AI products and projects – how are they different from others? | AI in business #49

Introduction to AI products management

AI products require constant development and customization, which is different from traditional technology solutions.

• AI, artificial intelligence – a general name for the ability of machines to perform tasks that mimic the workings of human reason and creativity, such as recognizing images, understanding written and spoken language, or making decisions based on available data,
• ML, machine learning – a sub-discipline of AI covering processes in which machines learn from data and experience how to perform tasks better. The uniqueness of machine learning (ML)-based products comes from the fact that they are not pre-programmed, but are equipped with learning and adaptation capabilities. In industries such as healthcare, AI contributes to more precise diagnostics, while in finance it enables more sophisticated risk analysis,
• GenAI, generative artificial intelligence – a new field of ML involving systems that can create new content, such as text, images, video, 3D models or music, based on the user’s invention or user-specified purpose and input data such as keywords, queries, or prompts, or sketches or photos.

AI product planning – from idea to implementation

Planning an AI product requires asking a key question at the outset: Will this product benefit from adding AI capabilities?

Implementing an AI product is risky and expensive, and as a result, it’s a good idea to start by defining the problem to be solved by the AI implementation, and then try to solve it optimally. Perhaps using brainstorming with ChatGPT or Google Bard, which can surprisingly advise on the optimal product development path – not necessarily based on AI.

However, if we decide to add artificial intelligence to a company’s offerings, we need to consider the specifics of the AI project life cycle. After all, Gartner data shows that only 54% of AI projects make it from the pilot phase to production.

This is often due to the very promising prototypes that can be created with the AI tools available today. On the other hand, it is very difficult to achieve “production quality” and the repeatability and relevance of results required by stakeholders.

The AI product life cycle differs from others, however, not only in that it goes beyond the concept phase somewhat less frequently. Where the life cycle of traditional products tends toward a gradual decline in interest once sales peak, AI products experience the so-called “flywheel effect.” This is a phenomenon in which a machine learning-based product improves as it is used and new data is collected from users. The better the product is, the more users choose it, which in turn generates more data to improve the algorithm. This effect creates a feedback loop that enables continuous improvement and scaling of AI-based solutions.

Source: DALL-E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

This makes them products with a renewing life cycle. In other words, the flywheel effect in AI means that continuous improvements lead to incremental improvements in product performance. For example:

• Iterative training of AI models – for example, a model for sales forecasting may require repeated training to achieve optimal accuracy, but it becomes more and more perfect over time,
• Data backlog management – for content personalization applications, collecting and analyzing user data can be a priority, which will gradually lead to more and more relevant results.

In summary, AI project management requires flexibility and readiness for continuous improvement. Therefore, AI project managers must be prepared to meet changing requirements and constantly adjust strategies.

Understanding data and its role in AI product development

The role of data in AI product development is crucial. McKinsey estimates that generative AI models could generate economic benefits of up to $4.4 trillion annually. However, reaching for a piece of that pie requires quality data management.

For example, for an e-commerce product recommendation system to work well, the quality of customer behavior data is crucial. Not only will you need the right amount of data, but also its proper segmentation and updating, and most importantly, skillful drawing of conclusions from the collected information.

When creating a data-driven AI product, it is equally important to maintain impartiality in the data. For example, in AI algorithms used in recruitment or insurance, the data mustn’t contain implicit biases – based on gender or location – that could lead to discrimination.

It is worth noting that proper data management requires not only technical expertise but also awareness of its impact on the performance of AI products.

The most common problems when managing AI-based products

Managing AI products involves challenges that require specific skills and ethical awareness. Among the most important problems are worth mentioning:

• AI skills development – for example, a product manager in the AI industry needs to understand the basics of machine learning to work effectively with the technical team,
• up-to-date orientation to legal requirements – regulations on AI products are just emerging, so you need to be oriented to adjust your company’s policies and regulations for using the AI product on an ongoing basis,
• integrating AI into existing systems – integrating advanced artificial intelligence into existing IT systems can pose technological and organizational challenges,
• scaling AI solutions – for technology start-ups, developing an AI prototype into a full-scale product requires resources, time and expertise, which can also be a problem due to the relatively low supply and high demand for specialists,
• keeping users engaged – for an app that uses AI to personalize content, constantly adapting to users’ changing preferences is key to keeping them engaged,
• addressing ethical dilemmas – for example, in an AI application for health monitoring, privacy and security of user data is a priority.

AI products – summary

In summary, managing AI projects and products requires an understanding of the unique challenges and opportunities that the technology brings. Understanding the role of data, being able to manage teams and projects as well as staying aware of the ethical aspects of AI are the essential. AI products are opening new horizons for business, but they require the right approach and skills.

For start-ups, it is important to focus on clearly defining the problem the AI product is meant to solve and building a team with the right knowledge and experience in AI. It’s also worth focusing on building ethical and transparent AI systems that comply with user expectations and regulations.

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