AI Readiness
Turn your data chaos into AI-powered growth
AI success depends on trusted, well-managed data. Most enterprises struggle with siloed, ungoverned, and inaccessible information that blocks innovation and creates risk. RDI bridges that gap, turning scattered data into a unified, governed, and monetizable asset, ready for generative AI, retrieval-augmented generation (RAG), and agentic AI. Our AI Readiness Plan & Enablement Service makes your data AI-ready and revenue-ready.
How it works
1. Business goals
We start by understanding your strategic goals, recommend high-impact AI use cases aligned with your business objectives, and record the agreed goals in writing.
2. AI ecosystem assessment
We evaluate your data and technology landscape against our proprietary AI Reference Architecture and deliver a comprehensive value assessment report.
3. Strategic roadmap
We draft an AI readiness framework, review it with stakeholders, and finalize a business-driven, innovation-ready plan that guides strategy, priorities, and ongoing
Every engagement is shaped by three considerations: business-first AI design, integrated technology readiness, and innovation foresight.
What the service includes
Modern data catalog foundation
Your central nervous system for AI, integrating structured and unstructured data with full metadata, lineage, and governance.
AI-ready data fabric
Context-aware, trusted data access that powers retrieval-augmented generation and agentic AI workflows.
Automated governance
Intelligent tagging, classification, and policy enforcement aligned with global standards, including GDPR and the EU AI Act.
Scalable maturity roadmap
A proven framework ensures every investment in data infrastructure compounds, from foundational readiness to transformative outcomes.
Principled execution
Our AI reference architecture is built on eight principles: govern once and enforce everywhere, a single source of truth for every entity, a zero orphan policy, lifecycle stewardship, lineage throughout, a single semantic backbone, continuous compliance, and dual control by humans and policy.
DPMM Journeys
The journey is aligned with the Data Product Maturity Model (DPMM). It moves your organization through four stages, from foundational readiness to transformative revenue, with each stage building the maturity the next one requires.
- Foundational readiness: establish a governed single source of truth with a modern data catalog.
- Data-driven operations: automate and optimize data flows for efficiency and scale.
- Strategic insights: activate trusted data for RAG, generative AI, and intelligent analytics.
- Transformative revenue: embed responsible AI to create new, sustainable revenue streams.
Expected business outcome
- Faster time-to-value for AI and analytics projects, through trusted, cataloged data pipelines.
- Higher operational efficiency, through automation and metadata-driven workflows.
- New data monetization potential, through reusable, governed data products and AI-ready assets.
- Stronger trust and compliance, through automated lineage, tagging, and policy enforcement.
- Sustainable growth, as data maturity and AI adoption build on each other.
Delivery timeline
- Weeks 1 to 2, Discovery: your business team walks us through goals; we begin the data and AI maturity evaluation.
- Week 3, Evaluation: we continue the data and AI maturity evaluation with your technical and subject matter experts, and review early findings with your data owners.
- Week 4, Assessment: we complete the ecosystem evaluation and share the value assessment report.
- Weeks 5 to 8, Framework creation: we draft the plan, you review and give feedback, and we complete the AI readiness framework.
- Week 9, Delivery: final plan released.
Timelines may extend depending on the complexity of your data ecosystem.
Critical risks and immediate actions
These risks are common in AI readiness work. We recommend acting on each one as soon as it is identified, rather than waiting for the final plan.
Access and availability delays
Confirm source-system access and subject matter expert time in week 1 to protect the timeline.
Poor data quality
Run a quality baseline on the priority source systems so the roadmap reflects real conditions, not assumptions.
Regulatory exposure
Brief legal and compliance on EU AI Act, GDPR, and PIPEDA obligations for current AI use cases before rollout.
Unclear data ownership
Name an accountable data owner for each priority domain before framework creation begins.
Ungoverned AI use
Issue an interim acceptable-use policy for AI tools now, covering approved tools, data types, and review steps.
What you get
Primary deliverable: the AI readiness plan
The AI readiness plan is the primary deliverable of this service. It brings together your business goals, the value assessment, and the AI readiness framework into one prioritized roadmap. It sets out what to do, in what order, who owns each step, and how success will be measured.
Goals charter
Your business goals and recommended high-impact AI use cases, agreed with your business team and recorded in writing in weeks 1 to 2.
Value assessment report
Findings from the data and AI maturity evaluation, with gaps, enablers, and value opportunities, delivered in week 4.
AI ecosystem evaluation
Your data and technology landscape scored against the RDI AI Reference Architecture, covering data sources, governance, and platform readiness.
Draft AI readiness framework
The working framework for stakeholder review in week 7, with your feedback incorporated before finalization.
Proposal to implement the plan
Once you accept the plan, we propose a phased implementation that follows the four stages of the journey: foundational readiness first, then data-driven operations, strategic insights, and transformative revenue. Each phase has defined owners and milestones, with quarterly health checks to confirm progress. Commercial terms are provided in a separate proposal once the scope is confirmed.
Governance guidance
Ongoing governance direction embedded in the framework, including ownership, quality, lineage, and policy principles for AI use.
Quarterly health checks
A one-week check against plan adherence: two in year one, then three each year, scheduled in advance.
Annual plan revision
An end-of-year revision of the roadmap, typically taking one to three weeks, to incorporate upcoming AI program enhancements.