Two-day working session
Where should AI actually be used in Problem Solving?
Organizations want to use AI. The real challenge is knowing where it genuinely adds value and how to implement it responsibly.
The central insight
Problem Solving is not one task.
Each sub-step places different demands on AI—from understanding context and structuring information to reasoning, generation, evaluation and verification.
01
Recognize
02
Define
03
Gather & Structure
04
Generate Causes
05
Evaluate Causes
06
Verify
07
Correct & Confirm
08
Reuse & Learn
Context understandingInformation structuringReasoningGenerationEvaluationVerification
A complete competence journey
Three questions. Three complementary perspectives.
Where can AI create value?
Sascha Laufenberg · Problem Solving × AI
15+ years in structured technical Problem Solving and Root Cause Analysis. CEO & co-founder of Causetec, working at the intersection of proven Problem Solving processes and applied AI.
View LinkedIn profile ↗
Can we technically realize it?
Mark Vismer · Enterprise AI & Technology
Hands-on experience leading AI transformation in a large regulated healthcare and medical-device environment, including AI-supported Problem Solving in practice.
View LinkedIn profile ↗
How do we make it stick and scale?
Jens Refflinghaus · Organizational Implementation
23+ years in organizational Problem Solving and extensive leadership experience, including regional Managing Director. Focused on capability building and Human Performance.
View LinkedIn profile ↗Problem Solving × AI
AI-supported
Problem Solving
that actually works
Problem Solving
that actually works
Enterprise AI
& Technology
& Technology
Organizational
Implementation
Implementation
Facilitator track record
What clients say about working with your facilitators
“Sascha has an incredible talent for making complex root cause analysis feel structured, approachable, and effective. I learned a tremendous amount from him - lessons that stuck with me and that I still apply in my work today.
…he doesn’t just solve problems - he equips people with the tools and confidence to solve them themselves.”
“For the future direction of our company, Auerhammer Metallwerk GmbH, the milestones we have achieved form a solid foundation. Thank you, Jens Refflinghaus, thank you, Processfuse, for your competent, open, and always authentic guidance and support. That was – and still is – amazing! Together, we have taken a very important step. More can now follow!”
Workshop output
Leave with more than ideas.
Select one specific lifecycle step or use case, then develop it through every critical implementation dimension during the workshop.
01
Business Pain
02
AI Opportunity
03
Human × AI Roles
04
IT / Data / Knowledge Readiness
05
Human Performance
06
Governance
07
Stakeholders
08
NOW / NEXT / LATER
09
Risks
10
Experiment & Investment Case
One prioritized AI Problem Solving use case and a practical case for implementation.
How the workshop works
Not two days of presentations.
Learn it. Apply it. Build your own implementation case.
This is a working session, not a two-day lecture. Each part of the workshop follows the same practical cycle:
Theory / Learn
Get the essential concepts and frameworks needed to make the next decision.
Case Study / Apply
Apply them to a shared industrial case and see how the approach works in practice.
Real-Life Application / Build
Transfer the approach directly to your own organization and selected AI Problem Solving use case.
The working document
One Canvas guides the entire two-day journey.
Rather than treating each session as an isolated topic, the workshop follows a practical AI implementation Canvas. You build it progressively throughout the two days — from the initial business problem and AI opportunity through technology, data, people and organizational requirements to implementation priorities, risks and the first experiment.
AI × PSImplementation Canvas
Two-day working document01Business Pain
02Desired Outcome / Success & Value
03Problem-Solving Lifecycle × AI
RecognizeDefineGather & StructureGenerate CausesEvaluate CausesVerifyCorrect & ConfirmReuse & Learn
04IT, Data & Knowledge ReadinessCurrent → Needed → Gap
05Human Performance Requirements
06Governance & Boundaries
07Stakeholders & Implementation System
08Implementation PrioritiesNow / Next / Later
09Pre-Mortem / Critical Risks
10Experiment & Investment Case
First Action When I Return to Work→
Idea
Requirements
Gaps
Priorities
Risks
Experiment
Next Actions
By the end of the workshop, you will have one clearly defined AI Problem Solving use case, the key requirements and gaps understood, and concrete actions for moving it forward.
Participant agenda
Your Two-Day Working Journey
Where should we use AI in Problem Solving?
Start with the Problem, Not the AI
What problem are we actually trying to solve?
Many AI initiatives fail because they start with the technology rather than with a meaningful business problem.
Understand How Problem Solving Works
Where does our Problem Solving process work and break down?
Problem Solving is not one task. It consists of different sub-steps, and each sub-step places different demands on AI — from understanding context and structuring information to reasoning, generating, evaluating and verifying.
Find the Right AI Opportunities
Where across the Problem Solving lifecycle can AI create the most value?
Breaking Problem Solving into its individual steps makes it possible to identify specific AI opportunities instead of treating “AI for Problem Solving” as one broad initiative.
Human × AI: Who Should Do What?
What should AI do — and what must remain a human responsibility?
The goal is not maximum automation. Clear roles, decision rights and human oversight allow organizations to gain AI leverage while retaining control.
From AI Opportunity to Technical Reality
What technology, data and knowledge would our AI opportunity require?
A promising AI opportunity still has to work in the real world. Understanding what it requires helps separate technically realistic opportunities from attractive ideas.
Choose Your Priority AI Use Case
Which part of our Problem Solving lifecycle should we implement first?
Not every opportunity should be pursued at once. Participants select one lifecycle step and AI use case to take forward and develop in depth during Day 2.
What does it take to put the use case into reality?
Assess Your IT, Data & Knowledge Readiness
What do we have today — and what is missing to make our use case work?
Mapping Current → Needed → Gap makes the technical prerequisites and dependencies visible before implementation starts.
Design the Human Side of the Change
What must people do differently for the use case to succeed?
Technically sound initiatives still fail when people do not adopt and carry the change. Roles, expectations, skills, processes and management support therefore need to be designed alongside the technology.
AI Boundaries & Enterprise Reality
What constraints and guardrails must be designed?
Learn from the reality of building AI-supported Problem Solving inside a large, regulated healthcare environment — including technical limitations, governance, oversight and practical struggles.
Build the Implementation System
Who needs to enable, approve, own and use the solution?
Implementation crosses functions. Making ownership, dependencies and stakeholder requirements explicit reduces the risk of a viable use case getting stuck during implementation.
Turn the Gaps into Clear Priorities
What do we need to do first?
Turn the identified gaps into clear priorities so you leave the workshop with a concrete next action rather than a long list of observations.
Pressure-Test the Implementation
What could prevent our next step from succeeding — and how do we secure it?
Quickly thinking through the risks around the chosen initiative and next action helps secure implementation and increases the chance of moving forward successfully.
Design the Experiment & Investment Case
How do we test the use case — and what decision do we need from management?
Turn the use case into a practical experiment with a hypothesis, scope, baseline, success criteria, required resources and clear Scale / Modify / Stop decision points.
Who should attend
For leaders who can turn insight into action.
Designed for people responsible for improving how their organization solves problems—with enough mandate to influence implementation.
Quality
Root Cause Analysis
Operational Excellence / CI
Manufacturing & Operations
Problem Solving Capability
Practical information
Plan the room. Bring a real challenge.
DateTo be confirmed
LocationOnline via MS Teams
LanguageEnglish
Group sizeUp to 12 participants
Price€1,390 per participant
Pre-workBring one real Problem Solving challenge
IncludedWorkshop materials, canvas and facilitation
FormatTwo full working days
Registration
Request a place
Interested in joining? Send us your details and we’ll confirm availability and provide the registration and invoicing information.
Up to 12 participants. We’ll confirm your place by email.
The next step