How to Use Your Product Management Experience to Ride the AI Wave

Product managers are ideally situated to use their experience and talents to navigate this new era of technology as AI continues to transform the corporate landscape. Product managers can open new doors for creativity, efficiency, and growth by embracing AI and incorporating it into their product management methods. To stay ahead of the curve in the rapidly evolving world of technology, it is crucial to comprehend how to leverage product management expertise to ride the AI wave.

Nevertheless, before we get into how AI might be used to streamline procedures, let's address a common concern.

The short answer is actually yes. In theory, product managers could be replaced by some type of artificial intelligence (AI). But such a type that would cover all the functions of a product manager does not yet exist. AI-powered product management has grown in popularity in recent years, leaving AI-driven software more sophisticated and capable of handling vast amounts of data more efficiently. So some aspects can be noted now, but you can't call it a replacement - it's more of a help.

AI stands for Artificial Intelligence. It refers to the creation of computer systems capable of performing activities that need human intelligence, such as visual perception, speech recognition, decision-making, and natural language processing.

There are several forms of AI, ranging from basic rule-based systems to complex neural networks. Different types of AI exist, ranging from basic rule-based systems to more advanced machine learning and deep learning models. Rule-based systems make judgments based on pre-defined rules, whereas machine learning models are trained on massive datasets to discover patterns and make predictions. Deep learning models use neural networks to learn from data and make decisions based on that learning.

Machine Learning for Product Managers – a subset of AI used to build models from existing sets of data to predict future outcomes.

That’s why ChatGPT — the GPT stands for generative pre-trained transformer — is receiving so much attention right now. It is a free chatbot that can generate an answer to almost any question it is asked. Developed by OpenAI and released for testing to the general public in November 2022, it’s already considered the best AI chatbot ever.

In picture 1, you can see that since 2017, the use of artificial intelligence by companies has doubled. And judging by how popular ChatGPT has gained lately next year, we should expect even bigger numbers. But how can you keep up with the trends of the new world and use AI for managementin your projects? Read below.

AI is capable of a wide range of tasks that can help to improve and streamline product management and development. Here are a few examples:

As you can see, there are several methods for incorporating artificial intelligence into your work. Nonetheless, this brings us to the conclusion that the most astounding productivity is reached through a mix of human and AI knowledge, as AI is presently incapable of replacing human intuition and experience.

It is important to note that AI can't replace the human touch just yet. Product managers bring a wealth of knowledge and experience that no AI can accurately replicate. Product managers are responsible for a variety of tasks, including making decisions, planning and executing strategy, creating product roadmaps, and managing customer relationships. AI is still not able to make decisions and think creatively the way a human can.

Product managers often have to work with teams and stakeholders, something that AI still needs to improve at. AI-driven software can help with data analysis and management but cannot replicate the human touch when building relationships and connecting with customers. In the work of a project manager, it is crucial to demonstrate many soft skills. The behavior of people and the atmosphere in the team depend on many decisions and reactions to them. Understanding these processes often takes place on a subconscious and emotional level, which only a person is capable of.

Additionally, procurement process flow is another critical area where human expertise shines. A structured procurement process flow requires a nuanced understanding of vendor relationships, negotiation tactics, and the ability to align purchasing decisions with organizational goals—tasks AI is not yet equipped to manage without human oversight.

Finally, product managers are often the ones who can see the big picture and provide a unique insight into the product. AI simply can't match the skills and experience necessary to do this effectively.

• AI in Management Software: these tools help automate tasks, predict risks, and monitor project progress. Examples of AI-powered project management software are Asana, Trello, and Monday.com.
• Chatbots: can help project managers solve administrative issues, remind them of deadlines and tasks, and help with API integration companies. Examples of AI-powered chatbots are Tars, BotStar, and Landbot.
• Natural Language Processing (NLP): Project managers can benefit from NLP by using it to analyze text data, including project reports, customer reviews, and comments on social media. Project managers can use NLP to find patterns, attitudes, and insights to help them make decisions. Examples of NLP tools are Google Cloud Natural Language API, IBM Watson NLP, and SpaCy.
• Predictive analytics: Project managers can foresee project results, spot potential dangers, and allocate resources more efficiently with the aid of predictive analytics. Examples of predictive analytics tools are Microsoft Azure Machine Learning, Google Cloud AI Platform, and Amazon SageMaker.
• Intelligent scheduling: Project managers can manage resources, optimize scheduling, and resolve problems with the use of AI-powered scheduling software. Examples of intelligent scheduling tools are X.ai, Calendly, and Acuity Scheduling. Microsoft AI can also be attributed to this type, in their Microsoft Teams application there is an artificial intelligence function that will document the entire conversation, this function will save you time and help with set tasks.

In 2016, the French telecommunications company Orange implemented a chatbot powered by an AI language model to handle customer service inquiries on its website. The chatbot was able to answer over 10,000 customer inquiries per day, with an accuracy rate of over 80%. It not only improved response times but also freed up human agents to handle more complex inquiries and issues. It is an example of how AI product management decisions can increase company results and simplify processes using AI technology for the daily work of other teammates.

For clarity, pay attention to picture 2, where you can see in which areas, businesses, and projects artificial intelligence is actively used.

APIs allow AI (Artificial Intelligence) systems to communicate with other applications or services (Application Programming Interfaces). APIs, which provide a standardized and secure interface, can be used to integrate AI models with other systems or data sources.

AI models, for example, might be used to recognize and evaluate pictures. This model might be made available through an API so that other applications may use it to transmit photographs for analysis. The API would provide an easy-to-use interface for providing the image to the AI model, obtaining the results, and optionally performing extra actions in reaction to the results (e.g. updating a database, sending a notification, etc.).

APIs can also be used to build AI systems. Many well-known AI systems and libraries, such as TensorFlow and PyTorch, provide APIs that allow programmers to train their own models. These APIs enable developers to concentrate on higher-level concepts and use cases by abstracting away many low-level intricacies of interacting with complicated AI algorithms.

So, AI has great potential to revolutionize product management by streamlining processes, improving decision-making, and identifying new opportunities. However, it is important to recognize that AI is not a substitute for human experience, soft skills, and intuition. While AI can certainly help with product management tasks such as data analysis and forecasting, it cannot replicate the invaluable understanding of human behavior and expertise that product managers bring. Ultimately, the combination of artificial intelligence and the human touch will likely be the most effective approach to product management in the coming years.