AI Readiness Assessment
A structured assessment of AI maturity, technology stack, data, workflows, applications, integrations, infrastructure, security and governance — delivered as a readiness score with a prioritized roadmap.
04 — AI Modernization
Make your technology AI-ready.
You cannot scale AI on top of inaccessible data, fragmented processes and systems that cannot be integrated.
The problem
The pilot worked. The rollout did not.
A proof of concept runs on an exported spreadsheet. Production needs live access, permissions, integration, monitoring and an owner. That distance is where most enterprise AI programmes quietly stop.
- 01Business data is scattered across systems with no retrieval layer.
- 02Core systems have no usable API surface to integrate against.
- 03Nobody has ranked the opportunities, so effort follows enthusiasm.
- 04Infrastructure and permissions were never designed for AI workloads.
What Pentagon X builds
The approach.
Most stalled AI programmes are not model problems. They are data-access problems, integration problems and process problems wearing an AI costume. We assess the environment you actually have, quantify what is blocking you, and produce a modernization roadmap ranked by value rather than by novelty.
01Assess
- Stack & integrations
- Data & retrieval readiness
- Governance & security
02Map
- Candidate use cases
- Value, risk, feasibility
- Dependencies
03Architect
- Data & retrieval design
- Integration layer
- Model strategy
04Sequence
- Ranked roadmap
- Owners and effort
- Reusable foundations
The output is an artefact you own — usable whether or not we build what it recommends.
Capabilities
What sits inside this discipline.
Engagements draw on a subset of these, scoped to the outcome you are buying.
AI Opportunity Mapping
Identify and rank candidate use cases by business value, complexity, feasibility, risk and expected ROI, so the first project is the one most likely to succeed.
Data Readiness for AI
Assess and improve data accessibility, quality, structure, metadata, permissions, information architecture and retrieval readiness.
Legacy System AI Enablement
Connect AI to systems that are not going to be replaced, through the integration route that carries the least operational risk.
API & Integration Modernization
Build the integration layer AI systems need to read and write to enterprise systems safely, with authentication, rate limiting and auditability.
Workflow Redesign
Move processes from human → software → human toward AI → tools → systems → human approval where the risk requires it.
AI Architecture
Model, data, retrieval, agent, integration, security and observability architecture designed together rather than accumulated project by project.
Model Strategy
Which models, where, and why — API versus private deployment, capability versus cost, and the routing policy between them.
AI Infrastructure Readiness
Assess cloud, network and application infrastructure against the demands of AI workloads, including cost behaviour under load.
How it works in practice
Worked examples.
These are illustrative constructions, not client case studies. We do not publish customer names, savings figures or results we have not verified.
AI readiness assessment for a multi-entity group
The problem
Several business units are each running independent AI pilots on different platforms, with no shared view of data, spend, risk or overlap.
What we would build
A group-wide readiness assessment producing a maturity score per dimension, a consolidated inventory of AI activity, and a sequenced roadmap with dependencies made explicit.
How it works
- 01Structured interviews across business, IT, data and risk owners
- 02Technical review of stack, data estate, integrations and infrastructure
- 03Inventory of existing AI usage, tooling and spend
- 04Score maturity per dimension against a defined rubric
- 05Produce a sequenced roadmap with dependencies and owners
Potential business impact
Duplicate effort surfaced, a shared foundation identified, and investment directed at the use cases with the strongest value-to-effort ratio.
Retrieval layer over a legacy document estate
The problem
Twenty years of operational documents sit across file shares and a legacy DMS with inconsistent metadata and no meaningful search.
What we would build
A retrieval layer that indexes the estate, derives metadata, maps existing permissions, and exposes governed search to both people and downstream AI systems.
How it works
- 01Inventory and classify the document estate
- 02Derive and normalize metadata where it is missing
- 03Map source permissions into the retrieval layer
- 04Build and evaluate retrieval quality against real queries
- 05Expose a governed interface for applications and agents
Potential business impact
One reusable foundation that every subsequent knowledge, copilot and agent project builds on instead of rebuilding.
Client outcome
What changes for the business.
- A ranked, costed roadmap instead of a backlog of competing ideas
- Data and integration foundations that later projects reuse
- A defensible answer to “why this use case first”
- AI programmes that survive contact with production
Our thinking
The argument behind this.
Where we set out the reasoning, in more detail than a capability list allows.