AI & business audit
Turn AI into an operational lever — not a gadget.
You have AI ideas, POCs, or tools in place — but it’s hard to tell what really drives value.
I help you align AI with your business processes, data, and real-world constraints.
Discuss your contextWhy so many AI initiatives fail
Today, many AI initiatives fail for the same reasons:
- poorly defined or overly generic use cases
- data that can’t be used or is poorly structured
- no integration with business tools
- unclear or unmeasurable ROI
The outcome: impressive demos… but no real impact.
Value proposition
A pragmatic audit that turns AI into actionable decisions.
I don’t sell a futuristic vision.
You get a clear read on:
- what is feasible in your context
- what is actually worth doing
- what to stop
What I analyse
Business processes
Where AI can genuinely create gains (time, quality, revenue).
Available data
Quality, structure, accessibility, and usability.
Existing or planned AI use cases
NLP, computer vision, automation, agents, etc.
Technical stack
APIs, data pipelines, infra constraints, security.
Organisation & decisions
Who decides, who uses it, where it gets stuck.
Deliverables
- Map of AI opportunities
- Prioritised use cases (impact vs complexity)
- Concrete recommendations (build / buy / no-go)
- Simplified target architecture
- Implementation roadmap
How I work
Short, structured, no wasted time:
- Targeted interviews
- Analysis of flows and data
- Identification of AI levers
- Clear, actionable playback
No pointless slides. No jargon.
Typical situations
- You’ve “tried AI” with no concrete outcome
- You want to start an AI project but don’t know where to begin
- Your teams talk about agents, LLMs, automation… with no shared frame
- You have data but no exploitation strategy
Outcome
By the end, you know exactly:
- where to invest
- what to build
- what to avoid
FAQ
How long does an AI & business audit take?
Typically from a few days up to three weeks, depending on organisation size, number of use cases, and data maturity.
Do we already need a data team or data scientists?
No. The audit surfaces gaps between ambition and reality: sometimes data or processes must be structured before any AI project.
Do you always recommend “building” with LLMs?
No. Deliverables compare build, buy, and no-go: a lot of value comes from well-scoped automation, existing tools, or simplified workflows.
How do you handle confidentiality and sensitive data?
NDA, data minimisation, workshops without exposing trade secrets. System access follows your security rules.
Does this address searches like “enterprise AI audit” or “profitable AI use cases”?
Yes. The audit prioritises use cases by business impact and complexity, aligns data and processes, and produces a realistic roadmap — without unrealistic ROI promises.