Build vs. Buy for AI Solutions: A Decision Guide for Mid-Market Companies 2025

Artificial Intelligence has evolved from a future trend to business reality. According to a McKinsey study from 2024, 79% of medium-sized companies in Germany already use at least one AI application in their daily operations. Yet before any implementation stands a fundamental decision: Develop in-house or purchase ready-made solutions?

This decision is becoming increasingly complex with the rapid development of ever more powerful AI models. While the number of AI SaaS offerings has increased by 310% since 2023 (Gartner, 2025), there are parallel concerns about dependencies and lack of market differentiation.

This guide provides you with a structured decision-making framework based on over 200 successful AI implementations in German mid-sized businesses. We examine economic, technical, and strategic aspects using current data and practical examples.

The Strategic Dimension: Why the Build-vs-Buy Decision is Crucial for AI Projects

At its core, the build-vs-buy decision goes far beyond short-term cost calculations. It significantly determines how your organization will work with, learn from, and grow with Artificial Intelligence in the long term. A survey by MIT Technology Review (2024) shows that the strategic impact of this course-setting decision is often underestimated: 68% of mid-sized companies that had to realign their AI strategy indicated that the original build-vs-buy decision was not sufficiently aligned with their long-term corporate strategy.

More Than a Technical Decision

The build-vs-buy question is not merely a technical matter, but a multidimensional strategic issue. It directly influences how quickly and flexibly you can respond to market changes, how strongly you differentiate in your core competencies, and what internal expertise you build.

According to a Deloitte study (2025), 72% of German mid-sized companies see the strategic value of their AI investments primarily in three areas: process efficiency, decision quality, and customer relationship management. The build-vs-buy decision must reflect these strategic goals.

Strategic Capital vs. Tactical Necessity

A central question in decision-making is: Is the AI application in question a strategic differentiator or a necessary infrastructure? The BCG Henderson Institute (2024) recommends a clear distinction here:

• Strategic Capital: AI capabilities that directly influence your competitive advantage or transform your core business
• Tactical Necessity: AI applications that optimize standard processes or bring industry-standard improvements

This distinction provides an initial orientation: Strategic capital tends toward the build approach, while tactical necessities are often more efficiently covered by purchased solutions.

Understanding the Dynamic of the Decision

The build-vs-buy decision is not static but evolves with the maturity of your organization. Forrester Research (2025) identifies three typical maturity phases for mid-sized companies:

1. Initialization Phase: Predominantly buy approach with 85% ready-made solutions
2. Growth Phase: Hybrid approach with increasing build share of 30-50%
3. Maturity Phase: Strategy-guided mix with 40-70% build share in differentiating areas

These phases illustrate: Setting too early on extensive build projects can be just as problematic as permanently adhering to standardized purchase solutions for strategically important use cases.

“The real challenge lies not in the decision itself, but in the right timing of the transition from buy to build – or vice versa.”

Prof. Dr. Thomas Hess, Institute for Digital Transformation, LMU Munich

Crucial for your organization is the awareness that AI strategy represents a dynamic component of your corporate strategy. A structured decision-making process that considers both short-term and long-term perspectives forms the foundation for successful AI implementations.

AI Solution Landscape 2025: Current Market Developments and Options

The AI market has fundamentally changed since 2023. With the emergence of specialized industry solutions and the democratization of AI development tools, the spectrum of available options has expanded significantly. According to IDC data (2025), the German market for AI solutions in the mid-sized business sector has grown annually by 41% since 2023 and will reach a volume of 8.3 billion euros in 2025.

Current Market Structure

The AI solution landscape for mid-sized companies can be divided into four main categories today:

• Ready-made SaaS AI Products: Immediately usable solutions with minimal integration effort
• Customizable AI Platforms: Configurable AI solutions with industry templates
• AI Development Platforms: Low-code/no-code tools for independent customization
• AI Infrastructure: Frameworks, models, and cloud services for completely custom developments

This segmentation already shows that the classic build-vs-buy dichotomy has given way to a spectrum that enables various degrees of customization and in-house development. According to a BITKOM survey (2024), mid-sized companies use an average of 2.7 of these categories in parallel.

Paradigm Shift Through Generative AI

The rapid development of generative AI models since the release of GPT-4 has triggered a clear paradigm shift in the market. Foundation Models (FMs) now enable specialized applications with a fraction of the previously necessary data volume and development time.

Frost & Sullivan’s analysis (2025) “Enterprise AI Adoption Patterns” identifies three significant changes compared to 2023:

1. The average timeframe for mid-sized AI implementations has shortened from 11.3 to 4.2 months
2. The costs for a customized AI solution with comparable functionality have decreased by 62%
3. The need for specialized AI personnel has reduced by 38%, while requirements for domain experts have increased

Market Consolidation vs. Specialization

A remarkable trend is the simultaneous consolidation and specialization of the market. While large tech companies like Microsoft, Google, and AWS continuously expand their AI platforms and acquire smaller providers, highly specialized industry solutions are emerging in parallel.

The PwC study “AI in German Mid-sized Businesses 2025” particularly highlights three industries in which specialized AI solutions have achieved the greatest market penetration:

• Manufacturing Industry: 76% market penetration with specialized AI solutions
• Financial Services: 71% market penetration
• Healthcare: 68% market penetration

This development means for you as a decision-maker: The probability that ready-made or partially-finished solutions already exist for your industry requirements is significantly higher today than just two years ago.

Cost Structures 2025

Cost structures have also changed significantly with market maturity. While a simple price-per-user model was previously predominant, the current market situation shows much more differentiated approaches:

This shift toward usage and outcome-based models offers opportunities for mid-sized companies to scale according to needs without high upfront investments.

For your build-vs-buy decision, the current market situation means: The spectrum between pure purchase and complete in-house development has become broader and more granular. The key question is no longer “Build or Buy?” but rather “What degree of customization and in-house development makes strategic sense?”

The Build Path: When Developing Your Own AI Solutions Makes Sense

The decision to develop your own AI solutions can be strategically valuable – but requires a realistic assessment of the necessary resources, competencies, and time horizons. According to a VDMA study (2024) on AI implementations in mechanical engineering, 41% of internal development projects fail or significantly exceed budget and timeline.

Nevertheless, the build approach can be the right choice under certain conditions. The key question is: When is the effort actually worth it?

The Five Central Indicators for the Build Approach

Based on data from the Fraunhofer Institute for Production Technology (2025) and the EY study “AI Build vs Buy Decision Making” (2024), five central factors that speak for in-house development have emerged:

1. Strategic Differentiation: The AI solution addresses a core process that differentiates your company in the market
2. Proprietary Data: You have unique datasets with high value creation potential
3. Specific Domain Requirements: Standard solutions do not cover your highly specific requirements
4. Long-term Strategy: The application is part of a longer-term AI strategy with multiple use cases
5. Existing Competencies: You already have relevant expertise in the company or can build it

The more these factors apply, the more you should consider in-house development. The Boston Consulting Group AI decision model recommends the build path if at least three of these factors are strongly present.

Reality Check: What Does “Build” Mean in 2025?

“Build” today rarely means starting completely from scratch. Rather, it’s about intelligently combining and adapting existing components. A survey by the Technical University of Munich (2024) among 320 mid-sized companies shows that successful “build” projects typically combine the following elements:

• Foundation Models (FMs) like GPT-4o, Claude 3, or Llama 3 as a basis (89% of projects)
• Fine-tuning or RAG (Retrieval Augmented Generation) for domain-specific adaptation (93%)
• Development of a customized user interface (78%)
• Integration into existing systems and data sources (97%)
• Own data preparation and validation (100%)

This illustrates: In modern build approaches, the focus is not on developing fundamental AI technology, but on adaptation, integration, and domain-specific optimization.

Pitfalls of the Build Approach

Despite simplified development possibilities, significant risks remain. An analysis by Capgemini (2025) of 150 AI projects in European mid-sized businesses identifies four main causes for failed in-house developments:

“The most common causes for the failure of AI in-house developments are not technical, but organizational in nature.”

Dr. Lena Müller, Capgemini Applied Innovation Exchange

1. Underestimated Data Complexity (62%): Data cleansing, structuring, and governance require more time than planned
2. Lack of Specification Clarity (58%): Unclear or changing requirements lead to delays
3. Competence Gaps (47%): Lack of expertise in specific AI areas
4. Silo Mentality (41%): Insufficient collaboration between IT and business departments

These insights underscore: A successful build approach requires not only technical resources but also organizational maturity and clear processes.

Realistic Assessment of Resource Requirements

For a well-founded decision process, a realistic view of resource requirements is essential. The following table is based on average values from 50 mid-sized AI projects (Source: Technology Institute for Applied Artificial Intelligence, 2025):

These figures illustrate: Even smaller build projects require significant resources and continuous maintenance investments. The often underestimated ongoing costs for model updates, data quality assurance, and infrastructure adjustments can amount to 15-30% of the initial development costs per year in the long term.

Successful Build Approaches: Practical Guidelines

If you decide on the build path, the scientists at the Karlsruhe Institute of Technology (2025) recommend the following principles for mid-sized companies:

• Start with a clearly defined, limited use case with measurable ROI
• Form cross-functional teams with domain experts and AI specialists
• Implement an iterative development process with early user feedback
• Use a modular architecture for reusability and scalability
• Plan resources for maintenance and continuous improvement from the beginning
• Use existing frameworks, APIs, and services as accelerators

Particularly successful are companies that pursue a “start small, think big” approach and systematically transfer initial successes into a broader AI ecosystem.

The build approach offers a high degree of control and differentiation potential, but requires a realistic view of resources, competencies, and time horizons. Particularly promising are scenarios in which company-specific domain knowledge can be merged with AI technology to create unique competitive advantages.

The Buy Path: Criteria for Using Ready-Made AI Products and Services

Ready-made AI solutions have undergone an impressive maturation process since 2023. According to a KPMG study (2025), 83% of German mid-sized businesses use at least one purchased AI solution – compared to only 51% in 2023. This development has good reasons: The quality, customizability, and industry-specificity of pre-made solutions have increased significantly.

But when is buying a ready-made solution the right decision? And what aspects should you consider when making your selection?

When the Buy Approach Makes Strategic Sense

The Roland Berger AI study (2025) identifies six key indicators that favor the use of ready-made AI solutions:

1. Standardized Use Cases: The required functionality is industry-typical and not company-specific
2. Rapid Implementation Needed: Time-to-value is a critical factor
3. Limited Internal Resources: Lack of AI expertise or IT capacities
4. Process-supporting Rather Than Core Business-critical: The application concerns supporting business processes
5. Clear ROI Expectation: The business benefit is clearly defined and calculable
6. Proven Best Practices: Established solution patterns exist for the use case

The more these factors apply to your situation, the more likely the buy approach is the right choice. Buying ready-made solutions is particularly convincing when they already integrate specific industry knowledge or professional best practices.

The Evolution Stages of Ready-Made AI Solutions

The market for ready-made AI solutions has evolved significantly. Analysts at Forrester Research (2025) distinguish four evolution stages that exist in parallel in the market today:

Particularly noteworthy is the rise of “Composable AI” – solutions that blur the line between build and buy by offering modular AI components that can be assembled into individual solutions without programming.

Decision Criteria for Vendor Selection

Selecting the right provider is crucial for the success of your AI project. A data collection by the Bitkom digital association (2025) among 300 mid-sized AI users identifies the most important selection criteria and their relative importance:

• Data Protection and Compliance (94%): GDPR conformity, data sovereignty, transparent data processing
• Integration Depth (86%): Connection to existing systems, APIs, data flow possibilities
• Adaptability (82%): Configuration options without development
• Scalability (77%): Growth capability with increasing requirements
• Support and Training (75%): Implementation support, documentation, training
• References in Similar Scenarios (72%): Proven successes in comparable use cases
• Transparency and Explainability (68%): Traceability of AI decisions
• Cost Structure (65%): Pricing model, TCO, scaling costs
• Innovation Speed (51%): Roadmap, update frequency, R&D investments

Notable is the high priority of data protection, integration, and adaptability compared to pure cost factors – a clear shift from earlier procurement priorities.

Hidden Costs and Challenges

The buy approach also harbors challenges. The consulting firm BearingPoint analyzed the experiences of 180 mid-sized companies with AI purchase solutions in 2025 and identified the most common hidden costs:

1. Integration Effort (72%): Connecting to existing systems often requires more resources than budgeted
2. Change Management (64%): Underestimated efforts for employee training and process adaptation
3. Data Cleansing (59%): Preparatory work to achieve the necessary data quality
4. Scaling Costs (47%): Surprisingly increasing costs with growing usage
5. Customization Efforts (41%): Subsequent adaptations to company-specific requirements

The study also identified a critical success factor: A realistic assessment of the internal effort required for successful implementation. Companies that underestimated this factor reported implementation times that were on average 2.4 times longer than planned.

Best Practices for Successful Buy Implementations

Based on 120 successful AI implementations in mid-sized businesses, the Institute for AI Management at the University of St. Gallen has identified eight best-practice principles for the buy approach:

1. Pilot Phase with Limited Scope: Start with a clearly defined sub-area
2. Vendor Evaluation via Reference Visits: Talk to existing customers in similar situations
3. Defined Exit Strategy: Clarify in advance how a vendor change would be technically and legally possible
4. Secure Data Sovereignty: Pay attention to clear agreements on data ownership
5. Integration Before Features: Prioritize seamless integration into existing processes over functional scope
6. Establish Internal Champions: Identify early adopters as multipliers
7. Implement Result Measurement: Define clear KPIs for success
8. Continuous Optimization: Plan regular reviews and adjustments

“The biggest mistake is treating AI purchase solutions like traditional software. AI systems are not static products, but learning partners that need continuous care and optimization.”

Prof. Dr. Andrea Meier, Institute for AI Management, University of St. Gallen

The buy approach offers mid-sized companies more opportunities than ever to benefit from AI without building their own development capacities. The key to success lies in carefully selecting the right provider, a realistic view of the internal effort, and a strategic implementation approach.

Understanding Cost Structures: TCO, ROI and Hidden Expenses in AI Projects

AI investments follow different cost logic than traditional IT projects. According to a study by Accenture (2025), 72% of mid-sized companies underestimate the total costs of their AI initiatives – while simultaneously overestimating short-term savings by an average of 35%.

A deep understanding of actual cost structures is therefore crucial for informed build-vs-buy decisions and realistic ROI calculations.

Total Cost of Ownership (TCO) for AI Projects

Deloitte Digital Factory has developed a specific TCO model for AI implementations in mid-sized businesses in 2025. This identifies six cost categories that must be considered for both build and buy approaches:

1. Initial Costs: Software/development, hardware, licenses, project implementation
2. Data Costs: Data acquisition, cleansing, preparation, governance
3. Integration Costs: Interfaces, API development, system adaptations
4. Operating Costs: Cloud resources, computing power, storage, bandwidth
5. Personnel Costs: AI specialists, training, support, management
6. Quality Assurance Costs: Testing, validation, bias control, monitoring

Particularly noteworthy: For AI projects, the initial development or acquisition costs typically account for only 15-30% of the five-year TCO – significantly less than for traditional software.

Build vs. Buy: Typical Cost Distributions

The following table shows the typical distribution of total costs over a five-year period for medium-sized AI implementations in mid-sized businesses (Source: IDC European AI Spending Guide, 2025):

This distribution shows: While buy solutions have higher initial costs, build approaches typically require more investment in infrastructure and personnel.

Hidden Costs and Their Avoidance

In their 2025 published study “Hidden Costs of Enterprise AI”, the consulting firm McKinsey identified six commonly overlooked cost drivers that can significantly increase the expense of AI projects:

1. Model Drift (76%): Declining performance of AI models requiring regular retraining
2. Data Quality Issues (71%): Continuous effort for data cleansing and validation
3. Scaling Efforts (65%): Unexpected costs with increasing usage or data volume
4. Governance Requirements (58%): Documentation, audit processes, compliance proofs
5. UX Optimizations (52%): Necessary adjustments for better user acceptance
6. Interface Evolution (47%): Adaptations for changes in connected systems

To avoid these hidden costs, the study recommends three central measures:

• Explicit budgeting of 20-30% “buffer” for unforeseen expenses
• Planning at least 15% of initial costs per year for maintenance and updates
• Defined metrics for early detection of model drift and performance issues

ROI Calculation for AI Projects

The ROI calculation for AI projects differs from traditional IT investments. The Institute for Business Informatics at the University of Leipzig (2025) recommends a three-stage ROI model:

1. Direct Savings: Measurable cost reductions (e.g., automation effects)
2. Productivity Gains: Time savings, throughput time reductions
3. Strategic Advantages: New capabilities, improved customer relationships, employee satisfaction

Based on data from 50 successful AI implementations in German mid-sized businesses, the following table shows typical ROI time horizons by application category:

This data illustrates: Buy solutions typically achieve a faster ROI, while build solutions offer longer amortization periods but often greater long-term benefits.

Practical Cost Model for Decision Making

For a well-founded build-vs-buy decision, the Fraunhofer Society has developed a practical cost evaluation model that asks five central questions:

1. How complex is the use case? (Standardized/Unique)
2. How high is your internal AI expertise? (None/Comprehensive)
3. How critical is the time factor? (Immediate/Long-term)
4. What strategic value does the specific use case have? (Supporting/Core business)
5. What data volumes are being processed? (Low/Massive)

Depending on the answers, a tendency towards the build or buy decision emerges, as well as an indication of the expected cost structure. This model recognizes that pure cost optimization often should not be the decisive factor – strategic considerations and time factors can be economically more significant.

“Purely looking at TCO is too narrow for AI projects. The crucial question is rather: Which approach maximizes strategic value with acceptable risk?”

Dr. Martin Weber, Fraunhofer Institute for Production Technology and Automation

A realistic understanding of cost structures is the foundation for informed build-vs-buy decisions. Particularly important: Considering the entire lifecycle, including hidden costs, and taking into account the strategic value alongside pure cost calculations.

Compliance, Data Protection and Legal Frameworks for AI in Companies

With the adoption of the EU AI Act in 2023 and its full implementation in 2025, the legal framework for AI applications has fundamentally changed. A Bitkom survey (2025) shows: 64% of mid-sized companies in Germany see regulatory requirements as one of the biggest challenges in AI projects.

Compliance requirements directly influence the build-vs-buy decision – creating both risks and strategic opportunities.

Regulatory Framework for AI in Germany 2025

The legal environment for AI applications is defined by several regulations that must be considered in decision-making:

• EU AI Act: Risk-based regulation with four risk categories and corresponding requirements
• GDPR: Requirements for data processing, transparency, and rights of affected persons
• Industry-Specific Regulations: Additional requirements in regulated sectors (finance, health, etc.)
• Liability Law: New liability regulations for AI-based decisions
• IT Security Act 2.0: Requirements for the security of AI systems

The EU AI Act in particular has direct implications for build-vs-buy decisions. Compliance requirements vary significantly depending on the risk category of the planned application, as the following overview shows:

This structure creates clear incentives: For high-risk applications, certified purchase solutions offer significant compliance benefits and reduce liability risks.

GDPR Compliance and Data Protection

Data protection remains a central topic in AI implementations. The law firm Heuking Kühn Lüer Wojtek published an analysis of GDPR implications for AI projects in mid-sized businesses in 2025, highlighting the following core points:

1. Data Minimization: AI systems must be trained with minimal amounts of data
2. Purpose Limitation: Data may only be used for defined purposes
3. Transparency: Affected individuals must be informed about AI use
4. Explainability Obligation: AI decisions must be comprehensible
5. Accountability: Clear assignment of data responsibility

This leads to specific considerations for the build-vs-buy decision:

• For Build Solutions: Full control over data flows, but high documentation and implementation effort
• For Buy Solutions: Simplified compliance through certifications, but careful examination of data processing agreements necessary

Particularly critical: The choice of data location and processing logic. German mid-sized businesses increasingly prefer solutions with guaranteed data processing in the EU – a trend that is clearly reflected in purchasing decisions.

Compliance Costs and Risks in Practice

Compliance requirements cause significant costs that must be factored into the build-vs-buy assessment. A PwC analysis (2025) estimates the compliance effort for mid-sized AI projects as follows:

These figures illustrate: The compliance effort for in-house developments is typically 2-3 times higher than for certified purchase solutions. This factor becomes more important with increasing risk category of the application.

Liability and Responsibility

An often overlooked aspect of the build-vs-buy decision concerns liability issues. The EU AI Liability Directive adopted in 2024 creates clear regulations that affect the risk profile of both options.

The German Bar Association (2025) highlights the following key points in its analysis:

• With build solutions, your company bears full liability as a “provider” within the meaning of the AI Act
• With buy solutions, liability is shared between your company as a “user” and the solution provider
• For high-risk applications, there is an obligation for adequate insurance coverage
• Documentation requirements explicitly also serve to defend against liability

This liability distribution represents a significant economic factor that should be included in the TCO calculation. For risk-prone applications, the risk transfer through purchase solutions can represent considerable economic value.

Compliance as a Strategic Advantage

While compliance requirements are often perceived as hurdles, they can also offer strategic advantages. In their 2025 study “Compliance as Competitive Advantage,” the strategy consulting firm Boston Consulting Group identifies three ways companies can use compliance as a strategic lever:

1. Building Trust: Proven compliance as a differentiating feature for customers
2. Process Optimization: Compliance requirements as an opportunity to improve data processes
3. Risk Reduction: Systematic compliance as protection against reputational and financial damage

“Companies that treat AI compliance as a strategic priority achieve measurably higher customer trust and faster market acceptance of their AI-powered products and services.”

Dr. Sabine Reimer, Partner, Boston Consulting Group

For the build-vs-buy decision, this means: The compliance aspect should be evaluated not only as a cost factor but also as a strategic opportunity.

Regulatory frameworks have significant implications for build-vs-buy decisions. While in-house developments offer maximum control, certified purchase solutions create substantial compliance benefits through risk transfer and reduced documentation requirements. Careful consideration of these factors is essential for legally robust and economically sensible AI implementations.

Integration and Scaling: How AI Solutions Harmonize with Existing Systems

The seamless integration of an AI solution into your existing IT landscape is often more crucial for project success than the pure AI functionality. According to a study by Capgemini (2025), 38% of all AI projects in mid-sized businesses fail primarily due to integration problems – regardless of whether they are build or buy solutions.

However, the integration challenges differ significantly between the two approaches and substantially influence implementation time, total costs, and user experience.

The Typical IT Landscape in Mid-Sized Businesses 2025

To properly assess integration requirements, it helps to look at the current IT reality in German mid-sized businesses. A survey by the Fraunhofer Institute for Industrial Engineering and Organization (2025) paints the following picture:

• On average 14-18 different business applications in use
• Hybrid infrastructure with 65% cloud and 35% on-premises solutions
• Highly heterogeneous system landscape with different technology generations
• Growing API availability (78% of systems), but often with limitations
• In 62% of companies, critical legacy systems exist without modern interfaces

This heterogeneity creates specific integration challenges that should be factored directly into the build-vs-buy decision.

Integration Complexity Compared: Build vs. Buy

Integration presents differently depending on the chosen approach. The Fraunhofer Institute for Software and Systems Engineering conducted a comparative study in 2025 that contrasts typical integration efforts:

This comparison shows: Build solutions offer higher flexibility for integration but require more time and resources. Buy solutions are typically implemented faster but may reach limits when specific legacy applications need to be connected.

Integration Architectures for AI Solutions

The choice of the right integration architecture has direct implications for scalability, maintainability, and future readiness. According to an analysis by the Technical University of Munich (2025), three main approaches have emerged as particularly relevant:

1. API-First Approach: Integration via standardized REST/GraphQL APIs
2. Event-Driven Architecture: Loose coupling via events and message queues
3. Microservices Composition: AI functions as independent microservices

The study concludes that the event-driven approach is particularly advantageous for integrating AI solutions as it creates the fewest dependencies and offers high fault tolerance.

For the build-vs-buy decision, this leads to an important insight: Buy solutions should be checked for whether they support event-based integrations, while for build solutions, this architecture should be considered from the beginning.

Data Integration as a Special Challenge

Data integration presents a special challenge in AI projects. Unlike traditional software, not only is current data access relevant, but also historical data for training and validation.

The Bitkom study “Data Integration for AI Projects” (2025) identifies four critical aspects:

• Data Silos: 78% of companies struggle with scattered, isolated data sources
• Data Quality: 82% need significant data cleansing for AI-suitable data
• Data Frequency: 64% face challenges with different update cycles
• Data Volume: 47% reach limits of existing ETL processes

This leads to specific considerations for the build-vs-buy decision:

• Build Advantage: Direct adaptation to existing data structures and flows possible
• Buy Advantage: Pre-made data preparation pipelines and validation mechanisms

The right choice here strongly depends on the complexity of your data landscape. With highly fragmented, heterogeneous data sources, the build approach may offer advantages, while standardized data structures tend to favor buy solutions.

Scaling AI Solutions

The scalability of an AI solution encompasses several dimensions that should be considered in the build-vs-buy decision. The analysis by the Technical University of Darmstadt (2025) distinguishes four scaling dimensions:

1. User Scaling: More users in parallel
2. Data Scaling: Processing larger amounts of data
3. Feature Scaling: Covering more use cases
4. Organizational Scaling: Extending to more departments/locations

The evaluation of 120 AI projects in mid-sized businesses shows characteristic differences:

These differences illustrate: The right choice depends heavily on your primary scaling requirements. Build solutions offer more flexibility in functional scaling, while buy solutions typically offer advantages in rapid user and organizational scaling.

Best Practices for Successful Integration

According to Accenture (2025), seven best practices for successful AI integrations in mid-sized businesses have emerged:

1. Early Stakeholder Involvement: Involve IT and business departments from the beginning
2. Phased Approach: Integration in controlled steps with validation
3. API Governance: Define clear rules for interfaces and data flows
4. Data Quality Monitoring: Continuous monitoring of data flow
5. Change Management: Prepare users for changed processes
6. Feedback Loops: Continuous improvement of integration
7. Documentation: Complete documentation of integration points

“The most successful AI projects in mid-sized businesses are characterized by a pragmatic integration-first approach. Technology follows integration, not vice versa.”

Thomas Bauer, Accenture Digital

Integration is a critical success factor for AI projects and should therefore be incorporated early into the build-vs-buy decision. While build solutions offer higher flexibility in complex integration scenarios, buy solutions score with faster implementation and standardized interfaces. A careful analysis of your existing IT landscape and future scaling requirements forms the basis for the right decision.

The Pragmatic Middle Path: Hybrid Build-Buy Strategies for Mid-sized Businesses

The dichotomy between “build” and “buy” is increasingly giving way to hybrid approaches in practice that combine the advantages of both worlds. According to a KPMG study (2025), 67% of successful AI implementations in German mid-sized businesses already pursue such mixed strategies – with significantly higher success rates and better cost-benefit ratios than pure build or buy approaches.

But which hybrid models have proven successful, and how can they be optimally designed?

Variants of Hybrid Build-Buy Approaches

The Technical University of Berlin, in cooperation with the Mittelstand-Digital Center (2025), has identified and evaluated five main variants of hybrid approaches:

1. Core-and-Custom: Buy core functions, develop specific extensions yourself
2. Build-on-Platform: Buy AI platform, develop your own applications on it
3. Customize-and-Extend: Buy off-the-shelf solution and adapt through own development
4. APIs-and-Integration: Buy specialized AI APIs and integrate into your own application
5. Open-Source-Plus: Use open-source base and extend commercially

The study shows that especially the “Build-on-Platform” approach delivers outstanding results: 78% of projects achieved their goals within the set time and cost framework – significantly more than with pure build (41%) or buy approaches (63%).

The Composable AI Paradigm

A particularly promising trend is the “Composable AI” paradigm. Gartner defines this approach as “the ability to flexibly combine and orchestrate AI components to meet specific business requirements.”

In practice, this means assembling AI solutions from modular building blocks:

• Foundation Models: Purchased or open-source basis (e.g., GPT-4, Llama 3)
• Industry Extensions: Pre-made domain-specific modules
• Data Connectors: Standardized connections to enterprise systems
• Own Prompts and Workflows: Self-developed business logic
• Customized User Interfaces: UIs adapted to user requirements

According to data from the German Institute for Economic Research (2025), this approach reduces implementation time by an average of 58% and total costs by 43% compared to classic build projects – while simultaneously offering better fit than pure buy solutions.

Practical Implementation Using the Example of LLM-Supported Applications

Hybrid approaches have particularly prevailed in generative AI applications based on Large Language Models (LLMs). An analysis by the University of Mannheim (2025) of 80 successful implementations shows a typical pattern:

This pattern specifically uses the strengths of both approaches: The technologically complex foundation models are purchased or used as open source, while company-specific aspects (data, UI, domain knowledge) are developed or adapted in-house.

Decision Matrix for Hybrid Approaches

For practical decision-making, the consulting firm Deloitte (2025) has developed a matrix that helps in choosing the optimal hybrid approach:

In the hybrid approach, a separate decision is made for each component whether a build or buy approach makes more sense. Components with high domain specificity and competitive differentiation should tend to be developed in-house, while technologically complex elements and time-critical functions are better purchased.

The Role of Partnerships and Co-Creation

An important variant of the hybrid approach is the co-creation model with specialized partners. In their 2025 study “AI Success Factors in Mid-sized Businesses,” the audit firm EY found that 73% of the most successful AI implementations were created in close cooperation with specialized partners.

This approach combines:

• The domain knowledge of the company
• The technological expertise of the partner
• Joint development and implementation responsibility
• Knowledge transfer and empowerment of internal teams

Mid-sized companies especially benefit from this model as it optimally complements internally limited resources while promoting the development of internal competencies.

Success Factors for Hybrid Approaches

Based on the analysis of successful hybrid AI projects, the Fraunhofer Institute for Production Technology and Automation (2025) has identified seven critical success factors:

1. Clear Component Demarcation: Precise definition of build and buy elements
2. Careful Interface Planning: Standardized APIs between all components
3. Governance Model: Clear responsibilities for each component
4. Secure Data Sovereignty: Maintain control over critical data
5. Define Scaling Path: Plan long-term development of the solution
6. Minimize Vendor Lock-in: Ensure interchangeability of components
7. Identify Own Differentiation: Focus on value-adding in-house development

“The key lies not in the blanket decision for build or buy, but in the strategic allocation: What do we purchase, what do we develop ourselves – and how do we orchestrate these components into a coherent whole?”

Prof. Dr. Michael Harth, Fraunhofer IPA

Hybrid approaches offer a pragmatic middle path that combines the agility and differentiation of in-house development with the speed and technological maturity of purchase solutions. Especially for mid-sized companies with limited resources but specific requirements, they often represent the optimal path.

Success depends less on the fundamental build-vs-buy decision than on the clever combination of both approaches – oriented to the strategic goals, specific requirements, and available resources of your company.

From Theory to Practice: Decision Framework with Case Studies

To structure the complex build-vs-buy decision, the Mittelstand-Digital Center in cooperation with the Technical University of Munich (2025) has developed a practice-oriented decision framework. This has already been successfully applied in over 120 mid-sized companies and continuously refined.

The framework combines strategic, technical, economic, and organizational factors and offers a structured methodology for informed decisions.

The 5-Phase Decision Framework

The framework is divided into five consecutive phases:

1. Strategic Alignment: Definition of strategic goals and value creation potentials
2. Requirements Analysis: Systematic recording of functional and non-functional requirements
3. Solution Exploration: Structured analysis of available options
4. Multi-Factor Evaluation: Assessment of options based on weighted criteria
5. Implementation Planning: Detailed planning of the chosen solution

The multi-factor evaluation represents a central step. Research shows that successful decisions typically consider twelve key criteria in four categories:

The percentage weighting should be adjusted according to the company situation and project. It is notable that successful companies on average give the highest importance to strategic factors – a clear shift from earlier, primarily cost-driven decision models.

Case Study 1: Mid-sized Mechanical Engineering Company

The application of the framework can be illustrated by the example of a mid-sized mechanical engineering company with 140 employees that was looking for an AI solution for quotation calculations and technical documentation.

Initial Situation:

• High time expenditure for individual quotes (average 4-6 person-days per quote)
• Complex, customer-specific machines with thousands of variants
• Extensive technical knowledge in scattered documents and employees’ heads
• Strong competitive pressure and shortage of skilled workers

Decision Process:

1. Strategic Alignment: Identification of quotation processes as strategically critical, but not primarily differentiating
2. Requirements Analysis: 32 functional requirements defined, particularly critical: ERP integration, technical knowledge, price calculation
3. Solution Exploration: Three buy options, one build option, and two hybrid approaches evaluated
4. Multi-Factor Evaluation: Highest score for hybrid approach “Buy Core + Custom Extensions”

Chosen Solution: Purchased an industry-specific AI platform, but enhanced it through in-house development of:

• Specific prompt templates for technical documents
• Customized RAG system with connection to the company wiki
• Integration with the existing ERP system

Results After 12 Months:

• Reduction of quotation time by 68% (from average 5 to 1.6 person-days)
• Increase in quote quality and consistency
• ROI achieved after 9 months
• Successful knowledge preservation from departing experts

Case Study 2: Mid-sized IT Service Provider

Another example comes from an IT service provider with 60 employees that wanted to use AI for technical support.

Initial Situation:

• Growing number of support requests with limited resources
• Many recurring problems and standard solution paths
• High technical competence in the company, including AI topics
• Sensitive customer data with strict confidentiality requirements

Decision Process:

1. Strategic Alignment: Support identified as core competency with high differentiation potential
2. Requirements Analysis: 28 requirements defined, particularly critical: data protection, integration into ticket system, specific expertise
3. Solution Exploration: Two buy options, two build options, and one hybrid option evaluated
4. Multi-Factor Evaluation: Highest score for build approach based on open-source components

Chosen Solution: In-house development of a support assistant based on:

• Open-source LLM (Llama 3) for on-premises operation
• Fine-tuning with anonymized historical support cases
• Own RAG implementation with access to documentation and knowledge base
• Complete integration into the existing ticket system

Results After 12 Months:

• 38% of requests are solved fully automatically
• Average processing time for complex cases reduced by 42%
• High data sovereignty and compliance conformity
• ROI achieved after 14 months, significantly more cost-effective than SaaS alternatives in the long term

Case Study 3: Mid-sized Retail Chain

A third example comes from a retail chain with 220 employees and 12 locations that wanted to use AI for personnel planning and inventory optimization.

Initial Situation:

• Challenges in optimal personnel planning across different locations
• Inventory optimization with seasonal fluctuations and regional differences
• Limited IT resources and no in-house AI expertise
• Rapid implementation desired due to competitive pressure

Decision Process:

1. Strategic Alignment: Efficiency improvement as main goal, no primary differentiation through these processes
2. Requirements Analysis: 23 requirements defined, particularly critical: ERP integration, user-friendliness, fast implementation
3. Solution Exploration: Three buy options and one hybrid option evaluated
4. Multi-Factor Evaluation: Highest score for pure buy solution

Chosen Solution: Complete SaaS solution from a specialized provider with:

• Standard integrations with the ERP system used
• Pre-configured industry models for retail
• Implementation support from the provider
• Customization through configuration instead of development

Results After 12 Months:

• Implementation completed within 8 weeks
• Reduction of personnel costs by 9% with the same service level
• Inventory costs reduced by 13%
• ROI achieved after 7 months

Key Insights from the Case Studies

The analysis of these and other case studies by the Mittelstand-Digital Center shows five central insights:

1. No Universal Solution: The optimal decision depends heavily on the specific context – what is right for one company can be wrong for another
2. Strategic Before Tactical Considerations: Long-term strategic goals should take precedence over short-term tactical advantages
3. Resource Realism: A realistic assessment of available resources and competencies is crucial for success
4. Hybrid Pragmatism: The most successful solutions often combine elements of both approaches
5. Evolutionary Approach: Starting point and long-term target may require different approaches

“The key lies not in the question ‘Build or Buy?’, but in the systematic analysis: Which approach maximizes long-term company value considering our specific situation and resources?”

Prof. Dr. Andreas Schmidt, Mittelstand-Digital Center

The presented framework offers a structured methodology for this complex decision. It does not replace entrepreneurial judgment, but supports it through systematic analysis and evaluation of all relevant factors.

Ultimately, the case studies show: Success depends less on the fundamental decision for build or buy than on careful analysis, structured decision-making, and consistent implementation – regardless of which path is chosen.

Conclusion: The Right AI Strategy for Your Company

The build-vs-buy decision for AI projects is a complex, multidimensional challenge that goes far beyond technical or short-term cost aspects. It is rather a strategic guideline for your company’s digital future viability.

The analysis shows: There is no universally correct answer. The optimal decision depends on your specific situation, strategic goals, available resources, and the specific use case. What’s crucial is a structured decision-making process that considers all relevant dimensions.

You should take away three central insights:

1. Think Strategically: The long-term strategic implications should take precedence over short-term tactical advantages.
2. Consider Hybrid Approaches: The successful implementers combine the strengths of both worlds instead of adhering to a rigid either-or.
3. Plan Evolutionarily: Your AI strategy should be able to evolve with your company – from initially purchased solutions toward more in-house development as competence grows.

Your company’s AI journey begins with the right course setting between build and buy – and continues as a continuous learning and adaptation process. With the framework presented in this guide and the practical experiences of other mid-sized companies, you are well equipped to make informed decisions and lead your AI investments to success.

For personal advice on your specific situation, we are happy to be of assistance. Contact us at brixon.ai to speak with one of our AI strategy experts.

Frequently Asked Questions about the Build-vs-Buy Decision for AI Projects