Process & productivity
AI Assistant
for Knowledge Workers
The technology was ready in three weeks. Adoption took five months. That ratio is where almost every business case falls apart.
Proof in numbers
AI in Europe
European brands already running AI in production.
Not my clients. Examples of what happens once AI moves past the pilot stage.
- Klarna
Sweden
AI customer service assistant
- Zalando
Germany
Fashion assistant for product advice
- Financial services
Investment firm
AI strategy and use-case portfolio
- Booking.com
Netherlands
AI Trip Planner for travel planning
- Siemens
Germany
Industrial Copilot for engineering
- Retail
Enterprise retailer
AI-driven vendor selection
- Spotify
Sweden
AI DJ and personalized playlists
- Mercedes-Benz
Germany
Generative voice assistant in the car
- Industrial / B2B
International manufacturer
AI roadmap and leadership
- ING
Netherlands
Generative chatbot for customer queries
- L'Oréal
France
Beauty Genius: personalized advice
- Financial services
Financial services firm
KYC accelerated with AI document analysis
- SAP
Germany
Joule: copilot in business software
- Carrefour
France
Hopla: grocery shopping assistant
- Strategy & AI
SaaS scale-up
AI readiness assessment
- Adyen
Netherlands
AI-driven payment optimization
- Philips
Netherlands
AI in medical imaging
- Proces & productivity
Professional services firm
Internal AI assistant for knowledge workers
ervaring, impact ↓
20+ jaar ervaring
real impact
Situation
A professional services firm with around four hundred knowledge workers gave everyone access to an AI assistant. After three months, 11% used it weekly. License costs kept running, and the promised productivity gain was nowhere to be found in the numbers.
Challenge
The tool worked fine. The problem was that "you can use it for anything" means, for most people, that they do not know where to start. An empty input box is not a workflow. And the people who did use it did not share how, so no habit spread through the organization.
Approach
We moved away from "assistant for everything." In working sessions with four teams we identified the tasks that actually consumed time: searching through files, drafting reports, rewriting quotes to a client’s context, and finding internal knowledge. For each of those four scenarios we built a ready-made starting point, embedded in the tool where the work already happened.
Decision
Two requested features were deliberately not built: automatic client correspondence and support for performance reviews in personnel files. The first because of reputational risk, the second because of employment-law sensitivity. That choice was documented along with the reasoning, so it would not resurface every quarter.
Result
Weekly active use grew from 11% to 68%. A time measurement across two representative teams showed an average of three hours per person per week recovered, concentrated in the four scenarios. That time was not cut but redirected to billable work, which is what finally made the business case add up.
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