From our experience in AI consultancy, we have noticed that companies starting their AI journey often overlook PoV. Many assume they already understand the business value of their AI projects and focus too much on PoC. However, figuring out business value is not just about assumptions. It requires careful planning and a clear approach. Here are some essential aspects of a PoV.

• Define and evaluate a use case: To begin creating a PoV, start by identifying a clear and specific use case for the AI solution. Many companies have several use cases with no clear objectives. Define measurable success metrics that will help evaluate the solution’s impact. These metrics could include reduced costs, faster response times, shorter processing periods, improved customer experiences, or increased revenue. Clearly defined outcomes ensure that measurable value remains the ultimate goal of the project.
  
  Selecting the right use cases is critical for a successful PoV. Decision-makers must avoid overemphasizing return expectations during early stages, such as the PoC phase. Doing so can limit innovation and slow down the development of effective solutions. At the same time, neglecting to actively measure the value contribution risks wasting resources on unimportant use cases or pursuing AI without a clear purpose (Applied AI, 2021).
  
  After selecting a use case, it’s important to assess its feasibility and potential impact by asking key questions. Can the problem be clearly defined? For example, creating an AI system to automatically generate requests for quotations. Is the necessary data available to train and test the AI models, such as text or image data from past quotations? What is the expected ROI, such as saving time, reducing errors, or increasing efficiency? Answering these questions helps focus on use cases that bring the greatest value to the business.
  
  

• Measure internal and external value: Identify and calculate the returns from a use case by separating internal and external value. Internal value comes from improving core business processes and operations, such as enhancing product quality, speeding up workflows, saving time, or making better decisions using data. External value, on the other hand, is created through market-facing activities like launching new products or services, developing innovative business models, upgrading existing offerings, or boosting customer loyalty and satisfaction.
  
  
• Align the AI project with business objectives: Make sure the AI project is closely aligned with the company’s business needs and objectives. For example, a manufacturing company planning to develop an AI solution to automatically recognize equipment and their labels in a virtual tour of the factory should ensure the project addresses specific operational challenges. This solution could be aimed at enhancing inventory management, improving maintenance processes, or enhancing safety checks.
  
  
• Demonstrating value to stakeholders and users: Show how the AI solution will provide real value to stakeholders, customers, and users. This can be done by using methods like literature reviews, case studies, or surveys to gather evidence of the solution’s potential benefits. For example, a retail company developing an AI-driven recommendation system for improving customer service could use studies from similar businesses to demonstrate how such a system has improved customer satisfaction and improved sales. Additionally, surveys of potential users could provide insights into their expectations and how the solution might meet their needs.
  
  
• Ensuring integration and sustainability: Evaluate how the AI solution will integrate with the company’s existing systems and processes. Check if it can remain effective as business needs, market conditions, and organizational goals evolve. A sustainable AI solution should consistently deliver value over time and adapt to new business challenges or advancements in technology. For instance, a company deploying a chatbot must ensure it can integrate with the company’s internal information sources. This chatbot could provide precise answers to employees’ queries about internal processes, assist decision-makers with data insights, and enhance customer experience by delivering accurate and timely information.
  
  
• Determine the scope of the AI solution: Determine if the planned AI project can be adapted for multiple purposes or extended beyond its initial use case. An AI solution that can be applied to various scenarios or departments within an organization significantly increases its overall value. For example, a Retrieval-Augmented Generation solution (an increasingly popular GenAI method in which a company’s internal data is integrated with a large language model) initially implemented to provide customer support could also be adapted for internal knowledge sharing among employees or as a tool for decision-makers to quickly access relevant business data. This flexibility makes the solution more impactful and cost-effective across different parts of the organization.
  
  
• Addressing capability gaps: Evaluate your company’s gaps in critical capabilities compared to industry leaders and invest in building these capabilities. While it may be important to initially focus on technical aspects like data quality and infrastructure, equally vital are people and processes, which require ongoing attention and development (BCG, 2024). For example, a healthcare organization aiming to develop an AI solution to predict cardiac diseases might discover gaps in its data integration processes or the technical expertise of its team. By addressing these gaps, such as upskilling healthcare staff in AI concepts, hiring required technical expertise, and/or improving data-sharing protocols with hospitals, the organization can strengthen its foundation for successfully implementing and scaling the solution.
  
  
• Calculating investment costs across the AI lifecycle: Evaluate the total costs involved in developing and maintaining the AI solution over its entire life cycle. This includes costs for initial planning, data collection, hiring skilled professionals, procuring necessary hardware and software, system development, deployment, validation, and ongoing monitoring and maintenance. For example, a healthcare organization planning to develop an AI decision support system for managing chronic illness care needs to calculate costs for collecting patient data, hiring technical experts, purchasing AI tools and infrastructure, building the system, deploying it in clinical settings, conducting thorough clinical validation, and maintenance.
  
  
• Addressing ethical and legal considerations: Understand the ethical and legal implications of the AI solution being developed. This includes becoming familiar with regulations like the AI Act and data privacy laws such as GDPR. These frameworks set guidelines for the responsible use of AI, especially in sensitive applications, and can introduce restrictions that may impact large-scale adoption. For example, a company developing an AI system for the early detection of dementia through facial analysis must ensure compliance with data protection regulations to safeguard patient privacy. Additionally, the system must address ethical concerns such as consent, data bias, and transparency to build trust with both users and stakeholders. Awareness of ethical implications is especially important under the current AI Act (European Parliament and Council, 2024), which classifies AI applications into different risk levels and applies specific restrictions based on those risks. This act will be gradually in place in the upcoming years.
  
  
• Adopt a co-development approach: Many companies assume that AI development is purely a technical process involving only developers. However, AI adoption requires a multidisciplinary approach that involves all stakeholders. The development process should include not just technical experts but also decision-makers and management teams. Providing basic AI training to these non-technical stakeholders is crucial to ensure they can contribute effectively and align the AI solution with business goals.
  
  

In conclusion, to ensure successful AI adoption, companies must go beyond PoC and focus on PoV to assess how the solution can deliver tangible benefits. Simply following AI trends or implementing AI without a clear purpose often leads to failed projects and wasted investments. A strategic approach that incorporates both PoC and PoV is essential for leveraging the true potential of AI and creating meaningful business impact.