Use cases/Helloprint

A 15-year-old, €100M print platform rebuilds itself around AI - while the business keeps running.

800,000 customers. 31 countries. 300 suppliers. 4,000 orders a day. Helloprint is demolishing its operating model and rebuilding it from the inside, with the same people still doing the work while the walls come down.

€100M
Revenue
~18
Skills in production
8
Core processes

- Use case · Helloprint

Helloprint is one of Europe's largest online print platforms. They've used machine learning for thirteen years and got serious about AI in 2022. This is the story of the moment they stopped applying AI to the old organisation - and started redesigning the organisation around it.

It feels like demolishing a house while the people are still living in it - and then building it up with the same people, but in a fundamentally different way.
- Hans Scheffer · CEO, Helloprint

Where they actually were.

This is a fifteen-year-old company with real customers, real suppliers, real margins, and real legacy. The kind of operation where 'apply AI' is easy to say and very hard to do without breaking something.

€100M
Revenue
800k
Customers
31
Countries
300
Suppliers
4,000
Orders / day
13 yrs
ML in use
Helloprint at the moment the rebuild began.

The turning point.

For years Helloprint layered AI on top of the existing structure. It worked in pockets, and then plateaued. The bottleneck turned out to be the operating model around the tools: departments, project managers, three-week cycles, tickets between idea and shipped code. That structure simply couldn't keep up with what the models could now do.

We came in to help redesign the model, alongside the leadership team and a growing group of people inside the business who wanted to build.

Phase 1 - what didn't work.

Most case studies skip this part. We won't. A dedicated central AI team ran for about a year, staffed with strong people but disconnected from the business. Projects took too long or stalled outright. A flagship content tool was already outdated by the time it shipped six months in. A model migration made on cost grounds alone took months to reverse.

The lesson that matters for other leadership teams: an AI team without business context produces the wrong thing, slowly. The context has to live inside the people doing the building.

2022Operating model rebuild2026
  1. 2022
    First serious AI experiments.
  2. 2024
    Central AI team set up.
  3. Late 2024
    Team disbanded - wrong structure.
  4. 2025–26
    Claude + SuperIntelligence rollout.
Four years of progress that was rarely linear and never pretty.

From departments to processes.

The org chart was redrawn around eight core business processes instead of around departments. Process owners replaced department heads. The PM layer largely disappeared. 'Everyone is a builder' became literal, with one nuance: process owners still lead. There is structure - it just sits closer to the work.

What disappeared was the coordination layer between idea and ship: tickets, sprints, three-week cycles. The distance between 'we should do this' and 'this is live' collapsed.

Before - departments
  • Department heads
  • PM coordination layer
  • Tickets · sprints · 3-week cycles
  • Builders wait for context
After - processes
  • 8 process owners
  • Everyone is a builder
  • Skills shipped, not tickets
  • Claude + MCP in the loop
The PM layer didn't get reorganised. It went away.

The heart - the SuperIntelligence platform.

An internal platform that wires the AI assistant into every system that matters - data warehouse, CMS, CRM, collaboration, analytics, commerce - through a governed integration layer with role-scoped permissions and a full audit trail. It's the difference between 'we use AI' and 'we have a company brain.'

Company brainLive
AI assistant
Governed integration layerData warehouseAnalyticsCMSCRMCollaborationDocsSEOCommerce
Role-scoped permissionsAudit · BigQuery
One assistant. One governed layer. Every system. Every query audited.

Skills - what the organisation actually built.

Roughly eighteen production skills, built by the business itself. Concrete people, doing concrete work. This is the part that proves the model.

F
Finance

Built a suite of analytics and pricing skills himself - without writing application code.

Finance ships tooling for the whole company
CE
Customer Experience

Automated the manual ticket flow the team handled every day.

A large share of repetitive tickets now handled by agents
Q
Quality

Quality reviews assisted by a skill that prepares the audit.

A fraction of the time per review
FT
First-time builders

Non-technical people shipping their first production skill without engineering help.

Builders, not requesters
O
Operations

Artwork-complaints triage and resolution, automated end to end.

D
Design

AI-generated UGC video assets for paid and organic.

A cross-org movement, owned across the business.

The cadence.

Every Tuesday 14:00 - an open training hour. Monthly hackathons. One-on-one champion deep-dives. Builders are celebrated publicly. The cadence is the system. 'It takes a village to move people from playing around to genuinely changing how they work.'

WEEKLY CADENCEALWAYS-ON
  1. 01Tuesday 14:00 - open training1h
  2. 02Monthly hackathon1d
  3. 03Champion 1:1 deep-divesongoing
RHYTHMEVERY WEEK
Training runs as a weekly rhythm, never a one-off kickoff.

What changed in the business.

Production skills shipped across finance, customer experience, quality, sourcing and commercial - built and owned by the people who do the work. Manual ticket handling dropped meaningfully. Time-per-quality-review collapsed. Whole departments shifted from doing repetitive work to instructing agents that do it for them.

Hiring is being rethought role by role, with some openings deliberately tested as agent-first before a human is added. Adoption moves in waves; usage dips and recovers as the model evolves. What's durable is the muscle: the business can now design, ship and govern its own AI work.

What Dissel contributed.

We co-designed the AI operating model with leadership - the rollout sequence, the org logic, the governance, and the rhythm that keeps it moving. We built the governed integration layer that lets agents act safely on real company data, and the brand-aware tooling that keeps output on-voice.

We shipped the skill-creator - the skill that lets non-technical people build production skills - and the surrounding guardrails: skill catalog, review gates, and a clear path from idea to live. And we ran the human side: the weekly training cadence, the hackathons, and one-on-one coaching that turned finance, CX and operations people into first-time builders shipping into production.

Want this in your operation?

Twelve weeks. One integration. No migrations. We design the model, build it inside your stack, and hand you the keys.