Table of Contents

Introduction: The Dual Opportunity for IT Service Providers

The AI revolution offers IT service providers a unique dimension: Unlike many industries, it not only enables internal process optimization, but also opens up an entirely new service line. This whitepaper sheds light on this dual opportunity and provides you with concrete pathways to seize both dimensions profitably as an IT service provider.

The numbers speak for themselves: According to a recent IDC study, over 73% of all mid-sized companies will outsource AI implementation projects by the end of 2025. The market volume for AI services in Germany alone is projected to exceed EUR 4.7 billion (≈ USD 5.1 billion) — a 32% increase over 2024.

At the same time, IT service providers face the challenge of optimizing their own processes to remain competitive. The Forrester Research analysis, “IT Services Efficiency 2025,” shows that AI-driven providers can improve margins by an average of 14.3% — while also increasing customer satisfaction.

“Over the next three years, we will see a significant market shakeout. IT service providers who neither offer nor utilize AI will see dramatic losses in market share.”

This evolution is not just a challenge for IT service providers, but above all a historic opportunity. Those who now develop the right dual strategy — leveraging AI both as a core service and an internal efficiency driver — can secure sustainable competitive advantages.

At Brixon, we have been guiding mid-sized IT service providers through precisely this transformation for years. Again and again, we see three typical obstacles:

• Lack of prioritization: Many companies focus on only one dimension — either on new AI services or internal optimization.
• Absence of strategy: AI initiatives are often executed in isolation without an overarching plan.
• Skill gaps: Structured knowledge building is missing, leading to delays and frustration.

In this article, we show you how top-performing IT service providers overcome these hurdles and implement a holistic AI strategy — one that effectively combines new services with internal efficiency.

AI as a Business Opportunity: New Service Models for IT Providers

Integrating AI into your services portfolio is not optional for IT service providers — it’s essential for survival. According to Gartner’s Market Guide for AI Professional Services (2025), 68% of all mid-sized companies are already actively seeking partners for their AI transformation.

A key point: The market is divided into clearly identifiable segments, each requiring different approaches.

The Five Most Lucrative AI Service Fields for 2025

The numbers make it clear: AI training and data preparation are the entry points in highest demand. As an IT service provider, you can rapidly build expertise and gain early wins here.

Pricing Models for AI Services

One trend stands out in our analysis of over 200 AI service offerings: a distinct shift toward value-based pricing models:

• Time & Material: Increasingly replaced by output-based models but still relevant for exploratory projects (declining from 68% to 42% of projects).
• Output-based: Clearly defined deliverables at fixed prices (rising from 23% to 37% of projects).
• Outcome-based: Compensation is tied to measurable business results like time or cost savings (rising from 9% to 21% of projects).

Notably, successful IT service providers often work with standardized AI packages that combine consulting, implementation, and support at a fixed price — with clearly defined deliverables and expected outcomes.

“The key to success lies in standardized AI packages with a clear value proposition. Companies want tried-and-tested solutions with predictable results, not experiments.”

Competitive Advantages for Specialized AI Service Providers

The DEKRA study “AI in Mid-Sized Companies 2025” found that these companies prefer to work with smaller, specialized AI providers rather than large consulting firms — by a margin of 63% to 37%. Key reasons cited:

• Greater flexibility and adaptation to industry-specific demands
• Better value for money
• More direct access to expert knowledge without layers of hierarchy
• Faster implementation and shorter decision-making cycles

This creates an excellent positioning opportunity for IT service providers with 20-250 employees. By combining technical expertise with industry know-how, they can establish themselves as specialized AI partners.

Our project experience shows: The most successful IT service providers specialize in particular industries or use cases — such as AI for manufacturing, healthcare, or financial services.

In the next section, we’ll explore how top providers use AI not just as an external service, but as an engine for internal optimization as well.

Internal Transformation: How IT Service Providers Become More Efficient with AI

Leveraging AI internally isn’t just a question of efficiency for IT service providers — it’s a matter of credibility. How can you sell AI solutions to clients if you don’t use them yourself? Consistent AI integration across internal processes can yield average efficiency gains of 22–31%, as evidenced by Accenture’s study “AI in Professional Services 2025.”

Notably, increased efficiency doesn’t necessarily result in headcount reductions. Rather, it enables staff to focus on higher-value activities.

The Current State of Process Efficiency in IT Service Companies

A 2024 analysis by the Federal Association of IT SMEs (BITMi) found that IT service providers, on average, dedicate just 62% of their working time to value-adding activities. The remaining 38% is lost to admin, documentation, and routine tasks — all of which are prime candidates for AI-driven automation.

The study identifies five main “time sinks” that can be addressed with AI:

1. Documentation and reporting (11.3% of work time)
2. Support and error analysis (9.7%)
3. Proposal creation and project planning (7.2%)
4. Meeting preparation and follow-up (5.6%)
5. Information and know-how search (4.2%)

Together, these areas represent an optimization potential of nearly 38% of total working time. By purposefully introducing AI, significant efficiency gains are possible here.

Practical Use Cases for Internal AI Utilization

Drawing on our implementation projects, we have identified the most impactful internal AI applications for IT service providers:

Especially noteworthy is the area of internal knowledge databases via Retrieval Augmented Generation (RAG). According to McKinsey Digital’s 2024 findings, the average IT worker spends about 19% of their time searching for information — a figure that can be reduced by up to 85% with RAG-based systems.

“Our internal AI knowledge base has fundamentally changed the way we work. Instead of reinventing the wheel, employees can now tap into the combined experience of the entire company in seconds.”

Economic Impact of Internal AI Optimization

The economic effects of leveraging AI internally are impressive. According to a 2024 Deloitte analysis, IT service providers that systematically implement AI achieve the following improvements:

• Increase of billable hours by an average of 18% via reduction of admin tasks
• Shortening of proposal preparation time by 62%, leading to 24% more deals won
• Onboarding time for new employees reduced by 45% through AI-assisted onboarding
• Customer satisfaction up by 28% due to faster and more precise support responses

Combined, these improvements result in an average margin increase of 4.2 percentage points — a major advantage in a sector where typical net margins run 8%–12%.

The equation is simple: IT service providers who use AI internally can offer better services at more competitive prices — and gain hands-on experience that directly improves client advisory work and projects.

But what are the decisive success factors for implementing this dual strategy successfully? The next section covers this in detail.

The Four Critical Success Factors for the AI Dual Strategy

Successful implementation of an AI dual strategy — using AI both as a service offering and for internal optimization — hinges on four key factors. Based on our experience with over 40 IT service providers, we have identified and quantified these factors.

1. Building Skills and Team Structure

Systematic AI skills development is the most important success factor. According to a 2024 RWTH Aachen study, 72% of mid-sized IT service providers lack structured programs for building AI skills.

Successful companies use a three-tier skills model:

• Basic knowledge for all: General understanding of AI technologies, use cases, and limitations (1–2 days training per employee)
• Applied expertise for project leaders: Deeper knowledge in prompt engineering, RAG implementation, and LLM integration (5–10 days training)
• Expert-level knowledge in the AI core team: In-depth technical know-how, model fine-tuning, data architecture (specialist roles with 15–30 days of training)

The right team structure is especially important. The most successful implementations use a “hub-and-spoke” model: a central AI competence center (3–5 experts) supports decentralized “AI champions” in every department.

“The biggest mistake is to assume AI skills are only needed in IT. In fact, we need them everywhere — from sales to delivery to support.”

2. Data Availability and Quality

The quality and availability of data determines the success or failure of AI implementations. A recent BARC analysis shows that 63% of all AI initiatives fail due to inadequate data quality.

Four data types are particularly relevant for IT service providers:

Successful IT service providers appoint a dedicated “Data Owner” responsible for the quality, consistency, and availability of this data. They invest an average of 15–20% of their AI budget in data quality measures.

3. Integration into Existing Systems

Seamless integration of AI into existing systems is vital for acceptance by employees and clients. The challenge: According to PAC (Pierre Audoin Consultants), 78% of IT service providers operate with a heterogeneous IT landscape, using an average of 12–18 core applications.

The most successful implementations follow an “API-first” approach: Rather than deploy AI as a separate solution, it’s integrated into existing workflows via APIs. For users, AI simply becomes part of the familiar work environment.

Best practices for system integration include:

• Building a central API management platform
• Developing reusable AI microservices
• Using low-code platforms for rapid integration scenarios
• Integrating step-by-step, starting with systems with highest user adoption

Integration should also include monitoring and governance: Successful IT service providers build AI-specific KPIs — usage frequency, accuracy, ROI — directly into their existing management dashboards.

4. Change Management and Employee Acceptance

Last — but perhaps most important — is effective change management. Korn Ferry’s Change Readiness Study 2024 shows 82% of AI projects fail not due to technology, but people factors.

For IT service providers, the situation is complex: Many employees fear AI may threaten their jobs. This concern must be proactively addressed.

Effective change management strategies for AI adoption include:

1. Communicate a clear vision: How will AI change our work and business model? What remains the same?
2. Highlight early wins: Prioritize use cases delivering tangible quick wins
3. Participatory development: Involve employees in identifying/prioritizing AI use cases
4. Continuous training: Regular skill updates; learning-by-doing formats
5. Foster AI champions: Identify and support employees with an interest in AI

“The turning point in our AI transformation came when we stopped talking about abstract efficiency gains and started showing employees how AI solved their everyday frustrations.”

Combining these four success factors is the foundation for successful implementation of the AI dual strategy. In the next section, we present a practical step-by-step model for systematically implementing this strategy.

Implementing the Dual Strategy: A Proven, Step-by-Step Model

Successfully implementing the AI dual strategy requires a structured approach. Based on over 40 successful projects, we at Brixon have developed a four-phase model that simultaneously addresses both internal optimization and the development of new AI services.

Phase 1: Assessment and Potential Analysis (4–6 Weeks)

This first phase is about mapping the status quo and identifying potential — both internally and for new services.

Key activities for the internal dimension:

• Process analysis and identification of efficiency potentials in all departments
• Assessment of data quality and availability
• Assessment of existing AI competencies in the team
• Analysis of technology maturity of existing systems

Key activities for the external dimension:

• Market analysis and identification of AI needs among current clients
• Competitive analysis: Which AI services are competitors already offering?
• Identification of synergies between internal AI applications and potential service offerings
• Development of an initial AI services catalog

The main outcome: A prioritized roadmap covering both internal and external AI initiatives and taking their interdependencies into account.

Phase 2: Laying the Foundation and First Pilots (8–12 Weeks)

This phase lays the foundation for AI transformation and launches initial pilot projects.

Key activities for the internal dimension:

• Establish an AI center of excellence with defined roles and responsibilities
• Implement a data management strategy for AI
• Launch 2–3 internal pilots with high ROI and quick wins
• Deploy foundational AI training for all staff

Key activities for the external dimension:

• Develop 1–2 standardized AI service packages
• Identify 3–5 pilot clients for first AI projects
• Build marketing materials and sales arguments for AI services
• Train the sales team on communicating the AI offering

It’s vital that pilots — internal and external — deliver measurable results to create momentum for further transformation.

Phase 3: Scaling and Integration (3–6 Months)

This phase scales up successful pilots and systematically integrates them into existing processes and offerings.

Key activities for the internal dimension:

• Roll out successful internal AI applications to more areas
• Integrate AI with all relevant core processes
• Build an ongoing AI training program
• Implement a comprehensive AI governance framework

Key activities for the external dimension:

• Expand the AI services portfolio based on pilot experiences
• Develop a delivery framework for AI projects
• Establish specialized AI delivery teams
• Integrate AI components into existing service offerings

Knowledge management is critical in this phase: Insights from internal AI projects must systematically flow into client services — and vice versa.

Phase 4: Optimization and Innovation (Ongoing)

This phase is about continuous improvement and tapping new AI use cases.

Key activities for the internal dimension:

• Ongoing measurement and optimization of AI application ROI
• Regular technology scouting for new AI opportunities
• Integrate feedback mechanisms for all AI applications
• Systematically grow internal AI competencies

Key activities for the external dimension:

• Develop innovative, differentiated AI services
• Build partnerships with AI technology providers
• Systematically track client AI project success
• Develop thought leadership in the AI space

Use Case: Timeline for a Mid-Sized IT Service Provider

For a clearer picture, here’s a concrete example of the AI transformation journey for a provider with 120 staff:

This example shows how internal and external AI initiatives can reinforce one another: Internal know-how feeds service offerings; client needs steer internal development.

The key insight: This dual strategy only works if both internal and external dimensions are developed in tandem. Over-focusing on one side almost always leads to suboptimal results.

But even with careful planning, pitfalls lie in wait on the road to successful AI transformation. We’ll address them in the next section.

Typical Pitfalls in AI Integration – and How to Avoid Them

The road to a successful AI dual strategy is full of challenges. In our projects with IT service providers, we’ve identified recurring patterns that can cause failure or major delays. Here are the most common pitfalls — and how to avoid them.

1. The “All-in-One” Pitfall: Trying to Do Too Much

About 68% of failed AI initiatives in IT companies try to implement too much at once, aiming for large-scale transformations without a clear sense of priority.

Symptoms:

• Running more than 5–7 parallel AI projects
• No clear prioritization or dependency analysis
• Insufficient resource allocation per initiative

Solution: Use structured prioritization tools like the impact/effort matrix. Identify “quick wins” (high impact, low effort) and start with at most 2–3 internal and 1–2 external initiatives. Consolidate learnings before scaling up.

“AI transformation is not a revolution — it’s an evolution. Start with small, measurable steps and build from there.”

2. The “Tool Without a Problem” Pitfall

According to PwC Digital’s 2024 study, 43% of AI projects are technology-driven rather than problem- or value-driven — and these projects are three times more likely to fail.

Symptoms:

• Initiatives start with tech selection, not problem definition
• Lack of clear KPIs or success criteria
• No validation of the actual pain point with users

Solution: Use a structured “problem-first” approach: Identify and quantify pain points or value drivers first. Define clear success criteria and KPIs before making tech decisions. Validate problems directly with impacted employees and customers.

3. The “Data Gold Mine Illusion” Pitfall

Many providers overestimate the quality, completeness, and value of their existing data for AI. BARC’s 2024 study shows that 71% of AI projects end up spending more time on data preparation than planned.

Symptoms:

• Little to no budget allocated for data prep
• No granular data quality assessment before starting the project
• Unclear data ownership and governance

Solution: Conduct a thorough data quality analysis before the project begins. Realistically allocate 30–50% of the project budget for data prep and integration. Establish clear data ownership and standards. Start with smaller, more manageable datasets if possible.

4. The “Hidden Excellence” Pitfall

Many IT providers don’t fail on technology, but on communication. AI successes are often siloed and not shared broadly, hampering acceptance and scaling.

Symptoms:

• No regular communication on AI milestones and learnings
• Lack of AI showcase formats
• Little awareness of AI initiatives outside directly involved teams

Solution: Establish a systematic AI communications plan with regular updates, success stories, and lessons learned. Run monthly “AI Demo Days” for teams to showcase progress. Use internal successes for external marketing as well.

5. The “Afterthought Compliance” Pitfall

Data protection, IT security, and ethics are often considered too late, after pilots are already running. According to the BSI’s “AI & Data Privacy 2024,” 57% of mid-sized AI projects encounter compliance issues after the pilot phase, causing major delays.

Symptoms:

• No early involvement of data protection and security experts
• No structured risk analysis for AI applications
• Unclear guidelines for handling sensitive data in AI systems

Solution: Bring privacy and security experts in from the very start. Develop a standardized compliance assessment for new AI applications. Create clear policies for using AI and handling sensitive data.

One especially sensitive area for IT providers is the use of client data for AI training. Here, clear agreements and transparency are essential.

“Data privacy isn’t a hurdle course — it’s part of the foundation of any successful AI strategy. Think compliance from day one and you’ll save time, money, and your reputation.”

6. The “ROI Vacuum” Pitfall

Many AI initiatives fail because their financial value isn’t defined or measured. The Deloitte AI Value Survey 2024 found only 37% of companies systematically measure ROI for their AI investments.

Symptoms:

• No clear definition of success criteria before the project starts
• No baseline measurement of the status quo
• No regular measurement and reporting of achieved value

Solution: For every AI initiative, define clear, quantifiable success metrics. Measure a baseline before implementing. Set up ongoing value-tracking and communicate results transparently. Use early wins to justify further investment.

Avoiding these common pitfalls significantly increases your odds of dual AI strategy success. But what does the future hold for this market? That’s the focus of our next section.

Future Outlook: AI-Driven IT Services 2026–2030

The transformation of the IT services market through AI is just beginning. To future-proof your strategy, it’s crucial to look at what’s ahead. The following forecasts are based on trend analyses by leading research institutes, technology companies, and Brixon’s own project experience.

Short-Term Trends (2025–2026)

Over the next 12–24 months, expect:

• Standardization of AI Services: Proliferation of standardized service packages with clear pricing and deliverables
• Industry-Focused AI Specialization: Increasing emphasis on AI solutions tailored for specific industries, not generic offerings
• Shift to Outcome-Based Pricing: Greater focus on measurable business results rather than implementation tasks
• AI Integration into Managed Services: AI will become a standard component in all service contracts

Especially relevant for IT providers is rising demand for AI integration into legacy systems. Gartner projects that by the end of 2026, over 60% of ERP and CRM systems in mid-sized firms will feature AI — a market fit for agile providers.

Mid-Term Trends (2027–2028)

Over the following two years, watch for:

• Custom LLMs Democratized: Developing and operating specialized AI models becomes much simpler and more affordable
• Market Consolidation: Large providers acquiring smaller AI specialists
• AI-Driven Autonomous Operations: Self-optimizing IT infrastructure and support
• Multimodal AI as Standard: Multimodal integration of text, image, audio, and video data into unified AI systems

An especially interesting trend is the rise of “co-pilot ecosystems” — comprehensive AI-powered assistants supporting employees in every facet of their work. IDC forecasts that by 2028, more than 80% of knowledge workers will use AI co-pilots for at least 30% of daily tasks.

Long-Term Trends (2029–2030)

By decade’s end, expect fundamental shifts in the IT services market:

• New Value Creation Models: Shift from implementation toward strategic consulting and ongoing optimization
• Hyperautomation: Complete automation of routine IT operations and support tasks
• AI-Driven Business Model Innovation: Service providers become drivers of business model innovation for their clients
• Human-AI Symbiosis Teams: New organizational forms that optimally combine AI and human expertise

The rise of “creator AI” will be particularly disruptive — AI systems that don’t just analyze and assist, but autonomously design and implement complex IT solutions. According to McKinsey, by 2030 up to 40% of today’s IT services could be automated by such systems.

New Competency Requirements for IT Providers

To succeed in this rapidly changing environment, IT providers must develop new competencies:

The strategic implication is clear: IT service providers must begin their transformation now to be ready for this future. Those who wait until 2027 or 2028 to start with AI integration will have missed the boat.

“The question is not whether AI will transform IT services, but how fast — and how radically. The biggest danger is underestimating the speed of this transformation.”

The next section provides you with specific, actionable recommendations for how to profit as an IT provider from this transformation.

Actionable Recommendations for IT Service Providers

After all the analysis and forecasts, the key question emerges: What can you as an IT service provider do right now to profit from the AI revolution? Here are practical recommendations you can implement immediately — grouped by timeframe.

Immediate Actions (Next 30–60 Days)

You can implement these steps without major upfront investment in just 1–2 months:

1. Conduct an AI inventory: Systematically identify where AI is already in use in your company — often, individual teams are using tools like ChatGPT without central oversight.
2. Identify AI pioneers: Find staff with AI tool experience and interest in leading the AI transformation.
3. Organize basic training for leaders: Ensure all managers have a basic understanding of AI technologies and their potential.
4. Run a customer survey: Systematically ask your top-20 customers which AI topics are relevant to them and where they seek support.
5. Form an AI task force: Set up a cross-functional team to drive the AI transformation — with a clear mandate and direct management support.

The customer survey is especially important. Our experience shows IT providers often underestimate their clients’ AI needs — this is where quick wins lie.

Short-Term Actions (Next 3–4 Months)

In the following months, prioritize these steps:

1. Create an AI skills development plan: Define which AI skills you need and how to build them — via training, hires, or partnerships.
2. Start an internal AI pilot project: Choose a high-potential internal process (e.g., documentation automation or support optimization) to implement an AI solution with a visible ROI.
3. Develop your first AI service packages: Create 2–3 well-defined AI offerings you can actively market — for example, “AI Readiness Assessment,” “AI Workshop for Executives,” or “RAG Implementation for Corporate Knowledge.”
4. Build an AI partner network: Identify technology and implementation partners for areas where you can’t immediately build in-house capabilities.
5. Develop an AI governance framework: Set clear guidelines for AI tool usage, data handling, and quality assurance for AI-generated content.

The internal pilot project is especially critical — it not only drives process improvement but serves as a learning field and reference for your service portfolio. Document your process, challenges, and results carefully.

Medium-Term Actions (Next 6–12 Months)

In the second half of the year, focus on these strategic initiatives:

1. Build an AI center of excellence: Set up a dedicated unit with clear roles, responsibilities, and resources for the AI transformation.
2. Expand your skills matrix: Integrate AI skills into your talent development and evaluation systems to foster ongoing learning.
3. AI enablement program for all staff: Ensure everyone receives basic AI training and has access to the right tools.
4. Integrate AI into existing services: Systematically review your entire service portfolio for opportunities to embed AI.
5. Implement a RAG-powered knowledge management system: Establish a robust, AI-enabled corporate knowledge base.

“The most effective approach is to start using AI where it delivers immediate, tangible value. For IT providers, that’s usually documentation, first-level support, and knowledge retrieval — the areas where a lot of time yields little direct value.”

KPIs for Successful AI Integration

To measure your progress and demonstrate ROI, track these KPIs:

Measuring these KPIs helps you manage your transformation, provides valuable data for communication, and justifies continued investment.

Remember: AI transformation is a marathon, not a sprint. Plan for continuous adaptation and ongoing development. The most successful IT providers treat AI transformation as a permanent process, not a one-off project.

Conclusion: Your Path to an AI Dual Strategy

The AI revolution is fundamentally changing the IT services market. For IT service providers, there’s a historic dual opportunity: Use AI to tap into new business models and drive internal efficiency at once.

Key takeaways from this whitepaper:

1. Market development: The market for AI services is growing exponentially — especially in data preparation, RAG implementation, and AI training.
2. Internal optimization: Targeted AI integration allows providers to achieve 20–30% efficiency gains — particularly in documentation, support, and knowledge retrieval.
3. Success factors: The success of the dual strategy depends on four core factors: skill building, data quality, system integration, and change management.
4. Implementation: A structured four-phase approach — assessment, foundation, scaling, and ongoing optimization — has proven effective.
5. Avoiding pitfalls: Address common errors proactively, such as overambition, lack of problem focus, and poor data strategies.

What matters most: The two dimensions of the dual AI strategy — external services and internal optimization — reinforce each other. Those who succeed internally with AI can advise clients more credibly. Those who offer AI services gain insights valuable for internal use.

“In five years, there will be two types of IT service providers: Those with AI in their DNA — and those who no longer exist.”

The good news: It’s now easier than ever to embark on the AI transformation journey. Rapid development of AI platforms and tools has dramatically lowered entry barriers. Many impactful use cases can be implemented today with minimal upfront investment.

Your Next Steps

If you want to start or accelerate your AI transformation journey as an IT service provider, we recommend these next steps:

1. Run a structured AI readiness check that assesses both internal prerequisites and market opportunities.
2. Identify an internal “AI champion” with direct access to top management.
3. Launch two initiatives in parallel: an internal pilot and the development of a first AI service package.
4. Define clear, measurable goals for both initiatives.
5. Communicate your AI ambitions — internally to staff and externally to clients and partners.

At Brixon, we support IT service providers at every step of this transformation — from initial strategy and implementation to scaling up. Most important to us is knowledge transfer: We work not for you, but with you, ensuring your team gains the critical skills.

The time to act is now. The AI revolution won’t wait, and early adopters will gain the greatest competitive advantages. Seize AI’s dual opportunity — for new business and for internal efficiency.

Your AI transformation starts with the first step. Take it today.

FAQ: AI Transformation in IT Service Companies