01
Discover
Interview of leadership and operational teams, audit data and platform readiness, review existing AI activities including shadow tooling.
Output: a current-state picture nobody in the organization has in one place.
AI Strategy & Roadmap
Know what's worth funding before you build it.
Find where AI creates value in your business, prove it, and build the plan to get there.
What is AI Strategy and Roadmap?
An AI strategy consulting and roadmap answers three questions: where AI creates real value, what it's worth, and what to build first. It starts with the readiness of your data, your platforms, and your teams. Then scores every AI use case prioritization, feasibility and data availability.
What you get is a sequenced plan, an operating model, and the governance to keep AI accountable once it's live.
What the Numbers Say
95%
Enterprise generative AI pilots that produced no measurable P&L impact.
MIT Project NANDA, The GenAI Divide: State of AI in Business, 2025
39%
Organizations reporting any enterprise-level EBIT impact from AI. Most put it below 5%.
McKinsey, The State of AI, November 2025
21%
Gen AI adopters who have fundamentally redesigned any workflow - the change most strongly correlated with EBIT impact.
McKinsey, The State of AI, 2025
Signs You Need an AI Strategy
- Pilots That Never Reach Production
- Tools Purchased Independently Across Teams
- Recognized Potential Without a Defined Application
- A Use Case Without an Approved Business Case
- AI Adoption Moving Faster Than Governance
What AI Strategy & Roadmap Covers
An honest read on where you are, including data quality, platform readiness, engineering capability, governance maturity and organizational appetite. Scored against a defined maturity model, so progress can be measured later.
Identify candidate use cases across operations, product and revenue, then rank them by business value, technical feasibility and data availability. Includes the business case model for each shortlisted case.
What gets built first, what it depends on, and what has to be true before each phase begins. Every phase carries its own success measure.
Who owns AI decisions, how initiatives get funded, where AI capability sits in the organization, and how central teams and business units divide responsibility.
Getting people to use what gets built. Role-level enablement, workflow redesign, and the internal communication that decides whether adoption happens or stalls.
The policy layer. Model approval, risk classification, data handling, human oversight, and the regulatory position for your sector and jurisdictions.
How We Approach AI Roadmap
This service runs the first three stages of The Pivot. Build, Launch and Evolve happen through AI Engineering and AI Operations once the strategy is set.
The Documents You Walk Away With
You finish with a plan you could hand to any competent team and have them execute it.
AI Readiness Scorecard- Prioritized Use Case Portfolio
- Business Case Model Per Shortlisted Use Case
- Sequenced AI Roadmap
- Target Operating Model
- Responsible AI Policy Framework
Our Technology Landscape
Model platforms
- OpenAI
- Anthropic
- Azure OpenAI
- AWS Bedrock
Data platforms
- Snowflake
- Databricks
- BigQuery
- PostgreSQL
- vector databases
Orchestration
- LangChain
- LlamaIndex
- custom agent frameworks
Standards We Work To
- EU AI Act
- NIST AI Risk Management Framework
- ISO/IEC 42001
- sector-specific requirements
Common Questions
Typically 4 to 8 weeks, depending on organization size and how many business units are in scope. The readiness scorecard usually lands by week three, so you get something usable early.
Often more so. Organizations already running AI usually have scattered ownership, unclear governance, and no view of total spending. The work then focuses on the operating model rather than finding opportunities.
That is a useful answer. Readiness gaps are usually in data or platform maturity, not AI capability. The roadmap sequences that foundational work first, which costs less than discovering it mid-build.
Each candidate is scored on business value, technical feasibility, and data availability. High-value cases with weak data readiness get sequenced later rather than dropped, with the enabling data work named.
Start with the Assessment
Four weeks. One document. An honest read on whether your organization can actually deliver what it's planning.
Book an Assessment