your first agent — one path, five phases. Each phase is backed by deployment evidence. Findings: Stanford Digital Economy Lab, 2026.
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01
WE DO
- Paid, fixed-scope teardown — your free AI Readiness Scan converts here
- Map workflows, data access and org readiness
- Score every integration point by ROI and risk
YOU GET
- Process map with AI-potential scores
- ROI model + prioritized roadmap
- Time & budget estimate per deployment
DATA// In 77% of studied deployments the hardest problems were process, data and change management — not the model — and failed first attempts had usually automated a broken workflow. Auditing before building is the cheapest risk control there is. — Stanford Digital Economy Lab, 2026
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02
WE DO
- Deploy your first agent straight onto real data and real users — no interim PoC, no throwaway demos
- Feasibility of these use cases is market-proven; your economics were checked by the audit
- KPIs wired in from day one
YOU GET
- Working agent in production
- Measured ROI vs audit projections
- Go / no-go for the next workflow
DATA// Every successful deployment in the study was iterative and started small — but value only registered once systems ran inside live workflows with real users. Organizations that kept manufacturing demo-stage proofs-of-concept captured almost none of it. — Stanford Digital Economy Lab, 2026
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03
WE DO
- Build the operational shell: admin UI, dashboards, reporting
- Wire CRM, telephony and billing via APIs, RAG, MCP
- Set oversight — agent takes volume, team takes exceptions; legal / risk seated in governance
YOU GET
- Agent embedded in daily operations, not in a log file
- Production integrations across your stack
- Trained users + handover docs
DATA// Only ~6% of deployments started with AI-ready data — winners built access layers over scattered systems instead of waiting for clean, centralized stores. The most frequent blockers were staff functions, not end users; a seat in governance turns them into enablers. — Stanford Digital Economy Lab, 2026
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04
WE DO
- Replicate to adjacent departments on the shared foundation
- Or productize: multi-tenant, compliance-ready SaaS architecture
- Sponsor cadence, adoption wired to OKRs, champions per team
YOU GET
- Rollout across functions — or a tenant-ready platform
- Sponsor playbook + champions network
- Follow-up deployments in weeks, not quarters
DATA// Every organization-wide transformation in the study tied AI adoption to corporate OKRs and incentives; weekly executive sponsorship was the most common accelerator, and teams reusing an existing foundation shipped follow-ups several times faster. — Stanford Digital Economy Lab, 2026
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05
WE DO
- Monitor quality, cost per task and drift — monthly retainer
- Raise autonomy stepwise: approval → escalation → agentic
- Train your team; fractional AI lead when you need the muscle without the hire
YOU GET
- Ops dashboard with agreed KPIs
- Systems that keep working after handover
- Quarterly model / cost review
DATA// Escalation-based operation — AI handles the volume, humans review exceptions — delivered roughly double the productivity of approve-everything setups. The durable advantage was the process and platform around the model, never the model itself. — Stanford Digital Economy Lab, 2026
NOTE// a separate PoC phase exists only on the SaaS track: custom platforms run the extended timeline — DISCOVERY → POC → PILOT → SCALE — see SRV·04 // AI SOFTWARE DEVELOPMENT.