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Applying AI to Product Management

By Sanika Bhide – Senior Product Manager AI/ML, Innoplexus

In the past, data was mostly used for analytics, where once the product was launched, you would run some data reports, and analytics and then find some insights based on that information. Based on these insights, you would go back and program something and create products. 

In AI product management, we start with data. With the given set of data, we try to find out what kind of problems it can solve. 

This session will include how product managers can use artificial intelligence and machine learning to mind their internal enterprise data and make proactive refinements in their strategic roadmaps, GTM approach, competitor positioning, and how they can use it for their overall development cycle and operational efficiency improvement.

Key Takeaways:

  • If AI is at the heart of your product, you have to make it the core of your product development strategy.
  • Staying ahead of the customer hype cycle includes leveraging AI to know what moves your customers’ industry.
  • Active listening means moving away from documented requirements or statements of work to understanding the intangible needs that the customers are not telling you.
  • Product managers should interpret insights, and apply business context and judgment to make decisions about innovation initiatives.
  • It is helpful to build a system of code writing for itself and write better code than us.
In this article
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    AI-Driven Product Strategy

    Product strategy is the key to translating your vision into a roadmap. If AI is at the heart of your product, you have to make it the core of your product development strategy because otherwise you are giving out a message that is incorrect and it will show somewhere because your technology roadmap will not be able to keep up with your externally facing product roadmap.

    This involves utilizing the following pillars of product strategy:

    • Network analysis: Modeling and persisting the entire data set as a network for real-time analysis.
    • Computer vision: Leveraging image processing to better extract specific text elements from web pages.
    • Text analytics: Understanding structured and semi-structured text for semantic analysis.
    • Machine learning: Building a reasoning system to serve the intent of user queries. Reducing irrelevant page hits by reinforcement learning.

    Staying Ahead of the Customer Hype Cycle

    Staying ahead of the customer hype cycle includes leveraging AI to know what moves your customers’ industry:

    • Validating strategic roadmap across competitor products and offerings to prioritize end-user requirements.
    • Updating the go-to-market strategy.
    • Re-examining and re-evaluating marketing priorities and messaging.
    • Fine-tuning and evolving the competitive positioning of the product of the product.
    • Prioritizing communications strategy for greater efficiency.
    • Identifying new partners and collaborators.

    Active Listening for Customer Needs

    Active listening means moving away from documented requirements or statements of work to understanding the intangible needs that the customers are not telling you over your recurrent calls and engagements. This involves leveraging ML to tap into customer needs and deliver proactively in the following ways:

    • Real-time data collation: This includes tracking users’ social media, user activity data, and user feedback.
    • Feedback analysis: This involves analyzing feedback using the power of AI and generating insights on customer pain points, and product revenue opportunities. These insights help you decide what to build next with smart prioritization. Many available AI product management tools make it easy to do this effortlessly.
    • Feature extraction/ Discovery: This includes page visits, page clicks, shares, and likes/dislikes.
    • Feature engineering: This involves improving product layout, building a recommendation engine, and personalizing content.
    • Iterative modeling: This step involves developing algorithms for real-time feedforward, and to push notifications based on user activity.

    Product managers should look for the questions to pose to customer data, rather than seeking answers to their questions.

    AI for Driving Innovation

    • AI is the driver for innovation. Innovation that is done without customer impact or input is like building a product in a black box without having any real-world value to the customers. For technology companies, innovation in the products has to be validated by some external market-driven metric. So the innovation that you put into your product has to either increase customer satisfaction or customer retention. Product managers need to identify fields of innovation with high customer impact and benefit. 
    • For companies to survive in this volatile environment and grow sustainably, lasting product success must be guaranteed.
    • This can only be achieved by focusing on fields of innovation that offer customers a real gain in benefits and satisfaction.
    • AI-powered predictive analytics can bolster industry-specific innovation initiatives.
    • Product managers should interpret insights, and apply business context and judgment to make decisions about innovation initiatives.
    Encourage AI-driven Code Development
    • For years, data scientists have been trying to understand if there can be a self-developing system- code writing for itself, and writing better code than us. This involves incorporating asynchronous learning models as key components of your system architecture.
    • Reinforced learning is required for dynamically adjusting actions based on continuous feedback to maximize a reward.
    • Self-learning algorithms, self-correcting code, and genetic algorithms can also be utilized in this process. This all helps in iterative precision, recall, and accuracy improvements.
    • Let AI handle the non-linear data and you focus on the business aspect of the product problem.

    Hence, if AI is at the heart of your product, you have to make it the core of your product development strategy. Product managers need to track their customers’ industry trends, ideas, standards, and paradigm shifts to modify their product strategy. It is also important to emphasize that rather than seeking answers to customer questions, look for the questions to pose to their data. Interpret insights, and apply business context, and judgment to make decisions about innovation initiatives. AI should handle the non-linear data and product managers should focus on the business aspect of the product problem.

    About the Author:

    Sanika BhideSenior Product Manager AI/ML, Innoplexus

    Frequently Asked Questions

    AI in product management utilizes artificial intelligence, deep learning, or machine learning to solve product problems.

    It is highly unlikely that AI will fully replace product management. AI is used to assist product managers and enhance aspects of product management, like data analytics and customer support. But it cannot completely replace a product manager.

    Challenges associated with AI product management include working with more stakeholders, the ambiguity of outcomes, difficulty in explaining the rationale behind the outcomes, addressing fairness and bias concerns, adapting to new infrastructure and tools, and selecting the right problems to solve with AI.

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