AI READINESS & GOVERNANCE
Your AI is only as ready as the data and controls behind it.
We help teams prepare trusted data, governed enterprise context and production controls for AI, so models and agents can use the right information, through the right access paths, with clear accountability.
Experimentation → Governed production readiness
Does this sound familiar?
If several of these are already true, this is the right conversation. If none of them are, it probably is not.
- Pilots work on curated samples and fail once connected to real enterprise data.
- Nobody can say which customer, product or supplier record an AI system should trust.
- Sensitive data can technically be reached, but permitted use by models or agents is unclear.
- Different teams are building separate RAG, agent and model patterns with no common controls.
- No one owns the approval path from experiment to production.
- Model, prompt, retrieval and data changes are hard to reconstruct after deployment.
- Human review happens by convention; escalation and override boundaries are not written down.
- Leadership is asking for AI progress while quality, lineage and ownership remain unresolved.
What it costs you
- Fragmented or poorly governed data
- Ambiguous enterprise context — no agreed answer to who or what a record refers to
- Uncontrolled AI access to that context
- Inconsistent outputs and weak traceability
- Production approval stalls, operational risk rises, trust falls
None of these are model problems, which is why buying a better model does not move them. The constraint is what the model is allowed to see, whether that information is trustworthy, and who is accountable when it is wrong.
What materially changes
| Today | With PaWa |
|---|---|
| Pilots built around convenient data | Use cases tied to governed data and knowledge sources |
| Conflicting customer and product identities | Authoritative mastered entities and controlled context, where the use case needs them |
| Broad or ad-hoc data access | Purpose-based access paths and policy controls |
| Unclear AI ownership | Named use-case, data, model and control owners |
| One-time pre-launch testing | Evaluation gates plus ongoing monitoring |
| Prompt, model and data changes hard to reconstruct | Versioning, lineage and retained evidence |
| Human review by convention | Defined human-in-the-loop and escalation boundaries |
| AI governance as a policy document | Controls embedded in the architecture and the lifecycle |
Today
Pilots built around convenient data
With PaWa
Use cases tied to governed data and knowledge sources
Today
Conflicting customer and product identities
With PaWa
Authoritative mastered entities and controlled context, where the use case needs them
Today
Broad or ad-hoc data access
With PaWa
Purpose-based access paths and policy controls
Today
Unclear AI ownership
With PaWa
Named use-case, data, model and control owners
Today
One-time pre-launch testing
With PaWa
Evaluation gates plus ongoing monitoring
Today
Prompt, model and data changes hard to reconstruct
With PaWa
Versioning, lineage and retained evidence
Today
Human review by convention
With PaWa
Defined human-in-the-loop and escalation boundaries
Today
AI governance as a policy document
With PaWa
Controls embedded in the architecture and the lifecycle
How we solve it
Mechanisms, not adjectives. Each of these is something we build, document and hand over.
Readiness assessment
Inventory the priority use cases, then evaluate the data, context, access, governance, architecture and operating gaps each one actually has. Readiness is a property of a use case, not of an organisation.
Trusted data foundation
Identify the authoritative sources, the quality requirements, the transformations and the serving patterns a given AI use case depends on — and say plainly which of them do not exist yet.
Mastered enterprise context
Where a use case depends on consistent customer, product, supplier, organisation or location identity, MDM and entity resolution supply it. Where it does not, we say so rather than selling a mastering programme the use case cannot justify.
Knowledge and retrieval architecture
Governed retrieval and context patterns, metadata, refresh and retirement, and the access boundaries appropriate to the use case. Most confidently wrong answers are retrieval failures, not model failures.
AI governance
Use-case intake, risk tiering, approvals, accountability, evidence and lifecycle controls. The enterprise framework lives on the Governance & MDM page; here it meets a real use case.
Agent and model access governance
Which data, tools and actions a model or agent can reach, with least privilege and human approval boundaries defined before build. An agent that can act is a different governance problem from one that can only answer.
Evaluation and monitoring
Pre-production evaluation gates, then production quality, safety and operational monitoring with an owner. A model evaluated once at launch is unmonitored, not governed.
Enablement and handover
Architecture decisions, control mappings, runbooks and ownership left with your team. An AI governance function that depends on an external firm cannot make a timely decision.
Reference architecture
Enterprise sources feed a trust and context layer — quality, entity resolution where identity matters, reference data, metadata and lineage. That produces governed data and knowledge: curated data products, semantic context and controlled retrieval. An AI access layer sits between that and the models: APIs, retrieval services, tool gateways, policy enforcement and identity. AI services consume it, and a human layer holds review, approval and escalation. AI governance, privacy and security, observability, evaluation and audit evidence span the whole stack, including the actions an agent takes. One thing this diagram does NOT say: that every AI use case needs a centralised MDM hub. Mastering earns its place where entity identity is what the use case turns on, and not otherwise.
1Enterprise sources
- CRM
- ERP
- Operational databases
- SaaS
- Documents
- APIs
- Event streams
2Trust & context
- Data quality
- MDM / entity resolution
- Reference data
- Metadata
- Lineage
3Governed data & knowledge
- Curated data products
- Semantic context
- Knowledge stores
- Controlled retrieval
4AI access layer
- APIs
- Retrieval services
- Tool gateways
- Policy enforcement
- Identity and access
5AI services
- Models
- RAG applications
- Copilots
- Agents
- Decision services
6Human & business layer
- Review
- Approval
- Escalation
- Operations
- Business workflows
Across the whole flow
AI governance · Privacy and security · Observability · Evaluation · Audit evidence · Lifecycle management
What you receive
Written artifacts you keep and can act on with any firm, including without us.
- AI readiness scorecard by priority use case, with the blockers named rather than scored
- Current-state risk and dependency map
- Authoritative data and context map, including where MDM is genuinely needed and where it is not
- Target AI data and knowledge architecture
- AI access-control and governance design covering data, tools and actions
- Use-case risk and control matrix with the approval flow
- Evaluation and monitoring requirements
- Prioritised remediation backlog and sequenced roadmap
- Architecture decision records, ownership model and handover documentation
How an engagement runs
- 1
Discover
Prioritise the use cases, including the ones running outside any programme, then inspect the data, knowledge, architecture, governance and operating constraints each one meets.
- 2
Design
Define the target data and context architecture, the controls, the access patterns and the decision rights. Designed per risk tier, not once for everything.
- 3
Prove
Where scope calls for it, validate a bounded architecture and control pattern against one priority use case, rather than asserting the design will hold.
- 4
Enable
Transfer the decisions, artifacts, runbooks, ownership and the roadmap for what comes next.
Relevant experience
Each item below is labelled with what kind of evidence it is. Nothing here claims a client outcome we cannot support.
Representative architecture: AI readiness in a regulated enterprise
A financial institution with approved AI use cases and no agreed answer on whether the underlying data may lawfully be used. The pattern shows how the pieces interact: entity resolution supplies a stable notion of who a customer is, governed retrieval decides what an assistant may see, lineage lets an output be traced back to a real record, and a written human approval boundary governs anything that acts rather than answers. Each piece is ordinary; the readiness is in how they connect.
What the engagement leaves behind: a use-case risk and control matrix, the access design agents will operate under, and a sequenced view of what must change before production.
Representative engagement — a realistic pattern used to explain our approach, not a client result.
Entity resolution as the foundation for a single customer view
A Tier 1 North American bank where retail, commercial and wealth each held their own version of a customer. The mastered record and the lineage back to every contributing source are exactly what an AI system needs to answer a question about a customer consistently — the same work that served financial crime investigators serves an agent that has to know which records are one person.
Delivered by our principal in a previous role, before PaWa Data Solutions.
Technology experience
AI platforms
- Azure OpenAI
- AWS Bedrock
- Google Vertex AI
- Databricks Mosaic
Retrieval and vector
- Azure AI Search
- pgvector
- Elasticsearch
- OpenSearch
Governance and context
- Informatica MDM
- Collibra
- Microsoft Purview
- OpenLineage
Evaluation and monitoring
- Evaluation harnesses
- Drift monitoring
- Structured audit logging
Platforms and tools we have worked with directly. This is experience, not a partnership claim: PaWa Data Solutions holds no reseller agreement or partner status with any vendor listed here, which is what keeps the recommendation neutral.
Papa S. Nguer
VP of Technical Sales & Engineering, PaWa Data Solutions
AI readiness work is led by our principal, whose background is enterprise data architecture, governance, MDM and entity resolution, lineage and financial-services controls — plus fifteen years translating vendor capability into production operating patterns. That matters here because the questions an auditor asks about a regulatory figure are the questions you should be asking about a model's inputs.
Full profileQuestions buyers actually ask
Do we need an MDM platform before we can use AI?
No. The question is whether your use case depends on authoritative entity identity — a customer assistant usually does, a document summariser usually does not. Where it does, we apply the lightest mastering pattern that meets the need, which is often not a platform purchase. We would rather tell you MDM is not your constraint than sell you a programme the use case cannot justify.
Is AI readiness the same as MLOps?
No. MLOps is one operating capability within it. Readiness also covers the data, the enterprise context, access paths, governance, evaluation and ownership — and in our experience those are what actually block production approval, long after the deployment pipeline works fine.
Can you work with our existing AI and cloud stack?
Yes, and we assess what is already in place rather than proposing a replacement. We have no reseller margin and no partner quota with any platform, which is what keeps that assessment honest.
Do you build models?
Scope-dependent, and it is not the core offer. What we do is the enterprise data, context and governance foundation that production AI depends on. If you need a model-development team, that is a different firm, and we will say so rather than staffing it.
How do you govern AI agents?
By controlling identity, data and tool access, permitted actions, approval boundaries, monitoring and evidence. An agent that can take an action is a higher risk tier by default, and the human approval boundary is written down before build rather than discovered after an incident.
What if our data governance is immature?
Then that becomes part of the readiness roadmap rather than a reason to stop. Often the honest sequence is governance and mastered entities first, because any future use case will need them and they pay for themselves in reporting and risk regardless of whether the AI programme proceeds.
Can we start with one use case?
Yes, and it is usually the better way in. A bounded priority use case exposes the reusable foundation and control requirements faster than an enterprise-wide assessment, and produces something you can act on rather than a document.
AI Readiness Assessment
A focused assessment of one or more priority AI use cases across data quality, authoritative context, access, governance, architecture, evaluation and operating readiness. You leave with a written gap view and a sequenced plan for what must change before production.
Scope and commercial terms are agreed in writing before the assessment starts.