When building AI, many companies habitually buy a platform product first and only later look for business scenarios to land — which easily produces systems detached from the business and investment that never yields business returns. The lighthouse-scenario 0-to-1 process provides a standardized landing path: it orderly completes current-state research, scenario screening, scenario implementation, effect evaluation, and capability reuse — making AI projects landable, verifiable, and replicable.
I. Map the Company, Complete Up-Front Diagnosis & Screening
This stage fully maps the company, sorts out business pain points, and screens high-value, low-risk first lighthouse scenarios from many candidate businesses — laying a solid landing foundation.
Run layered interviews — the executive round, the front-line role round, and the IT round — to collect the demands and current pain points of different roles.
Produce base maps across the six dimensions of organization, data, systems, roles, processes, and permissions, fully clarifying internal business boundaries and resource status as the basis for later construction.
Sort out business pain-point blockers under a unified numbering rule, mapping each blocker to an owner and priority to precisely locate business improvement opportunities.
Collect multiple potential landing scenarios, scoring them on business value and landing feasibility to form a candidate scenario pool.
Converge on the best from the pool, locking in high-value, low-risk scenarios as the first lighthouse projects.
Standardized deliverables for this stage include the six enterprise base maps, the numbered blocker list, and a lighthouse-scenario SOW document that clearly defines the scenario description, acceptance criteria, and implementation timeline.
II. Break Through One — End-to-End Landing of the Lighthouse Scenario
Focus on the selected lighthouse scenario to complete the engineering landing, run and validate on real business data, and sediment quantifiable run baselines.
Gather all assets the lighthouse scenario needs to run — authoritative business materials, long-term role memory, and business-system views — building a scenario-dedicated knowledge foundation.
Connect business systems through APIs, data views, and file sync to open data sources, with permission control refined down to the data-field level.
Run on real enterprise business data rather than mock samples, with human-review backstop on every key execution action in the scenario to avoid business risk.
Fully retain run evidence across the flow, build a standardized run ledger recording call counts, hit rates, and cost details, and build an evaluation set to form scenario quality and cost baselines.
Standardized deliverables for this stage include scenario context resources, the system-integration plan, the run ledger, and the evaluation dataset.
III. Replicate into a System — Reuse Capabilities and Keep Expanding
Reuse the context, skill components, and system connectors sedimented by the lighthouse scenario as assets, empower the internal team, and support autonomously adding more AI business scenarios for scaled expansion.
IV. Reference: Typical Lighthouse Landing Scenarios
For overseas customers, automatically prepare meeting materials and generate customer-follow-up summaries
Respond fast to pre-sales questions based on product materials, pricing rules, and project cases
Auto-identify red-line contract clauses and verify key commercial terms to avoid contract risk
Aggregate business data across systems and use AI to analyze and output action recommendations
Codify mature working methods, turning veterans’ tacit experience into deliverable AI capabilities
V. Project Handover & Delivery Criteria
The core criteria for formal delivery: the client’s internal AI team can update autonomously, independently add new business scenarios, and read the complete run ledger. Until those are met, delivery is not complete.