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Product Management skills to survive in the AI era

Product Management skills to survive in the AI era

In 2016, tech giants such as Baidu & Google alone spent $20B-$30B on AI, and 62% of all enterprises expect to hire a Chief AI Officer in the future. The share of jobs requiring Artificial Intelligence skills in the US has grown 450% since 2013 and corporations are seeking relentlessly for technical professionals as well as product leaders who can utilize AI technologies on their products and services to improve the company’s bottom line or top line. It is named the Fourth Industrial Revolution, and it is happening right now, right here.

However, as a product manager (PM) or as a potential product manager, how do you gain the necessary knowledge to analyze, understand, plan, and design products based on Artificial Intelligence technologies? Since you can not get today a college degree in AI Product Management, how do you adapt to this rapid change?

As an AI consultant in Silicon Valley, I get to talk to many C-level execs, product managers as well as software engineers who want to move to product management. These are professionals from all sizes of companies and have two things in common: First, they see the fourth industrial revolution happening and want to make smart moves into AI domains and technologies at the right time. Second, they have a hard time defining a framework and where their responsibilities start and end in this new domain.

Regardless of if your organization is willing to use AI technologies in their products, services or internally, the PM role alone is hugely impacted, and drawing the lines between technical responsibilities and product responsibilities is not very easy. To help you with both of these questions as well as guide your way to become an AI Product Manager (AIPM) I want to introduce you to a four-step framework.

In the rest of this article I am giving an overview of this framework, and if you are interested in more details, please feel free to contact me. Alternatively, please check out our AIPM certification course where we give the entire training in-depth:

AI Product Manager’s Skills:

  • Have solid core Product Management skills

  • Have industry-specific business domain expertise

  • Gain particular AI solution understanding

  • Gain AI Product Lifecycle knowledge

1) Solid Core Product Management Skills

In the AI era, It is crucial to success not just to understand the technology but also to have the core PM skills. Since AI solutions can touch the vision, strategy, team, product, marketing, partnerships, and support, it is essential to have an understanding of how these business aspects operate together. For example, trying to implement an AI solution without the know-how to balance between customer needs, team capabilities and business constraints it is almost guaranteed that the time-features-cost-quality equation will be unbalanced.

The ways to gain core Product Management skills is beyond the scope of this article. In addition to online product management courses, I have seen that the Business Model Canvas (by strategyzer, frameworks and techniques help enormously to see the bigger picture and be able to put on the second CEO hat in any organization.

2) Industry-specific Business Domain Expertise

Without specific domain knowledge, a PM will not be able to ideate, design, create and release viable products in that domain. The domain is, in this case, a specific industry or product. This requirement is no different from AI Product Management; the market, the regulations and the business model of the organization need to be understood.

However, even with core business model understanding, it is not always possible to implement AI solutions in those businesses. The reason is that unlike other technologies, AI can bring change to every aspect of the organization and can require different business perspectives. Therefore, it is ubiquitous that the business processes need analysis with an AI perspective. Take for example a visual quality inspection process. The solution is not as isolated as it sounds; we could integrate a feedback mechanism and make the whole production line automatically optimize itself. In such a case, not just the end but also the rest of the processes needs evaluation.

Below I will go more into the details of how to analyze from an AI perspective, but in general, a Business Analysis Framework like in the following diagram helps to organize gathered information.

Based on my experience I have seen that for any given business process there are at least four AI opportunities. It is the product manager’s role to go over each business process and identify which of these opportunities are available:

  • Automation Opportunities

  • Optimization Opportunities

  • Expansion Opportunities

  • Innovation Opportunities

Automation opportunities exist in proven and well-working business processes. One can not automate a broken process. Therefore it is essential to understand the requirements and performance metrics before automation decision. Some examples of these processes are where human error is high, human performance is too low or recalls rate is low.

Optimization opportunities exist in well working automated processes where usually the software and hardware technology is old and new alternatives are available. PMs need to know the baseline key metrics, the goal and be able to walk through. During optimization projects, the interaction with the science team is usually more frequent than during automation projects. I will mention more about the AI product lifecycle later but at a very high level the PM needs to be able to follow a rapid experimentation cycle with various AI solutions. Also, optimization efforts get more difficult when the actual performance approaches Bayes error, which is the lowest possible error rate for your AI solution. For example, the object detection task in the Large Scale Visual Recognition Challenge (LSVRC) Competition already exceeded human performance ( and improving this algorithm further requires effort and new approaches.

Expansion opportunities arise when the goal is to apply working automated processes to different geographical regions, to different products or services. These opportunities are common in large organizations, where in some cases AI capabilities are underutilized and newer technology is available. For example, applying a chatbot solution of one product to another product, after making small changes. Alternatively, expanding an e-commerce recommendation engine to international markets.

Innovation opportunities arise when a new and maybe unproven business process is needed. These opportunities are comparable to creating a new product or starting a new start-up where there is a continuous search for a model. It is an iterative process of defining, measuring, validating the various hypothesis to achieve the desired goal. On the technical side, in most cases, a new approach or algorithm is needed which increases uncertainty and overall project complexity.

Complexity is a significant factor when deciding on a technology, methodology as well as team structure in every AI project. It is usually the case that research-oriented solution is more complicated in the areas of the organization, technology, process, and regulation. The complexity of the four AI opportunities is in the diagram below:

It is also essential to know the driving factors of an AI project. Since these factors differ from organization to organization, and even from project to project, it is the PM's responsibility to identify them. Below are the top five elements I’ve seen:

  1. Competition

  2. Customer Demands

  3. Market

  4. Corporate Goals

  5. Venture Capital

3. Specific AI Solution Understanding

Today, there is endless information available about AI Solutions on the Internet, and articles range from marketing solutions to how to train an image recognition algorithm. But, the commonly missing information is the strategy and explanation about how a PM can design a solution for their specific business. The truth is that there is no one-size-fits-all solution, and the PM needs to gain the necessary understanding and equip themselves with the right framework and techniques to build a custom strategy, per-project basis.

Below is an excellent framework to follow to match any business process to the AI solution space:

  • Study the relevant AI technology landscape

  • Study the corresponding AI solution domain

  • Evaluate AI solution alternatives

The first step is to study the AI landscape to understand where the AI technologies related to the business are standing. When we look at different companies and even different business processes, we see that technology did not evolve equally in every area. For example, fraud detection and even advertising are using today's very advanced algorithms. One of the reasons is that these industries have been competing for a decade. On the other hand, some industries like healthcare and especially drug development is not using AI as much as advertising.

We dive into the aspects of our AIPM course ( but to give an idea I provided below a high-level timeline for the PMs to determine the stage of the business process.

The second step is to study the relevant AI solution domains. AI technologies can be utilized to play the following roles in any business process:

  • Task Processing

  • Decision Support

  • Decision Making

These roles directly map to AI stages shown in the previous diagram and are not always viable for every business process. For example, it looks like critical healthcare systems will never play the 3rd role. On the other hand, today, online advertising automatic bidding systems are making every second substantial number of decisions. Therefore, the PM's role is to consider these roles when looking for an AI opportunity and analyze aspects like operability, reliability, and compliance related to each of these roles during requirement analysis.

Nowadays, we categorize the AI solution domain based on three fundamental techniques:

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement learning

The PM needs to have an understanding of the business solutions in each of these categories and be able to foresee the project lifecycle involved with it. For example, a solution to predict diabetes cases from historical patient lab data will fall into supervised learning algorithms and the nature of this category is to have labeled data and therefore PM has to plan for the labeling aspect.

4. AI Product Lifecycle knowledge

If you are an experienced PM, then you most probably know that when it comes to project methodologies, frameworks, and techniques, there is no one-size-fits-all solution. As soon as I say I’ve seen it all, another method comes along. I think the most crucial PM trait is to know the pros and cons of each methodology and then work with the project manager.

AI projects tend to be different from regular software projects by having procedures like data analysis, model research, and experimentation, etc. Therefore, given the requirements and business case, the PM is responsible for identifying the right method for the project.

Below is an example AI product development lifecycle with the research and A/B test iterations. I find it also valuable to adopt an agile model and adhere to agile software development manifesto.

In this article, I presented just a few aspects of the larger frameworks and methods I teach at my course. If you want to learn more about how to manage AI products and teams successfully, please enroll in one of my courses.

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