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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: https://www.movetoai.com/courseAIPM.html.


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, https://strategyzer.com) 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 (http://www.image-net.org/challenges/LSVRC/) 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: