What Challenges Do Product Owners Face with AI?

Artificial intelligence is becoming a valuable ally for product teams, enhancing decision-making and speeding up tasks while finding patterns in customers’ behavior.

AI has many advantages, but it also introduces new challenges, especially for product owners who need to balance customer needs with business objectives and the team’s work. Many professionals are now exploring AI Enabled Product Owner Training to strengthen their skills and understanding to handle challenges better.

Product owners are extremely important in determining what a product will do.

They are responsible for planning features, determining what the users want, and keeping the product team on the same page.

Adding AI either to the product or to the process of making the product makes this role much harder. The following are major issues product owners face when working with AI.

1. Understanding How AI Works

A common difficulty is simply understanding AI well enough to be confident. AI is not like a conventional feature that works the same every time.

AI learns through the data it uses, changes over time, and sometimes produces results that are very difficult to understand. This is the reason why product owners opt for PSPO-AI Essential training to build fundamental skills that are required to work with AI.

For example, product owners often have challenges with:

• The types of things that AI will or will not do
• The kind of information AI uses to create predictions
• The way that AI created the decision-making by the product provider to share that with its teams/partners
• When an AI model requires improvement

If product owners lack a basic understanding of AI principles, they may feel unsure about their choices of AI features and whether the AI-based product/solution performs as expected.

2. Getting the Right Data to Train AI

AI requires data, tons of it. AI’s performance is based on the quality of data; however, product owners find it extremely challenging to obtain the appropriate data, primarily due to the following:

• Poor data quality,
• The amount of data available in different systems,
• Incomplete or out-of-date data,
• Limited access to sensitive data because of privacy regulations.

AI learns best from accurate, relevant, and current data, so when it is trained on dirty or missing data, its capabilities yield poor or inconsistent results.

3. Customer Trust and Expectation Management

The performance of AI is often expected to be perfect by its very consumers. They expect it to always result in perfect predictions, perfect personalisation, or simply smarter service. Just like any other form of technology, AI will have its limits.

Product owners will frequently be faced with these types of questions:

• How do you explain what the AI does?
• How do you handle customer complaints after an error has occurred in AI?
• How do you ensure that your AI does not invade user privacy or treat users unfairly?
• How can you provide sufficient clarity without overwhelming the user with technical details?

If a user does not understand or trust how an AI system operates, they may not utilise its associated features. Building trust becomes a never-ending responsibility for product owners.

4. Ethics and Fairness in AI

AI may be trained using biased datasets. An example would be if one group of users, or a majority of users, were present in the training dataset when developing the AI solution; the AI would then provide more relevant responses to these users than to users whose type was not represented in as great a volume.

As such, product owners must recognize this potential risk and take steps to mitigate it by ensuring the following:

• The AI treats all users equitably.
• Decisions are not affected by bias.
• There is a clearly outlined process for reporting and fixing issues.
• The product follows legal and ethical standards.

Monitoring fairness requires careful review, conversations with data teams, and regular testing. It can become a challenging but critical part of AI-driven product management.

5. AI’s Natural Capabilities vs. Human Capabilities

A human being can perform most tasks performed by AI; however, AI cannot perform any of the cognitive functions associated with being human.

The challenge for product owners is to strike a balance between using both AI and human judgment effectively when making product-related decisions.

For Example: While AI can create suggestions on how to improve products, it is still up to humans to determine how the suggestions from AI should be incorporated into the product design.

Final Thoughts

Although AI has great potential, it also creates new responsibilities for product managers. Product managers need to understand how AI operates and how to manage it within the data ecosystem. They need to build customers’ trust, evaluate fairness in product development, find ways to motivate all company departments to implement AI, and assess the effectiveness of their AI implementation. All of these responsibilities require product managers to commit to continuous learning and to be patient.

Product managers should look at AI as an extension of themselves rather than as a robot or tool that will take their job. The most successful product managers who implement AI will be the ones who use curiosity, caution and clear thinking to help them maximize the opportunities that AI presents to create smarter, more supportive and more useful products for the user.