AI Strategy Roadmap Development
Turn AI uncertainty into a clear, prioritized, and budgeted plan your team can execute in the next 90 days. This engagement aligns your business goals with high‑impact AI opportunities, defines the right technical approach, and provides a step‑by‑step roadmap to deliver measurable outcomes, without vendor hype or analysis paralysis.
Who this is for
01
Founders, product leaders, and CTOs who need a pragmatic plan before spending on tools or headcount
02
Operations and support leaders looking to automate workflows and reduce cycle times
03
Marketing and growth teams exploring personalization, content ops, and experimentation at scale
04
Data leaders who want governance, privacy, and model risk handled from day one
Typical Timeline
Week 1
Discovery and objectives alignment.
Week 2
Opportunity mapping and quick feasibility checks.
Week 3
Solution shaping, architecture, and data plan.
Week 4
Roadmap, business case, and executive readout.
Days 1–10: Alignment
- Confirm goals, metrics, and constraints.
- Prioritize use cases; select a pilot
- Set up environments, access, and security
Days 11–35: Data readiness and prototype
- Data extraction and cleaning; establish retrieval pipelines
- Build baseline prototype; define evaluation harness and test set
- Set acceptance thresholds (latency, accuracy, cost per request)
Days 36–65: Pilot build and integration
- Integrate with internal systems (CRM, ticketing, knowledge base, data warehouse)
- Add observability and feedback loops; introduce human‑in‑the‑loop where needed
- Run controlled trial with real users; capture metrics and qualitative feedback
Days 66–90: Launch and scale plan
- Address gaps; harden security and guardrails
- Train team; finalize runbooks and support model
- Executive review of results and decision on scale‑up
Executive brief
A 2 to 3 page summary of recommended AI initiatives, business impact, and next steps
Prioritized use-cases
Prioritized use‑case portfolio mapped to effort, impact, and risk, with ROI hypotheses
Buy/build/partner recommendations
Buy/build/partner recommendations and vendor shortlist (model providers, vector DBs, orchestration, MLOps)
Architecture sketches
Target architecture sketch showing how AI services integrate with your data, apps, and security controls
Data readiness assessment
Data readiness assessment and remediation plan (sources, quality, labeling, PII/PHI considerations)
Action plan
30/60/90‑day action plan with owners, milestones, and acceptance criteria
KPI Framework
KPI and measurement framework to prove value (operational, revenue, and quality metrics)
Change management plan
Change management plan covering roles, training, and adoption
Budget and resourcing plan
Budget and resourcing plan (internal capacity vs. partner support)
Discovery
- Stakeholder interviews (leadership, product, ops, data/security)
- Current systems and data landscape review
- Business objectives and constraints (budget, risk appetite, compliance)
Opportunity mapping
- Generate and score candidate use cases with your team
- Identify “now / next / later” initiatives with rough‑order‑of‑magnitude sizing
- Quick feasibility checks: data availability, latency, accuracy, and privacy needs
Solution shaping
- Choose approach per use case: retrieval‑augmented generation (RAG), fine‑tuning, agents/workflows, classic ML, or rules
- Draft target architecture: model endpoints, vector store, orchestration, observability, and human review steps
- Define data plan: sources, cleaning, enrichment, and PII handling
Plan and de‑risk
- 30/60/90‑day roadmap with milestones, owners, and acceptance criteria
- KPI tree and measurement plan (what we track, how we instrument, how we report)
- Risk and compliance guardrails (access control, prompt injection, data leakage, bias/accuracy checks)
- Budget, resource plan, and vendor shortlist
Executive readout and handover
- Clear recommendations, trade‑offs, and next steps
- All templates and artifacts delivered for execution
What makes this different
- Business‑first: we start with outcomes and work backward to the minimum technical approach
- Vendor‑neutral: recommendations are based on your context, not reseller incentives
- Governed by design: privacy, security, and model risk are built in, not bolted on
- Measurable: every initiative has KPIs, instrumentation, and a decision cadence
- Practical: you get the templates, playbooks, and runbooks to execute without hand‑holding
Common use cases we evaluate
- Customer support: assisted responses, auto‑summaries, intent/routing, and knowledge retrieval
- Sales and marketing: personalization, enrichment, content workflows, and campaign insights
- Operations: document processing, approvals, and exception handling
- Product: intelligent search, recommendations, and in‑app copilots
- Analytics: natural‑language querying, report generation, and anomaly detection
Technology approach (stack‑agnostic)
- Foundation models: commercial and open‑source options depending on performance, privacy, and cost
- Retrieval: vector databases, hybrid search, chunking strategies, and metadata governance
- Orchestration and agents: workflow tools, function calling, tool use, and safety rails
- LLMOps: evaluation harness, observability, cost controls, and CI/CD for prompts and policies
- Integration: APIs, event buses, and connectors to your core systems
- Cost management: usage caps, caching, and usage dashboards
How big do we need to be to benefit from this?
If you have repeatable processes, customers to serve, or content/data to leverage, you’re big enough. The scope flexes from startup to enterprise.
Will we need to buy new tools?
Not necessarily. We start with what you have and only recommend purchases where the ROI is clear.
How do you handle our data?
Your data stays within your chosen boundaries. We prioritize privacy, least‑privilege access, and use managed services or private deployments as needed.
Do you train our team?
Yes. Handover includes runbooks and live knowledge transfer. Optional hands‑on workshops are available.
Can you help with implementation?
Yes. I can support your team directly or coordinate with trusted partners.
What about legal and compliance?
We align with your existing controls and bring patterns that reduce risk. This is guidance, not legal advice, and your compliance team remains the authority.
How soon will we see results?
Many teams see early indicators during the pilot phase (weeks 5–8). The roadmap sets conservative, testable checkpoints.
Ready to turn AI curiosity into a concrete plan your team can execute?
Use the contact page to book a short intro call and we’ll scope the right engagement for your goals.