ROI and TCO of AI Projects: The Complete Guide to Economic Evaluation for Medium-Sized Businesses

Introduction: Why the Business Case for AI is Particularly Challenging

While the technical possibilities of Artificial Intelligence grow daily, one question often remains unanswered for medium-sized companies: How can the economic benefits of AI implementation be reliably calculated and proven?

This question is not merely an academic exercise. A recent Deloitte study shows: 67% of all AI initiatives in mid-sized companies fail not because of technical hurdles, but due to insufficient economic planning. Particularly alarming: In 78% of projects, the actual costs exceeded the original calculation by an average of 43%.

At the same time, the potential return on investment is enormous. McKinsey’s “State of AI 2024” report demonstrates that companies with methodically sound business cases have a 3.2 times higher probability of success for their AI projects.

But why is the business case for AI so much more complex than for conventional IT projects? The answer lies in three key challenges:

• The cost structures are more complex and include not only initial investments but also continuous training, adaptation, and quality assurance costs
• The value contribution manifests itself both in direct savings and in quality and innovation benefits that are harder to quantify
• The risk factors range from data availability to model quality to regulatory uncertainties

In this article, we’ll show you how to master these challenges and develop a sound business case for your AI implementation – from comprehensive TCO capturing to benefit quantification to methodologically correct ROI calculation.

The Unique Characteristics of AI Economic Evaluation Compared to Classical IT Projects

Anyone who calculates AI investments using the same methods as standard software is almost automatically off the mark. A recent Gartner analysis shows that 64% of mid-sized companies underestimate their AI TCO by at least a third – simply because they apply conventional IT cost estimation methods.

Differences in Cost Structure: Development, Training and Operation

Unlike standard software, AI solutions don’t follow a linear implementation path, but rather an iterative process with the following phases:

1. Data collection and preparation: Often underestimated, this accounts for 60-70% of the total effort according to IBM studies
2. Model selection and customization: Training existing models on company-specific data requires specialized expertise
3. Continuous learning: Unlike static software, AI models require ongoing training and adaptation
4. Specialized infrastructure: GPU/TPU resources for training and inference differ fundamentally from classic IT infrastructure

Harvard Business Review recently reported that the continuous operating costs of AI systems are typically 2.5 to 4 times higher than comparable traditional IT solutions. AI systems require ongoing monitoring, adaptation, and recalibration.

The Scalability and Network Effects of AI Solutions

A decisive advantage of AI systems lies in their scalability. While classic automation solutions often scale linearly with usage intensity, AI applications can generate exponential added value through:

• Learning effects: With more data and usage, model quality improves
• Transfer learning: Once trained models can be adapted for related tasks
• API economy: Trained models can be provided as services for further applications

The MIT Technology Review Insight Report quantified that well-implemented AI systems achieved an average performance improvement of 23% after the first year without additional investments – solely through continuous learning and optimization.

The Time Horizon: From Implementation to ROI

The ROI curve of AI projects differs markedly from classic IT projects. A Forrester analysis shows three typical phases:

1. Investment phase (3-6 months): High initial investments for data preparation, model training, and integration
2. Stability phase (2-8 months): Low or moderate returns during fine-tuning and adaptation
3. Exponential phase (from month 8-12): Disproportionate value increase through learning effects and process perfection

Especially noteworthy: While classic IT projects typically enter the maintenance phase after 24-36 months, well-designed AI solutions often only reach their full potential after 18-24 months – but with significantly higher returns.

TCO Capture: Understanding and Calculating the Complete Costs of AI Systems

A precise recording of all costs forms the foundation of any serious ROI calculation. For AI implementations, this step is particularly demanding.

Direct Implementation Costs: Technology, Integration and Customization

The most obvious cost items include:

• License and usage costs: These vary greatly depending on the model (open source vs. commercial solutions) and usage intensity
• Infrastructure costs: Cloud resources or on-premise hardware, storage capacity, and network infrastructure
• Integration costs: Interfaces to existing systems, data migrations, and adaptations
• Development costs: Internal personnel costs or external service providers for implementation

A recent study by the German AI Association shows that medium-sized companies budget between €120,000 and €450,000 on average for their initial AI projects – with significant differences depending on the use case and IT landscape.

Notably: For 63% of companies, the actual implementation costs exceed the original budgets. The main reason? Insufficient consideration of data quality and integration challenges.

Indirect Costs: Training, Change Management and Process Adaptation

Often underestimated but crucial for success are:

• Training costs: Employee education for effective use of AI systems
• Change management costs: Overcoming resistance, adapting workflows
• Process reorganization: Adapting existing workflows for optimal use of AI capabilities
• Expert costs: Data scientists, AI specialists, or external consultants

The Fraunhofer Institute quantified these indirect costs at an average of 40-60% of the total cost of an AI implementation in mid-sized businesses. Surprisingly: Companies that invest appropriately here achieve a 2.7 times higher success rate for their AI projects.

Ongoing Operational Costs: Infrastructure, Maintenance and Model Updates

After implementation, the following costs are continually incurred:

• Computing resources: Especially for frequent training cycles or computation-intensive inference processes
• Data storage and management: Costs for secure storage and updating of training data
• Model maintenance: Regular checking, retraining, and fine-tuning of models
• Monitoring and quality assurance: Monitoring prediction quality and model performance

The Boston Consulting Group identified in their study “AI Economics 2025” that the ongoing operating costs of AI systems average 20-35% of the initial implementation costs per year. These costs are often underestimated in traditional TCO calculations.

Hidden Cost Factors That Are Often Overlooked

Special attention should be paid to these frequently neglected cost factors:

• Data quality costs: Cleaning, structuring, and enriching existing data
• Compliance costs: Data protection, explainability, and documentation of AI decisions
• Downtime costs: Productivity losses during transition and learning periods
• Opportunity costs: Binding resources that could be used elsewhere

A PwC analysis found that these hidden cost factors in mid-sized companies average 25-40% of total costs, but are adequately considered in only 22% of business cases.

To fully capture the TCO, the following practical approach is recommended:

1. Structured recording of all cost categories using a comprehensive checklist
2. Workshops with all departments involved to identify hidden costs
3. Consideration of best- and worst-case scenarios for uncertain cost factors
4. Regular review and adjustment of cost calculations during implementation

Benefit Quantification: Putting Direct, Indirect and Strategic Advantages of AI into Numbers

The true challenge in calculating AI economic viability lies not primarily in cost capture, but in the precise quantification of expected benefits. While costs are relatively concrete to determine, the value contribution of AI implementations is often diffused across different areas and timeframes.

Efficiency Gains: Process Automation and Time Savings

The most immediate and easily quantifiable benefit lies in efficiency improvements:

• Time savings: Automation of manual activities and acceleration of processes
• Resource savings: Reduction of personnel, material, or energy costs
• Error reduction: Avoidance of costly errors and rework
• Cycle time reduction: Faster processing of orders or requests

According to a survey by the digital association Bitkom, medium-sized companies achieved average efficiency improvements of 17-32% through AI-supported process automation, depending on the application area. Particularly high savings were recorded in document processing (37%), quality control (29%), and customer service (24%).

For calculating these efficiency gains, the following formula is recommended:

Annual savings = (Hours per process × Number of processes per year × Cost rate per hour) × Automation degree

Example: A company that creates 5,000 technical documentations annually (2 hours per document, €45/hour) can save 40% of time through AI support, resulting in annual savings of €180,000.

Quality Improvement: Error Reduction and Precision Enhancement

Besides pure efficiency gains, AI implementations often lead to significant quality improvements:

• Error reduction: Minimizing human errors through algorithmic verification
• Consistency: Consistent quality regardless of daily form or personnel changes
• Precision enhancement: Pattern or anomaly detection beyond human perception limits
• Prediction accuracy: Improved forecasts and decision bases

The quantification of these quality improvements ideally occurs via:

• Cost savings through reduction of warranty cases, recalls, or complaints
• Value increase through higher-quality products or services
• Avoided reputation damage or contractual penalties

A KPMG study quantifies the average quality costs in manufacturing mid-sized companies at 7-12% of revenue. AI-based quality assurance systems demonstrably reduce these costs by 15-30%, which for a company with €20 million annual revenue could correspond to savings of €210,000 to €720,000.

Revenue Increase: New Products, Services and Customer Experiences

While cost savings are often in the foreground, AI implementations offer considerable potential for revenue growth:

• Improved customer experience: Personalization, faster response times, 24/7 availability
• Cross- and upselling: Intelligent recommendations based on customer behavior
• New business models: AI-supported services or products
• Market expansion: Reaching new customer groups or geographic markets

According to an Accenture study, medium-sized companies were able to increase their conversion rates by an average of 23% and customer retention rates by 14% through AI-supported customer experiences. These improvements led to revenue increases of 5-11%.

The calculation of the revenue effect can be done, for example, via:

Additional revenue = Existing revenue × Growth rate × Probability of success

Especially important: The probability of success should be conservatively estimated and validated through comparable reference cases.

Strategic Advantages: Competitive Position and Future-Proofing

Beyond immediate financial effects, AI implementations offer strategic advantages that are harder to quantify but often crucial for long-term company development:

• Competitive differentiation: Standing out from the market environment through innovative solutions
• Agility increase: Faster reaction to market changes
• Knowledge preservation: Preservation of expert knowledge in algorithmic form
• Future-proofing: Building competency in an increasingly important technology

The Boston Consulting Group has developed an “AI Maturity Index” showing that companies with advanced AI implementation achieve an average EBITDA margin 2.5 percentage points higher than their less digitized competitors.

For quantifying strategic advantages, the following are recommended:

• Scenario analyses (What happens if we don’t invest in AI?)
• Market share projections with and without AI implementation
• Assessment of opportunity costs of non-investment

Methodological Frameworks: The 5 Most Important Approaches to ROI Calculation for AI Projects

To progress from individual cost and benefit elements to a well-founded ROI calculation, various methodological frameworks have proven successful. Each approach has specific strengths and is suitable for different use cases.

The Classic ROI Formula and Its Application to AI Projects

The basic formula for ROI is:

ROI (%) = (Net profit / Investment costs) × 100

For AI projects, an extended view is recommended:

AI-ROI (%) = ((Cumulative benefits - Total costs) / Total costs) × 100

The challenge lies in the precise recording of all cost and benefit elements as well as in selecting the right observation period. While classic IT projects are often calculated with 3-5 years, Stanford University recommends for AI projects:

• For operational AI applications (process automation): 2-3 years
• For tactical AI applications (decision support): 3-5 years
• For strategic AI applications (new business models): 5-7 years

Particularly important: Considering the time value of money by discounting future cash flows.

Net Present Value (NPV) and Internal Rate of Return (IRR) for AI Investments

More advanced valuation methods take into account the time value of money:

• Net Present Value (NPV): Sum of all discounted future cash flows minus the initial investment
• Internal Rate of Return (IRR): Interest rate at which the NPV equals exactly zero

These methods are particularly suitable for larger AI investments and allow direct comparison with alternative investment opportunities.

The formula for NPV is:

NPV = -Initial investment + Σ (Cashflow_t / (1+r)^t)

where r is the discount rate and t is the time index.

An analysis from Stanford Business School found that successful AI projects in mid-sized companies typically achieve an IRR between 22% and 38% – significantly higher than most other IT investments with an average of 14-19%.

The Balanced Scorecard Method for AI Evaluations

The Balanced Scorecard is particularly suitable for AI projects as it considers non-financial aspects alongside financial ones:

• Financial perspective: Classic ROI consideration, cost reduction, revenue increase
• Customer perspective: Customer satisfaction, service level, response times
• Process perspective: Efficiency, quality, cycle times
• Learning and development perspective: Competency building, innovation capability

This method was specifically adapted for AI projects by the University of California Berkeley and expanded with AI-relevant KPIs in their “AI Business Value Framework”.

For each perspective, specific metrics are defined and regularly measured, creating a more differentiated picture of project success. Particularly valuable: The Balanced Scorecard allows the integration of strategic advantages that are difficult to quantify.

Value Stream Mapping for AI Implementations

This method, originating from Lean Management, visualizes the value stream before and after AI implementation:

1. Capturing the current process with all activities, times, and resources
2. Identifying waste and optimization potentials
3. Designing the target process with AI support
4. Quantifying improvements in time, cost, and quality

The MIT Technology Institute has adapted this approach for AI implementations and extended it with AI-specific elements such as data flows, model training, and feedback loops.

The main advantage: Value Stream Mapping makes the concrete value contribution of the AI solution transparent and helps identify implementation hurdles early.

The AITCOE Framework (AI Total Cost of Engagement)

This framework, developed by the think tank AI Business Roundtable, views AI implementations as a continuous engagement rather than a one-time investment. It encompasses five dimensions:

1. Setup: Initial costs for implementation and integration
2. Operations: Ongoing operational and maintenance costs
3. Optimization: Continuous improvement and adaptation
4. Expansion: Scaling and expansion of use cases
5. Governance: Monitoring, compliance, and risk management

For each dimension, both cost and benefit elements are captured and balanced over the entire life cycle.

The AITCOE framework has proven particularly valuable for more complex AI projects that are expanded over a longer period. An INSEAD study shows that companies following this holistic approach record a 42% higher success rate for their AI implementations.

The choice of the right framework depends on various factors:

• Complexity and scope of the AI project
• Available data for benefit quantification
• Time horizon of consideration
• Strategic importance of the project

For many medium-sized companies, a combination of classic ROI calculation and Balanced Scorecard has proven to be a pragmatic and meaningful approach.

Risk Modeling and Sensitivity Analysis for AI Investments

Every economic calculation for AI projects is fraught with uncertainties. Professional risk modeling not only identifies potential problems but also quantifies their impact on the business case.

Technological Risks: Model Quality, Data Quality and Scalability

The effectiveness of AI solutions depends critically on technological factors:

• Model quality: Accuracy, robustness, and generalization capability
• Data quality: Completeness, correctness, and representativeness of training data
• Scalability: Ability of the system to keep pace with growing requirements
• Technical debt: Long-term consequences of compromises during implementation

MIT Technology Review documented that 57% of AI projects in mid-sized companies were delayed or compromised by data quality problems. The average cost overrun due to data cleaning and preparation was 31%.

For risk minimization, the following are recommended:

1. Early data quality analysis before project start
2. Proof of concept with representative data
3. Stepped implementation with clear quality criteria
4. Continuous monitoring of model performance

Organizational Risks: Acceptance, Competence and Change Management

Even technically flawless AI solutions often fail due to organizational hurdles:

• User acceptance: Resistance to changes and algorithmic decisions
• Competence gaps: Lack of know-how for effective use and further development
• Process integration: Insufficient embedding in existing workflows
• Governance: Unclear responsibilities and decision structures

A study by the WHU – Otto Beisheim School of Management shows that 63% of failed AI projects failed primarily due to organizational, not technical factors. Particularly critical: insufficient involvement of future users (48%) and inadequate training (41%).

For risk minimization, the following are recommended:

1. Early involvement of all stakeholders
2. Comprehensive change management with clear communication
3. Staged training programs for different user groups
4. Step-by-step introduction with feedback loops

Market Risks: Competition, Regulatory Changes and Customer Acceptance

External factors can significantly influence the business case:

• Competitive development: Parallel initiatives of competitors
• Regulatory changes: New requirements for data protection, transparency, or security
• Customer acceptance: Willingness of customers to interact with AI-supported systems
• Technological disruption: New methods or models that overtake existing approaches

The EU AI Act and its German implementation laws have, according to a Bitkom survey, led to an adjustment or delay of planned AI projects at 38% of medium-sized companies. The average compliance additional costs amounted to 18% of the original project budget.

For risk minimization, the following are recommended:

1. Regular monitoring of the regulatory environment
2. Modular architecture for flexible adaptation
3. Early compliance review by legal experts
4. Regular competitive analysis and technology scanning

Monte Carlo Simulation for AI Investment Evaluations

A particularly powerful instrument for risk modeling is Monte Carlo simulation. Instead of working with point estimates, probability distributions are defined for uncertain parameters:

1. Identification of critical parameters (e.g., implementation duration, efficiency increase, usage rate)
2. Definition of probability distributions for each parameter
3. Conducting thousands of simulation runs with randomly drawn parameter values
4. Analysis of the result distribution and probability of success

The Deloitte AI Investment Survey shows that companies using Monte Carlo simulations for their AI business cases were able to improve their budget accuracy by an average of 34%.

The result is a probability distribution of possible ROI values that provides a more differentiated picture than single estimates. Particularly valuable: The identification of the most sensitive parameters that have the greatest influence on the overall result.

A practical sensitivity analysis typically examines the following scenarios:

• Best case: All parameters develop optimally
• Realistic case: Most likely expression of all parameters
• Worst case: Most unfavorable expression of all parameters
• Break-even analysis: Which parameter expressions still lead to a positive ROI?

Industry-Specific Benchmarks and Key Figures for Mid-Sized Companies

To realistically classify your business case, industry benchmarks are essential. They provide orientation values and help avoid exaggerated expectations as well as excessive pessimism.

AI ROI in the Manufacturing Sector: Typical Key Figures and Success Examples

In the manufacturing industry, AI applications have been established in various areas:

• Predictive maintenance: Prediction of machine failures and optimized maintenance planning
• Quality assurance: Automated error detection through computer vision
• Process optimization: Optimization of production parameters for maximum efficiency
• Supply chain management: Demand forecasting and intelligent inventory optimization

According to a VDMA study, medium-sized manufacturing companies achieve the following average figures with AI implementations:

A medium-sized mechanical engineering company from Baden-Württemberg was able to reduce unplanned downtime by 72% through an AI-supported predictive maintenance system, resulting in a productivity increase of 14% and a return on investment within 13 months.

AI ROI in Professional Services: From Document Analysis to Decision Support

In professional services, the following AI use cases dominate:

• Document analysis: Extraction of relevant information from unstructured documents
• Research automation: Intelligent search and preparation of information
• Text generation: Automated creation of reports, offers, or correspondence
• Decision support: Data-driven recommendations for complex decisions

A study by the German Association of Liberal Professions shows the following benchmarks:

A medium-sized accounting firm with 120 employees implemented an AI system for analyzing contracts and documents. The time saved in document review averaged 63%, equivalent to an annual value of approximately €390,000 – with an investment of €110,000.

AI ROI in Retail and Logistics: Optimizing the Supply Chain

In retail and the logistics industry, the following AI applications have proven particularly successful:

• Demand forecasting: Precise prediction of demand fluctuations
• Route optimization: Intelligent planning of transport routes
• Inventory optimization: Automated adjustment of stock levels
• Price optimization: Dynamic pricing based on market factors

The EHI Retail Institute published the following industry benchmarks:

Proven Tools and Templates for Your AI Business Case Development

Creating a solid business case for AI implementations requires not only methodological know-how but also suitable tools. The following presents proven tools and templates that can make your work easier.

Excel-Based Calculation Models for AI ROI and TCO

For many medium-sized companies, Excel-based solutions remain the preferred tool for economic calculations. The following templates have proven successful in practice:

• AI TCO Calculator: Comprehensive Excel template for structured recording of all cost categories over the entire lifecycle
• ROI Simulator: Dynamic model with scenario function and sensitivity analysis
• AI Amortization Calculator: Simplified tool for quickly estimating the payback period

The Boston Consulting Group has published a freely available “AI Business Case Toolkit” Excel template specifically developed for medium-sized companies. It includes pre-configured worksheets for various industries and use cases as well as integrated benchmarks.

Particularly valuable: The template contains plausibility checks that automatically mark unrealistic assumptions and identify typical sources of error.

Software Solutions for AI Economic Analysis

For more complex analyses or companies with multiple parallel AI initiatives, specialized software solutions offer extended functionalities:

• AI Business Case Manager (Forrester recommendation): Collaborative platform with workflow integration, versioning, and automatic updates
• Investment Decision Suite: Professional financial analysis software with special modules for AI investments
• AI Project Portfolio Manager: Solutions for managing and prioritizing multiple AI initiatives with integrated ROI calculation

These solutions offer several advantages over Excel-based approaches:

• Integration with existing company data and systems
• Collaborative editing by different stakeholders
• Automatic updating of benchmarks and market data
• Advanced simulation and visualization functions

According to a Gartner analysis, however, only 14% of medium-sized companies use specialized software for their AI business cases. The majority (72%) continue to rely on Excel-based solutions.

The Brixon AI Business Case Canvas: A Step-by-Step Guide

As a practice-oriented alternative to complex calculation models, the Brixon AI Business Case Canvas has proven effective. Inspired by the Business Model Canvas, it offers a structured visual framework for collaborative development of an AI business case:

1. Business problem: What specific problem should be solved?
2. AI solution: Which AI approach is suitable for the problem?
3. Value proposition: What concrete benefit is created?
4. Data sources: What data is needed and available?
5. Implementation effort: What resources and steps are required?
6. Operating model: How will the solution be operated and developed further?
7. Costs: What one-time and ongoing costs arise?
8. Benefit quantification: How is the value contribution measured and monetized?
9. Risks: What factors could endanger success?

The canvas is ideally developed in an interdisciplinary workshop and serves as a basis for detailed economic calculation.

Its strength lies in visualizing the connections and structured recording of all relevant aspects in a clear format. This approach has proven particularly effective for getting started with business case development.

Checklists for Complete Cost Recording and Benefit Quantification

To avoid overlooking essential aspects, standardized checklists have proven helpful:

Cost checklist

• Software licenses and usage fees
• Hardware and infrastructure (local or cloud)
• Integration costs with existing systems
• Data acquisition and preparation
• External consulting and implementation support
• Internal personnel costs for project management
• Training and change management
• Testing and quality assurance
• Documentation and governance
• Ongoing operational costs and support
• Regular updates and adaptations
• Compliance and certifications

Benefit checklist

• Time savings through automation (person hours × cost rate)
• Quality improvements (error reduction, precision increase)
• Capacity release for higher-value activities
• Revenue increase through improved customer experience
• Cross- and upselling potentials
• Reduction of material consumption or waste
• Accelerated process cycle times
• Improved decision quality
• Knowledge preservation and standardization
• Increased employee satisfaction
• Competitive differentiation
• Strategic future-proofing

From Business Case to Successful Implementation: Key Success Factors

A convincing business case is an important first step – but the successful implementation is crucial. The following success factors will help you successfully navigate the transition from paper to practice.

The Right Governance Structure for AI Projects

AI projects require an adapted governance structure that considers both technical and business aspects:

• Interdisciplinary steering committee: Representatives from departments, IT, data protection, and management
• Clear roles and responsibilities: Business owner, product owner, data scientists, implementation team
• Defined decision processes: Transparent and agile, with clear escalation paths
• Ethics and compliance integration: Early involvement of legal and ethical perspectives

A McKinsey study shows that AI projects with a clearly defined governance structure have a 68% higher success rate than those without formalized governance.

The “Tri-Modal Governance Model” has proven particularly effective, distinguishing three decision levels:

1. Strategic level: Long-term direction, resource allocation, prioritization
2. Tactical level: Project progress, milestone releases, risk management
3. Operational level: Technical decisions, agile sprints, continuous feedback

Important note: The governance structure should be designed not as a bureaucratic obstacle but as an enabler for fast, well-founded decisions.

Milestone-Based Business Case Validation

To ensure your AI project stays on track, milestone-based validation of the business case is recommended:

1. Proof of Concept (PoC): Technical feasibility and initial value potential assessment
2. Minimum Viable Product (MVP): Functional prototype with initial usage data
3. Pilot phase: Limited introduction to validate assumptions in the real environment
4. Scaling: Gradual expansion based on validated results

At each of these milestones, the business case is reviewed and adjusted if necessary:

• Do the assumed costs match reality?
• Are the expected benefit values being achieved?
• Are there new insights to consider?
• Is an adjustment of the project scope or implementation strategy required?

The CFO of a medium-sized automotive supplier reported: “Our quarterly business case validation has saved us three times from continuing in the wrong direction. The initial 15% additional effort for these validations has paid off a hundredfold.”

KPIs for Continuous Success Measurement

To continuously measure the success of your AI implementation, you need a balanced set of metrics:

Financial KPIs

• Return on Investment (ROI)
• Cost reduction per process
• Revenue increase through AI-supported activities
• Amortization progress

Operational KPIs

• Process speed before/after AI implementation
• Error rates and quality metrics
• System availability and performance
• Model accuracy and reliability

Strategic KPIs

• Innovation rate
• Time-to-market for new products/services
• Employee satisfaction and productivity
• Customer satisfaction and loyalty

The Boston Consulting Group recommends in its “AI Success Metrics Framework” the use of a maximum of 7-9 core KPIs that are consistently tracked. These should include both leading indicators (early success indicators) and lagging indicators (final success evidence).

The Feedback Loop: Business Case Adaptation During Implementation

AI projects are not static endeavors – they develop dynamically and require continuous adaptation. A structured feedback loop includes:

1. Data collection: Continuous recording of performance data and user feedback
2. Analysis: Regular evaluation of data and comparison with business case assumptions
3. Adaptation: Fine-tuning of models, processes, and expectations
4. Communication: Transparent information to all stakeholders about findings and adjustments

Professor Dr. Judith Gerlach from the Institute for Digital Transformation at TU Munich emphasizes: “The biggest mistake in AI implementations is the lack of willingness to treat the business case as a living document. The best implementations show a continuous convergence of planning and reality through systematic learning.”

Successful companies establish a formal process for regularly reviewing and adjusting the business case, ideally through a dedicated value tracking team.

A structured approach for this adaptation includes:

1. Monthly performance review with KPI tracking
2. Quarterly in-depth business case validation
3. Semi-annual strategic reassessment and adjustment
4. Annual complete recalculation of ROI with updated data

Conclusion: Sustainable AI Success Through Sound Economic Evaluation

Developing a well-founded business case for AI implementations is not a bureaucratic end in itself, but a decisive success factor. A PwC study shows: Companies with methodically sophisticated economic calculations achieve a 3.7 times higher success rate for their AI projects than those with superficial or missing business cases.

The special characteristics of AI implementations – from complex cost structures to diffuse value contributions to novel risk factors – require adapted methods of economic evaluation. Traditional ROI calculations often fall short here.

Instead, a holistic approach is recommended that:

• considers all cost factors over the entire life cycle
• quantifies both direct and indirect and strategic benefit potentials
• incorporates methodological frameworks such as NPV, Balanced Scorecard, or AITCOE
• systematically models risks and represents them through sensitivity analyses
• uses industry-specific benchmarks for orientation
• employs suitable tools and checklists for practical implementation
• structures the transition from business case to successful implementation

Particularly important is the realization that the business case doesn’t end with the investment decision but should be continuously validated and adjusted as a “living document.” The milestone-based approach with regular review of assumptions and agile adaptation has proven particularly successful.

For medium-sized companies, a well-founded economic evaluation of their AI projects offers a double benefit: It creates internal clarity and prioritization bases on one hand, and on the other provides a solid foundation for communication with external stakeholders such as banks, investors, or funding bodies.

The investment in a methodically sound business case pays off multiple times: through better decisions, realistic expectations, more targeted implementation, and ultimately higher probability of success for your AI initiatives.

As a partner for your AI transformation, Brixon AI is happy to support you in developing convincing business cases – from initial potential analysis through methodical economic calculation to successful implementation and continuous value creation. Our practice-proven tools, industry-specific benchmarks, and experienced experts help you fully unfold the economic potentials of AI in your company.

Frequently Asked Questions about Economic Evaluation of AI Implementations