Michael Weiße · Senior Project Manager (gn) – AI Enablement & Transformation
I bring structure to complex AI and transformation initiatives: strategic goals become clear priorities, executable Workstreams and decisions that move Delivery forward.
As a PMP®-certified transformation and IT project manager with around ten years of international Delivery experience, I lead programmes across countries, regions, vendors and technical teams – from UAT and migration through Go-live, Hypercare and stable operations. I use AI pragmatically to improve Delivery and collaboration: hands-on, with clear Guardrails and Human Review.
01 · Executive profile
I manage close to 20 AI customer projects in parallel — not as a loose collection of initiatives, but as a portfolio.
I prioritise based on strategic relevance, Business Value, dependencies and Delivery Readiness. The result is clear decisions, reliable next steps and a working cadence that enables assigned teams to execute with confidence.
My current delivery experience ranges from production AI agents to AI Adoption. The foundation: around ten years of international IT and transformation Delivery across countries, regions, vendors and technical teams.
02 · Role fit
Not another person managing one isolated pilot. Someone who can prioritise a portfolio, move Use Cases into production, increase autonomy without losing control and treat Adoption as part of Delivery.
I manage close to 20 AI customer projects in parallel and decide where attention, capacity and Delivery focus will create the greatest impact. Initiatives are assessed based on strategic customer relevance, economic contribution, delivery demand and dependencies. Every initiative gets a clear next step — or loses priority.
I lead Use Case and requirements clarification, feasibility and risk assessment, and the introduction of customer-specific Copilot Studio Agents. No pilot without an Owner. No production without Guardrails. No Go-live without a reliable operating model.
Human-in-the-Loop is the default starting point. Autonomy does not increase simply because the technology allows it. It increases when output quality, traceability, risk and potential production impact justify it.
AI Adoption is not a communication project added after rollout. It combines Readiness, Governance, Use Case consulting, rollout, Change and communication. Customer feedback and surveys are not the final report. They are input for the next iteration.
03 · Impact
04 · Cases
Close to 20 Microsoft AI customer projects run in parallel — each with individual Use Cases, different stakeholders and varying levels of impact on productive processes.
Michael does not manage them as a loose collection of pilots. He manages them as a portfolio. He prioritises based on strategic customer relevance, economic contribution, Delivery Readiness and implementation demand. Together with customers and teams, he clarifies requirements, risks and next steps.
Productive solutions include Copilot Studio Agents for Knowledge Search, Classification, Support and cross-system tasks. The portfolio also includes Microsoft 365 Copilot Adoption initiatives.
Human-in-the-Loop is the starting point. Greater autonomy is introduced only when output quality, traceability and potential production impact justify it.
Outcome: productive AI solutions, controlled evolution and reduced manual effort. Portfolio Leadership means deciding what ships next — and why.
A historically fragmented Contact Center landscape had to be transformed from Legacy to Cloud — across more than 40 countries, four international regions and dozens of locations.
Michael was responsible for international rollout and Stakeholder Management: decision paths, dependencies, risks, UAT, migration and Go-live. He coordinated multiregional teams without disciplinary authority, surfaced conflicts early and kept Business, vendors, Engineering and operations aligned around one shared direction.
Outcome: more than 1,000 Agents successfully migrated into a standardised Cloud environment — within a programme exceeding €10 million and with responsibility extending into Hypercare and stabilisation. Scale is not copy and paste. It is controlled adaptation.
The starting point was a productive, highly customised Dynamics 365 Contact Center environment with fragmented processes, technical dependencies and complex Governance requirements.
As Project Lead, Michael translated operational and technical requirements into prioritised, decision-ready Workstreams. Compliance and Data Protection were not added shortly before Go-live. They were embedded early in decision-making and execution.
Productive capabilities included Case Management, Copilot-supported Agent Assistance, Case Automation and Reporting. The project was completed and formally accepted. Michael remained close to execution after Go-live, supporting Hypercare and stabilisation where needed.
Outcome: fragmented requirements became a productive solution connecting processes, technology and Governance. AI value needs more than a feature. It needs a process that can carry it.
05 · Hands-on
I do not use AI only in customer projects. I build my own solutions, apply them in daily work and improve them where real usage exposes weaknesses.
Built independently in Microsoft Power Automate: Teams transcripts are transformed into structured Meeting Minutes and Action Items. Guardrails prevent fabricated content. Human Review remains mandatory. Confirmed tasks can then be transferred systematically into Microsoft Planner.
Less follow-up effort. Clearer ownership. Faster execution.
Designed and developed with AI assistance as a web application in Next.js — with 297 questions, Practice, Exam, Review and Dashboard functions, plus a successful Production Build. Multi-user login and Cloud Sync are deliberately planned as the next development stage.
I did not just study the content. I built a system for learning it.
Created Change and communication plans with AI support and reviewed them through an AI Quality Review for completeness, consistency and risk. Confidential information remains strictly separated. Final accountability stays human.
AI accelerates the work. Human Review protects the outcome.
Designed a reusable Workflow Model with clearly separated roles for creation, Quality Review and improvement, then tested those roles on real deliverables. No claimed Multi-Agent system. No artificial complexity. First a reliable model — then potential orchestration.
Hands-on means testing it yourself, making limitations visible and scaling only what holds up in daily work.
06 · Enablement
Enablement does not happen through licences, presentations or a single training session. It works when people understand where AI creates value – and the organisation defines how safe, measurable usage becomes possible.
I connect Readiness, Governance, Use Case consulting, rollout, Change, communication and Enablement into one continuous approach. Feedback and surveys are not a final report. They reveal where usage stalls, relevant Use Cases are missing or Guardrails need to be adapted.
Not a licence rollout. A managed change process.
As an active contributor to an internal AI Community, I bring a clear focus on AI Project Delivery. I co-developed sessions on why AI projects require a different delivery approach and presented them to a cross-functional audience including management.
Knowledge becomes valuable when it changes decisions and Delivery.
I co-developed a phase model, Governance principles and Quality Gates for AI projects, aligned the approach internally and tested it in a concrete project context. Not as an organisation-wide framework. As a solid foundation that can now evolve through real Delivery experience.
Enough structure to reduce risk. Not enough process to kill momentum.
I am currently deepening my knowledge of AI project lifecycles, Data Readiness and Trustworthy AI through CPMAI. Not as a replacement for practical experience – but as a systematic extension of my Delivery practice.
Enablement is not awareness. It is repeatable execution.
07 · Ask Michael
Because AI Enablement is not a future topic for me. It is part of my current Delivery responsibility. I manage close to 20 Microsoft AI customer projects in parallel, prioritise them based on strategic relevance, economic contribution and implementation demand, and lead the assigned teams through Delivery. This is backed by around ten years of international transformation experience. I understand the path from Use Case to stable operations — including the problems in between.
Customer-specific Copilot Studio Agents for Knowledge Search, Classification, Support and cross-system tasks. The portfolio also includes Copilot Adoption initiatives. My role spans portfolio prioritisation, customer consulting and Use Case clarification through to project leadership, Governance and Delivery Management. Technical implementation is handled by specialist teams.
I do not need to build every component myself. I need to ensure that the right solution gets built.
With Human-in-the-Loop as the starting point. The level of autonomy depends not on what is technically possible, but on Use Case complexity, output quality, traceability, risk and production impact.
Greater autonomy is earned, not assumed. Control first. Autonomy second.
Adoption does not begin after rollout. I connect Readiness, Governance, Use Case consulting, Change, communication and Feedback Loops into one manageable approach. Customer feedback and surveys directly influence the next decisions. I lead teams through transparency, clear priorities and a shared goal — not through hierarchy.
Alignment is reached when people have not only agreed, but know what happens next.
08 · Motivation
TQG combines fast-growing Consumer Brands with product development, production, logistics, E-Commerce and retail. This creates exactly the kind of complexity in which AI cannot succeed as an isolated innovation project.
It requires clear priorities, Business Ownership, technical integration, Governance and a Delivery Model that moves initiatives from Use Case to stable operations.
That is already the focus of my work in customer environments: I manage AI portfolios, guide individual Use Cases into execution, connect rollout with Adoption and increase autonomy only where quality and risk justify it.
What attracts me to TQG is the next level of scale: not delivering individual customer projects one after another, but helping make AI structured and usable across a growing organisation.
There is also a personal connection. Sport, performance, nutrition and continuous improvement are part of my everyday life. I use products from ESN and More Nutrition myself.
Performance. Scale. Execution.
At TQG, these areas come together. Not as an artificially constructed fit, but as a challenge that matches the way I work:
Prioritise. Execute. Learn. Scale.
09 · Personal
I am currently preparing for a marathon.
To me, that requires more than endurance: choosing the right pace, measuring progress honestly, accounting for setbacks and continuing consistently.
I rarely think in short sprints at work either. Complex transformations require structure, a realistic rhythm and the discipline to keep testing decisions against reality.
I approach AI learning in the same way: test, understand, improve — and never stop simply because something already works today.
Structure. Endurance. Continuous improvement.
michaelwe97@gmail.com
Frankfurt · open to coordinated, occasion-based on-site presence in Hamburg · Three months’ notice to the end of the month.