01
Reality
Understand operational reality
Start with the work as it really happens: jobs to be done, constraints, safety requirements, local workarounds, and execution pain points.
Jobs to be done · Operational constraints · Execution pain points
Approach & Work
Felix Schüßler · Digital & AI Transformation Executive
Where operating theory meets execution reality.
The work starts with how industrial operations actually run, then turns that logic into digital ecosystems, decision routines, and delivery capability.
Transformation methodology
Process-centric operational transformation.
The sequence starts with operational reality, then connects process, accountability, data, products, AI support, and organizational capability into one transformation system.
02
Process
Map processes and decision flows
Make workflows, interfaces, responsibilities, information flows, and decision points visible before deciding which technology belongs where.
Workflow interfaces · Decision flows · Information needs
03
Ownership
Define human accountability
Clarify who remains accountable for execution and decisions, then design role-specific journeys that make digital support usable in real operations.
Role journeys · Ownership model · Human-in-the-loop
04
Data
Design operational data models
Translate the operating model into integrated data structures that support visibility, reporting, decision support, and future AI use cases.
Data structures · Source systems · Operational visibility
05
Ecosystem
Build digital and AI ecosystems
Develop intuitive workflow products, reporting layers, automation, and AI-supported routines that sit inside daily operations.
Workflow products · AI augmentation · Adoption rhythm
06
Scale
Scale organizational capability
Establish governance, product organizations, support structures, and feedback loops so transformation survives beyond the first launch.
Product organization · Governance · Support model
Execution patterns
Recurring problems before sector detail.
Planning, ownership, source-system logic, and decision cadence are the repeatable layer. Offshore wind is one strong context, not the only pattern.
Operational planning ecosystems
Turning fragmented work inputs into decision-ready planning flows for asset-heavy teams.
Execution governance
Clarifying who decides, who owns, and how priorities move across business and IT.
Digital product organizations
Structuring product roles, stakeholder cadence, and delivery routines that can scale.
AI-supported workflows
Embedding AI where it improves prioritization, knowledge access, or decision support without blurring accountability.
Contexts represented
Offshore Wind
Planning, maintenance workflows, SAP PM/MM smart layers, and offshore execution routines.
Aviation
MRO, engine lifecycle work, and safety-critical technical service environments.
Retail
Digital product platforms, app delivery, and internal startup execution.
Logistics
Innovation projects and operational technology concepts in networked environments.
Product Organizations
Product ownership models, senior stakeholder routines, and matrix structures.
Offshore wind planning
Anonymized conceptAI-supported work planning flow for offshore wind assets
A concept layer on top of SAP PM that turns technical, commercial, and access constraints into a human-approved work plan.
Anonymized concept scenario, shown to illustrate operating-system design logic.
Planning inputs
SAP PM work orders and notifications
SCADA and condition signals
Weather and wave windows
Vessel and crew availability
Spare parts and tooling readiness
Turbine criticality and production impact
Power prices and production forecasts
Grid, curtailment, permit, and safety constraints
Decision flow
01
Operational inputs
02
Economic planning logic
03
AI-supported plan proposal
04
Human approval and assignment
05
SAP PM execution feedback
Context
SAP PM remains the system of record for offshore wind O&M work orders, notifications, maintenance history, and execution feedback.
Challenge
Planning teams need to reconcile work orders, asset condition, weather windows, vessels, crews, spare parts, grid constraints, and commercial exposure before deciding what should happen next.
Intervention
An integrated planning layer would consolidate relevant inputs, rank work packages by operational risk and economic value, propose a multi-day plan, support assignment, and sync approved execution plans back to SAP PM.
Outcome
The approach would enable clearer prioritization, fewer manual reconciliation loops, and stronger alignment between access windows, operational criticality, market value, and execution capacity.
SAP PM as system of recordMulti-source planning inputsHuman-approved AI support
Selected work
Transformation work in operational environments.
Compact examples of context, intervention, and operating outcome.
Operational planning modernization for offshore operations
~90% lower planning effortSAP PM/MM integrationEight-figure value opportunity
Context
International offshore operations with complex planning workflows, SAP PM/MM integration needs, and globally distributed field teams.
Challenge
Operational planning was spread across fragmented processes, disconnected data sources, and local workflows, making consistent decisions slow and difficult to scale.
Intervention
Led a proprietary planning product suite integrated with SAP PM/MM and aligned operational, technical, and commercial specialists around one workflow layer.
Outcome
Reduced planning effort by approximately 90% and underpinned an estimated eight-figure value opportunity.
Product operating model for industrial digitalization
3 → 6 → 14 Product Owners~45 specialists€4M annual portfolio
Context
An enterprise offshore product landscape moving from fragmented IT projects toward a global product-led operating model.
Challenge
Digital initiatives lacked a unified operating rhythm, making prioritization, delivery ownership, and adoption harder to scale.
Intervention
Defined the digital and AI transformation roadmap, introduced portfolio governance, and built international Product Owner and user communities.
Outcome
Scaled the organization from 3 to 6 and ultimately 14 Product Owners across a delivery ecosystem of approximately 45 specialists.
Data and AI foundations across global wind farms
18 wind farms300+ usersLLM-enabled knowledge retrieval
Context
Global offshore operations with inconsistent data structures, reporting needs, and knowledge distributed across sites and teams.
Challenge
Operational reporting, decision support, and scalable AI adoption were constrained by fragmented data and inconsistent cross-site foundations.
Intervention
Structured reusable data and platform foundations and initiated LLM-enabled knowledge retrieval and AI decision-support use cases.
Outcome
Established foundations serving 18 offshore wind farms and 300+ users while creating a practical path to frontline AI support.
Digital warehouse workflows across offshore sites
25% workload reductionReal-time workflowsMulti-site standardization
Context
Multi-site warehouse operations relying on paper-based tracking and locally varied administrative routines.
Challenge
Inventory visibility and operational consistency were limited by manual handoffs and non-standard workflows.
Intervention
Replaced paper tracking with standardized real-time workflows and coordinated rollout across multiple offshore sites.
Outcome
Improved inventory accuracy and enabled leaner administration, including a 25% workload reduction at one measured site.
Scaled adoption of governed operational tools
1,000+ usersGoverned rolloutOperational adoption
Context
Enterprise teams needing faster tooling, stronger governance, and practical digital workflows.
Challenge
Digital tools risked remaining local experiments rather than governed capabilities adopted at scale.
Intervention
Scaled initiatives from strategy through deployment, embedding governance and operational adoption routines.
Outcome
Established a low-code platform adopted by more than 1,000 users.