Your Path to Digital Excellence

Digital Strategy

A well-founded digital strategy is the key to successfully transforming your organization. Together with you, we develop a tailored roadmap for your digital future — one that connects technological innovation with your business objectives.

  • Increasing competitiveness through digital business models
  • Improving customer retention through optimized digital touchpoints
  • 30% efficiency gains through intelligent process optimization
  • Measurable ROI through strategic digital initiatives

Your strategic success starts here

Our clients trust our expertise in digital transformation, compliance, and risk management

30 Minutes • Non-binding • Immediately available

For optimal preparation of your strategy session:

  • Your strategic goals and objectives
  • Desired business outcomes and ROI
  • Steps already taken

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Certifications, Partners and more...

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Comprehensive Digital Strategy for Forward-Looking Organizations

Our Strengths

  • Over 11 years of expertise in digital transformation in the DACH region
  • Comprehensive consulting approach with measurable value contribution
  • Proven track record in complex digital projects
  • Interdisciplinary team of experts in strategy, technology, and change management

Expert Tip

Invest sufficient time in stakeholder analysis and understanding their needs. A successful digital strategy must consider and involve all stakeholder groups. Companies that engage their employees early in the transformation process achieve a 62% higher success rate in digital projects.

ADVISORI in Numbers

11+

Years of Experience

120+

Employees

520+

Projects

We support you with a structured approach to developing and implementing your digital strategy. Our proven methodology ensures that all relevant aspects are considered and a tailored, actionable roadmap is created.

Our Approach:

Phase 1: Digital Maturity Assessment - Comprehensive analysis of your digital maturity and competitive environment

Phase 2: Digital Vision & Ambition - Development of your individual digital vision and strategic objectives

Phase 3: Digital Roadmap - Elaboration of the strategic roadmap with prioritized initiatives

Phase 4: Implementation Planning - Detailed planning of implementation with concrete measures

Phase 5: Agile Execution - Supporting implementation with agile methods and continuous adaptation

"According to a Bitkom study, 73% of digital projects fail because a clearly defined strategy is lacking. We change that by consistently aligning technology and business value, and by mapping out a pragmatic path to implementation."
Asan Stefanski

Asan Stefanski

Head of Digital Transformation

Expertise & Experience:

11+ years of experience, Applied Computer Science degree, Strategic planning and management of AI projects, Cyber Security, Secure Software Development, AI

Our Services

We offer you tailored solutions for your digital transformation

Digital Strategy Development

Development of a tailored digital strategy for your organization that connects technological innovation with your business objectives and creates a clear competitive advantage.

  • Digital Maturity Assessment and potential analysis
  • Development of the digital vision and objectives
  • Elaboration of strategic directions
  • Creation of the digital roadmap with prioritization

Digital Business Models

Identification and development of effective digital business models that open up new revenue streams and sustainably strengthen your market position.

  • Analysis of market trends and effective technologies
  • Design Thinking workshops for idea generation
  • Development of business cases and monetization strategies
  • Prototyping and MVP development for rapid market validation

Digital Transformation

Comprehensive support for your organization through digital change with a focus on people, processes, and technologies to achieve lasting transformation.

  • Change management and digital culture development
  • Process optimization and digitalization
  • Technology integration and modernization
  • Building digital competencies and organizational structures

Digital Leadership

Development of digital leadership competencies and structures that enable your organization to successfully shape and manage digital change.

  • Digital Leadership Assessments and coaching
  • Building digital governance structures
  • Development of digital innovation ecosystems
  • Establishment of agile leadership and working methods

Our Competencies in Digitale Transformation

Choose the area that fits your requirements

AI – Artificial Intelligence

Unlock the impactful potential of artificial intelligence with ADVISORI's comprehensive AI expertise. As a leading AI consultancy, we develop strategic AI solutions that make your organization future-proof, create competitive advantages, and simultaneously ensure the highest standards in governance, ethics, and EU AI Act compliance.

Frequently Asked Questions about Digital Strategy

What are the most important success factors for effective digital strategy consulting?

Effective digital strategy consulting requires a targeted interplay of key factors:

🎯 Strategic Alignment

Development of a clear digital vision and measurable transformation goals
Elaboration of a detailed digital strategy with a business focus
Establishment of a solid governance framework for implementation
Consistent alignment with the corporate strategy
Integration into existing strategy processes

👥 Leadership & Culture

Active commitment from top management as a driver of digitalization
Development of an innovation-friendly corporate culture
Implementation of a systematic change management process
Building digital leadership competencies at all levels
Creating space for innovation and experimentation

Processes & Methods

Introduction of agile working methods and DevOps practices
Continuous process optimization through analytics and AI
Integration of modern development methods (Design Thinking, Lean Startup)
Establishment of cross-functional teams for digital initiatives
Implementation of iterative development cycles with rapid feedback loops

📊 Data & Decisions

Building a data-driven decision-making culture
Implementation of data governance structures
Development of KPI frameworks for measuring the success of digital initiatives
Use of advanced analytics for deeper customer insights
Creating data continuity across silos

How long does it take to establish a digital strategy in practice?

Establishing a viable digital strategy unfolds over several phases and years:

️ Short-term (3–6 months)

Analysis of digital maturity and the market environment
Definition of strategic goals and digital ambition
Development of the digital strategy with a focus on quick wins
Creation of the initial roadmap with prioritized measures
Building governance structures for implementation

🔄 Medium-term (6–18 months)

Implementation of first quick wins with visible results
Building digital competencies and teams
Process optimization and digitalization in core areas
Pilot projects for effective business models
Initial technological modernization of the IT landscape

🎯 Long-term (18–36+ months)

Full digital transformation of the organization
Cultural change and new ways of working
Scaling successful digital initiatives
Continuous optimization and adaptation of the strategy
Establishment of digital ecosystems and platforms

️ Key factors affecting the timeline:

Company size and organizational complexity
Digital starting position and legacy systems
Available resources and willingness to invest
Change management capabilities and corporate culture
Industry-specific requirements and competitive pressure

How can the success of a digital strategy be measured objectively?

The value of a digital strategy is made objective through a comprehensive set of KPIs:

📊 Business KPIs

Revenue growth through digital channels and offerings
Digital share of total revenue (typically an increase of 15–25%)
Customer Lifetime Value (increase of 20–40% through digitalization)
Customer acquisition costs (reduction of 10–30% through digital channels)
Time-to-market for new products (reduction of 30–50%)

🎯 Operational KPIs

Process efficiency and throughput times (improvement of 20–35%)
Degree of process automation (increase of 40–60%)
System availability and performance
Error rates in digital processes
Resource utilization and efficiency

👥 Customer KPIs

Customer Satisfaction Score (CSAT) for digital interactions
Net Promoter Score (NPS) for digital offerings
Customer Effort Score (CES) for digital touchpoints
Usage of digital touchpoints and self-service rate
Conversion rates in digital channels

🚀 Innovation KPIs

Number of implemented digital initiatives
Time-to-value of digital projects
Success rate of digital innovations
Share of revenue from new digital products
Return on Digital Investment (RoDI)

How does digitalization differ from building a digital strategy?

Digitalization and digital strategy are closely linked, yet differ in fundamental aspects:

🔍 Conceptual Differences

Digitalization: Technical conversion of analog processes and data into digital form
Digital strategy: Comprehensive plan for using digital technologies to create business value
Digitalization focuses on the "how" of technical implementation
Digital strategy defines the "what" and "why" of digital transformation
Digitalization is operational; digital strategy is strategic

🎯 Objectives and Focus

Digitalization: Efficiency gains and cost savings through automation
Digital strategy: Business model innovation and value creation through digital opportunities
Digitalization often has an internal focus (processes, systems)
Digital strategy has both an internal and external focus (customers, markets)
Digitalization optimizes what exists; digital strategy creates something new

️ Implementation and Approach

Digitalization often occurs bottom-up and incrementally
Digital strategy requires top-down leadership and a impactful approach
Digitalization can take place in isolated projects
Digital strategy requires orchestrated, coordinated initiatives
Digitalization is technology-oriented; digital strategy is business-oriented

🔄 Relationship Between the Two

The digital strategy provides the framework for digitalization initiatives
Digitalization without strategy leads to uncoordinated isolated solutions
Strategy without digitalization remains theoretical and ineffective
Successful transformation requires both: vision and execution
78% of successful transformations are based on a clear digital strategy

Which digital technologies should be considered in a modern digital strategy?

A modern digital strategy must consider relevant technologies for both short-term optimization and long-term innovation:

🧠 Artificial Intelligence & Advanced Analytics

Generative AI for content creation, creative processes, and support functions
Machine learning for forecasting models, anomaly detection, and personalization
Natural language processing for text analysis, chatbots, and voice control
Computer vision for image analysis, quality control, and augmented reality
Predictive analytics for predictive maintenance, demand forecasting, and risk management

️ Cloud & Modern Infrastructure

Multi-cloud strategies for flexibility, scalability, and cost optimization
Container technologies and Kubernetes for portable applications
Serverless computing for event-driven architectures
Infrastructure as Code for automated provisioning
Edge computing for low-latency data processing close to the source

🔗 Connectivity & Internet of Things

5G/6G for ultra-fast, reliable connectivity
IoT sensors and devices for data collection and process control
Digital twins for virtual representation of physical objects and processes
Low-Power Wide-Area Networks (LPWAN) for energy-efficient IoT applications
Mesh networks for solid, self-organizing connections

🛡 ️ Cybersecurity & Trust Technologies

Zero-trust architectures for end-to-end security
Blockchain and distributed ledger for secure, transparent transactions
Quantum-resistant cryptography for future-proof encryption
Security automation and orchestration for rapid response to threats
Confidential computing for secure data processing

How do you integrate existing legacy systems into a new digital strategy?

Integrating legacy systems into a digital strategy requires a well-considered approach that combines business continuity with the capacity for innovation:

🔍 Analysis and Assessment

Comprehensive inventory of the legacy landscape and dependencies
Assessment based on business value vs. technical debt
Classification by criticality, modernization needs, and risks
Identification of interfaces and data flows between systems
Capturing implicit knowledge and undocumented functions

🏗 ️ Architecture Strategies

API-first approach to encapsulate legacy systems
Strangler pattern for gradual replacement of legacy components
Microservices architecture for modular renewal
Bimodal IT with stable core systems and agile innovation layers
Hybrid cloud models for flexible system integration

🔄 Integration Patterns

Service-oriented architecture (SOA) for unified interfaces
Event-driven architecture for loose coupling between old and new
Data virtualization layer for consistent data access
Enterprise Service Bus (ESB) or modern iPaaS solutions
API management platforms for governance and monitoring

📋 Implementation Strategies

Phased approach with incremental modernization
Parallel operation during the migration phase
Core and context strategy: focus on value-creating systems
Lift and shift vs. refactoring vs. replatforming
Minimum Viable Product (MVP) for rapid value creation

How can a digital strategy contribute to sustainable competitive differentiation?

A digital strategy can make a decisive contribution to sustainable competitive differentiation by creating strategic advantages across multiple dimensions:

🛡 ️ Digital Market Presence and Customer Experience

Personalization based on data analysis (leading to 20–30% higher conversion rates)
Smooth omnichannel experience across all touchpoints (increases customer retention by 25–40%)
Effective digital products and services as a unique selling proposition
Customer journey optimization through AI-supported predictions
Building digital ecosystems with network effects

️ Operational Excellence and Efficiency

AI-supported decision-making for faster and more precise responses
End-to-end process automation for significant cost savings (20–35%)
Real-time-capable supply chain with dynamic adaptability
Predictive maintenance to avoid downtime (reduction of 30–50%)
Digital twins for product optimization and innovation

🔄 Business Model Innovation

Development of platform-based business models with network effects
Transformation from products to services (Product-as-a-Service)
Data monetization and new digital revenue streams
Building digital marketplaces with two-sided network effects
Open innovation through API ecosystems and co-creation

🚀 Organizational Agility and Learning Capacity

Building a digital-first culture with continuous innovation
Digital skills and talent acquisition as a strategic advantage
Agile organizational structures for rapid market adaptation
Data-driven decision-making and continuous learning
Strategic technology partnerships for an innovation edge

What role does data governance play in a digital strategy?

Data governance plays a central role in a digital strategy, as it provides the structured framework for value-oriented management of data as a strategic resource.

🏛 ️ Strategic Importance

Data as the central value driver of the digital strategy (companies with mature data governance achieve 1.5–2x higher ROI from data investments)
Foundation for data-driven decision-making and competitive advantages
Basis for AI/ML initiatives and advanced analytics
Enabling personalization and customer-centric business models
Building trust through transparent and responsible data handling

📋 Core Components

Data ownership: Clear assignment of responsibilities for data domains
Data quality management: Ensuring accuracy, completeness, and timeliness
Master data management: Single version of the truth for critical master data
Data architecture: Structure for data flow, storage, and access
Metadata management: Documentation of context, origin, and meaning of data

🔐 Compliance and Security

Adherence to regulatory requirements (GDPR, BDSG, industry-specific regulations)
Data protection by design and by default in all digital initiatives
Access controls and authorization concepts for sensitive data
Audit trails and traceability of data usage and changes
Risk management for data loss, misuse, and compromise

🔄 Implementation Approach

Establishment of a data governance board with C-level participation
Development of a data culture through training and change management
Implementation of technological enablers (data catalog, data quality tools)
Agile introduction with iterative expansion of data domains
Measurement of value contribution through defined KPIs (time-to-insight, data quality metrics)

How do you integrate sustainability aspects (ESG) into a digital strategy?

Integrating sustainability aspects (ESG: Environmental, Social, Governance) into a digital strategy creates multiple layers of value and is increasingly becoming a strategic imperative:

🌱 Fundamental Integration Approaches

Dual-impact principle: Every digital initiative is assessed for both digital and ESG value contribution
ESG KPIs in digital scorecards: Integration into existing management frameworks
Green IT: Sustainable technology use as a strategic principle (can reduce energy costs by 30–50%)
Sustainable by design: ESG criteria as a design principle for digital products
Digital ethics framework: Guidelines for responsible digitalization

🛠 ️ Digital Enablers for Sustainability

IoT & sensors for resource optimization and emissions reduction
Digital twins for sustainable process and product optimization
AI-supported forecasting models for ESG risk management
Blockchain for transparent and sustainable supply chains
Data analytics platforms for ESG reporting and management

📊 ESG Data Management and Reporting

Automated data collection for ESG-relevant metrics
Integration of ESG data into business intelligence systems
Real-time monitoring of sustainability KPIs
XBRL/ESG taxonomies for standardized reporting
AI-supported scenario analyses for climate risks

🤝 Stakeholder Engagement Through Digitalization

Digital platforms for stakeholder dialogue and co-creation
Smart working to reduce commuter traffic and CO₂ emissions
Digital inclusion programs to bridge digital divides
Sustainability apps for customers and employees to drive behavioral change
Transparency portals for ESG performance and progress

How can companies maximize the ROI of their digital strategy?

Maximizing the ROI of a digital strategy requires a systematic approach across the entire digitalization cycle:

📋 Strategic Planning and Prioritization

Value-driven roadmapping: Prioritization of initiatives by business value and feasibility
Venture capital approach: Portfolio management with different risk/return profiles
Minimum Viable Product (MVP) strategy: Rapid testing with minimal investment
Fail-fast principle: Early identification and termination of unsuccessful initiatives
Innovation accounting: Alternative evaluation metrics for effective innovations

️ Efficient Implementation

Agile methods: 2–3x higher success rate compared to traditional approaches
DevOps practices: 70% less time for deployments and 30% higher code quality
Cloud-based development: 40–60% cost savings compared to on-premise
Low-code/no-code platforms: 3–5x faster application development
Reusable components: Microservices and API-first approach

📊 KPI-Based Management

Balanced scorecard with digital value KPIs
Real-time performance dashboards for immediate adjustments
A/B testing and experimentation for data-driven decisions
Customer success metrics (net retention rate, customer lifetime value)
Agile financial models with regular investment decisions

🔄 Continuous Optimization

Growth hacking: Systematic experimentation for exponential growth
Continuous customer feedback loops through digital channels
Lean analytics for iterative product improvement
Performance marketing optimization through AI and machine learning
Digital experience monitoring for proactive issue resolution

What organizational changes are necessary for a successful digital strategy?

A successful digital strategy requires far-reaching organizational changes that go well beyond the mere introduction of technology:

🏛 ️ Structural Adjustments

Bimodal organization: Two-track approach with a stable core organization and agile innovation units
Digital units: Dedicated teams with end-to-end responsibility for digital products
Center of Excellence (CoE): Pooling of specialist knowledge on technology topics
Matrix organization: Flexible deployment of specialists across functional boundaries
Platform teams: Shared services for reusable digital capabilities

👥 Leadership and Talent Management

Digital leadership: New leadership competencies for distributed, agile teams
T-shaped skills: Combination of deep subject expertise and broad digital understanding
Continuous learning: Ongoing development instead of one-off training (70‑20–10 model)
Digital talent acquisition: New approaches to recruiting digital experts
Digital natives enablement: Cross-generational knowledge transfer formats

🔄 Ways of Working and Methods

Agile scaling frameworks: SAFe, LeSS, or the Spotify model for larger organizations
OKR framework: Alignment between strategy and operational execution
Design thinking: Customer-centric development of digital solutions
DevOps and CI/CD: Integration of development and operations
Lean startup: Experimentation and rapid learning in innovation projects

🧠 Cultural Change

Data-driven decision-making culture instead of gut feeling and hierarchy
Tolerance for failure and willingness to experiment instead of a risk-averse mindset
Results orientation instead of a presence culture and micromanagement
Collaboration and transparency instead of silo thinking
Customer-centricity instead of an inward-looking perspective

How should a digital strategy be adapted to specific industry requirements?

A digital strategy must be specifically tailored to industry requirements in order to create maximum value:

🏭 Industry-Specific Adaptation Factors

Digital maturity: Consideration of the degree of digitalization within the industry and among competitors
Regulatory environment: Compliance requirements and industry-specific regulations
Competitive dynamics: Disruption potential and speed of innovation in the sector
Customer expectations: Industry-specific digital maturity levels of target groups
Value creation structures: Specific margin and cost structures of the industry

🔍 Industry-Specific Focus Topics (Examples)

Financial services: Open banking, embedded finance, AI-supported compliance
Manufacturing: Industrial IoT, digital twins, predictive maintenance, smart factory
Healthcare: Telemedicine, personalized medicine, wearables integration
Retail: Omnichannel, personalization, frictionless shopping experience
Energy: Smart grids, decentralized energy generation, energy management platforms

🧩 Adapting Strategy Elements

Technology stack: Industry-specific core platforms and solutions
Data model: Alignment with industry-specific data requirements and standards
Partner ecosystem: Integration of relevant industry partners and specialist providers
Employee competencies: Development of industry-relevant digital skills
Use cases: Prioritization based on industry-specific value potential

📊 Industry-Specific KPI Adaptation

Financial services: Cost-income ratio, digital share of sales, straight-through processing rate
Manufacturing: OEE (Overall Equipment Effectiveness), time-to-market, first-pass yield
Healthcare: Patient satisfaction, clinical outcomes, care coordination metrics
Retail: Conversion rate, basket size, customer lifetime value
Energy: Grid stability, renewable integration rate, energy efficiency metrics

How do you address cybersecurity in a comprehensive digital strategy?

Cybersecurity must be considered an integral part of every digital strategy from the outset — not as an afterthought:

🛡 ️ Strategic Integration

Security by design: Security as an integral component of all digital initiatives
Risk-based approach: Prioritization of protective measures according to business risk
Alignment with business objectives: Security as an enabler, not a blocker
Quantification of cyber risks in financial metrics (average cost of a data breach: USD 4.35 million)
Cybersecurity as a competitive advantage and trust factor

🏗 ️ Security Architecture for Digital Transformation

Zero trust architecture: Consistent verification of all access regardless of network location
Secure cloud adoption: Cloud security posture management and cloud-based security controls
API security: Securing digital interfaces as a critical attack surface
DevSecOps: Integration of security into CI/CD pipelines (reduces security vulnerabilities by up to 50%)
Secure Access Service Edge (SASE): Convergence of network and security for distributed working models

📊 Data and AI Security

Data security classification and governance
Privacy by design for personal data
AI model security and protection against adversarial attacks
Quantum-safe cryptography for future-proof data encryption
Secure data sharing for ecosystem collaboration

🔄 Resilient Security Culture and Organization

Security champions program to embed security in all teams
Continuous security testing (penetration tests, red team exercises)
Security awareness training with phishing-resistant authentication mechanisms
Cyber range for realistic attack simulations
Security Operations Center (SOC) with AI-supported threat detection

Which cloud strategy is optimal for digital transformation?

The optimal cloud strategy for digital transformation must be tailored to the specific requirements and goals of the organization:

️ Strategic Cloud Models

Multi-cloud strategy: Use of multiple public cloud providers to minimize risk and optimize performance (preferred by 87% of large enterprises)
Hybrid cloud approach: Combination of private and public cloud for sensitive and less critical workloads
Cloud-based approach: Full utilization of cloud-specific technologies and services
Edge-cloud model: Distributed cloud resources close to the point of use for low-latency applications
Cloud-sovereign approach: Use of European cloud infrastructures to meet regulatory requirements (e.g., GAIA-X)

🏗 ️ Architecture Principles for Cloud Strategies

Cloud-ready application design: Modularization and decoupling of applications
Infrastructure as Code (IaC): Automated provisioning and configuration
Containerization and orchestration with Kubernetes for portability
Stateless application design for horizontal scalability
Microservices architecture for independent development and deployment

💰 Economic Considerations

CapEx to OpEx transformation: Shift from capital expenditure to operating expenditure
Right-sizing: Continuous adjustment of resources to actual demand
Reserved instances & savings plans for predictable workloads (savings of up to 72%)
Spot instances for interruptible workloads (savings of up to 90%)
FinOps practices: Continuous cost control and optimization

🔄 Migration and Transformation Approach

Rehosting (lift & shift): Rapid migration without modifications
Replatforming: Moderate optimizations for the cloud
Refactoring/rearchitecting: Comprehensive adaptation for cloud-based
Rebuild: New development as a cloud-based application
Retain/retire: Deliberate decision to remain on-premises or decommission

How do you develop an effective data-driven digital strategy?

An effective data-driven digital strategy places data at the center of all strategic decisions and value creation activities:

📊 Strategic Data Foundations

Data value chain: Systematic consideration of the value chain of data
Data-driven business models: Identification of new monetization opportunities
360° customer view: Integration of all customer data for personalized experiences
Advanced analytics strategy: Prioritization of use cases with the highest business value
Data ROI framework: Measurement of the value contribution of data investments (companies with a data-driven culture achieve 30% higher EBITDA margins)

🧩 Architecture Components

Modern data platform: Flexible and flexible infrastructure for various data types
Data mesh: Domain-oriented, decentralized data architecture with self-service capabilities
Data fabric: Enterprise-wide data integration with metadata intelligence
Real-time data processing: Event streaming and CEP (Complex Event Processing)
Embedded analytics: Integration of analyses directly into business processes and applications

🎯 Applications and Use Cases

Customer analytics: Churn prevention, next-best-action, CLV optimization
Operational excellence: Predictive maintenance, supply chain optimization
Risk & compliance: Fraud detection, AML, automated regulatory reporting
Smart products: Product telemetry, usage-based pricing, predictive services
Market and competitive intelligence: Trend analyses, pricing optimization

🏢 Organizational Prerequisites

Chief Data Officer (CDO) with a clear mandate and C-level reporting
Data Center of Excellence (CoE) as a catalyst for data utilization
Data product manager for application-oriented data products
Data literacy programs to empower all employees
Data champions network to embed data culture in business units

What role do modern workplace concepts play in digital strategy?

Modern workplace concepts are a critical component of a comprehensive digital strategy and act as a catalyst for digital transformation:

💻 Digital Workplace as a Strategic Asset

Productivity gains through a smooth digital experience (up to 25% higher productivity)
Talent attraction and retention through attractive working models
Enabler for organizational agility and faster decision-making
Reduction of real estate costs through flexible space utilization (20–40% savings)
CO₂ reduction through reduced commuter flows and optimized space utilization

🔄 Hybrid Working Models

Activity-based working: Adapting the workplace to the task at hand
Hub-and-spoke models: Central office with decentralized satellite offices
Digital-first collaboration: Digital collaboration as the standard, not the exception
Asynchronous working: Time sovereignty with clear synchronization points
Outcome-based performance measurement instead of a presence culture

🛠 ️ Technological Components

Unified communication & collaboration: Smooth integration of all communication channels
Digital employee experience platforms: Integrated employee portals
Zero trust security: Location-independent protection of all access
Virtual Desktop Infrastructure (VDI) and Desktop-as-a-Service
Smart building technologies: IoT-supported space utilization optimization

🧠 Change Management and New Leadership Competencies

Digital leadership: Managing distributed teams
Hybrid meeting formats for inclusivity of all participants
Virtual onboarding for new employees
Digital rituals to strengthen corporate culture
Wellbeing concepts to prevent digital overload

How do you measure the degree of digitalization of a company and its competitors?

Measuring the degree of digitalization requires a structured assessment of various dimensions of digital maturity and enables strategic comparisons with competitors:

📏 Assessment Frameworks and Methods

Digital maturity assessment: Structured evaluation of all digital dimensions
Digital disruption risk analysis: Assessment of vulnerability to disruption
Digital capability radar: Visualization of digital capabilities
Benchmark-based comparisons with industry averages and best practices
Digital transformation index: Aggregated metric for overall maturity

🧩 Key Dimensions of Digitalization

Digital business model: Degree of digital business model integration
Customer experience: Digital maturity of customer interactions
Operations & supply chain: Digitalization of value creation processes
Technology & architecture: Future-readiness of the IT landscape
Organization & culture: Digital DNA of the organization

🔍 Competitive Analysis Methods

Digital footprint analysis: Assessment of digital presence and activities
Patent and IP analyses on digital innovations
Talent flow analysis: Analysis of job postings and personnel changes
Digital marketing performance benchmarking
API and digital ecosystem analysis

📊 Key Performance Indicators (KPIs)

Digital revenue share: Proportion of revenue generated through digital channels
API call volume: Intensity of use of digital interfaces
Digital customer engagement: Interaction metrics on digital channels
Technology debt ratio: Ratio of legacy to modern technologies
Digital skills index: Maturity level of digital competencies

How should a digital strategy be connected to portfolio and innovation management?

Linking digital strategy with portfolio and innovation management is essential for effective resource allocation and forward-looking transformation:

🧩 Strategic Linkage

Bi-directional alignment: Digital strategy informs portfolio prioritization and vice versa
Ambidextrous innovation management: Balance between optimization and exploration
Digital venture portfolio: Dedicated budget for effective digital innovations (15–20% recommended)
Strategic technology roadmapping: Long-term technology development
Dynamic resource allocation: Regular portfolio reviews instead of annual budgeting

🧪 Innovation Methods for Digital Initiatives

Design thinking: User-centered problem-solving methodology
Lean startup: Hypothesis-based market validation through MVPs
Jobs-to-be-done framework: Focus on customer needs rather than features
Open innovation: Involving the ecosystem in the innovation process
Corporate venture capital: Strategic investments in digital startups

📊 Portfolio Management Approaches

Two-speed IT: Two-track transformation with different governance models
Venture capital stage-gate process: Staged funding based on milestones
Real options approach: Flexible valuation methodology for digital initiatives
Scaled Agile Framework (SAFe): Coordination of many agile teams
OKR-based management: Flexible goal cascading instead of rigid targets

🔄 Governance and Control Mechanisms

Digital investment board: C-level committee with a dedicated digital focus
Digital portfolio office: Central management of all digital initiatives
Innovation accounting: Adapted KPIs for early-stage digital innovations
Quarterly business reviews: Regular adjustment of portfolio allocation
Digital value management office: Consistent tracking of realized value

How do you manage the integration of new digital technologies into existing business processes?

Successfully integrating new digital technologies into existing business processes requires a systematic approach that addresses both technical and organizational aspects equally:

🔄 Process Integration and Optimization

Business process mining: Data-supported analysis of existing processes prior to digitalization
Digital process redesign: End-to-end redesign from a digital perspective
Straight-through processing: Full automation without manual intervention
Exception-based processing: Automation of the standard case with human intervention only for exceptions
Hybrid human-AI processes: Collaborative human-machine processes with optimal task allocation

🛠 ️ Technical Integration Approaches

API-first integration: Standardized interfaces for modular process components
Robotic Process Automation (RPA): Rapid integration without invasive system changes
Event-driven architecture: Process decoupling through event-based integration
Process orchestration engines: Central management of complex process landscapes
Low-code process automation: Democratization of process automation

👥 Change Management and Adoption

Process ownership: Clear responsibilities for end-to-end processes
Digital process champions: Ambassadors within business units
Continuous learning: Iterative improvement and knowledge building
Experience-based design: User and employee experience at the center
Gamification: Game-based elements to promote adoption

📊 Process Metrics and Continuous Improvement

Process cycle efficiency: Ratio of value-adding to total time
Automation rate: Share of automated process steps
First-time-right rate: Quality of automated processes
Digital adoption score: Utilization rate of new digital tools
Continuous process mining: Ongoing analysis for iterative improvement

What best practices exist for implementing AI in digital strategy?

Successfully implementing AI within a digital strategy requires a structured approach — from selecting suitable use cases through to sustainable scaling:

🎯 Strategic AI Alignment and Use Case Selection

Value-first approach: Prioritization by business value rather than technological complexity
Balanced AI portfolio: Well-balanced mix of efficiency-oriented and growth-oriented use cases
AI opportunity map: Systematic identification and assessment of AI potential
Low-hanging fruits: Quick wins with foundation models (generative AI, LLMs)
Ethical guidelines: Responsible AI use as a strategic principle

🏗 ️ Technological Foundations and Architecture

AI-as-a-Service vs. custom models: Weighing standard solutions against tailored approaches
Data readiness assessment: Evaluation of data availability and quality for AI initiatives
MLOps framework: Industrialization of the ML lifecycle from development to operations
Federated learning for privacy-compliant AI implementation
Foundation models with Retrieval-Augmented Generation (RAG) for company-specific contexts

👥 Organizational Prerequisites

Citizen data science: Democratization of AI technologies for business users
AI Center of Excellence: Central expertise for methods and governance
AI literacy: Building a basic understanding of AI across all areas of the organization
Human-in-the-loop concepts: Collaborative human-AI systems instead of full automation
Agile AI teams: Cross-functional teams of domain experts and AI specialists

️ Risk Management and Governance

AI risk assessment framework: Systematic evaluation of AI-specific risks
AI model documentation: Structured recording of all model properties (model cards)
Explainable AI (XAI): Transparency and traceability of AI decisions
Continuous AI monitoring: Oversight of performance, bias, and drift
Regulatory compliance: Adherence to emerging AI regulations (e.g., EU AI Act)

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ECB Guide to Internal Models: Strategic Orientation for Banks in the New Regulatory Landscape
Risikomanagement

The July 2025 revision of the ECB guidelines requires banks to strategically realign internal models. Key points: 1) Artificial intelligence and machine learning are permitted, but only in an explainable form and under strict governance. 2) Top management is explicitly responsible for the quality and compliance of all models. 3) CRR3 requirements and climate risks must be proactively integrated into credit, market and counterparty risk models. 4) Approved model changes must be implemented within three months, which requires agile IT architectures and automated validation processes. Institutes that build explainable AI competencies, robust ESG databases and modular systems early on transform the stricter requirements into a sustainable competitive advantage.

Explainable AI (XAI) in software architecture: From black box to strategic tool
Digitale Transformation

Transform your AI from an opaque black box into an understandable, trustworthy business partner.

AI software architecture: manage risks & secure strategic advantages
Digitale Transformation

AI fundamentally changes software architecture. Identify risks from black box behavior to hidden costs and learn how to design thoughtful architectures for robust AI systems. Secure your future viability now.

ChatGPT outage: Why German companies need their own AI solutions
Künstliche Intelligenz - KI

The seven-hour ChatGPT outage on June 10, 2025 shows German companies the critical risks of centralized AI services.

AI risk: Copilot, ChatGPT & Co. - When external AI turns into internal espionage through MCPs
Künstliche Intelligenz - KI

AI risks such as prompt injection & tool poisoning threaten your company. Protect intellectual property with MCP security architecture. Practical guide for use in your own company.

Live Chatbot Hacking - How Microsoft, OpenAI, Google & Co become an invisible risk for your intellectual property
Informationssicherheit

Live hacking demonstrations show shockingly simple: AI assistants can be manipulated with harmless messages.

Success Stories

Discover how we support companies in their digital transformation

Digitalization in Steel Trading

Klöckner & Co

Digital Transformation in Steel Trading

Case Study
Digitalisierung im Stahlhandel - Klöckner & Co

Results

Over 2 billion euros in annual revenue through digital channels
Goal to achieve 60% of revenue online by 2022
Improved customer satisfaction through automated processes

AI-Powered Manufacturing Optimization

Siemens

Smart Manufacturing Solutions for Maximum Value Creation

Case Study
Case study image for AI-Powered Manufacturing Optimization

Results

Significant increase in production performance
Reduction of downtime and production costs
Improved sustainability through more efficient resource utilization

AI Automation in Production

Festo

Intelligent Networking for Future-Proof Production Systems

Case Study
FESTO AI Case Study

Results

Improved production speed and flexibility
Reduced manufacturing costs through more efficient resource utilization
Increased customer satisfaction through personalized products

Generative AI in Manufacturing

Bosch

AI Process Optimization for Improved Production Efficiency

Case Study
BOSCH KI-Prozessoptimierung für bessere Produktionseffizienz

Results

Reduction of AI application implementation time to just a few weeks
Improvement in product quality through early defect detection
Increased manufacturing efficiency through reduced downtime

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