A plan to adopt and scale AI
AI consulting services for the enterprise — AI strategy and roadmap, use case discovery, an AI readiness, risk and ROI assessment, and a Responsible AI operating model. Decision-ready in 3–6 weeks.
Quick answerAI consulting services turn AI ambition into a sequenced, fundable plan — AI strategy and roadmap design, use case discovery and prioritisation, an AI readiness, risk and ROI assessment, foundation model and platform guidance, and the design of a Responsible AI operating model. AI consulting decides what to build and why; AI development builds it.
Where AI consulting helps
Take on the full engagement, or a focused piece — the strategy service goes deeper on vision and operating model.
AI Strategy Consulting Services
AI vision and ambition, target operating model, a prioritised use case portfolio and a funded roadmap.
ExploreAI readiness, risk & ROI assessment
An 8-dimension evaluation across data, platform, people, governance and ROI, with a prioritised set of gaps.
ExploreResponsible AI operating model
Acceptable use, human oversight, model risk management, evaluation and monitoring standards.
ExploreGenAI-specific consulting
For GenAI strategy, model selection and a GenAI maturity view, see Generative AI Consulting.
ExploreDelivery-ready roadmap
A 30-60-90 day plan that hands off cleanly into proof of concept and delivery.
ExploreEnterprise AI Services
When the answer is 'scale across the org' — governance, platform and operating model.
ExploreWhat comes out of AI consulting
Ranked use case portfolio
Value, effort, feasibility and confidence for every candidate, with quick wins identified.
AI readiness assessment
Where you stand across data, platform and tooling, skills and operating model, and governance and risk.
Business case
Value and cost per use case so investment goes to the highest-return, lowest-regret work first.
Responsible AI design
The governance operating model, sized to your industry and regulatory obligations.
Reference architecture
Model and platform guidance you can build against.
30-60-90 day roadmap
Sequenced, funded, decision-ready for your stakeholders.
Where consulting stops and building starts
| Question | AI Consulting (this page) | AI Development |
|---|---|---|
| Which use cases, in what order? | Decided here | — |
| Are we ready, and where are the gaps? | Assessed here | — |
| What is the ROI and the risk? | Sized here | — |
| Build the solution | — | Yes |
| Run it as a managed practice | Designed here | Operated |
Consulting that leads to delivery
Practitioners, not slideware
Advice from teams that also build and run AI in production.
Governance depth
Responsible AI operating models fit to regulated industries.
Platform view
RLCatalyst and Spectra inform a realistic target architecture.
Honest ROI
Value and cost estimated per use case, weighted by confidence.
Delivery-ready output
The roadmap is executable by your team, us, or a mix.
Fast
3–6 weeks to a decision-ready plan.
AI strategy, tuned by industry
The constraints that shape an AI roadmap differ sharply by sector.
Financial services
Document AI, risk and fraud models, and governed copilots that meet audit and compliance needs.
Healthcare & life sciences
Clinical and research assistants, knowledge retrieval and automation with data governance built in.
Retail & consumer
Personalization, demand forecasting, and customer and associate assistants across channels.
Manufacturing & supply chain
Predictive maintenance, quality vision, and agentic workflows for planning and operations.
Public sector
Secure, private-LLM deployments for citizen services, case work and knowledge access.
Technology & ISVs
Embed generative AI features, agents and RAG into your product with a partner who ships.
- AWS
- DataStax
- Salesforce
- Snowflake
Scaling this across the enterprise?
Our Enterprise AI Services practice brings generative AI, agents and LLMs to production at scale — with the governance, platform and operating model to keep them reliable, compliant and cost-controlled.
AI strategy insights from Relevance Lab
Agentic AI vs Generative AI: key differences and enterprise use cases
Where generative AI ends and agentic AI begins — and which enterprise problems each is built for.
Read More BlogFrom reactive to autonomous: GenAI's role in AIOps & SRE
Moving operations from rule-based and reactive to predictive, conversational and intent-driven with GenAI.
Read More BlogRethinking enterprise analytics with GraphRAG and context-aware AI
Why data engineering stalls on missing context — and how GraphRAG makes data relationships explicit for AI.
Read MoreAI Consulting Services: frequently asked questions
Content last reviewed: September 2026
Not sure where AI pays off for you?
Book a working session and leave with a shortlist of use cases, a readiness view and a roadmap.
Talk to an AI specialist
Tell us where you are on your AI journey — a use case, a proof of concept, or a platform decision — and an AI consultant will come back with next steps and a rough shape for the engagement.