Precisely measuring AI ROI: How Swiss companies maximize the value of their AI investments

The artificial intelligence (AI) revolution is in full swing and is transforming industries worldwide. For Swiss companies, AI is no longer a futuristic trend, but a potentially decisive competitive factor. However, every investment, especially in new technologies, must pay off. Measuring the return on investment (ROI) of AI initiatives is the indispensable compass that ensures that financial means and resources are used effectively and generate the greatest possible benefit for the company.

The urgency of dealing with AI is evident for Swiss companies. Many companies recognize the immense potential, but are still hesitant to implement it. Data shows that 62% of Swiss companies are not yet using AI applications, although 73% of them are hoping for greater efficiency and productivity through AI. This discrepancy underlines a considerable need for information and action. At the same time, the introduction of generative AI tools such as ChatGPT has accelerated the adaptation of AI; more than a third of Swiss companies are already using generative AI to create advertising texts. This makes it clear that AI is already finding its way into everyday business and that the question of concrete benefits and how to measure them is becoming all the more relevant. The challenge can often be summarized with the phrase "Many want it, few understand it". ROI measurement is often perceived as complex, but it is essential to make the value of AI projects transparent, make informed decisions and avoid costly bad investments. This article serves as your comprehensive guide to systematically capture the ROI of AI initiatives, avoid typical pitfalls and thus generate maximum value for your organization. The gap between the desire to increase efficiency through AI and the still hesitant adoption rate in many companies points to a significant hurdle: uncertainty regarding the actual benefits and their measurability. A clear, comprehensible guide to ROI measurement can reduce this uncertainty and thus increase the willingness to introduce AI solutions, as companies are typically very resource-conscious. The rapid spread of easily accessible generative AI tools also harbors the risk of an "implementation trap" if companies introduce such tools without clear objectives and defined measurement criteria. This underscores the need to talk intensively about the strategic anchoring and ROI measurement of AI right now to ensure that experiments do not end up as a mere "gimmick" without added business value.  

1 What exactly is the ROI of AI initiatives? More than just numbers.

The return on investment (ROI) of AI initiatives goes far beyond a simple cost-benefit calculation. It is a comprehensive measure of the profitability and strategic value that AI projects bring to a company. While the basic formula of ROI - (return on investment - cost of investment) / cost of investment - is an important starting point, it needs to be interpreted more broadly in the context of AI. A holistic ROI model takes into account both hard, directly quantifiable factors and softer, qualitative aspects that often have a transformative and long-term impact on the company.  

Quantitative success factors (hard facts): These factors are usually directly measurable and are expressed in concrete financial benefits.

• Cost savings through automation: AI can simplify labor-intensive processes, reduce manual errors and thus cut overheads. One example of this is the automation of routine tasks in accounting, such as invoice processing, or in customer service through the use of chatbots. In the US healthcare sector, it is estimated that AI could save up to 360 billion dollars a year.   
• Increase in sales: AI-supported personalization, which enables tailored product recommendations and offers, for example, can lead to improved lead generation and higher conversion rates. The technology company Dell, for example, was able to increase its conversions in email campaigns by 79% through AI-driven personalization.   
• Productivity gains: By optimizing processes and processing procedures more quickly, companies can significantly increase their productivity. For example, AI can help to significantly shorten product development cycles.   
• Error reduction: In production, AI systems can lead to lower reject rates and less reworking. This not only reduces costs, but also improves product quality.
  

Qualitative success factors (soft but decisive values): These factors are often not directly measurable in monetary units, but contribute significantly to the long-term success and competitiveness of a company.

• Improved decision-making: AI systems can analyze large amounts of data and provide data-driven insights that lead to more informed strategic and operational decisions.   
• Increased customer satisfaction and loyalty: Faster response times to customer inquiries, personalized interactions and an overall higher quality of service can sustainably increase customer satisfaction and loyalty. The Net Promoter Score (NPS) is a common metric to measure this.   
• Increased employee satisfaction: Relieving employees of monotonous and repetitive tasks through AI allows them to focus on more challenging and higher-value activities. This can increase employee satisfaction and potentially reduce the staff turnover rate.   
• Innovative strength and competitiveness: AI can shorten the time-to-market for new products and services and enable the development of new business models. In addition, the use of AI can strengthen a company's image as innovative and future-oriented.   
• Improved data quality and utilization: The implementation of AI projects often requires the systematic collection and preparation of data. This often leads to an improved data basis that benefits the entire company and addresses a weakness that exists in many companies - the assumption that systems and data are already "clean and solid" often proves to be a fallacy.
  

The distinction between and simultaneous emphasis on quantitative and qualitative factors is particularly important for decision-makers in companies. They are often under direct cost pressure and must be able to justify investments in the short term. At the same time, they are looking for long-term strategic advantages. A pure focus on hard numbers could undervalue the transformative potential of AI, as companies are often more agile and can benefit more quickly from qualitative improvements such as increased flexibility or a strengthened culture of innovation if these are consciously targeted and monitored. The qualitative dimension of AI ROI, in particular aspects such as "innovative strength" and "improved decision-making", also directly addresses the "pains" felt by many decision-makers, such as "pressure to innovate" and the perception of "AI as a black box". ROI measurement thus becomes a tool for opening up this "black box" and making the concrete contribution of AI to innovation and strategic development visible.  

2. step-by-step: systematically record and calculate the KI-ROI

Successfully measuring the return on investment (ROI) of AI initiatives is not a product of chance, but the result of a structured and well thought-out process. From the clear definition of project objectives to the careful interpretation of results, each step is crucial to making the true value of AI visible to your company.

Step 1: Clear target definition for AI projects - What should AI achieve?

Before measurement can even be considered, the objectives of the AI project must be clearly defined. Without clear objectives, measuring success is simply impossible and there is a great risk of launching ineffective or misguided projects. Ask yourself fundamental questions: Which specific business problem should the AI solution address? Which specific process should be optimized or which new capability should be developed? It is essential to avoid so-called "solutions in search of problems", where the focus is on the technology and not the actual business need. Ideally, the goals should be formulated SMART: Specific, Measurable, Acceptable (or Attractive), Realistic and Timed.  

Step 2: Selecting relevant key performance indicators (KPIs) - Finding the right metrics.

Once the objectives have been defined, suitable key performance indicators (KPIs) must be selected to measure progress and success. These KPIs must be directly related to the previously defined project objectives. A mix of leading indicators, which show changes and trends at an early stage, and lagging indicators, which confirm the actual success achieved in retrospect, is recommended.  

Examples of KPIs, grouped by typical application areas of AI :  

• Customer satisfaction and engagement: Net Promoter Score (NPS), customer retention rate, conversion rates on the website, average processing time for customer inquiries, engagement metrics for digital interactions.
• Employee productivity and satisfaction: time spent per task or process, number of processes processed per unit of time, error rates for manual activities, employee satisfaction (e.g. collected via regular surveys), fluctuation rate in the departments concerned.
• Process efficiency and error reduction: throughput times of end-to-end processes, error rates and reworking effort, degree of automation of processes, resource utilization and efficiency (e.g. energy consumption, use of materials).
• Innovation and competitiveness: time-to-market for new products and services, number of new products or features made possible by AI, innovation rate (number of innovations implemented per unit of time), development of market share compared to competitors.

While technical AI metrics such as precision, recall or the F1 score are of great importance for the development team to evaluate the performance of the AI model itself, the overarching business KPIs that are positively influenced by these technical improvements are crucial for decision-makers.  

Step 3: Comprehensive cost recording - all investments at a glance.

A realistic ROI calculation requires a complete record of all costs associated with the AI project. These include:

• Direct costs: Acquisition costs for hardware and software, development costs (internal or external), license fees for AI platforms or tools.   
• Indirect costs: costs for training employees to use the new AI systems, internal working time spent on implementation, ongoing maintenance and support costs, data management costs (collection, cleansing, processing, storage and security of data) and integration costs to incorporate the AI solution into existing IT systems and business processes. It is crucial not to ignore so-called hidden costs. Experience shows that these are often underestimated and can lead to distorted, overly optimistic ROI expectations. For companies with often limited budgets, the occurrence of unexpected costs, for example for complex data cleansing or lengthy integration processes, can seriously jeopardize a project. An honest and comprehensive cost assessment from the outset creates a solid basis for decision-making and prevents disappointment later on.   

Step 4: Benefit quantification and qualitative evaluation - quantify and describe the added value.

On the other side of the equation is the benefit generated by the AI initiative.

• Quantifiable benefits: These include direct financial savings, for example through reduced personnel costs due to automation or lower material costs through process optimization. Increased sales, for example through improved sales processes or personalized marketing campaigns, also fall into this category. A simple example calculation for quantifying time savings could be: (Daily time savings per employee in hours * hourly rate of the employee * number of employees affected * number of days of use per year) - Annual costs of the AI application.   
• Evaluate qualitative benefits: Not all benefits can be expressed directly in francs and centimes. Nevertheless, qualitative improvements such as higher customer satisfaction, a stronger culture of innovation or improved decision-making quality should be systematically documented and, wherever possible, supported by indicators (e.g. an improvement in the NPS by X points following the introduction of an AI-supported customer service tool). In some cases, an approximate monetization of qualitative aspects is also conceivable, or at least a well-founded estimate of their value. It is also important to consider the ROI timeline: when exactly will the AI investment pay off? Many benefits, especially qualitative ones, often only take effect in the medium to long term.
  

Step 5: Application of the ROI formula and interpretation - Understanding the result.

Once the costs and benefits have been recorded and evaluated, the ROI can be calculated. The most common formula is: ROI=total costs (total benefits-total costs) ×100%

It is important to understand that the calculated ROI represents a snapshot in time. A regular reassessment, especially for long-term AI projects or changing framework conditions, makes sense. Conducting sensitivity analyses, in which different scenarios (e.g. optimistic, pessimistic, realistic) for costs and benefits are run through, can also help to check the robustness of the ROI calculation and get a better feel for possible fluctuation ranges. The need to use both leading and lagging indicators emphasizes that ROI measurement is not a one-off act at the end of a project, but a continuous process. Lagging indicators, such as the actual cost reduction realized after one year, confirm long-term success. Leading indicators, on the other hand, such as a measurably faster processing time for individual tasks directly after the implementation of an AI solution, provide early signals and enable timely adjustments if the project is not on track as expected. This iterative approach fits well with the agile nature of many AI developments and enables a learning system.  

3. mastering the challenges of AI ROI measurement - especially for Swiss companies

Measuring the return on investment of AI initiatives is undoubtedly a complex undertaking and poses various challenges, especially for small and medium-sized enterprises (enterprises) in Switzerland. However, with the right strategies and a deliberate approach, these hurdles can be successfully overcome.

Typical hurdles for companies:

• Lack of expertise and resources: Many companies lack internal experts who have both in-depth AI know-how and experience in conducting complex ROI analyses. The statement "I have long since lost track of the market" reflects this uncertainty. The fact that 62% of Swiss companies do not yet use AI applications and 54% express concerns about using AI is often also due to this lack of expertise.   
• Data quality and availability: AI models are only as good as the data used to train them. In companies, however, data is often fragmented, unstructured, stored in silos or of insufficient quality. The "missing foundation" in the form of a solid data infrastructure is a widespread challenge.   
• Difficulty in quantifying intangible benefits: The concrete financial value of aspects such as improved customer satisfaction, a strengthened culture of innovation or greater employee loyalty is often difficult to express in francs and centimes, even though these factors contribute significantly to the company's success.
  
• Integration into existing business processes and systems: The seamless integration of AI solutions into the often historically evolved IT landscape and established workflows of companies is often complex and incurs costs that are difficult to calculate precisely in advance.   
• Uncertainty regarding AI technology maturity and data protection: Concerns regarding the reliability and maturity of AI technologies as well as compliance with legal frameworks such as the General Data Protection Regulation (GDPR) or the developing EU AI Act can inhibit the willingness to invest.
  

Solutions for companies:

• Prioritization with the impact-effort matrix: This simple but effective tool helps to evaluate potential AI projects according to their expected effect (impact) and the effort required for implementation (effort). The focus should initially be on "quick wins" - projects with a high impact and low effort - in order to achieve initial visible success, create acceptance within the company and gain valuable experience.   
• Gradual implementation and pilot projects: Instead of directly introducing large, complex AI systems, it is often advisable for companies to start small, for example with clearly defined pilot projects. This reduces the financial risk, allows experience to be gained and provides a more solid basis for the ROI estimate for a later scaling or a broader rollout.   
• Focus on clear, measurable goals for each project: Even if not every benefit aspect can be quantified exactly, clear and, if possible, measurable goals and expectations should be defined for each AI project. This creates a framework for evaluating success.
• Development of internal know-how and/or involvement of external expertise: Companies can invest specifically in the further training of their employees in order to build up internal AI knowledge. Alternatively or as a supplement, cooperation with external specialists can be useful.   
• Develop a data strategy: In parallel with the planning and implementation of AI initiatives, companies should develop and pursue a strategy to improve data quality, availability and governance. A solid data foundation is a basic prerequisite for successful AI.   
• Transparency and communication: Early and open communication with employees about the goals, opportunities and possible changes resulting from AI projects is essential. Involving the workforce or their representatives can help to reduce fears and uncertainties and promote acceptance of new technologies.
  

The challenges that companies face when introducing and measuring the ROI of AI are often not primarily technological in nature. Rather, they are often organizational, strategic and skills-based. This means that successful AI implementation and a meaningful ROI assessment require a holistic approach that goes beyond pure technical feasibility. For example, if a company implements a technically brilliant AI solution, but it is not used effectively due to a lack of employee acceptance (cultural dimension) or a lack of integration into the overarching corporate strategy (strategic goals), the ROI will inevitably be disappointing - regardless of the quality of the technology itself. The ROI measurement must therefore ideally also take into account the maturity of these non-technical dimensions or at least recognize their potential influence on the result. The recommendation to call in "external expertise" if necessary is particularly relevant for companies. It is often not economically viable for them to hire their own full-time experts for each specific AI challenge. An external partner can act as an enabler here, not only providing support with technical implementation and ROI measurement, but also helping to carry out an objective assessment using the impact-effort matrix and designing pilot projects in such a way that they deliver rapid and measurable success. Such early successes are in turn crucial for sustainably increasing internal acceptance and trust in the potential of AI.  

4. CorpIn: Your partner for measurable AI success in Switzerland

Successfully navigating the complexity of AI projects and measuring their ROI requires not only technological understanding, but also strategic foresight and business know-how. CorpIn(www.corpin.ch) understands the specific challenges and needs of Swiss companies in the context of artificial intelligence and offers practical support to make the ROI of AI investments transparent and maximize it sustainably.

How CorpIn supports companies:

CorpIn addresses the critical points that are crucial for a successful AI implementation and a meaningful ROI assessment:

• Building understanding and developing strategy: Many companies face the challenge of recognizing and evaluating the actual potential of AI for their specific business model. Through targeted AI workshops, CorpIn helps to create a sound understanding of AI fundamentals and possible applications. This directly addresses the often expressed perception of "AI as a black box" and the lack of "missing expertise". In these workshops, concrete use cases and individual AI strategies are developed together with the decision-makers and relevant specialist departments. This participatory approach ensures that AI projects are geared towards clearly defined, measurable goals from the outset - a basic prerequisite for any meaningful ROI assessment (see step 1 of ROI measurement).
  
• Laying the foundations - readiness analyses: Before investing in AI technologies, an honest assessment is essential. CorpIn conducts comprehensive readiness analyses in which the existing infrastructure, the data landscape, the processes and the organizational requirements of the company are examined. The "Hexagon Model" developed by CorpIn serves as a structured framework. It assesses a company's AI maturity level across six central dimensions: "Awareness & Competence", "Security & Privacy", "Data Foundation", "Technical Prerequisites", "Strategic Objectives" and "Cultural Dimension". This holistic analysis is essential in order to formulate realistic ROI expectations and to identify and address potential cost traps, for example due to poor data quality (the "missing foundation"), at an early stage.   
• Selection and implementation of suitable solutions: Not every AI solution fits every company or every problem. With the CorpIn Toolbox, an ecosystem of proven technologies and AI tools, as well as the development of customized solutions for specific projects, CorpIn ensures that the technology used is optimally tailored to the individual requirements and goals of the customer. This needs-driven approach avoids investing in solutions in search of problems and instead maximizes potential benefits.
  
• Focus on measurable success: A central element of CorpIn 's philosophy is the "data-driven" approach, which places a clear focus on measurable success and well-founded recommendations. This forms the core prerequisite for transparent and comprehensible ROI measurement and ensures that the success of AI initiatives can not only be claimed, but also proven.   
• "Bridging The Gap": One of CorpIn 's particular strengths lies in its ability to bridge the gap between technology and business. The team combines AI expertise with business knowledge. This combination is crucial to translate the technical possibilities of AI into concrete, understandable business benefits and to prepare the ROI in such a way that it is comprehensible and meaningful for decision-makers without a deep technical background.
  

CorpIn's service portfolio, especially the readiness analyses and workshops in conjunction with the Hexagon model, ideally positions the company to close precisely the gap that many companies have in the strategic introduction of AI and the evaluation of ROI. It is not just about the technical implementation, but about the comprehensive preparation and strategic alignment of the company for the use of AI.

5. conclusion: KI-ROI is more than just a key figure - it is your strategic navigation tool

Measuring the return on investment of AI initiatives is far more than an accounting exercise or a mere metric to justify expenditure. Rather, it is a powerful strategic navigation tool that helps Swiss companies make informed decisions, continuously improve their AI projects and ensure that artificial intelligence delivers real, measurable value to their business.

The most important findings of this guide can be summarized as follows:

• A holistic view of AI ROI, encompassing both quantitative financial gains and qualitative strategic benefits, is essential to capture the full potential of AI.
• A systematic process - from the clear definition of objectives, the selection of relevant KPIs and the comprehensive cost-benefit analysis through to the interpretation of the results - forms the basis for a meaningful ROI measurement.
• The challenges of measuring AI ROI, especially for companies, are real, but with the right approach, a step-by-step implementation and, if necessary, support from external experts, they can be overcome.

Start today to visualize the value of your current or planned AI initiatives. The transparency you gain will help you to make optimal use of your resources, minimize risks and harness the transformative power of artificial intelligence for your company's success. It's worth it. The path to successful AI implementation may seem complex, but the ability to accurately assess the benefits is key to ensuring that AI becomes a driver of growth and efficiency for your business.

Would you like to better understand the potential ROI of your planned AI initiative or are you looking for support in evaluating ongoing projects? The CorpIn team is at your side with expertise and practical solutions. The experts at CorpIn will help you to make AI, which is often perceived as a "black box", tangible and to decipher its value for your company. Contact us for a no-obligation initial consultation and find out how we can help you get the maximum value from your AI investments and future-proof your business.  

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