AI adoption and digital transformation
I help companies adopt AI where it actually delivers: both in operational processes and in development itself. No hype, no adoption for its own sake — matched to your team's maturity and real goals.
When companies bring me in
- You want to use AI but it's unclear where it will pay off and where it will burn budget.
- Routine operations eat people's time and have become a bottleneck.
- You have engineers, and they already use AI — but chaotically and without quality control.
- You need to separate what can genuinely be automated from marketing promises.
- Competitors are "adopting AI", and you need to know whether there's anything behind it.
What you get
- An honest picture: where AI will deliver in your case, and where it won't.
- Priorities: where to start to get a result rather than a presentation.
- Discipline in AI-assisted development: reviews, quality control and predictable results instead of chaotic "vibe coding".
- A solution matched to your maturity, without needless complexity.
How I work
- We start from processes and economics: where time and money are being lost today.
- We pick the scenarios where AI gives a measurable effect and cut the ones where it's decoration.
- For teams with engineers, I set up the rules for working with AI tools: what's acceptable, what gets reviewed, how not to lose quality.
- We adopt step by step: first what pays off quickly and doesn't break what already works.
- We assess the result honestly: if it didn't work, we say so.
From practice
AI isn't adopted in a vacuum: it fits into existing systems and processes. Below are examples of such systems.
Format and cost: AI-readiness assessment
Cost
from $5,000
Implementation itself is separate; its scope is set after the assessment.
- What's included: an assessment of where AI pays off in your case and where it doesn't; priorities by return; the risks and what needs preparing in data and processes. The result is a written conclusion with a recommended direction.
- Timeline: usually up to one and a half to two weeks.
- Outcome: a written conclusion and a walkthrough of the findings.
FAQ
- Where do we start?
- With processes, not tools: first we find where time and money are lost.
- Our developers already use AI. Does anything need to change?
- Often yes — not banning it, but bringing order: reviews, quality control, clear rules.
- Is it expensive?
- Start with a narrow scenario that pays off quickly. Large transformation programmes without early results rarely survive.
- What if AI won't deliver for us?
- I'll say so plainly. Not adopting is also a decision, and often the right one.
- Do you implement specific tools?
- Tools are chosen to fit the task and the team's maturity, not the other way round.
For more, see 'The value isn't in the code, it's in the system'