The Right AI Consulting for Medium-Sized Businesses: Selection Criteria for Sustainable Digitalization Partners 2025

Table of Contents

• The AI Consulting Market for SMEs in 2025: Facts, Figures, Developments
• Why Choosing the Right AI Consulting Partner Is Crucial for Medium-Sized Companies
• The 7 Key Selection Criteria for AI Consulting Partners in a B2B Context
• Four Typical Implementation Models for AI in SMEs: Advantages and Disadvantages
• Success Factors for AI Projects: What Makes the Difference?
• Costs and ROI: Realistic Expectations for AI Consulting Projects
• The Selection Process: From Shortlist to Successful Project Launch
• Future Outlook: How AI Consulting for SMEs Will Evolve by 2027
• Frequently Asked Questions About AI Consulting for SMEs

The AI Consulting Market for SMEs in 2025: Facts, Figures, Developments

The AI consulting landscape has fundamentally changed since the major AI breakthrough in 2022/2023. While the market for AI consulting in Germany was previously dominated by a few large players, today we see a differentiated market with specialized providers for almost every industry and company size.

According to the current study “AI in German SMEs 2025” by the Federal Association for Artificial Intelligence, 68% of medium-sized companies with more than 50 employees have already implemented at least one AI project – an increase of 45 percentage points compared to 2022. The average investment in AI projects including consulting services by medium-sized companies now amounts to €175,000 annually.

Market Growth and Specialization: The New AI Consulting Landscape

The market for AI consulting in German-speaking countries has grown by 34% annually over the last three years alone and has reached a volume of approximately €4.7 billion in 2025. Particularly striking is the increasing specialization of providers – both by industry and by functional areas.

Today we can identify four clearly distinguishable types of AI consulting providers:

• Traditional IT consulting firms with AI departments (35% market share) – often strongly technology-driven and with good connections to hardware and cloud providers
• AI pure players (27% market share) – specialized consultancies that exclusively operate in the AI field and are often founded by former employees of large tech companies
• Industry specialists with AI focus (22% market share) – consulting firms that combine deep industry knowledge with AI expertise
• AI startup ecosystems (16% market share) – networks of startups that cover various aspects of AI implementation and often operate as a collective

A study by the Technical University of Munich shows that despite the strong growth, the market is still far from saturation. Professor Dr. Helmut Weber, Head of the Institute for Digitalization in SMEs, predicts: “By 2027, we will see another doubling of the market volume, with the segments for industry-specific and function-specific AI consulting growing disproportionately.”

The Special Situation of SMEs: Between Digitalization Pressure and Resource Scarcity

Medium-sized companies find themselves in a challenging situation. The pressure to digitalize has significantly increased due to the rapid development of generative AI systems since 2023. At the same time, they do not have the human and financial resources of large corporations.

The Bitkom study “Digitalization Index for SMEs 2025” shows that 73% of the surveyed medium-sized companies consider AI to be decisive for competitiveness – but only 31% feel well prepared for integration. Particularly critical: 65% of medium-sized companies report difficulties in finding and retaining qualified personnel for AI projects.

This discrepancy between strategic necessity and operational implementation capability makes external consulting indispensable for many. Dr. Sabine Pfeiffer from the Institute for Labor Research describes this in a contribution to Wirtschaftswoche: “Today’s typical German medium-sized business faces the challenge of integrating complex AI technologies into established structures without losing its own DNA. The right consulting serves as a bridge between technological possibilities and entrepreneurial reality.”

Looking at the distribution of AI budgets in SMEs makes this challenge even clearer:

This distribution shows that AI has now made its way into all areas of business – the challenge, however, lies in setting the right priorities and using limited resources optimally.

Why Choosing the Right AI Consulting Partner Is Crucial for Medium-Sized Companies

Selecting the appropriate AI consulting partner for medium-sized companies is not a minor matter, but a strategic decision with far-reaching consequences. Unlike many other consulting services, AI projects are not just about temporary support, but about integrating technologies that can fundamentally change the business model.

Mark Hoffmann, CEO of a medium-sized mechanical engineering company with 120 employees, puts it succinctly: “Our first collaboration with an AI consulting company was an expensive lesson. The provider had everything technically under control, but zero understanding of the specifics of our industry and our customers. The result was a technically impressive system that completely missed the actual needs.”

The Cost of a Wrong Decision: More Than Just Lost Budget

A wrong decision when selecting an AI consulting partner can go far beyond wasted project budgets. The long-term costs can be categorized as follows:

• Direct financial losses: According to a study by the German Society for Project Management, about 38% of all AI projects in SMEs fail or do not deliver the expected ROI. The average cost of a failed project is €142,000 – money that is particularly painful for medium-sized businesses.
• Opportunity costs: While the company loses time with an unsuitable consulting partner, competitors may be successfully implementing AI solutions and securing market advantages. These missed opportunities are difficult to quantify but often weigh heavier than the direct costs.
• Reputational damage: Failed AI projects can lead to reputational losses both internally (employee skepticism) and externally (customer trust).
• Change management setbacks: If an AI project fails, the next initiative will face significantly more resistance – a vicious cycle that massively complicates digital transformation.

Dr. Christina Meyer, change management expert at WHU Vallendar, explains: “After a failed AI project, we typically see a doubling of resistance to further digitalization initiatives. The human ‘I told you so’ is a powerful opponent – and one that can be avoided by choosing the right partner.”

Strategic vs. Tactical AI Implementation: The Difference Between Sustainable Transformation and Isolated Solutions

A fundamental difference in the AI consulting landscape lies in the approach: Are AI projects understood as isolated tactical measures or as part of a strategic transformation?

The research paper “Digital Maturity in SMEs” by the Berlin School of Economics and Law identifies a clear correlation between strategic implementation and success rate: Companies that embed AI projects in an overarching digital strategy achieve a success rate of 72%, while isolated projects are only successfully completed 34% of the time.

Tactical implementations – often recognizable by terms like “quick win” and “low hanging fruits” – can deliver short-term successes, but frequently lead to technical island solutions that later can only be integrated into an overall architecture with great effort.

The consulting firm McKinsey has identified five typical patterns in their study “The State of AI 2025” that make the difference between strategic and tactical implementation:

1. Long-term roadmap vs. individual projects: Strategic partners develop 3-5 year roadmaps instead of isolated solutions.
2. Holistic data strategies vs. use-case related data collection: Sustainable concepts consider the entire data architecture of the company.
3. Building internal competencies vs. complete outsourcing: Strategic partners enable the company to build competencies.
4. Integration into existing IT landscape vs. parallel systems: Successful projects consider integration into existing infrastructure from the beginning.
5. Consideration of organizational and cultural factors vs. purely technological focus: The best partners understand that AI is a socio-technical transformation.

A good AI consulting partner for SMEs should therefore bring not only technological expertise, but also a deep understanding of the specific challenges of medium-sized companies – from limited budgets to flat hierarchies and the special corporate culture.

Michael Schmidt, CIO of a medium-sized automotive supplier, summarizes his experiences: “The decisive difference between our first, failed AI project and our later success was not the technology, but the partner. The first provider sold us a solution, the second understood us and empowered us.”

The 7 Key Selection Criteria for AI Consulting Partners in a B2B Context

The selection of the right AI consulting partner should be based on a structured set of criteria that goes far beyond technical capabilities. Based on an analysis of over 150 successful AI implementations in German SMEs, the following seven criteria have proven to be particularly critical for success.

Industry Expertise and Understanding of the Business Model

AI solutions are not off-the-shelf standard products. Their effectiveness depends crucially on how well they are tailored to the specific requirements of your industry and your individual business model.

A study by the Fraunhofer Institute for Intelligent Analysis and Information Systems shows that 76% of successful AI implementations in SMEs were carried out with partners who already had experience in the respective industry. Without this industry know-how, even the most advanced AI solution remains a foreign body in the company.

Make sure potential consulting partners:

• Can demonstrate concrete reference projects in your industry
• Know the typical processes and challenges of your industry
• Are familiar with the technical terminology and specifics of your business model
• Bring an understanding of your competitive situation and market dynamics

A simple test: Have the potential partner explain how they would take into account the specifics of your industry in AI solutions. The answer reveals a lot about their actual expertise.

Technological Competence and Vendor Independence

The technological landscape in the AI field is developing rapidly. In 2024 alone, the performance of leading Large Language Models (LLMs) has more than tripled. A good consulting partner must not only master current technologies, but also continuously follow developments.

Particularly important: Your partner should be able to act vendor-independently. A consultancy that exclusively relies on the products of a specific technology provider will hardly be able to offer you the optimal solution for your specific requirements.

According to a survey by the German Society for AI Compliance, successful AI projects use components from an average of 3.7 different vendors. “The idea of optimally covering all AI requirements of a medium-sized company with the products of a single manufacturer is unrealistic,” explains Prof. Dr. Markus Winterstein, head of the institute.

When assessing technological competence, pay attention to:

• The breadth of the technological portfolio (various LLMs, computer vision, speech processing, etc.)
• Experience with different cloud platforms and on-premises solutions
• Competence in integrating with existing systems
• Verifiable continuing education of the consulting team on current technological developments

A warning sign: If a consulting company quickly focuses on a specific technology or platform without thoroughly analyzing your specific requirements, this often indicates limited technological flexibility.

End-to-End Competence: From Strategy to Implementation

Successful AI projects typically go through several phases – from strategic planning to conception and development to implementation and continuous improvement. Ideally, your consulting partner can guide you through this entire process.

The study “AI Project Success Factors 2025” by the University of St. Gallen shows that projects with phase breaks between strategic consulting and technical implementation have a 43% lower success rate than continuously supervised projects.

Dr. Sophia Müller, head of the Institute for Digital Transformation, explains: “When strategic conception and technical implementation are carried out by different partners, crucial information and intentions are often lost. It’s like having an architect design a house and then building it without their further involvement – this rarely leads to the optimal result.”

A partner with true end-to-end competence should be able to demonstrate the following capabilities:

• Strategic consulting on integrating AI into corporate strategy
• Experience in designing customized AI solutions
• Technical expertise in development and implementation
• Change management competence for organizational integration
• Training and enablement concepts for your employees
• Support and maintenance concepts for ongoing operations

Check whether potential partners actually have all these competencies in-house or whether certain areas need to be covered by subcontractors.

Training and Change Management Capabilities

The technically best AI solution will fail if people in the company are not willing or able to use it. The human factor is particularly critical in AI projects, as these technologies often deeply interfere with existing work processes and sometimes trigger fears.

A representative survey by the Institute for Employment Research from 2024 shows that 63% of employees in medium-sized companies are fundamentally positive about AI – but only if they are adequately involved and trained. Without structured introduction and training, acceptance drops to below 30%.

A good AI consulting partner must therefore be more than just a technology expert – they must also be able to shape change processes. Look for:

• Structured training concepts for different user groups (from basic to expert training)
• Experience with change management in comparable corporate environments
• Approaches for early involvement of employees in the development process
• Concepts for dealing with resistance and fears
• Sustainable enablement strategies instead of mere user training

Dr. Thomas Müller, change management expert, emphasizes: “The most common reason for the failure of AI projects is not technical, but human in nature. Even the most powerful AI becomes worthless if employees circumvent it or only use it reluctantly.”

Data Protection and Compliance Expertise

Especially in the German and European context, data protection and compliance are not optional aspects, but fundamental requirements for any AI solution. The tightened requirements of the EU AI Act, which has been in force since 2024, have further increased the complexity.

An analysis by the University of Münster from 2024 found that 47% of all stopped AI projects in SMEs were abandoned due to unresolved data protection or compliance problems – often after considerable resources had already been invested.

When evaluating potential partners, you should therefore pay special attention to their data protection and compliance expertise:

• Demonstrable experience with GDPR-compliant AI implementations
• Understanding of industry-specific regulations (e.g., in the finance or healthcare sector)
• Knowledge of the EU AI Act and its specific implications
• Approaches for privacy-friendly design of AI systems
• Transparency regarding data storage, processing, and transfer

A concrete practical test: Ask them to explain how they handle the issue of training data in public cloud AI services. The answer shows how deep their understanding of practical data protection challenges is.

Attorney Dr. Stefanie Weber, specialist for AI law, warns: “Non-compliance with data protection and AI-specific regulations can have existence-threatening consequences – from fines to reputational damage and liability risks. A partner who does not centrally consider these aspects from the beginning is a significant risk for medium-sized companies.”

References and Verifiable Successes in SMEs

Claims and reality often diverge widely in AI consulting. It is therefore all the more important to verify the actual successes of potential partners – ideally with companies of similar size and structure.

When evaluating references, you should go beyond the mere presence of case studies on the website. Authentic and meaningful references are characterized by the following features:

• Detailed description of the initial situation, challenges, and achieved results
• Quantifiable results instead of vague claims of success
• Possibility of direct contact with reference customers
• Transparency regarding difficulties encountered and their resolution
• Long-term references that prove the sustainable success of the solution

A study by the Berlin University of Applied Sciences for Engineering and Economics shows that 78% of successful AI projects in SMEs were carried out with partners who could demonstrate at least three comparable projects. Professor Dr. Marcus Schmidt, head of the study, emphasizes: “AI projects have specific pitfalls that can only be learned through concrete project experience. A company should not have to finance this learning curve with its own project.”

Particularly valuable are references where the partner has accompanied not only the technical implementation, but also the organizational integration and long-term operation. They provide insight into the actual end-to-end competence.

Cultural Fit and Communication at Eye Level

Working with an AI consulting partner is usually not a short project, but a longer-term partnership. This makes the cultural fit between your company and the consulting provider all the more important.

An analysis by RWTH Aachen University shows that cultural compatibility is one of the strongest predictors for the success of AI projects in SMEs. Dr. Julia Schneider, the head of the study, explains: “If we want to predict the probability of success of AI projects, the cultural fit between consulting partner and company is more informative than many technical parameters.”

When assessing cultural fit, pay particular attention to:

• Communication style and frequency – does this match your expectations?
• Hierarchical understanding and decision-making processes
• Handling of feedback and criticism
• Flexibility with changing requirements
• Understanding of the special values and priorities of medium-sized companies

A particularly important aspect is communication at eye level. A good partner explains complex technological relationships understandably, without being condescending or trying to impress with technical terms. They ask questions that show they really want to understand your company and business model.

Christian Weber, CEO of a medium-sized logistics company, describes his experience: “We chose our current AI partner because they were the only one who spent more time listening to us and understanding us than presenting their own solutions. This attitude has been maintained throughout the project – and was crucial for success.”

Ideally, check this cultural fit in several conversations and with different representatives of the potential partner – from strategic consultant to technical implementer. This gives you a more complete picture of the corporate culture.

Four Typical Implementation Models for AI in SMEs: Advantages and Disadvantages

The way AI consulting services are delivered has a decisive influence on project success. Depending on the situation, available internal resources, and strategic goals, different implementation models can make sense.

Based on the analysis of over 200 successful AI projects in German SMEs, four basic models have emerged, each offering specific advantages and disadvantages.

The Generalist Model: One Partner for Everything

In the generalist model, a single consulting partner takes on all aspects of the AI project – from strategic planning to technical implementation to training and support.

Advantages:

• Clear responsibilities and a central point of contact
• Consistent approach without interface problems
• Less coordination effort for the company
• Often faster implementation through a well-coordinated team

Disadvantages:

• Possible limitations in specialized expertise in sub-areas
• Potential dependency on a single provider
• Less diversity of perspectives and approaches
• Often higher overall costs due to premium prices for full service

The generalist model is particularly suitable for companies with limited internal resources for project coordination and for projects where speed and seamless integration are more important than maximum specialization in sub-areas.

According to a study by the Berlin School of Economics and Law, about 43% of medium-sized companies choose this approach for their first AI projects. Dr. Markus Weber, head of the Institute for Digital SMEs, explains: “Especially for AI beginners, the generalist model offers a clear advantage: The complexity is reduced for the company, which can be decisive in the initial phase of AI adoption.”

The Specialist Network: Best-of-Breed for Every Area

In the specialist network, the company works with several highly specialized partners, each leading in their field – for example, one partner for strategic consulting, one for technical implementation, and another for change management and training.

Advantages:

• Highest expertise in each sub-area
• Greater independence and flexibility
• More diverse perspectives and innovation impulses
• Potentially more cost-efficient through precise specialist deployment

Disadvantages:

• High coordination effort for the company
• Risk of interface problems and information loss
• More complex responsibilities and contract design
• Possible delays due to need for coordination

This model is particularly suitable for companies that already have experience with AI projects, have internal resources for coordination, and want to solve a specific, clearly defined problem.

A survey by the Federal Association of Digital Economy shows that about 27% of medium-sized companies rely on this model – with an increasing trend among companies that have already implemented several AI projects.

Professor Dr. Sabine Meyer from the Institute for Digital Transformation explains: “The specialist network can be superior especially when very specific expertise is required, such as integrating AI into highly specialized production processes or developing industry-specific AI applications.”

The Hybrid Model: Internal Champions and External Expertise

The hybrid model combines internal resources with external expertise. Internal “AI champions” work closely with external specialists to ensure knowledge transfer and build long-term internal competencies.

Advantages:

• Continuous knowledge transfer and competence building in the company
• More sustainable solutions through early internal anchoring
• Combination of external expertise and internal company understanding
• Reduced dependence on external partners over time

Disadvantages:

• Requires internal resources to be dedicated to the project
• More complex role distribution between internal and external teams
• Potentially longer onboarding and coordination phases
• Risk of knowledge loss due to fluctuation of internal champions

The hybrid model is recommended by the German Academy of Technical Sciences as particularly sustainable. A long-term study over three years showed that companies with this approach had a 3.2 times higher probability of implementing further AI projects without or with significantly reduced external support after project completion.

Dr. Thomas Schmidt, AI consultant and author of the book “Sustainable AI Transformation in SMEs,” emphasizes: “The biggest advantage of the hybrid model lies in its sustainability. The company not only buys a solution but simultaneously builds the ability to act more independently in the future. This is often particularly attractive for SMEs, which traditionally focus on independence and long-term thinking.”

About 22% of medium-sized companies choose this model for their AI projects, with a strongly increasing trend in the last two years.

The Lighthouse Model: Pilot Projects with Radiating Power

The lighthouse model focuses on implementing a single, particularly visible and effective AI project that should serve as a “lighthouse” for further initiatives. The external partner concentrates on a clearly defined project with high probability of success and visible business impact.

Advantages:

• Clear focus on concrete, measurable business benefit
• Manageable risk due to limited project size
• Faster success experiences promote acceptance in the company
• Results can serve as “proof of concept” for further investments

Disadvantages:

• Risk of island solutions without strategic embedding
• Danger of excessive expectations for follow-up projects
• Possibly not representative for more complex use cases
• Knowledge transfer must be explicitly planned

The lighthouse model is particularly suitable for companies that want to gain initial experience with AI or need to do internal convincing of the potential of AI. It is chosen by about 18% of medium-sized companies, especially those at the beginning of their AI journey.

An analysis by the Mittelstand-Digital Competence Center shows that 76% of companies that started with a lighthouse project initiated at least two more AI projects within 18 months. Marion Weißenberger-Eibl, head of the Fraunhofer Institute for Systems and Innovation Research, explains: “Lighthouse projects act as internal conviction amplifiers. They make the often abstract AI tangible and create trust for more ambitious projects.”

The choice of the appropriate implementation model should be made based on the specific company situation, the available internal resources, and the strategic goals. Many successful AI transformations begin with a lighthouse project and evolve over time towards a hybrid model to ensure sustainable competence building.

Success Factors for AI Projects: What Makes the Difference?

While selecting the right partner and the appropriate implementation model are fundamental decisions, a number of specific factors ultimately determine the success or failure of AI projects in SMEs. The following success factors have proven particularly relevant in practice.

Clear Problem Definition and Measurable Goals

AI is not an end in itself, but a tool for solving concrete business challenges. A precise definition of the problem to be solved and clearly measurable goals are therefore the cornerstone of every successful AI project.

A study by MIT Sloan Management Review in collaboration with Boston Consulting Group has shown that 78% of successful AI projects had clearly defined and quantifiable target metrics at the outset. For failed projects, this was the case for only 23%.

Dr. Martin Weber, AI implementation expert, emphasizes: “The most common mistake is to start with the technology instead of the problem. ‘We need AI’ is not a problem definition. ‘We need to reduce our offer creation time by 60%’, on the other hand, is.”

When defining the problem, pay attention to the following aspects:

• Concretize the problem in measurable and observable dimensions
• Define a clear current state as a baseline
• Formulate specific, measurable goals (KPIs)
• Ensure that the problem can actually be solved with AI
• Prioritize by business impact and technical feasibility

A structured problem prioritization workshop at the beginning of the collaboration with your AI consulting partner can provide clarity and lay the foundation for a targeted implementation.

Data Quality and Availability as a Foundation

AI systems are only as good as the data they are trained on and work with. Data quality and availability are therefore decisive success factors – and often underestimated challenges.

A recent survey by the Competence Center SMEs 4.0 shows that 67% of all AI projects in SMEs struggle with data problems. The three most common challenges are:

1. Inadequate data quality (erroneous, inconsistent, or outdated data)
2. Data silos (data is stored in different, unconnected systems)
3. Insufficient data volume for training purposes

Dr. Julia Schmidt, data scientist at the Fraunhofer Institute, explains: “Many companies underestimate the effort required for data preparation. In our projects, an average of 60% of the time flows into data preparation and only 40% into the actual model development and implementation.”

A serious AI consulting partner will therefore always start with a thorough data analysis and honestly communicate if data problems could jeopardize the project implementation.

For the success of your AI project, you should check the following aspects of data availability early on:

• Is the required data available and accessible at all?
• What is the quality of the data in terms of completeness, correctness, and timeliness?
• Are there legal restrictions on the use of the data (GDPR, trade secrets, etc.)?
• Is the data available in a machine-readable format or does it first need to be transformed?
• Is continuous data supply secured for ongoing operations?

The investment in a solid data infrastructure and quality pays off multiple times, as it supports not only the current project but also future AI initiatives.

Iterative Approach Instead of Big Bang Implementation

AI projects differ from classical IT projects through their inherent uncertainty and willingness to experiment. Successful implementations therefore rely on iterative approaches rather than rigid waterfall models.

An analysis by McKinsey of 125 AI implementations shows that agile, iterative approaches have a 37% higher success rate than classical waterfall methods. The reason: They enable faster learning and adaptation in the face of unforeseen challenges.

Dr. Thomas Schulz, project management expert for AI implementations, explains: “AI projects have an inherent unpredictability. Even with the best planning, you will encounter surprises – be it in data quality, model behavior, or user acceptance. An iterative approach turns these surprises into learning opportunities rather than project risks.”

A successful iterative implementation is characterized by the following elements:

• Short development cycles with regular feedback loops
• Early tests with real users under realistic conditions
• Willingness to adapt requirements based on insights
• Incremental value creation instead of “all-or-nothing” approach
• Transparent communication of progress and challenges

Make sure your AI consulting partner has experience with agile methods and doesn’t just use them as a buzzword. Ask for concrete examples of how the team has dealt with unexpected challenges in previous projects.

Management Commitment and Resource Planning

Successful AI projects require not only technical expertise but also consistent support from the leadership level and adequate resources. A study by the Institute for Organizational Development shows that 82% of successful AI projects in SMEs received active support from management – in failed projects, it was only 34%.

Professor Dr. Markus Müller from the University of St. Gallen explains: “AI projects often touch on fundamental processes and structures. Without clear commitment from the leadership level, they will sooner or later encounter organizational barriers that cannot be solved technically.”

This commitment must be reflected in concrete resource provision:

• Releasing internal experts for the project team
• Adequate financial resources, not only for the initial implementation but also for continuous improvement
• Clear prioritization compared to competing initiatives
• Active support in overcoming organizational hurdles
• Patience for the often necessary cultural change

A particularly critical aspect is realistic resource planning. According to a survey among AI consulting companies, 73% of medium-sized customers underestimate the internal effort required for successful AI projects. A good consulting partner will transparently communicate from the beginning what internal resources are needed and support planning.

Dr. Christina Müller, change management expert, emphasizes: “AI projects cannot simply be ‘purchased’ and then left to themselves. They require active engagement from the company. This willingness for active participation is one of the strongest predictors of project success.”

The early identification of an internal “executive sponsor” at the management level who actively supports and drives the project has proven particularly effective in practice.

Costs and ROI: Realistic Expectations for AI Consulting Projects

AI projects require investments – in external consulting, technology, and internal resources. A realistic assessment of costs and Return on Investment (ROI) is therefore crucial for project planning and evaluation.

Typical Cost Structures in AI Consulting Projects

The cost structures of AI projects differ depending on complexity, implementation model, and project scope. However, based on an analysis of over 150 AI projects in German SMEs, typical cost categories can be identified.

For a medium-sized company with 100-250 employees, the costs for a typical AI project are distributed approximately as follows:

Dr. Marcus Weber, author of the study “Digital Investments in SMEs,” explains: “The cost distribution clearly shows that AI projects are not pure technology projects. The share for data preparation, training, and change management together often accounts for more than half of the total budget – but these positions are often underestimated in planning.”

A critical aspect that companies should consider is the ongoing costs after the initial implementation. Unlike traditional IT systems, AI solutions require continuous maintenance and adaptation – be it through model retraining, data updates, or functional extensions.

In addition to direct external costs, companies should not underestimate internal resources. Successful AI projects typically bind 10-30% of the working time of relevant subject matter experts and IT staff during the implementation phase.

Return-on-Investment: How and When AI Projects Pay Off

Calculating the ROI of AI projects is more complex than for many traditional IT investments, as in addition to direct cost savings, qualitative advantages such as improved decision quality, increased customer satisfaction, or faster innovation cycles must also be taken into account.

A study by the Fraunhofer Institute for Production Technology and Automation shows that successful AI projects in SMEs typically exhibit the following ROI patterns:

• Amortization period: 12-24 months after complete implementation
• ROI after 3 years: 150-300% (depending on use case)
• Distribution of ROI: 40% direct cost savings, 30% productivity increases, 30% revenue growth and strategic advantages

Particularly interesting: The ROI curve for successful AI projects is often not linear but exponential. The full economic benefit frequently unfolds only after an initial learning curve and several optimization cycles. While traditional IT projects often deliver their full benefit immediately after commissioning, with AI projects we see a continuous increase in benefits over time.

Professor Dr. Julia Weber from the Institute for Digital Business Models explains: “AI systems get better through use and feedback. Additionally, organizations need time to optimally align their processes and behavior to the new possibilities. The greatest ROI is often only achieved when not only the technology is implemented, but also the organization is correspondingly transformed.”

For a realistic ROI calculation, the following factors should be considered:

1. Direct cost savings: Reduced personnel costs through automation, decreased error rates, optimized resource utilization
2. Productivity increases: Faster processes, improved decision-making, reduced manual effort
3. Revenue growth: Improved customer interaction, new products or services, better market adaptation
4. Strategic advantages: Increased agility, better data utilization, future-proof capabilities
5. Risk reduction: Improved predictions, early error detection, more robust decisions

A serious AI consulting partner will help you create a well-founded ROI analysis for your specific use case and set realistic expectations.

Hidden Costs and How to Avoid Them

When budgeting for AI projects, certain types of costs are often overlooked, which can later lead to unexpected budget overruns. Identifying these hidden costs can help to plan for them early or even avoid them.

The five most common hidden cost factors in AI projects for SMEs are:

1. Data cleaning and preparation: The effort for preparing the data is underestimated in 68% of projects. An early data analysis can minimize this risk.
2. Integration effort with legacy systems: Connecting to existing systems is often more complex than expected, especially when older systems without modern APIs are involved. Detailed system analyses in advance reduce these risks.
3. Continuous model retraining: AI models must be regularly retrained with current data to remain relevant. These ongoing costs are often not considered from the beginning.
4. Change management and organizational adaptation: The effort for adapting processes and supporting employees during the transition is often underestimated. Early involvement of the affected departments can help.
5. Scaling costs with increased usage: What works cost-effectively in pilot operation can become surprisingly expensive at full scale, especially with cloud-based solutions. Early load and cost forecasts help with planning.

Dr. Thomas Schmidt, specialized in AI project management, recommends: “Plan an additional 20-30% as a buffer to the calculated budget, especially for your first AI project. With increasing experience, your budgeting will become more precise.”

A particularly important aspect is the contract design with the AI consulting partner. Pay attention to:

• Clear definition of the scope of services and acceptance conditions
• Transparent regulations for changes to the project scope
• Clear responsibilities for data quality and provision
• Agreements on maintenance and support after project completion
• Regulations for intellectual property and usage rights to the developed models

A good partner will address these aspects proactively and ensure transparency regarding possible hidden costs. Anyone who makes overly optimistic promises here without addressing potential risks should be viewed critically.

The Selection Process: From Shortlist to Successful Project Launch

Selecting the right AI consulting partner is a structured process that encompasses more than just evaluating offers and prices. A thorough selection process minimizes risks and lays the foundation for successful collaboration.

The Shortlist: How to Find Potential Partners

The first step is creating a shortlist of potential providers. Various sources are available for this:

• Industry networks and recommendations: 67% of successful AI projects in SMEs are based on partners found through personal recommendations. Ask for experiences in your network.
• Industry associations and chambers of commerce: Many industry associations and chambers of commerce maintain lists of specialized service providers or organize networking events.
• Funding programs and competence centers: Programs such as “Mittelstand Digital” or “Go-Digital” offer not only funding but also qualified provider directories.
• Technical articles and case studies: Consulting providers who publish substantiated articles or present detailed case studies often demonstrate deep expertise and transparency.
• Trade fairs and conferences: Events such as DMEXCO, CeBIT.AI, or industry-specific digitalization fairs offer opportunities for direct exchange.

During the initial research, you should already pay attention to industry-specific experience and SME competence. An initial shortlist ideally includes 5-8 potential partners for further evaluation.

Professor Dr. Marcus Weber from the Institute for Digital SMEs recommends: “During the pre-selection, pay particular attention to communication quality. How quickly and thoroughly are your inquiries answered? Are technical terms explained or assumed to be known? These initial interactions are often a good indicator for the subsequent collaboration.”

The Structured Selection Process: From RFI to Contract Conclusion

After the initial shortlist, a structured selection process follows, typically including the following steps:

1. Request for Information (RFI): A brief document containing basic information about your company and your requirements. It serves to obtain initial feedback from potential partners and check their basic suitability.
2. Initial personal meetings: Personal conversations should be conducted with 3-5 pre-selected providers. Pay particular attention to the understanding of your specific situation and the chemistry between the teams.
3. Detailed Request (RFP): A detailed request specifying concrete requirements, framework conditions, and expected services is sent to 2-3 favored providers.
4. Reference visits or conversations: Talk to existing customers of the providers, ideally from similar industries or with comparable challenges.
5. Workshop or Proof of Concept: A joint workshop or small proof of concept can be conducted with the preferred provider to practically experience the working method and expertise.
6. Contract negotiation and conclusion: After the final decision, concrete contract negotiations follow.

This process may seem elaborate but pays off through a significantly reduced project risk. Dr. Christina Müller, expert for digitalization projects, explains: “Every hour you invest in the careful selection of the right partner saves you days or weeks of problems and rework later.”

Particularly valuable during the selection process are structured evaluation frameworks that enable objective comparability of providers. These should include both hard factors (experience, references, technology competence) and soft factors (communication, cultural fit, project management approach).

A common mistake is excessive weighting of price in the decision. A study by the Federal Association for Artificial Intelligence shows that in failed AI projects, price influenced the decision by an average of 38%, while in successful projects, the price factor was weighted only at 22%.

Critical Contract Clauses and Agreements

The contract with your AI consulting partner is more than a formality – it defines the framework for collaboration and can significantly contribute to the success or failure of the project.

The following aspects have proven particularly critical for success:

• Clear service description and deliverables: Precise definition of services to be provided and measurable results
• Acceptance criteria and processes: Clear definition of when a milestone is considered achieved
• Flexible change management processes: AI projects often require adjustments during the course – the contract should clearly regulate how changes are handled
• Data protection and security: Clear regulations for handling sensitive company data, especially training data for AI models
• Usage and ownership rights: Clear definition of who owns the developed models and algorithms and what usage rights exist
• Maintenance and support: Agreements for the time after the initial implementation
• Knowledge transfer and documentation: Ensuring that knowledge does not remain exclusively with the consultant
• Exit strategies: Regulations for the case that the collaboration does not proceed as desired

The question of usage and ownership rights is particularly complex for AI projects. “Unlike with classical software, with AI models it’s often not clearly separable what is generic model knowledge and what is company-specific implementation,” explains attorney Dr. Sabine Weber, specialist for IT law. “This makes precise contractual regulation all the more important.”

Pay attention to a balanced risk sharing in the contract. While many consulting companies understandably cannot give unlimited guarantees of success (especially for experimental AI applications), they should be willing to take on clearly defined responsibilities.

An increasingly popular contract form is phase-based contracts with defined go/no-go decision points. After an initial analysis phase, a joint decision is made whether and how the project will continue – based on the insights gained and a refined success forecast.

Dr. Thomas Müller, experienced project manager for AI implementations, emphasizes: “A good contract creates clarity and security for both sides without stifling the necessary flexibility. It should not be understood as a necessary evil, but as a valuable steering instrument for the collaboration.”

Future Outlook: How AI Consulting for SMEs Will Evolve by 2027

The AI consulting landscape is in continuous change, driven by technological developments, regulatory requirements, and changing market needs. Looking at the expected developments helps companies make long-term viable decisions.

Consolidation and Specialization: The Consulting Market in Transition

According to the forecast of the Federal Association of Digital Economy, the market for AI consulting will undergo two parallel developments in the coming years: increasing consolidation on the one hand and deeper specialization on the other.

“We expect a wave of consolidation by 2027, in which about 30% of today’s smaller consulting providers will either disappear from the market or be taken over by larger players,” explains market analyst Dr. Marcus Weber. “At the same time, we are seeing an increasing specialization of the remaining providers – be it in certain industries, technologies, or company sizes.”

This development is driven by several factors:

• The growing complexity of AI technologies requires increasingly specific expert knowledge
• Customers increasingly demand verifiable industry expertise and prefabricated solution components
• Competitive pressure through economies of scale favors larger providers
• Regulatory requirements (especially through the EU AI Act) increase the entry barriers for new providers

For medium-sized companies, this means clearer market orientation on the one hand, but also the need to be even more careful about future viability and stability when choosing a partner.

An interesting development is the expected increase in industry-specific AI platforms and solutions. “By 2027, we will see pre-configured AI solutions in many industries that are tailored to the specific requirements of, for example, mechanical engineering, healthcare, or retail,” predicts Dr. Julia Schneider from the Fraunhofer Institute for Intelligent Analysis and Information Systems. “These solutions will significantly reduce the implementation effort.”

From Implementation to Continuous Improvement Process

A fundamental change is emerging in the understanding of AI projects. The traditional project approach with defined beginning and end is increasingly being replaced by continuous improvement processes.

“AI systems are not static solutions, but living systems that must be continuously improved, adapted, and further developed,” explains Dr. Thomas Schmidt, author of the specialist book “Continuous AI Evolution in SMEs.” “Accordingly, the role of consulting is also changing from one-time implementation to long-term partner for continuous innovation.”

This development is favored by several factors:

• AI models need regular retraining with current data to remain relevant
• Business processes and requirements change continuously
• The underlying AI technologies are developing at a rapid pace
• The true value of AI often unfolds only through continuous optimization and expansion

For medium-sized companies, this means a shift from one-time project budgets to continuous investments in AI capabilities. According to a study by the Technical University of Munich, by 2027, about 65% of AI budgets in SMEs will be spent on continuous improvement and expansion of existing systems – compared to only 35% today.

This development is also reflected in new contract models. “We are seeing a clear trend towards managed services and success-based pricing,” reports Dr. Christina Müller, AI strategy consultant. “Instead of classic project budgets, increasingly results-oriented, continuous cooperation models are being agreed upon.”

Democratization of AI and the Role of Consulting

One of the most striking developments in the coming years will be the further democratization of AI technologies. Through low-code and no-code platforms, prefabricated AI services, and increasingly self-learning systems, AI applications will become accessible to a wider range of users.

“The technical entry barriers for AI implementations are rapidly decreasing,” explains Prof. Dr. Markus Winterstein from the Institute for Digitalization in SMEs. “What was a complex data science project five years ago can often be integrated into existing systems today via drag-and-drop.”

This democratization fundamentally changes the role of AI consulting:

• Less focus on technical implementation, more on strategic integration and business impact
• Shift from development to orchestration of prefabricated AI services
• Stronger importance of change management and organizational enablement
• Focus on data strategies and governance as success factors

For medium-sized companies, this development opens up new possibilities to enter AI usage faster and more cost-efficiently. At the same time, however, the danger of uncoordinated “shadow AI” initiatives in individual departments that are not embedded in an overall strategy increases.

“The democratization of AI technologies makes overarching AI governance more important than ever,” emphasizes Dr. Sabine Weber, specialist for digital transformation. “The role of good consulting shifts from pure implementer to strategic navigator who helps to use the diverse possibilities in a coordinated and sustainable way.”

A particularly relevant development for SMEs is the trend towards industry-specific AI ecosystems where technology providers, consulting companies, and user companies work together on solutions. These ecosystems offer especially smaller companies the chance to benefit from economies of scale and gain access to highly developed AI applications despite limited resources.

Professor Dr. Julia Schneider predicts: “By 2027, we will see AI cooperation models in many industries where medium-sized companies jointly develop data, models, and use cases – accompanied by specialized consultants as orchestrators. These models enable even smaller companies to remain competitive in an AI-driven economy.”

Frequently Asked Questions About AI Consulting for SMEs