Enterprise AI & AI Agents
Putting AI to work in an organization starts with a plain question: which repetitive task, or which specific decision, could it handle better? The answer depends on how mature your data and processes are.
Context
Starting from a tool rather than a problem can lead to pilots that never reach daily work. And if data and processes are not ready, agents will not produce results you can rely on.
What I do
- Assessing data and process maturity for AI
- Identifying and prioritizing use cases
- Designing and building a prototype with defined evaluation criteria
- Step-by-step deployment connected to existing processes
How I usually approach it
01
Assess readiness
Review data, processes, and existing infrastructure to find a sensible starting point.
02
Choose the use case
Prioritize by value, risk, and feasibility — one or two specific cases rather than a long list.
03
Prototype
Build something small, measured against criteria agreed in advance.
04
Deploy
Connect it to the real process, train the people who use it, and monitor results before scaling.
Potential deliverables
- A data and process readiness assessment
- A prioritized list of use cases
- A prototype with evaluation criteria
- A deployment and scaling plan
The deliverables of each engagement are agreed at the start, based on the organization's needs.
Tools and methods
- Python
- n8n
- Claude
- ChatGPT
Let's talk about Enterprise AI & AI Agents
Describe your problem or goal, and we will see how this service fits your organization.