How can AI product strategy will rebound from COVID-19 crisis?

 

      AI Product Management involves AI, Deep Learning, or Machine Learning to improve, create and shape the products. There are lots of AI applications in B2B, B2C products and services like Alexa, Amazon Recommendation, Tesla Autopilot, Netflix, and Machine Learning etc., The data product reflects standard product development of identifying the opportunity to solve the user need, building an initial version and iterate. Also, you may have to work with the complex and iterative nature of AI models and processes. So, make sure that you have the right data for the right purpose and invest in acquiring and maintaining strategic datasets. Think about the stakeholders and his key question of what is in it for them like MVPs, models, narrow the domain, buy/borrow the data. Follow the agile development that involves the users in the development cycle to test, refine and improve the AI features in the product by sharing the feedback with development teams. There are 3 phases in AI or Machine Learning to develop the product. Those are
  1. Inception: In this stage, we are deciding what and why to do on data? Combine the data, analysis, and judgement. The product manager has to discover or create a product that is valuable, usable and feasible.
 2. Development: AI Product Manager keeps all the AI elements together to create a series of MVPs with the models, and buy or borrow the data. So, understand and coordinate with the organization structure.
 3. Commercialization: It is to monitor continuously the performance and improve the product with the right people, processes and tools.

Product Strategy: It is the integrated plan for how will you meet your objective. It is the roadmap for the product manager to work on the critical elements of product development priorities and finding profitable growth opportunities etc., Product Strategy mess with broader corporate strategy and complement the strategy of your company. The key steps in product strategy are,
    * Build the Cross-functional Team
    * Review the Company Strategy
    * Apply the Market Intelligence
    * Make the Product Vision & Objective
    * Think, Analyze and Discuss the strategy
    * Share with the manager, executives to enhance
    * Use your strategy and Iterate over time
The cross-functional team helps for the discussion, critical thinking, dissecting and best done on a group. For ex, you can discuss with other product manager or financial representatives, or engineering manager or client services or account manager and go-ahead to get an executive sponsor. The charter paves the way for what you are doing with your strategy.  Charter may be the executive sponsor to say it is a great idea and work on the product strategy with this team and I want to come back in a month and present to our executive leadership. Next, the company strategy is to look at the mission, objectives, and strategy of our company. The product strategy should nest within and complement the corporate strategy. Third, Applying Market Intelligence depends on various factors such as market size and segmentation, customer needs, competitive positioning, technology assessment and regulatory assessment that you are facing in the new regulations. Also, it is to look at the trends and emerging opportunities for long-term strategy work. Product Vision is to find out a better place in the world if we succeed. It should be compelling, ambitious and motivated. For ex, the Wikipedia product manager has compelling and motivating commitments that imagine a world in which every single human being can freely share the sum of all knowledge. Once you got the vision, you need to think about your objectives. This is a specific and measurable goal that you need to set for yourself.
      The hard work of strategy development is to get together with the core team. It is to analyze, discuss and push your product with different ideas for market opportunities of size and debating on those are the strategy development. According to prof. Hambrick D Fredrickson,  there are 5 elements of strategy as shown in the picture. Arenas are where will be active? The product team where they will be active like the sports team is active in the sports arena. The arena is the market segment, target customers, product, geography, and technology. The vehicles will help you to get your objective. For ex, to enter into a new market, do you have the capabilities to get there or how will you fill the gaps. Will you use the engineering team or partner with another company that has capabilities. The differentiator is about the competitive advantage of how you will meet the needs of the customers. Staging and Pacing are the speed and sequence of moves that are described in Product Maps. Finally, economic logic mainly depends on the money to make at the end of the day.
       Once you have done all that work together with your cross-functional team, you are ready to share with your management team about the market background, proposed directions, timeline and actions, and financial projections. In the final step, you are really using the product strategy and iterating over time. It is to drive your product roadmap, product development and growth plans based on the strategy.  Then, modify and enhance the strategy based on the market feedback in the long term directions for every year so that you can have some stability of your product. Roadmaps are time-based charts showing the planned evolution of product or service. It helps prioritize development investments, and focus development teams.

Identifying the Opportunities: The best data product opportunities demand the product-and-business perspective with the tech-and-data perspective. Product Managers and business leaders have to identify the unsolved user and business needs. Meanwhile, the data scientist and engineers identifying feasible data-powered solutions on what can be scaled and how. The right data product opportunities are identified and prioritized by,
    - Educating to data scientists about the user and business needs: Ensure that part of the data scientist's role is to dig on data directly to understand users and their needs will help.
   - Develop the data-savvy product and business group: Individuals across a range of functions are upskilling in data, and employers can accelerate the trend by the learning program. The higher the data literacy of product and business functions will better able to collaborate with tech and data science team
  - Give Data Science at the right place: Data science can live at a different place in an organization (eg, centralized or decentralized) and the product and business strategy discussion will accelerate data product development.
         Companies often run into trouble with development priorities and struggled to get compelling products to get released with a competitive advantage due to,
   * Constant Changing of Strategy - If you think about the product to get really compelling features, compelling products and this will not work your strategy changes of every 3 to 4 months.
   * Death by Numerous Requests - B2B companies that grab your products of enterprise clients or you have been in the situation to meet with one of the big clients to represent 7 to 10 percent of your business and talk for 25 requests that they want from you to respond so that they can run their service or doing better. So, you go back to your development team and ask 25 requests. Then, the next big enterprise client for 20 requests followed by 20 things. Now, you got so many requests and completely tie-up with engineering bandwidth and never get to develop a compelling breakthrough product.
   * Overly Long Bureaucratic Planning Cycle - Some companies do their market research, talk to the customers, work with the development teams to design and build by the time to get out of the market it's 20 months later and the market has changed.

Objectives and Key Results(OKR): The product vision and strategy has been set at a 5-year path for our products. This OKR converts a big picture direction to this quarter's goals. Objectives are business goals and what we want to accomplish in any given time periods or quarters. One of the quantifiable or

measurable outcomes linked to this objective is going to use these results later to measure and track our progress against OKR. It empowers the team to set the objective and what our target is and how to measure it the key results. There are 4 steps in OKR,
 1. Create 2 to 3 OKR each quarter and include measurable results with a focus on big impact items and the strategic areas of the parts of the product. You might have other elements in your products that you need to get done and fix some bugs to client enhancement requests. So, keep all of these things in the OKR.
2. Share your OKR with the executive team and make sure you got the consensus and headed with the development team.
3. Use OKR to tightly focus on your development efforts.
4. Review the OKR with your team at the end of each quarter to see how you are doing,  figure out what to do better next time, move forward from that and grade yourself,