Question 1

How do you decide which AI use cases to prioritize?

Accepted Answer

We score every candidate on three axes: business value, data readiness, and implementation risk. The best first project is high value, runs on data you already have, and tolerates imperfect accuracy with a human review step. A quick win shipped in 2–3 months builds the internal momentum that bigger initiatives need.

Question 2

Do we need our own data scientists to adopt AI?

Accepted Answer

Not necessarily. API-based LLM solutions can be built and maintained by ordinary software engineers, which is where most companies should start. Custom predictive models do need data science skills, but those can be contracted for the build phase — the readiness audit tells you exactly which skills your roadmap requires.

Question 3

How long does a strategy engagement take, and what does it cost?

Accepted Answer

A typical engagement runs 3–6 weeks depending on company size and the number of departments involved. The cost is a small fraction of a single failed AI project — which is precisely what it exists to prevent. You receive fixed-scope pricing after a free initial call.

Question 4

Should we build custom AI or buy off-the-shelf?

Accepted Answer

Buy when the problem is generic — transcription, OCR, standard chat — because commercial APIs are cheaper and better than anything you would build. Build when your proprietary data creates a real advantage competitors cannot copy. Most companies land on a hybrid: a commercial LLM connected to their own data through retrieval, which costs far less than training anything.

Question 5

How do you measure ROI on AI projects?

Accepted Answer

We baseline the current process first — hours spent, error rates, cycle times — because without a baseline nobody can prove value later. Typical automation wins land between 20% and 60% time reduction on the targeted process. The business case states the expected range and the measurement method before any development starts.

Question 6

Is our data good enough for AI?

Accepted Answer

It depends entirely on the use case. LLM-based document and chat solutions need little or no training data — they work off documents you already have. Predictive models need thousands of clean historical examples, and if those do not exist yet, data collection becomes phase one of the roadmap rather than a reason to abandon the idea.

Question 7

What about the EU AI Act and compliance?

Accepted Answer

The Act classifies systems by risk tier, and most business applications fall into minimal or limited risk, requiring mainly transparency measures. Use cases touching HR decisions, credit scoring, or biometrics count as high-risk and carry strict documentation and oversight obligations. We classify each use case during scoring so compliance cost is part of the business case, not a surprise.

Question 8

When would you advise against using AI?

Accepted Answer

When a rules-based system solves the problem more cheaply, when the data the model would need does not exist, or when the process has zero tolerance for error and no room for human review. Roughly a quarter of the use cases we assess end with that recommendation — telling you no early is the cheapest outcome a consultant can deliver.

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