Anja Warrot

The through-line

Anja Warrot's recurring preoccupation is turning AI, specifically Claude, into a practical, everyday tool for entrepreneurs and their numbers, rather than a novelty for writing texts or making images . Over the period covered she moves from testing and demonstrating Claude on her own work to systematizing that experience into training and workshops for KMO's, sharing prompts and skills for free and running recurring sessions . Underneath the AI focus sits an older habit of questioning "zo doen we dat hier nu eenmaal" and looking for the one habit, process or way of working worth challenging or improving a little every day . Her earlier work in recruitment, visible in the podcast material, already carried the same data-driven instinct: watching a metric double, trusting the number, and acting on what it told her about the business model ¹⁶.

On using Claude for real work

Warrot treats Claude as a working colleague she tests deliberately, not a gimmick. She fed it a bare AutoCad drawing with no explanation of the client's method and asked for a meetstaat, then compared it against the client's own calculation: "Ik was onder de indruk van het resultaat" and in the second round "werd het pas écht interessant" when Claude "leerde extra zaken die typisch waren voor een meetstaat" and even "vond een menselijke fout" in the client's own numbers . She frames the value not as replacement but as time and decision quality: "Minder zoeken. Minder knippen en plakken. Minder manueel controleren." and "Niet om je werk over te nemen. Wel om sneller inzicht te krijgen en betere beslissingen te nemen. Daar zit voor mij de echte meerwaarde." . When new Claude models appear, her instinct is to re-run the same assignment and ask "Maakt dit model minder fouten? Ziet het sneller verbanden? Bouwt het een beter dashboard?" .

On entrepreneurs' fear of AI and numbers

A repeated theme is naming the objections entrepreneurs voice and dismantling them one by one. She quotes them directly: "AI, dat is niets voor mij," "Ik ben niet technisch genoeg," "Straks staan er fouten in mijn cijfers en zie ik het niet," "Ik heb geen tijd om dat allemaal uit te zoeken." She answers the time objection head on: "Ik heb daar eigenlijk geen tijd voor." Net daarom is dit zo interessant... Dat is geen hype. Dat is slimmer ondernemen." She also flags that most entrepreneurs already use AI but shallowly: "81% van de Belgische KMO's gebruikt AI. En toch merk ik dat veel ondernemers er vandaag nog maar een klein stukje van gebruiken." Her prescription is focus rather than breadth: "Kies één AI-tool en leer er echt goed mee werken. Dan ben je al veel verder dan de gemiddelde gebruiker."

On cash and the cijfers that matter

Beyond AI tooling, Warrot's COBIZ work centers on which numbers actually matter to a business. She distinguishes profit from cash bluntly: "Winst is een cijfer op papier. Cash is wat er effectief op je rekening staat. En daartussen zit soms een wereld van verschil." The related workshop pushes entrepreneurs to cut clutter: "Geen overvloed aan rapporten, maar een helder overzicht waarmee je betere beslissingen neemt." with the guiding line "Less is more." She frames every offering, including AI training, against the same test of what it delivers for "jouw cash, je tijd en je Comfort" .

On trust from clients

Warrot repeatedly returns to gratitude for the trust entrepreneurs place in her and her team on decisions that matter to their business: "zalig om het vertrouwen te krijgen van klanten om mee te werken aan cruciale stappen in hun onderneming" and "We staan er zelf niet vaak genoeg bij stil hoe bijzonder dat is." This sits alongside her older belief, from her recruitment years, that struggling with finance is not something to be ashamed of because "finance is a specialty like any other trade" ¹⁶.

From the stage

In the BlackBird podcast, Warrot and Dirk Colman give a business history not present in the written posts: they sold their recruitment agency Van A naar Beter to Select Group after ten years because the no-cure-no-pay model broke down when the number of client conversations needed per successful hire doubled during the war for talent and never came back down ¹⁶. They describe abandoning a fully prepared diversification plan, brand names and logos ready, after concluding on holiday that the company would be better off inside a larger group, and they detail the mechanics of the handover: Select's people were on the parking lot the same morning the sale was announced, closing the gap between announcement and buyer presence, which let the team switch mentally within a day ¹⁶.

Takeaways

• Test new Claude model releases by rerunning the same assignment and comparing whether it makes fewer errors, spots relationships faster, and builds a better dashboard .
• When introducing AI to skeptical entrepreneurs, name their exact objections first (not technical enough, no time, afraid of errors) before making the case .
• Push clients toward depth with one AI tool rather than shallow use of many, since most KMO's already touch AI but underuse it .
• Separate profit from cash explicitly when advising on business decisions: profit is paper, cash is what is actually in the account .
• Design financial workshops around "less is more," giving a small set of decision-relevant numbers rather than a flood of reports .
• When selling or restructuring a company, close the gap between announcing change and having the new reality visibly present, since that speed of transition is what lets a team adjust emotionally ¹⁶.