The 10 Crucial Questions for AI Providers: Your Decision-Maker’s Checklist 2025 for Secure Contract Agreements

Table of Contents

• Introduction: The AI Decision Pressure in Mid-Sized Companies
• 1. “How do you secure our data?” – Data Protection as a Foundation
• 2. “How transparently does your AI work?” – Transparency and Explainability
• 3. “How does your solution integrate with our existing IT landscape?” – Compatibility and Integration
• 4. “What hidden costs should we expect?” – Complete Cost Transparency
• 5. “How do you support us during implementation?” – The Path to Productive Use
• 6. “How future-proof is your technology?” – Scalability and Technological Evolution
• 7. “What happens when problems arise?” – Support and Service Levels
• 8. “How do you comply with current and upcoming regulatory requirements?” – Compliance and Legal Security
• 9. “Can you provide references from our industry?” – Experience Values and Proof
• 10. “How will our long-term collaboration look like?” – Partnership Instead of Sales
• The Complete Checklist: Your Guide for the Next Negotiation
• Conclusion: Making Informed Decisions
• Frequently Asked Questions

Choosing the right AI provider today resembles selecting a strategic business partner – with far-reaching consequences for your competitiveness. For mid-sized companies, this choice is particularly consequential: Without specialized AI teams, you need to rely on your provider’s expertise and reliability.

The facts speak clearly: According to a 2024 Bitkom survey, 62% of German mid-sized companies plan to implement AI solutions by the end of 2025. At the same time, a recent Deloitte study shows that 67% of AI implementations don’t deliver the expected results. The main reason: insufficient care in selecting providers.

As experienced consultants for AI implementations in mid-sized businesses, we at Brixon AI have repeatedly seen how crucial the right questions are before signing a contract. These 10 essential questions for potential AI providers will help you separate the wheat from the chaff and find a partner who sustainably strengthens your business success.

1. “How do you secure our data?” – Data Protection as a Foundation

How your company data is handled is not just a technical question, but an existential one. According to a 2024 KPMG study, only 32% of AI providers fully comply with all GDPR requirements – an alarming figure given the drastically increased penalties for data protection violations.

GDPR-specific requirements for AI systems

Ask specifically about the legal basis for data processing by the AI. Are personal data used for training? Is there a Data Protection Impact Assessment (DPIA) for the AI system? A reputable provider can answer these questions clearly and transparently.

Particularly relevant: Since the adoption of the EU AI Act, enhanced transparency obligations apply to AI systems. Therefore, request information about the provider’s compliance roadmap.

The question of data ownership and usage rights

Will your company remain the owner of all data you input? Will your information be used to train other models? A BSI guideline recommends explicitly defining these points in the contract. Don’t assume this is self-evident – inquiries often reveal surprising gaps in standard contracts.

Data locations and international transfers

After the Schrems II ruling and the end of the Privacy Shield, the question of where your data is stored is more critical than ever. Ask specifically about:

• Locations of all data centers that process your data
• Guarantees for exclusive processing in the EU/EEA (if required)
• Technical and organizational measures to protect against unauthorized access

Practical checklist: How to verify GDPR compliance

Request the following documentation from the provider:

1. Current ISO 27001 certification
2. Documented Privacy by Design principles
3. Data Processing Agreement (DPA) according to Art. 28 GDPR
4. Evidence of regular penetration tests
5. Data Protection Impact Assessment for the AI being used

According to the Federal Office for Information Security (BSI), 2024 saw a 45% increase in security incidents related to AI systems. This underscores the necessity of treating data protection and security as fundamental selection criteria.

2. “How transparently does your AI work?” – Transparency and Explainability

The notorious “black box” is now considered an unacceptable risk in many industries. A Gartner analysis predicts that by 2025, around 85% of AI projects will deliver incorrect results – due to bias in data or algorithms. But how do you ensure transparency?

Black Box vs. Transparent AI: The decision basis

Inquire about the specific mechanisms the provider uses to make the decision processes of their AI transparent. Advanced solutions now offer confidence scores that evaluate the reliability of an AI recommendation, or Explainable AI (XAI) features that visualize decision paths.

A practical example: If the AI in your company is meant to assess creditworthiness, it must be transparent why one customer is classified as creditworthy and another is not. Without this transparency, you’re not only exposing yourself to legal risks but also losing control over critical business processes.

Auditing capabilities and traceability

Ask specifically about audit trails and logging mechanisms. An MIT study shows that AI systems with robust audit functions produce up to 40% fewer erroneous decisions. Specifically, you should know:

• Are all AI decisions logged?
• Can decision paths be reconstructed?
• Are there options for independent audits?
• How are model updates documented?

Bias and fairness: Critical checkpoints

The question of bias in AI systems is not an academic discussion, but a real business risk. The EU AI Act classifies discriminatory AI systems as high-risk – with corresponding obligations for operators.

Ask the provider specifically:

• How are training data checked for bias?
• What methods do you use to test for fairness?
• Can you provide demographic tests?

Decision-maker tip: How to test explainability during the demo

During the product demonstration, you should specifically ask about traceability. Ask the provider to explain the AI’s decision path using an example typical for your industry. Pay attention to whether the explanation:

1. Is understandable for non-technical people
2. Names specific influential factors
3. Works even in complex scenarios
4. Is integrated into the user interface

This practical test often exposes theoretical transparency promises that don’t hold up in everyday use.

3. “How does your solution integrate with our existing IT landscape?” – Compatibility and Integration

Technical integration is often the biggest practical hurdle in AI projects. A 2024 IDG study shows that 58% of mid-sized companies cite integration challenges as the biggest obstacle to AI adoption.

API interfaces and standard compatibility

The quality and documentation of APIs significantly determine the implementation effort. Ask specific questions about:

• Availability of REST, GraphQL, or SOAP APIs
• Support for industry standards (e.g., ONNX for ML models)
• Public API documentation and developer resources
• Rate limits and performance guarantees

A positive sign: Providers who offer public documentation, SDKs, and sample code often demonstrate their integration maturity.

Challenges with legacy systems

Especially in mid-sized businesses, established IT structures with proprietary systems are not uncommon. According to a Capgemini study, 62% of AI initiatives fail due to insufficient integration with legacy systems.

Confront the provider with your specific system landscape and ask about:

• Experience with similar integration scenarios
• Reference implementations in comparable environments
• Necessary adaptations to your existing systems
• Middleware solutions or connectors for older systems

On-Premise vs. Cloud: Decision criteria

The deployment option has far-reaching consequences – both technical and economic. Since 2023, increasingly more providers offer hybrid models that can combine the advantages of both worlds.

Check the following aspects:

Best Practice: The integration roadmap

Request a specific integration plan from the provider with:

1. Detailed technical analysis phase (2-4 weeks)
2. Clear milestones and dependencies
3. Resource requirements for your internal team
4. Test phases and quality assurance measures
5. Rollback strategies in case of problems

A reputable provider will not make blanket promises but will insist on a thorough system analysis. Don’t be blinded by unrealistic promises – solid integration takes time and careful planning.

4. “What hidden costs should we expect?” – Complete Cost Transparency

The pitfall with AI projects often lies in the financial details. A Forrester Research study shows that the actual costs of AI implementations are on average 43% higher than the original budget plans. Transparency is not a luxury here, but a business necessity.

Typical cost structures in AI implementations

Get clarity about the complete cost model. The Bertelsmann Foundation identified the following typical cost blocks in a 2024 study on AI in mid-sized businesses:

• License costs (35-40% of total costs)
• Implementation and integration (20-25%)
• Data preparation and migration (15-20%)
• Training and change management (10-15%)
• Ongoing maintenance and support (10-15%)

Pay particular attention to the pricing for updates, support services, and onboarding new users, as hidden costs often lurk here.

The truth about licensing and usage models

There are various billing models for AI solutions today that can mean significant cost differences:

1. User-based: Cost per user (most common)
2. Transaction-based: Cost per AI request or action
3. Volume-based: Cost according to processed data volume
4. Outcome-based: Cost linked to measurable results
5. Hybrid: Combinations of the above models

Ask specifically about scenarios with increasing usage and have the cost development transparently presented. Also check the notice periods and conditions – flexible exit options can be more valuable in the long run than short-term discounts.

Scaling costs: When success becomes expensive

A successful AI implementation almost always leads to increased usage – a positive effect that must be budgeted for. Capgemini reports that operating costs for AI systems increase by an average of 30% in the second year, mainly due to expanded use.

Have a scaling scenario calculated: What does the solution cost if

• the number of users increases by 50%?
• the data volume doubles?
• additional modules or functions are needed?

Also check whether the provider offers volume discounts or enterprise licenses that can be activated with growing usage.

ROI calculation: How to determine the real business value

Ask the provider for industry-specific ROI calculations and concrete metrics. A 2024 McKinsey analysis shows that successful AI implementations in mid-sized businesses achieve an average productivity increase of 15-25% – but this value varies greatly depending on the application area and industry.

Reputable providers will help you create a realistic business case that considers the following factors:

• Time savings in person-hours
• Quality improvements and error reduction
• Capacity gains in existing teams
• Shortened processing times for critical processes
• Competitive advantages through faster decisions

Insist on specific, measurable KPIs rather than vague efficiency promises. A partner who evades this has either not understood their value contribution or doesn’t want to make it transparent – both problematic signals.

5. “How do you support us during implementation?” – The Path to Productive Use

The implementation phase is crucial for the success or failure of an AI project. A Boston Consulting Group study identifies lack of implementation support as the second most common reason for the failure of AI initiatives in mid-sized businesses.

From theory to practice: Implementation models

AI providers follow different approaches to implementation – from the simple “Here’s your API key” to complete project management. For mid-sized companies without their own AI specialists, a guided process is usually essential.

Ask specifically about:

• Detailed implementation plan with timeframe
• Responsibilities (What does the provider deliver, what does your company need to do?)
• Required internal resources (IT, departments, management)
• Formalized project management approach
• Risk management and fallback strategies

Particularly valuable: According to Forrester Research, providers that offer a dedicated implementation phase with Customer Success Managers have a 64% higher success rate with AI projects.

Change management and employee acceptance

Technical integration is only half the battle – organizational integration determines actual usage. A recent MIT study shows that in 71% of failed AI projects, not technical problems but lack of user acceptance was decisive.

Ask about the provider’s change management concept:

• Measures to involve departments
• Communication concept for different stakeholders
• Handling of concerns and resistance
• Methods to measure and promote adoption

A good sign: Providers who understand change management not as a separate item but as an integral part of implementation and allocate corresponding resources.

Training and education concepts

Effective training significantly shortens the time-to-value. According to a PwC analysis, comprehensive training programs reduce the productive learning curve for AI tools by an average of 60%.

Discuss the following aspects with the provider:

1. Formats: What training formats are offered? (Webinars, workshops, 1:1 training)
2. Target groups: Are there specialized trainings for different user groups?
3. Materials: What documentation and learning resources are available?
4. Language: Are the trainings available in your company language?
5. Sustainability: Are there ongoing training offers for new employees?

Case Study: Successful AI implementation in a mid-sized company

The theoretical concepts should be reflected in real success stories. Request specific case studies from the provider in your industry with:

• Detailed initial situation and objectives
• Description of the implementation process
• Actual challenges encountered and their solutions
• Measurable results and timeframe until ROI
• Possibility to directly contact the reference customer

An example: A mid-sized engineering company was able to reduce the time required for creating quotes by 62% through the structured implementation of an AI-supported documentation solution – but only after an intensive six-week introduction phase with targeted change management.

6. “How future-proof is your technology?” – Scalability and Technological Evolution

The half-life of AI technologies is constantly shortening. According to a recent Stanford study, the performance of leading AI models currently doubles every 3.4 months – a pace that makes the question of the future security of your investment central.

Technology roadmap and update policy

Transparency about future developments is a crucial indicator for a long-term partnership. Ask specifically about:

• Documented product roadmap for the next 12-24 months
• Update cycles and processes
• Policy on breaking changes and their management
• Integration of customer feedback into product development
• Beta programs and early access possibilities

A positive signal: Providers who transparently communicate which functions are in development, how customers are involved in this process, and how updates are implemented during operation.

Flexibility with growing requirements

Your company and your requirements will evolve – can the AI solution keep pace? Consider these aspects:

• Modularity: Can functions be flexibly added?
• Capacity reserves: How much growth is possible without a system change?
• Performance scaling: How does performance behave under increasing load?
• Adaptability: Can workflows and processes be configured without developers?

According to an IDC survey, 37% of mid-sized companies change their AI provider within the first two years – mainly due to lack of scalability with growing requirements.

Avoiding vendor lock-in: Exit strategies

The easiest way to protect against future surprises is a clearly defined exit strategy. The Initiative D21 recommends contractually defining the following points:

1. Data portability: How can you export your data?
2. Formats: In which formats are exports available?
3. Support: What help does the provider offer during transition?
4. Costs: Are there additional fees for data extraction?
5. Deadlines: How long will your data remain available after termination?

Reputable providers don’t shy away from this conversation – on the contrary: They understand that transparent exit options strengthen confidence in the long-term relationship.

Future security in a fast-moving AI landscape

The current Gartner analysis of AI market development shows: Only 26% of AI providers classified as “leading” in 2021 were able to maintain this position in 2024. This underscores how important a future-oriented assessment is.

Consider these indicators for future-proof providers:

• Continuous investments in R&D (>20% of revenue)
• Active participation in research and standards (publications, conference contributions)
• Partnerships with leading technology providers
• Financial stability and sustainable business model
• Reference customers with long-term success stories (>3 years)

7. “What happens when problems arise?” – Support and Service Levels

The support case is the moment of truth in any business relationship. A Forrester analysis shows that companies with production-critical AI applications experience an average of 4.3 serious support cases per year – with an upward trend as implementation complexity increases.

SLAs under the microscope

Service Level Agreements define the support you can expect. Pay attention not only to the numbers but also to the definition of terms:

• How is “availability” measured? (Observation period, downtime)
• What qualifies as a “critical incident” and what doesn’t?
• What compensation do you receive if SLAs are not met?
• Are planned maintenance windows excluded from the availability calculation?

According to a KPMG study, only 47% of AI providers in the mid-market segment offer real SLAs with compensation mechanisms – an important differentiating factor for professional providers.

Response times and escalation paths

For business-critical applications, every minute counts. Clarify specifically:

Additionally ask about:

• 24/7 support or business hours?
• Support in your language?
• Direct access to experts or multi-tier ticket system?
• Clear escalation paths if a solution is not satisfactory?

Availability guarantees and their meaning

The often quoted “99.9% availability” translates to nearly 9 hours of possible downtime per year – possibly too much for critical applications. A study by the Fraunhofer Institute shows that each hour of AI system downtime in productive environments causes average costs of €8,500.

Always evaluate availability guarantees in the context of your business model:

• 99.5% = 43.8 hours downtime/year
• 99.9% = 8.76 hours downtime/year
• 99.95% = 4.38 hours downtime/year
• 99.99% = 52.6 minutes downtime/year

Also check whether separate guarantees exist for different system components and how overall availability is calculated.

Expert check: How to verify support promises for substance

Test the support even before signing a contract – a proven trick of experienced IT decision-makers:

1. Ask a complex technical question during the evaluation phase
2. Observe response time, competence, and solution orientation
3. Check accessibility at different times of day
4. Request a demonstration of the ticketing or support system
5. Ask current customers specifically about support experiences

According to IDC, support quality in the evaluation phase correlates 82% with subsequent customer satisfaction – a strong indicator for future collaboration.

8. “How do you comply with current and upcoming regulatory requirements?” – Compliance and Legal Security

The regulatory landscape for AI is changing rapidly. With the EU AI Act coming into effect in 2024 and its gradual implementation until 2025, companies face new compliance requirements that must be considered when selecting providers.

EU AI Act: Impact on your AI implementation

The EU AI Act categorizes AI systems by risk classes – from “minimal” to “unacceptable”. Different requirements apply depending on the intended use of your planned AI solution.

Ask the provider specifically:

• Into which risk class of the EU AI Act does the offered solution fall?
• What specific compliance measures have been implemented?
• Is there a documented AI Impact Assessment methodology?
• How is human oversight ensured?
• How are transparency requirements for high-risk AI met?

A study by the University of St. Gallen estimates that by the end of 2025, about 35% of all AI systems used in the EU will be classified as “high-risk” – with corresponding documentation and testing obligations.

Industry-specific regulations and their fulfillment

In addition to horizontal regulations like the EU AI Act, there are industry-specific requirements that may be relevant depending on your sector:

Ask specifically whether the provider has experience with the regulatory requirements relevant in your industry and how these are addressed in the solution.

Documentation and verification obligations

With increasing regulation, documentation requirements rise. The EU AI Act prescribes extensive technical documentation for high-risk systems that you as an operator must maintain.

Clarify with the provider:

• What documentation is provided as standard?
• Are there specific compliance reports and evidence?
• How are model changes and updates documented?
• Are there templates for your internal compliance processes?

A McKinsey analysis shows that companies with complete AI documentation spend an average of 76% less time on regulatory audits – a significant efficiency gain with increasing compliance requirements.

Compliance process: Continuous adaptation to new requirements

The regulatory landscape is constantly evolving. Ask about the process to ensure continuous compliance:

1. How often are compliance updates made?
2. Is there a dedicated team for regulatory requirements?
3. Are customers informed about relevant changes?
4. How are short-term regulatory adjustments implemented?
5. Are there contingency plans for sudden regulatory restrictions?

A positive sign: Providers who not only react to new regulations but proactively participate in industry associations and standardization committees to anticipate upcoming developments.

9. “Can you provide references from our industry?” – Experience Values and Proof

Nothing is more revealing than the experiences of other customers. An IDC survey shows that 78% of successful AI implementations are based on providers who already have proven experience in the specific industry.

Identifying the right references

Not every reference is relevant to your context. Pay particular attention to:

• Similarity in company size and complexity
• Comparable industry and application scenarios
• Similar technological starting point
• Reference currency (ideally not older than 18 months)
• Verifiable success metrics and results

Particularly valuable: References that cover the complete lifecycle of the implementation, including challenges and how they were overcome.

Understanding industry-specific success examples

The benchmark for “success” varies greatly by industry and use case. According to a study by the University of Mannheim, success metrics for AI implementations differ considerably:

Ask about industry-specific benchmarks and how the reference implementations measure against them.

Recognizing red flags in provider history

Warning signs in the provider’s history are just as revealing as success stories. The top 5 red flags according to an analysis by consulting firm Accenture:

1. Lack of concrete result metrics in case studies
2. Frequent changes in product strategy or positioning
3. Unusually high customer turnover
4. Large discrepancy between marketing promises and customer feedback
5. Lack of transparency with direct inquiries

An effective test: Ask the provider about an implementation example that didn’t go optimally and what was learned from it. The reaction to this question is often more revealing than many success stories.

Practical: The 3-step method for reference validation

To effectively check references, this systematic approach has proven successful:

1. Document analysis: Check case studies, testimonials, and customer feedback for consistency and concrete results.
2. Direct reference conversations: Speak with at least two existing customers – ideally without the provider present.
3. Independent research: Consult independent sources such as analyst reports (Gartner, Forrester), online reviews, or industry experts.

This set of questions has proven effective for direct reference conversations:

• What unexpected challenges occurred during implementation?
• How quickly and effectively was support for critical problems?
• Which promises were fully fulfilled, which only partially?
• How flexibly did the provider respond to changing requirements?
• Would you make the same decision today?

10. “How will our long-term collaboration look like?” – Partnership Instead of Sales

Successful AI implementation is not a sprint but a marathon. An MIT Sloan study shows that the full business value of AI solutions is realized on average only after 14-18 months – a timeframe that requires a stable, partnership-based relationship.

From provider to strategic partner

The quality of a long-term partnership often shows in the details. Pay attention to these indicators:

• Is there a dedicated Customer Success Manager?
• Are regular business reviews offered?
• Are there escalation paths at the management level?
• Is there a structured feedback system?
• How is continuous value creation measured and optimized?

The Boston Consulting Group determined in a study that companies with strategic AI partnerships are 67% more likely to achieve sustainable competitive advantages through AI compared to pure vendor-customer relationships.

Innovation and joint development

In the fast-moving AI landscape, continuous innovation is crucial. Ask about specific mechanisms for joint development:

• How are customer wishes integrated into product development?
• Is there a formalized method for feature prioritization?
• Do co-innovation programs or labs exist?
• How are best practices shared between customers?
• Is there an active user community or user groups?

A positive signal: Providers who have established transparent processes for collecting, evaluating, and implementing customer feedback and understand this as part of their corporate culture.

Customer involvement in product development

According to a PwC analysis, AI systems developed with active user involvement have 43% higher user acceptance than those primarily conceived by technicians.

Check these specific involvement possibilities:

1. Customer Advisory Boards with influence on product strategy
2. Beta tester programs for new features
3. Design thinking workshops for user feedback
4. Joint development of industry-specific modules
5. Access to product roadmap and right to have a say

Particularly valuable for mid-sized companies: Providers who give real opportunities for input to smaller customers as well and don’t just privilege large customers.

Establishing long-term success metrics

Continuous measurement of business value is crucial for a lasting partnership. A KPMG study shows that only 23% of companies systematically measure the ROI of their AI investments over extended periods – a missed potential for optimization.

Ask the provider about their approach to long-term success measurement:

• Which KPIs are typically measured for your solution?
• How often are success metrics reviewed and adjusted?
• What tools or dashboards are available for performance measurement?
• Are there benchmarks from comparable implementations?
• How is continuous improvement methodically implemented?

A structured approach to measuring success that includes both technical metrics (system performance, usage) and business metrics (time savings, quality improvement, customer satisfaction) is a strong indicator of a value-oriented partnership.

The Complete Checklist: Your Guide for the Next Negotiation

To facilitate practical application, we have summarized the 10 core questions with their most important aspects in a compact checklist. Use this in your next conversation with a potential AI provider.

The 10 core questions at a glance

1. Data protection: “How do you secure our data?”
   • GDPR compliance and data ownership
   • Data locations and security standards
   • ISO certifications and penetration tests
2. Transparency: “How transparently does your AI work?”
   • Explainability of decisions
   • Bias testing and fairness
   • Auditability and traceability
3. Integration: “How does your solution integrate with our existing IT landscape?”
   • API interfaces and standard compatibility
   • Legacy system integration
   • On-premise vs. cloud options
4. Costs: “What hidden costs should we expect?”
   • Complete TCO calculation
   • License and usage models
   • Scaling costs and ROI calculation
5. Implementation: “How do you support us during implementation?”
   • Detailed implementation plan
   • Change management and acceptance promotion
   • Training and education concepts
6. Future security: “How future-proof is your technology?”
   • Product roadmap and update policy
   • Scalability with growing requirements
   • Exit strategies and vendor lock-in avoidance
7. Support: “What happens when problems arise?”
   • SLAs and availability guarantees
   • Response times and escalation paths
   • Support languages and hours
8. Compliance: “How do you comply with current and upcoming regulatory requirements?”
   • EU AI Act and risk classification
   • Industry-specific regulations
   • Documentation and verification obligations
9. References: “Can you provide references from our industry?”
   • Comparable use cases
   • Direct reference conversations
   • Independent assessments and analyst evaluations
10. Partnership: “How will our long-term collaboration look like?”
    • Customer success management
    • Involvement in product development
    • Continuous success and value measurement

Evaluation matrix for multiple providers

For a structured comparison of multiple providers, we recommend an evaluation matrix. Weight the criteria according to your company-specific priorities on a scale of 1-5:

Next steps in the decision process

After evaluating providers using the checklist, we recommend the following next steps:

1. Create shortlist: Limit yourself to the 2-3 most promising providers.
2. Proof of Concept: Implement a defined use case with limited scope as a test run.
3. Reference visits: Personally visit reference customers in your industry.
4. Contract negotiation: Ensure that all critical points on the checklist are contractually fixed.
5. Implementation planning: Develop a detailed implementation plan with clear responsibilities.

Be sure to involve all relevant stakeholders in this process – from departments to IT to the data protection officer and management.

Conclusion: Making Informed Decisions

Selecting the right AI provider is a strategic decision for mid-sized companies with long-term implications. The 10 core questions presented in this article offer you a structured framework to make this decision in a well-founded and careful manner.

The key insights summarized

• Holistic assessment: Don’t make your decision based solely on technological factors or costs. Include all 10 dimensions in your evaluation.
• Long-term perspective: Think beyond the initial implementation period. A successful AI initiative is a continuous process, not a one-time implementation.
• Trust through transparency: Reputable providers don’t shy away from critical questions and offer maximum transparency – from data processing to long-term costs.
• Industry-specific expertise: Prioritize providers with proven experience in your industry and similar use cases.
• Partnership instead of transaction: Look for a partner who understands your business goals and contributes to the long-term success of your AI initiative.

How Brixon AI can support you in the decision process

As a specialized partner for AI implementations in mid-sized businesses, Brixon AI supports you at every step of the decision and introduction process:

1. Requirements analysis: We help you identify and prioritize your specific requirements.
2. Provider evaluation: We support you in the structured assessment of potential providers based on the 10 core questions.
3. Implementation support: We stand by your side as a neutral advisor throughout the entire implementation.
4. Training and enablement: We optimally prepare your employees for working with AI systems.
5. Success and value measurement: We help you measure and continuously optimize the business value of your AI initiative.

Our experience from numerous successful implementations in mid-sized businesses shows: With the right preparation and a structured selection process, companies without specialized AI teams can also realize successful and value-creating AI projects.

Recommendation for action in 2025

The AI landscape is evolving at a breathtaking pace. For mid-sized companies in 2025, we therefore recommend:

• Act now: The competitive advantage through early AI adoption is growing. According to Accenture, early adopters have an average productivity lead of 37% over latecomers.
• Start focused: Begin with clearly defined, manageable use cases that quickly deliver measurable added value.
• Invest in knowledge: Continuously train your employees to realize the full potential of your AI investment.
• Think scalably: Choose solutions and partners that can grow with your increasing requirements.
• Stay informed: The regulatory landscape and technological possibilities are constantly changing – keep yourself updated.

By asking the right questions to potential AI providers, you lay the foundation for a successful transformation of your company through artificial intelligence – methodically, controlled, and with sustainable business value.

Frequently Asked Questions