Generative AI Consulting: A 90-Day Path From Use Case to Production

Ninety days is a discipline, not a promise

Most GenAI programs don’t fail from lack of ambition. They fail from too much of it. Five pilots start at once, none has a baseline, and six months later the steering committee can’t say whether any of them worked.

The industry data backs this up. Gartner reports that by the end of 2025 at least half of generative AI projects were abandoned after proof of concept, due to poor data quality, inadequate risk controls, escalating costs or unclear business value. And McKinsey finds that while AI use is nearly universal, meaningful enterprise-wide bottom-line impact remains rare.

At Perceptive Analytics, we use a 90-day window as a forcing function. It makes everyone choose, measure, and ship.

The Perceptive POV: Prompt quality isn’t what separates a GenAI demo from a GenAI product. Context engineering is: how you structure, version, and govern the knowledge the model works from. Get that right and reliability follows. Get it wrong and every edge case becomes another prompt patch.

Days 1 to 15: Choose one use case and set a baseline

Rank candidates by value, data availability, and cost of error. The strongest first use cases tend to be internal and knowledge-heavy: contract review, policy Q&A, research assistants, and ticket summarization.

Deliverable: one use case, one KPI, one measured baseline.

Days 16 to 30: Data and architecture

The platform choice usually follows your existing cloud:

• Azure OpenAI for Microsoft 365 and Fabric environments
• Amazon Bedrock for AWS shops that want a choice of model providers
• Google Vertex AI for Google Cloud and BigQuery users

Most knowledge use cases use retrieval-augmented generation (RAG). This phase covers document ingestion, chunking, embeddings, a vector store, and permissions that mirror your existing access controls. Perceptive’s data engineering background matters most here: the retrieval layer is a data pipeline, and it needs to be built like one.

Deliverable: architecture, data pipeline, security design.

Days 31 to 60: Build and evaluate honestly

Build the smallest version real users can test. Then evaluate it the way production will:

• A test set of real questions with verified answers
• Scores for accuracy, groundedness, and appropriate refusals
• Latency and cost per query
• Feedback from actual users, not the project team

Deliverable: working pilot, evaluation report, go or no-go decision.

Days 61 to 75: Guardrails and hardening

Add protections against prompt injection and off-topic use, PII handling, logging, human review for high-risk outputs, and monitoring for quality and cost. Align controls to the NIST AI RMF Generative AI Profile so risk teams can sign off quickly.

Deliverable: production-ready system with security approval.

Days 76 to 90: Launch and measure

Roll out to a defined group, train them, and measure against the day-one baseline. Report the KPI, not the demo.

Deliverable: live system, adoption metrics, first ROI read.

What this looks like in the real world

Case study: Perceptive Analytics built a document intelligence system for a financial services client that automated contract review and cut manual processing time by 75%. It followed this same pattern: one narrow use case, a measured baseline, rigorous evaluation, then production. [CASE STUDY LINK: financial services document intelligence]

When we tell clients 90 days won’t work

We’d rather say it upfront. Ninety days isn’t realistic when data is scattered with no access path, when regulatory review is heavy, or when the “use case” is really five use cases. In those situations, Perceptive Analytics scopes a data foundation phase first. It’s slower at the start and much faster overall.

Executive takeaway: Pick one use case. Measure it before you build. Evaluate it like production. Then scale what works.

Map the 90 days to your use case. Get the Perceptive Analytics 90-Day GenAI Roadmap, a working template with stage gates, deliverables, and evaluation criteria. See our generative AI consulting services.