Development of AI Strategies
for the manufacturing industry
Rising cost pressure, increasing complexity, and ongoing productivity challenges are forcing manufacturing companies to act. Artificial intelligence is widely seen as a key lever – yet in practice, many organizations struggle to move beyond pilots and isolated use cases.
The result: promising initiatives without scale, unclear priorities, and limited business impact.
TMG Consultants supports manufacturing companies in developing clear, implementable AI strategies that create measurable value – across processes, organization, and leadership.
Why TMG Consultants
>35 years of industry experience meets AI expertise
We are not merely technology consultants. Since 1986, TMG Consultants has combined in-depth process expertise in the manufacturing industry with cutting-edge methodological expertise. Our consultants understand your production processes, supply chain challenges, and quality requirements, drawing on experience from more than 3,000 projects.
What sets us apart: We don’t just implement software — we create the organizational and process-related foundations for sustainable AI success and support you throughout the implementation of your projects.
For further information on AI and hybrid organizations, please refer to our TMG study “AI as a Productivity Lever – How Far Has the Industry Really Come?” (only German edition)
Shaping the Sustainable Use of AI
The AI Maturity Model
Artificial Intelligence as your Productivity Booster
Our 6-Dimensional Approach to the Hybrid Organization Combining People and AI
For AI tools to create lasting value, it takes more than just the right technology.
TMG Consultants has developed a comprehensive AI maturity model that addresses all critical success factors for the successful integration of human expertise and artificial intelligence.
- Technology & (AI)Tools:
We help you build and scale the right AI infrastructure - Data:
We ensure data quality, data protection, and fair use of data - Ethics & Regulation:
We integrate ethical standards and legal compliance into your AI strategy - Governance:
We establish clear structures and processes for AI decisions - Leadership & Culture:
We are shaping the cultural shift toward AI acceptance - Competence:
We help your team develop AI literacy and specialized technical skills
1. Technology & (AI)Tools
AI Governance requires a robust technological foundation
Key questions for your organization:
- How can technical infrastructure contribute to AI scalability?
- How sensible is it to make greater use of cloud-native and modular architectures in the future?
- What specific considerations should be taken into account when dealing with legacy systems and technical integration?
- How can you find suitable AI tools and platforms quickly?
- How can artificial intelligence be used to identify opportunities for technical optimization?
2. Data
Responsible AI relies on high-quality and ethically sourced data
Key questions for your organization:
- What data sources and structures are required for AI systems?
- How can data quality and availability be ensured in the long term?
- What special considerations should be taken into account when handling sensitive and personal data?
- How can we quickly find data providers that offer the right data formats?
- How can artificial intelligence be used to identify the right data sources?
3. Ethics & Regulation
Trustworthy AI requires adherence to ethical standards and legal compliance
Key questions for your organization:
- How can ethical AI governance contribute to social responsibility?
- How sensible is it to make greater use of fairness standards in the future?
- What specific considerations should be taken into account regarding algorithmic discrimination and bias?
- How can ethical guidelines be established quickly and implemented as a standard?
- How can artificial intelligence be used to identify compliance risks?
4. Governance
Sustainable use of AI is achieved through clear governance structures
Key questions for your organization:
- How can clear governance structures contribute to organizational stability?
- How sensible is it to formalize governance processes more extensively in the future?
- What key considerations should be taken into account when comparing decentralized and centralized AI governance?
- How can responsibilities and roles be clarified quickly?
- How can artificial intelligence be used to identify governance gaps?
5. Leadership & Culture
Successful AI transformation starts with cultural change
Key questions for your organization:
- How can cultural change contribute to the acceptance of AI?
- How effective would it be to place greater emphasis on promoting transformative leadership in the future?
- What specific considerations should be taken into account when it comes to change management and cultural development?
- How can leaders be identified and developed in the short term?
- How can artificial intelligence be used to identify cultural resistance?
6. Competence
Effective use of AI is achieved through systematic skill development
Key questions for your organization:
- How can building expertise contribute to AI readiness?
- How sensible is it to place greater emphasis on promoting AI literacy across the board in the future?
- What specific considerations should be taken into account regarding specialized skills and basic knowledge?
- How can training programs be identified and implemented quickly?
- How can artificial intelligence be used to identify skill gaps?
From requirements to the right tool selection
Methodology: The TMG 5-Phase Approach to AI Tool Selection
TMG Consultants helps companies systematically develop their AI maturity and integrate AI into their processes, organization, and business models in a sustainable manner. With this strong foundation, we can work together to select the right AI tools, deploy them strategically within your processes, and thereby create sustainable value. Choosing the right AI tools is crucial to the success of your AI implementation. TMG guides you through a structured, independent selection process – from defining requirements to making a decision.
Phase 1: Requirements definition
What exactly do you want AI to do for you?
- Strategic Vision and Business Case Development
- Prioritizing use cases based on value contribution and feasibility
- List of Requirements (Functional, Technical, Regulatory)
- Compliance requirements (EU AI Act, GDPR, industry regulations)
Result: A detailed job description as a basis for decision-making
Phase 2: Market analysis
Who offers what on the market?
- Systematic Vendor Scan (100+ AI Tool Categories)
- Longlist of relevant solutions
- Prequalification based on exclusion criteria
- Creating a shortlist (typically 3–5 tools)
Result: A prioritized shortlist of tools based on your specific requirements
Phase 3: Use Case Testing
Does this tool work for your situation?
- Proof of Concept Using Your Real-World Use Cases
- Structured user testing with business departments and user feedback
- Technical integration checks with your IT department
Result: Validated practical applicability of the tools
Phase 4: Evaluation
Which tool offers the best overall package?
- Multi-criteria evaluation (10–50 weighted criteria)
- Calculation of a meaningful overall score for each tool
Result: Data-driven decision recommendations – transparent and easy to understand
Phase 5: Decision
How do you proceed with the implementation now?
- Management Presentation of the Evaluation Results
- Clarification of contractual and compliance requirements
- Implementation Roadmap
Result: A decision-making document and a clear implementation plan
The TMG Approach: From Analysis to Value Creation
Our Approach: A Systematic Path from AI Maturity to Scalable Implementation
Implementing artificial intelligence within a company is not a one-time technology project, but rather a structured transformation process. Drawing on our project experience, TMG guides companies through a clear process model – from defining objectives to operational implementation.
We begin by jointly clarifying your motivation and vision: What role should AI play in your company in the future, and what strategic goals are you pursuing? Building on this, we analyze your company’s current situation across all departments using our AI maturity assessment. In the next step, we systematically evaluate the results and prioritize them together. In doing so, we identify specific areas for action as well as relevant use cases with the greatest economic potential. In workshops with functional departments and management, these findings are discussed and translated into concrete measures and initiatives. Based on this, we jointly develop a practical roadmap for AI implementation that takes both technological and organizational aspects into account. This includes, among other things, the selection of suitable technologies and software solutions, the establishment of governance structures, and the integration of AI into existing processes and systems. This creates a structured path from the initial assessment to the scalable and value-adding use of AI within the company.
FAQ
Frequently Asked Questions About Developing an AI Strategy
Why do so many AI projects in industry fail?
Studies show that 78% of AI initiatives fail as early as the pilot phase. The main reasons are:
- Fragmented data landscapes without centralized data governance
- Lack of organizational prerequisites (roles, responsibilities, governance)
- Unclear business cases and a lack of ROI measurement
- Lack of AI expertise in academic departments and IT
- Isolated pilot projects without a scaling strategy
TMG systematically addresses all of these dimensions in its AI maturity model.
What are the key success factors for AI projects?
Four pillars are crucial: First, the strategic alignment of AI with corporate goals, supported by clear governance and clearly defined roles. Second, a robust data foundation and strong system integration based on uniform architectural standards, rather than fragmented siloed solutions. Third, clear cost-effectiveness and prioritization of fewer strategic use cases. Fourth, targeted skill development for employees and managers at all levels.
What are the biggest challenges in implementing AI?
The challenges are manifold. From a technical standpoint, problems arise from fragmented tool landscapes that lead to siloed solutions, as well as from poor data quality and difficulties integrating with existing systems. From an organizational standpoint, there is often a lack of strategic integration of AI, resulting in unclear roles and responsibilities, and Governancestructures have not been established. On the human resources side, there is a lack of AI expertise and Employee acceptance. From a business perspective, unclear prioritization means that many AI projects lack business value.
Does AI transformation really make sense for small and medium-sized businesses?
A resounding yes – especially for hidden champions and mid-sized companies. AI often delivers higher returns on investment for mid-sized companies than for large corporations because decision-making processes are shorter, cultural changes are implemented more quickly, and use cases often have an immediate impact on business results.
How can I develop an AI strategy specifically tailored to small and medium-sized businesses?
Small and medium-sized businesses have advantages that large corporations do not: flat hierarchies, in-depth process expertise, and real-world challenges. TMG recommends a 5-step approach:
- Realistic goals: Boosting productivity instead of “becoming an AI leader”
- Leveraging strengths: Quick decision-making, customer focus, in-depth process knowledge
- Start with the data you have: Don’t wait for perfect data — an 80% solution is enough to get started
- Strategically selection of external partners: TMG assists with strategy and tool selection
- Pragmatic governance: Lean AI Steering Committee (Executive Management + IT + 1–2 Functional Departments)
What is the difference between AI pilot projects and scalable AI adoption?
Pilot projects test individual use cases under optimal conditions – often with limited data and significant staffing requirements. Scalable AI usage means:
- Automated data flows without manual intervention
- Governance structures that make decisions quickly
- Reproducible processes that can be applied to 10+ use cases
- Clearly defined roles and responsibilities
- Continuous performance monitoring
Without a systematic approach to building maturity, you’ll get stuck in the pilot phase.
We are always there for you
Your contact person
Talk to
Nicolas Stahmer
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