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NEW -Strategic AI Consulting

Do you want to lead companies through AI transformation?

Experienced consultants who want to advise clients on AI transformation

Consultants who want to lead AI implementation projects

Strategy consultants who want to expand their portfolio with AI expertise

For whom

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This is What You Achieve:

Developing a lucrative consulting branch

Become a sought-after AI transformation expert in a rapidly growing market

Confident leadership

Guide companies safely through complex AI implementation projects

Measurable successes

Create demonstrable competitive advantages for your customers through tailored AI strategies

Position of trust

Build long-term client relationships as a strategic AI consultant

Comprehensive expertise

Guide companies safely through complex AI implementation projects

1

Basics of AI transformation consulting

  • AI Landscape for Consultants

  • AI Transformation Models and Frameworks

  • AI Potential Analysis in Practice

2

Industry-specific AI applications

  • Industry and Manufacturing

  • Financial Sector and Professional Services

  • Public Sector and Healthcare

3

Change management for AI transformations

  • Special features of AI-related change management

  • AI readiness assessment and culture development

  • Rollout and scaling strategies

4

AI governance and risk management

  • Designing AI governance frameworks

  • AI-specific risk management

  • Compliance and certification

5

Strategic AI roadmap and business case

  • Developing an AI Vision and Strategy

  • Business Case and Investment Planning

  • Practical Case Study: AI Strategy Development

Contents

STRATEGIC AI CONSULTING

MODULE II

STRATEGIC AI CONSULTING - Detailed Content
AI landscape for consultants

  • Current status of AI technology and application areas

  • Market overview: providers, platforms, specialized solutions

  • Technological development paths and their evaluation

  • Regulatory framework in various markets (EU AI Act, etc.)

AI transformation models and frameworks

  • The METIS maturity model for AI implementations

  • Phase models of successful AI transformations

  • Critical success factors and typical pitfalls

  • Balanced scorecard for AI initiatives

Practice of AI potential analysis

  • Methodical identification of AI use cases

  • Assessment grid for business impact and technical feasibility

  • Portfolio approach for AI use cases

  • Economic evaluation models for AI potential

1. Basics of AI transformation consulting
Industry and production

  • Predictive Maintenance and Asset Management

  • AI-supported Quality Assurance and Process Optimization

  • Supply Chain Intelligence and Logistics Optimization

  • Case Studies: Successful Implementations and ROI Measurements

Financial sector and professional services

  • Risk assessment and fraud detection

  • Automated compliance and document review

  • Customer journey optimization and personalized advice

  • Next-generation analytics for financial service providers

Public sector and healthcare

  • Citizen services and intelligent administration

  • AI-supported diagnosis and therapy support

  • Ethical framework and specific features

  • Data protection-compliant use of AI in sensitive areas

2. Industry-specific AI applications
Special features of AI-related change management

  • Overcoming AI-specific resistance and fears

  • Designing participation processes for AI implementations

  • Training concepts for various stakeholder groups

  • Leadership and culture in AI-transformed organizations

AI readiness assessment and culture development

  • Measuring organizational AI readiness

  • Change readiness check for various stakeholders

  • Developing an AI-friendly organizational culture

  • Promoting a spirit of experimentation and tolerance for mistakes

Rollout and scaling strategies

  • From the pilot phase to widespread implementation

  • Multiplier concepts and internal champions

  • Graduated implementation models and quick wins

  • Sustainable integration into structures and processes

3. Change management for AI transformations
Designing AI governance frameworks

  • Automated customer and market research

  • AI-supported competitive intelligence

  • Personalized proposal creation and pitch preparation

  • Differentiated value proposition with AI components

AI-specific risk management

  • Data-driven pattern recognition and anomaly identification

  • Accelerated hypothesis generation and validation

  • Combined qualitative and quantitative analyses

  • Multi-perspective framing of complex problems

Compliance and certification

  • Co-creative idea generation with AI

  • Systematic validation and risk assessment of solution approaches

  • Tailored designs for various stakeholders

  • Cost-benefit analyses and business case development

4. AI governance and risk management
Developing an AI vision and strategy

  • Positioning AI in a strategic context

  • Alignment with corporate goals and value drivers

  • Future scenarios and strategic options

  • Roadmap development with clear milestones

Business case and investment planning

  • AI-specific ROI calculation and value measurement

  • Total cost of ownership for AI implementations

  • Evaluation of build-vs-buy options

  • Financing models and budgetary integration

Practical case study: AI strategy development

  • Interactive work on a real-life business case

  • Development of a complete AI roadmap

  • Presentation and constructive feedback

  • Reflection and transfer to your own consulting situations

5. Strategic AI roadmap and business case
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