Many enterprise AI projects fall into disordered pilots, hard-to-measure results, and an inability to scale. This three-stage landing service system provides a standardized implementation path, divided into three cycles — diagnostic planning, lighthouse validation, and system replication — with clearly defined actions, deliverables, and quantified acceptance criteria at each stage. It steadily moves enterprise AI from pilot to long-term operation.
I. Stage A: Map the Company First — Diagnostic & Planning (≈2–4 weeks)
The core goal of this stage is to fully map the company’s current state, pinpoint business blockers, lock in the first batch of landing scenarios, and produce an executable medium-to-long-term roadmap.
Key actions include interviewing three kinds of subjects — company leaders, business-role employees, and the IT team; producing six enterprise business base maps; numbering and archiving business blockers uniformly; and screening a pool of 30+ potential scenarios, then converging on the first lighthouse scenarios.
The standardized deliverables are the six enterprise panorama base maps, a numbered business-blocker list, formal SOW plans for 1–2 lighthouse scenarios, and a complete 90-day project roadmap.
Clear, achievable acceptance criteria are set: at least 15 valid interviews; 20+ identified business blockers; lighthouse-scenario plans confirmed and approved by the business owner; and the overall roadmap signed off by the company’s highest decision-maker.
II. Stage B: Break Through One — Minimal Viable Context + Lighthouse Scenario (≈4–8 weeks)
Building on the Stage A plan, focus on the selected lighthouse scenario and complete an end-to-end breakthrough — validating that the whole AI approach can create stable value in a real business environment and establishing a baseline standard.
Key actions include connecting authoritative knowledge sources and business systems; completing knowledge governance and storage plus fine-grained permission mapping; landing the lighthouse scenario for real business execution; configuring human review for key actions; and opening the write-back flow from AI results into business systems.
Stage deliverables include a minimal viable enterprise knowledge hub, 1–2 AI employees ready for production launch, a complete run ledger and effect-evaluation set, and project quality and cost baselines.
Acceptance criteria require: connected real business data running stably and continuously; 100% human review of all key AI execution actions; complete cost ledgers and a problem-case library with fault localization; and business departments actually receiving measurable value from AI.
III. Stage C: Replicate into a System — Expand Enterprise Expert Capability + Continuous Closed-Loop Operation
On the foundation of a validated lighthouse scenario, complete capability reuse and team enablement, build an AI operating system the company can sustain and iterate on independently, and achieve scaled replication.
Key actions include reusing already-landed context, skills, and system connectors; building normalized operating mechanisms for effect evaluation, cost control, and exception handling; cultivating a dedicated in-house AI operations team; and establishing periodic new-scenario admission review norms.
Deliverables are multi-role AI employee assets launched into production, a standardized skill library and connector repository, a full management system covering scenario admission/permission gates/effect evaluation, and supporting internal training materials and standard SOP documents.
Acceptance criteria: the in-house team can independently add new landing scenarios; new-scenario replication cost and delivery time drop significantly; AI task accuracy and user satisfaction keep improving; and ultimately the in-house AI team can independently run continuous operation and iteration.
IV. Core Philosophy & Highlights of the Service System
The whole landing approach has three core characteristics. It deeply adapts to the DingTalk, Feishu, and WeCom ecosystems without forcing replacement of your existing IT foundation — existing CRM, ERP, and MES systems connect and reuse quickly. It insists on doing real front-line landing engineering, not just PPTs: it fully delivers executable assets like base maps, blocker lists, scenario plans, and run ledgers. And all context, skill components, and system connectors ultimately belong to the company — no technical black boxes, keeping your digital assets self-owned and controllable.