ExpertiseAI-native operating model

Everyone is flying AI – few are redesigning the runway for real productivity gains

AI is scaling everywhere - Productivity is not

AI adoption is accelerating across industries. Tools are widely available, pilots are everywhere, and use cases continue to grow. Yet, measurable productivity gains remain limited.

The reason is structural. Most organizations apply AI on top of operating models designed for a pre-AI world. Roles remain unchanged, processes fragmented, and governance disconnected from execution.

As a result, AI improves isolated activities but fails to transform overall performance.

The Companies need a Strategic Reframe: From Use Cases to Structural Productivity

Most organizations focus on identifying more AI use cases or reducing costs. This leads to fragmented initiatives without lasting impact.

The relevant question is different: how to systematically increase output per euro through AI.

This requires a consistent transformation logic that connects use cases, operating model design, and execution. The objective is not more AI, but structurally better performance.

Redefining the Future of WorkX

Automation cuts effort. Augmentation multiplies impact. AI-native operating models are engineered across the entire value chain - from operational to leadership roles.

Philipp Mudersbach

Managing Director
Philipp.Mudersbach@fortlane.com

All it takes is ROLES and TASKS to make
AI potential measurable

Redesign work. Become AI-native.

We break down every role into its underlying tasks & work becomes transparent at the value creation level

We measure AI exposure and economic impact at task level — in euros, not theory

We redesign roles and workflows by reassembling tasks based on automation depth and value

X"AI-driven productivity gains stem from reimagining how work is done, not merely accelerating existing processes."

Adrian Drettmann, Principal at Fortlane Partners
Adrian.Drettmann@Fortlane.com

AI roadmap: Transition into AI governance, roles and responsibilities, process and system requirements – aligned with existing AI initiatives and gaps

Define target operating model and governance
Establish roles, decision rights, and steering aligned with AI-driven processes
Translate insights into concrete change requirements
Derive role shifts, capability needs, process adaptations, and system enablers

Create a fact-based, task-level data foundation as basis for AI decisions:

Integrate and structure role and task data
Consolidate fragmented job descriptions into a consistent, task-level dataset with clear activity granularity
Enrich with proprietary task intelligence
Map tasks against external benchmarks (e.g. Impact Prism) to add automation logic, AI applicability patterns, and effort drivers
Create transparency on data gaps and quality
Identify inconsistencies, missing data, and structural biases to ensure robustness of downstream analysis

Validation with selected client stakeholders and scientific analysis/ evaluation of AI potential

Assess AI feasibility at task level
Evaluate automation and augmentation potential using scientific models and real workflow constraints
Quantify impact, effort, and dependencies
Size productivity gains, implementation complexity, and system/data prerequisites per task cluster
Validate with business and technical stakeholders
Stress-test results in workshops to align on feasibility, prioritization, and organizational reality

AI roadmap: Transition into AI governance, roles and responsibilities, process and system requirements – aligned with existing AI initiatives and gaps

Define target operating model and governance
Establish roles, decision rights, and steering aligned with AI-driven processes
Translate insights into concrete change requirements
Derive role shifts, capability needs, process adaptations, and system enablers

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AI-native operating model

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Philipp Mudersbach

Managing Director

Organizational Performance and People, AI-native Operating Models, Tech and AI, Innovation & Business Model Evolution, Change Management, Industrials and Automotive, Energy and Utilities, Mobility, Transportation, and Infrastructure

Dusseldorf, Germany

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Adrian Drettmann

Principal

Restructuring and Turnaround, Organizational Performance and People, Workforce Transformation, AI-native Operating Models, Target Operating Model, Process Excellence, Strategy and Growth, Performance Programs, Digital Transformation, Technology, Media, and Telecommunication, Industrials and Automotive

Munich, Germany