Challenger House | An AI Transition Practice

The shape of the problem

AI changes three things.

Most companies stop at the first. We take leadership teams through all three, live, on the problems they already care about.

First

The work.

Productivity, speed, automation. Where everyone starts, and where AI is fastest.

Then

The team.

Trust, learning, coordination. Whether people will admit what they do not know in front of each other.

Then

The organisation.

Handovers, decision rights, workflows, the operating model itself. Where the value is, and where almost no one is working.

AI is the diagnostic, not the transformation. It speeds the work up, then exposes everything the work was hiding: the handovers, the trust gaps, the decisions nobody owns.

Capability compounds. So does the gap between the teams that start and the teams that wait.

Who this is for

This is not for everyone.

If you want a vendor who hands you a finished thing and leaves, we are the wrong call. People who choose a practice are not buying a deliverable. They are joining a discipline. The leaders we work with are not trying to rent a capability for a quarter. They are building the capacity to keep adapting, long after we are gone.

For leaders who already suspect the org chart, not the technology, is the real bottleneck.

Who we typically work with, and the sentence we hear from each.

Innovation Leader"We are expected to create the future while trapped in yesterday's meeting, reporting and handover logic."

Strategy & Transformation Lead"I am expected to recommend where AI takes us, before I have seen it work in the wild, on a real team's real problems."

What we offer

We don't teach AI. We build the fluency that lets your organisation adapt, fast.

Executive Experience · 1 day

You have to experience it yourself.

We separate the technical challenge (the tools) from the adaptive one: what AI does to your teams, your workflows, and how decisions get made.

You build real AI tools yourself: skills, workflows, maybe even an agent. That turns enthusiasm into the questions that decide value: what data it needs, where it fails, who maintains it after the demo. It also surfaces a quieter gap. Most leaders cannot yet say what excellent output looks like, and AI cannot reproduce a standard you cannot articulate. If you can set direction and judge whether work is good enough, you are better placed for this than you think.

We make you feel the cognitive effects most leaders only hear about. Every one of these hits a senior leader hardest. You sit furthest from the raw work, get the least honest pushback, and your judgments scale across the whole organisation.

Click the arrows to expand.

01The fluency effect

Polished output reads as rigorous output. Execs skim fast and trust clean prose, so the smoother the answer, the less it gets challenged, and you are the one who signs it off.

02The sycophancy effect

The more senior you are, the more everything around you already agrees with you. AI adds a tireless agreement machine to a person already starved of dissent. It compounds the blind spot seniority creates.

03The anchoring effect

The first draft sets the frame. When you anchor on an early AI output, the whole organisation anchors with you. The first agent draft quietly becomes the strategy.

04The Plausibility Illusion

The output looks right, feels right, passes casual scrutiny, and is wrong. Execs decide on summaries, not source material, so the plausible-but-wrong artefact reaches you with its caveats already stripped off, and you act on it at scale.

The Challenger House Sprint · 1 quarter

Turn the spark into a new way of working.

For an intact team, or a hand-picked cross-functional one. A three-day residential ignites it. The weeks around it turn the spark into workflows, agents, and prototypes that survive the handover. We build capability, not dependency.

Pre-flight

Explore

Structured briefing. North-star definition. Tech-stack and sandbox setup. Baseline analysis: where the team's time actually goes. Tailored masterclasses. Drop-in clinics. Bottom-up use-case selection.

Ignition · the 3-day bootcamp · 1 week

Day 1 · Excite

Show what is inside the frontier. Speed gains that reframe what is possible.

Collective intelligence · live production

Day 2 · Enable

Push to the edge. Teams feel where AI breaks down on their own problems. Surface the tacit knowledge first.

Frontier mapping · common ground

Day 3 · Execute

Cross the frontier. Build real output with judgment on what to trust and what to override.

Production sprint · board-ready artefacts

Post-flight

Empower

Facilitated integration. Handover-quality review. Champion support and sparring. Capability transfer to the teams. Follow-up use-case support.

What teams build in our bootcamp

Typical use cases from our engagements. Click to expand.

Innovation Portfolio Prioritisation

AI-augmented scoring, ranking and visualisation of a scouted solutions portfolio. Turns weeks of analyst work into a structured, showcase-ready output in days.

Cross-Team Alignment & Handover Package

A common language and shared artefact between upstream and downstream teams. AI compresses alignment work that normally takes multiple meeting cycles into a single working session.

KPI Dashboard Prototype

Functional HTML front-ends pulling from existing data sources. What normally needs an external agency and weeks of scoping, built live by the team in hours.

Competitive Intelligence Deep Dive

Competitive landscape analysis across target segments, strategies and value pools. What normally takes analysts 2 to 3 weeks, produced in a single working session.

Board-Ready Business Case

From rough hypothesis to evidence-backed business case with financial projections, risk assessment and a stakeholder-ready narrative. Napkin sketch to board-ready in under a day.

Productivity-Gain Analysis

Pioneered with Future Energy Ventures in phase one: a clear baseline of where a team's time actually goes, so the gains are measured, not asserted.

What changes

Before and after the bootcamp.

Dimension	Before	After
How teams approach AI	Individual tinkering	Coordinated team capability
Output format	PowerPoint	Code, demos, working tools
Post-event momentum	Inspiration fades in a week	Self-organised next steps
Research & competitive intelligence	1-2 weeks	2-3 days
First-draft documents	3-5 days	1-2 days
Dashboard prototypes	Weeks (external)	Hours (internal)

The Challenger House Sprint

Two ways in.

Bring a teamWorking on complex problems

Innovation, strategy, transformation or marketing. A real team, working on its real problem.

Shared experience, immediate adoption and real workflow change.

The team's existing dynamics arrive with it, and we cater for that properly throughout the entire process. Not everyone will love this. That is part of the point.

Build a pathfinder teamA cross-functional ambassador tribe

Hand-pick your most curious talent and early-adopter types from across the organisation.

Internal champions, future operating models and the ideal vehicle for building the AI strategy itself.

Full implementation in their respective areas comes later.

Either way, AI adoption does not compound in individuals. It compounds in teams.

What we believe

Convictions earned in the room.

Not theory. Not frameworks borrowed from consultancies. What we can say with conviction after working live with teams on real problems. Click the arrows to expand.

01AI reveals the organisation.

Every engagement surfaces what was already there: trust gaps, decision bottlenecks, ownership ambiguity. AI just makes it visible faster. The question is whether you are willing to look.

02The real barrier is not fear of AI. It is fear of exposure.

People are not scared of the technology. They are scared of revealing how they think with it in front of colleagues. That is a psychological safety problem, not a training problem.

03Most of the work is invisible until you make it explicit.

Before AI can do anything useful, the work has to be made explicit. The knowledge that matters lives in people's heads, in side channels, in undocumented handovers. We surface it: common ground, shared language, the assumptions nobody had written down. That is where psychological safety stops being soft and becomes the thing the whole quarter depends on.

04Upskilling happens in the work, not before it.

Classroom training teaches tools. The Sprint builds capability. The difference: your team solves your actual problem with AI. The learning is the byproduct.

05The frontier is the product.

AI is spectacular at some tasks and dangerous on others, and the boundary shifts constantly. Teaching people to use tools is table stakes. Teaching leadership teams to navigate the jagged frontier between what AI handles and what requires their judgment, their empathy, their political instinct: that is the capability that compounds.

The research

The jagged frontier.

The seminal Mollick et al. study of 758 BCG consultants, published in Organization Science (2026), found that using AI on the right tasks produced 40% higher quality and 25% faster output. Using AI on the wrong tasks produced worse results than not using AI at all. The boundary between those two is invisible, irregular, and shifts constantly. This cannot be fully grasped via classic training or half-day workshops — it must be experienced. What makes the frontier dangerous is that AI does not just fail on the wrong tasks. It fails plausibly. That is why frontier fluency cannot be taught in a classroom.

The Challenger House Practice

Four ways of knowing.

Most organisations overindex on propositional knowing: facts, frameworks, slides, analyses. That is exactly where AI is fastest, and exactly what typical AI consulting delivers more of. Two weeks after the engagement ends, everyone reverts to old workflows and the investment evaporates. Navigating the frontier requires more than knowing facts. It requires knowing how, knowing what matters, and knowing through. We use John Vervaeke's 4P framework to activate all four ways of knowing, not just the one AI is about to automate.

Propositional

Knowing that

Facts, frameworks, claims. Reports, slides, analyses. This is where 95% of corporate time goes. Every AI tool automates this.

AI replaces this

Procedural

Knowing how

Skills built through repetition. How to brief AI, iterate, and decompose complex challenges into delegable tasks. Built through practice, not instruction.

Days 1-2

Perspectival

Knowing what matters

Judgment. Seeing what AI got wrong. Knowing when an artefact will land with a stakeholder and when it will be rejected.

Day 3 handover

Participatory

Knowing through

Trust through co-creation. Identity reshaped by what you do with whom. This is why the bootcamp is residential and cross-functional.

The human layer

You cannot read or talk your way to the bottom row, only work your way there.

Client success

What teams say after.

"Imagine someone promises to serve you the most amazing dessert. The ah moment comes when you taste it. Being here creates the moment when you taste the dessert in real life."

VP, Scouting & Programmes

"On Monday I stood in front of a spaceship and hit my leg very hard. I did not even manage to open the door. Now I feel I actually opened the door to the spaceship. I feel AI enabled."

Programme Manager

"The group self-organised next steps without being prompted. Ambassador roles. Weekly syncs. That is not a typical bootcamp outcome. That is proof the method works."

Internal retrospective

The Team

Not a consultancy. A frontier practice.

We build in real time what others promise in slides. We call this an AI Transition Practice. We assemble the right team for each engagement. Current core team:

Marcus Druen

Founder · Organisational Developer

25 years as an OD practitioner, facilitator and coach inside blue chips including Microsoft, Telefónica, Sanofi and E.ON. Built Challenger House because the pattern was the same everywhere: the system slows the people it hired to move fast. His craft is making the tacit explicit, the messy middle visible, so a team can actually work with it. Sees AI not as a tool problem but as an organisational design problem. Does the inner work, because you cannot guide people through transition without doing it.

André Cramer

Chief Learning & AI-Capability Designer

25+ years in tech, from Silicon Valley (Yahoo!) to Deutsche Telekom, where he led Strategic Communications and the Think Tank for the CTIO board member and co-founded a 500-person community for responsible technology. His work focuses on what AI does to judgment and decision quality. He developed the Plausibility Illusion framework and the cognitive-effects model that lets leaders experience how AI shifts decisions before being taught why. Curator of the DRANBLEIBEN newsletter.

Brittney Bean

Builder & Coder · Agentic Systems

Exited serial founder-operator. Builds and implements AI across SMEs and regulated environments, where security, compliance and data governance are not optional. Runs autonomous agents on her own infrastructure and stress-tests frontier tooling most AI consultancies have not heard of yet. The safety net: the person who makes sure what gets built in the room can survive procurement, legal review and IT governance.

What is your talking tax?

How much does your team spend talking about the work?

Status meetings, handovers, alignment sessions. Add it up.

Team size 12

Average annual cost per person €100,000

Hours per week on meetings, handovers, alignment 15h (38%)

€0

See what this looks like on your real work

You talk this much because the work itself was never properly written down.

Challenger House rule: name the elephant.

Most workflows are undocumented. Handovers are inconsistent. Knowledge lives in heads, not systems. AI does not fix this. It exposes it. And when teams use AI on problems it is not suited for, it makes the mess worse.

We name the gap before the bootcamp. Because naming it is the first step to fixing it.

AI does not transform your organisation. It reveals it.