With the rapidly evolving technology landscape, today’s customers have increasingly high expectations for both current and future products. This puts immense pressure on product teams to meet these expectations effectively. One technology that has emerged as a game-changer across various industries from marketing to education, to design and software engineering is Generative AI. This type of artificial intelligence enables computers to learn from existing data and create new content, offering substantial value in several areas, including product management.
Let’s explore how product teams can utilize Artificial Intelligence, specifically Generative AI, to deliver cutting-edge products and experiences to their customers.
Here are some of the product management use cases where Generative AI can prove beneficial to the product teams.
Generative AI can greatly enhance the discovery and analysis process, boosting ideation and brainstorming sessions by generating a wide variety of ideas and concepts. Traditionally, these sessions incorporate collaborative discussions involving multiple stakeholders and whiteboarding, but they can lack tooling to augment participant’s thinking. AI tools, such as ChatGPT and Gemini, can accelerate the ideation process by providing novel ideas and perspectives. For example, if a product team aims to increase engagement on their e-commerce website by 10%, they can prompt a large language model (LLM) such as Gemini to generate a list of strategies to achieve this goal. This output can serve as a foundation for refinement to brainstorming outcomes enabling further ideation accordingly.
Likewise, teams can visualize an entire customer journey or business process by simply entering a prompt, allowing an AI tool like Visily to automatically generate a comprehensive flowchart. This flowchart illustrates the customer journey, including decision points, enabling teams to view the user flow holistically and identify areas where business processes or information flow can be optimized.
As an example, take a look at the flowchart below which was created with Visily. It was generated from a basic prompt requesting a flowchart for a food delivery application, that must include features like viewing and selecting menu items, choosing payment methods, and paying with either a credit card or mobile wallet. While this prompt results in a high-level overview, the complexity can be adjusted based on the prompt given to the app.
After generating ideas, it’s crucial to test them in real-world scenarios. Generative AI can assist in evaluating these ideas through prototyping and virtual simulations. AI-driven simulations can create virtual environments where AI agents mimic real users, allowing product teams to observe potential interactions and refine their concepts. Tools like Mockitt and Visily enable teams to create prototypes based on simple prompts and test them with real users, offering valuable insights into the feasibility and effectiveness of their ideas.
Generative AI can significantly aid in both design and development. Prototypes created in the previous phase can be imported into design tools such as Figma. AI plugins in these tools can streamline and automate design processes, enhancing productivity. Previously, design handover involved handing over design files with design guidelines to the developers, and developers having to code functionality from scratch. This took a lot of time and effort. Now Generative AI-powered plugins in design tools such as Figma can generate boilerplate code with high-level functions for the developers which gives them the foundation to start implementing the core logic behind the product allowing developers to focus on critical aspects instead of spending time on trivial tasks.
Additionally, developers can use AI tools like GitHub Copilot to assist them in writing application code, unit tests, and deployment scripts resulting in higher levels of productivity, quality, and accuracy.
Post-development, evaluating the product traditionally requires extensive user interviews, which are both time-consuming and costly. Modern AI tools, like Odaptos, can analyze user sentiment through technologies such as facial recognition and natural language processing, providing actionable insights from fewer testing sessions. Generative AI can optimize user testing by identifying what works well and what needs improvement, enabling product teams to make data-driven adjustments to enhance user experience.
Product launches once involved numerous manual tests and checks. Today, AI can automate many of these processes, running multiple tests with minimal human intervention. If issues arise, AI systems can flag them for resolution. As AI technology advances, it will be able to handle end-to-end deployments and upgrades, guided by customer feedback and market trends. Automated test and deployment reports, along with dashboards, will enable product teams to continuously monitor releases, ensure compliance, and track key performance indicators (KPIs).
Feature prioritization involves ranking features or product backlog items based on their business value. Ideally, product teams would like to prioritize items that offer the highest business value with the least amount of effort and rank the remaining items based on their relative importance and effort required.
Given that a product can include numerous features it is important to evaluate each feature and its potential to meet customer needs and business goals. Product teams should measure adoption, engagement, and user sentiment through various data sources, including in-app analytics, CRMs, surveys, and interviews. Machine learning (ML) models can analyze this data to recommend the next steps for improving product performance. These ML models can be developed using various tools including open-source Python libraries like Pandas and NumPy as well as commercial analytics platforms like PowerBI or SAS Analytics which offer powerful predictive analytics and data visualization capabilities These insights can guide prioritization and iterative enhancements based on current feature adoption and product usage patterns until the product reaches the end of its lifecycle.
While every technology has its challenges, privacy and data security are some of the biggest concerns when using commercial AI platforms such as Gemini or ChatGPT. To mitigate these risks, safeguards must be implemented to prevent sensitive enterprise data from being used to train commercial AI models. This will ensure the responsible utilization of AI tools while ensuring data privacy and confidentiality.
There are specific measures that companies can take to safely leverage AI tools such as open-source language models like Llama which we will discuss in a future article.
By effectively integrating Artificial Intelligence tools, including Generative AI, LLMs, and machine learning models, into their workflows, product teams can significantly boost their efficiency, performance, and capabilities. This integration not only enhances productivity but also helps teams remain competitive in the ever-changing field of product management. Lastly, it is important to have enterprise governance to ensure the responsible utilization of AI tools.
Discover how the team at Ippon created a solution for waste classification using OpenAI's ChatGPT and Microsoft Azure, aimed at enhancing waste management and recycling compliance. Read, "Developing AI vision application with OpenAI Vision API: The Ippon BinSmart Use Case," to learn more!