Clear, owned outputs.
- A prioritized use-case portfolio
- Data readiness assessment
- Business case for the top use cases
- Responsible AI checklist
- An AI roadmap
AI consulting helps you separate valuable opportunities from hype. We work with your teams to find use cases where AI can measurably improve a decision or task, check whether the data and processes are ready, decide whether to build or buy, and plan a path from pilot to production with responsible practices built in.
The problems AI Consulting solves.
Use-case discovery, data readiness, build-or-buy advice and practical AI roadmaps.
Leadership wants an AI strategy but the starting point is unclear.
Many ideas exist, but none has a clear business case.
Pilots were built but never reached production.
There are concerns about data privacy, accuracy and risk.
What AI Consulting covers.
Use-case discovery
Workshops and interviews to find and describe opportunities.
Prioritization
Scoring use cases on value, feasibility, data readiness and risk.
Data readiness
Assessing data availability, quality and governance.
Build, buy or partner
Comparing custom development with existing products.
Responsible AI
Risk assessment, human oversight and evaluation practices.
Roadmap
A sequenced plan from pilot to production and scale.
How we judge whether an AI use case is worth building.
Most AI projects fail on the business case or the data, not the model. We test each candidate use case against the same criteria before anyone writes code.
| What we check | Why it matters |
|---|---|
| The decision or task | A specific decision or process, who makes it today and how often. "Use AI for customer service" is not yet a use case. |
| Value | What improves — time saved, errors avoided, revenue gained — and a rough estimate of how much. |
| Data | Whether the data exists, how much, how clean and whether you're allowed to use it for this purpose. |
| Feasibility | Whether current techniques can reach the accuracy needed, and what a wrong answer costs. |
| Adoption | Who will use the output, how it fits into their workflow and what would make them trust it. |
| Risk | Privacy, bias, regulatory exposure and how humans stay in control of consequential decisions. |
Why it's worth it.
Focused investment
Effort goes to use cases with real value.
Fewer stalled pilots
Pilots are designed with production in mind.
Managed risk
Privacy, accuracy and oversight are planned from the start.
When AI Consulting is the right move.
- A leadership team preparing an AI strategy.
- Choosing the first AI use case to fund.
- Reviewing an AI vendor proposal independently.
Technologies & platforms
- OpenAI
- Anthropic Claude
- Azure AI
- Google Vertex AI
- AWS Bedrock
- Databricks
- Python
How AI Consulting work runs.
- 01
Frame
The decision or task to improve, the measure of success and the constraints.
- 02
Assess data
Availability, quality, access, privacy and labelling needs.
- 03
Prototype
A small, honest test of whether the approach can work.
- 04
Build and evaluate
Model development with documented evaluation against agreed criteria.
- 05
Deploy and monitor
Integration, monitoring for drift and performance, and retraining plans.
AI Consulting questions.
Where should we start with AI?
With a specific, measurable problem where data exists and a better decision or faster task would matter. Our article on scoping a first AI use case explains the approach.
Do we need lots of data?
It depends on the use case. Generative AI tools can work with small amounts of your own content; predictive models usually need meaningful historical data.
How do you handle AI risk?
By assessing each use case for privacy, bias, accuracy and misuse risks, keeping humans involved where decisions matter, and evaluating systems before and after launch.
Not sure where AI would actually help?
Tell us which decision or process you want to improve and what data you have. We'll reply with whether it's a good fit and how we would test it.