Use case discovery
Workshops and process reviews to find where AI removes real effort, ranked by value, feasibility and risk.
AI Implementation Strategy
Where AI actually pays. Not another proof of concept.
An AI implementation strategy identifies the specific processes where AI will save time or create revenue in your business, ranks them by value and feasibility, and takes the best ones into everyday use with the right data, rules, people and partners. I use AI every day to run my own work, so the advice comes from practice, not from slides.
- Engagement
- Project-based, Fractional, Advisory retainer
- Led by
- Nicolò Mazzari, personally
- Clients in
- Europe, UK, US
Who it's for
- Leadership teams under pressure to 'do something with AI' who want a plan, not a demo
- Companies with several AI pilots and nothing in production
- Operations-heavy businesses (customer service, finance, sales operations, document processing) with repetitive knowledge work
You might need this if
- You have run AI pilots, but none of them changed how the business works
- Employees use AI tools on their own, without guidelines
- You don't know which data you can safely give to an AI system
- Suppliers are selling you AI features you can't evaluate
What I do 01 02 03 04 05
Scope of work
Business case
Costs, savings and adoption assumptions written down, so you invest where the return is clear.
Tools and partners
Buy, configure or build: the right platforms and partners for each use case, without lock-in you will regret.
Data, security and governance
Clear rules on data, access, quality checks and human review, aligned with GDPR and the EU AI Act, with your legal and IT advisors.
Rollout and adoption
From pilot to everyday use: integration delivered by your teams or partners, training, and metrics that show whether it works.
How it unfolds
The first steps
- Weeks 1 to 2
Discover
Interviews and process walk-throughs across teams. Inventory of data, tools and current AI use.
- Weeks 3 to 4
Prioritize
Opportunity map, business cases and a governance baseline. A short list of use cases worth doing.
- Weeks 5 to 12
Ship
The first use case live with real users, delivered with your teams or partners and measured against the business case. Then the next one.
What you get
Deliverables
- AI opportunity map ranked by value and feasibility
- Business case for the top use cases
- AI usage policy and governance model
- Tool and partner recommendation
- Rollout plan with adoption KPIs
What changes
Outcomes
- AI working in everyday processes, not in a sandbox
- Measurable time saved on repetitive knowledge work
- Clear, safe rules for how your people use AI
- A pipeline of next use cases with known value
Engagement
Ways to work together
Advisory retainer
A senior sounding board for the CEO, the owners or the board, with regular reviews and on-call advice.
Cadence: A few hours a month, plus on-call
Best for: Boards, owners, investors and CEOs who want an independent view
Fractional
I own the transformation and AI agenda as your fractional executive: in leadership meetings, accountable for outcomes.
Cadence: From a few days a month to two or three days a week
Best for: Companies that need senior ownership, but not five days a week
Project-based
A defined scope under a statement of work: assessment, roadmap, supplier selection, due diligence.
Cadence: Fixed scope, fixed timeline
Best for: A specific decision or deliverable
I work as an independent executive under a services contract: clear mandate, clear scope, clear notice period. How I work
FAQ
Frequently asked questions
Where should a mid-sized company start with AI?
Start with repetitive knowledge work that already has clear inputs and outputs, like answering recurring customer questions, processing documents, preparing reports or qualifying leads. These deliver value quickly and teach the organization how to work with AI.
Do we need a data science team to use AI?
Usually not at the start. Most valuable use cases today run on existing models and platforms, configured and integrated well. What you do need is clean access to your data and someone accountable for the outcome.
How do you handle data privacy and the EU AI Act?
Every use case is classified by risk, data flows are documented, and human review is designed in where it matters, together with your legal and IT advisors. Governance is part of the plan from day one.
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