You're looking to improve your product management. How can AI help you achieve that?

This is one of the best way to use AI. In my last products, such as AI-empowered smart cities product, we conducted a lot of customer interviews. AI can help us summarize the customer interview outcome and draft questions for customer interviews. However, it can’t replace the real customer input. As each persona is different and we need to experience the customer interaction ourselves to gain customer empathy.

At NBCU I’ve been using AI for the past 4 years to conduct qualitative UX to learn more about the habits and behaviors of users across a multitude of platform experiences. Here are the top 3 things I’ve learned in harnessing the power of AI as a UX research tool. 1. AI can collect and analyze large volumes of natural language extremely efficiently. The ability to understand broad patterns quickly helps to improve the quality of more granular analysis by researchers. 2. AI ethnographers can capture more verbatim than any focus group testing at a fraction of the time and cost. 3. The verbatim are more honest and authentic in private chat environments and use rich media to add additional context.

When it comes to developing B2B products, it's not just about ticking off features on a checklist; it's about developing a solution that truly aligns with our business goals. AI helps dive deep into every possible scenario. Let me walk you through how I incorporate AI into user persona creation. Instead of relying solely on a handful of interviews, I use AI to digest all the data we've gathered. It's like having a brainstorming session, where the insights from every interview are synthesized into personas that helps understand the entire list of users. This approach not only saves me time but also ensures that our personas are rich and reflective of the diverse perspectives.

Artificial intelligence (AI) has significantly impacted the realm of product management, streamlining various aspects of operations and processes in product management . AI has revolutionized product management in Customer Survey, Market Research and Insights, personalised customers recommendation, documentation, rapid prototyping, testing and performance monitoring.

As a product manager, I'm always on the lookout for cutting-edge tools to elevate my approach. When it comes to refining my skills, AI emerges as a game-changer, particularly in customer research. With AI-driven analytics, I've experienced a profound shift in how I uncover insights. By swiftly sifting through vast data troves from diverse channels, AI unveils nuanced customer preferences and behaviors in real-time. This agility allows me to stay agile, predicting market trends and fine-tuning strategies with precision. Thanks to AI, I'm empowered to lead with data, ensuring that every decision reflects the evolving needs of my audience.

The first thing to keep in mind is that AI works off the data it has been trained with, and therefore it's imagination (unlike humans) is limited to concepts that already exist in this world. If the product you are designing is similar to other products that exist today, AI can save you a lot of effort by putting together the design based on your inputs, or even hand sketches. However, if the product you are building is novel, or relies on significant innovation that will be driven through AI, you would be better of thinking afresh, in a customer backwards manner, to design a solution that delights the customer. So choose wisely, based on what scenario you are up against.

In addition, AI can be used for integrating new technologies in an existing product. In a recent case, we have used AI suggestions to add technological features to a traditional product, and result was awesome, afterthat we studied more in focus groups and some product features were added to the experiment pipeline.

AI can revolutionize product design by analyzing customer data to uncover trends and preferences, enabling personalized product experiences. For instance, AI algorithms can sift through vast amounts of user feedback to identify common pain points and desires, informing iterative design improvements. This data-driven approach ensures that our product evolves in line with user needs, enhancing satisfaction and loyalty.

Data is King when developing AI models. It is important to understand the kind of data used to train your model and the biases contained therein. For instance, does the AI model used capture the complete spectrum of your targeted demographic?

AI transforms product designing in this digital era, collaborating with designers to enhance creativity and user experience. It suggests innovative features based on market trends and user data, ensuring products resonate with users. Personalization is paramount; AI tailors interfaces and functionalities to individual preferences, optimizing usability. It analyzes user interactions to refine designs. By automating repetitive tasks like image editing, AI allows designers to focus on strategic aspects such as user empathy and journey crafting. Moreover, AI enables data-driven design decisions by analyzing user research data and feedback, uncovering user needs and pain points for products that truly engage the audience.

AI can assist product managers in making better-informed decisions by providing real-time analytics, predictive insights, and scenario modeling. By augmenting human decision-making capabilities with AI-driven recommendations and analysis, product managers can make faster, more accurate decisions that align with business objectives and user needs.

AI is just here to ease your task "a bit" — cost a lot of 'prompt engineering' time to be invested (which mostly results in a non-satisfactory outcome). However, categorizing features and efforts and assigning intensity grades — will eventually help you understand the priority of every feature, how two features can relate, which request can be a road-blocker, what can be pushed further to another phase, and how exactly can your sprint story points be managed. Although a small task, it requires substantial effort from the tech team to refine prompt engineering. AI might be fruitful later, but is extremely hard to achieve. Focussed more on AI's complexity, but it is what it is!

AI revolutionizes product development, streamlining processes and enhancing team collaboration ??. AI assists in managing and coordinating development teams, tasks, and resources by assigning and tracking tasks, monitoring progress, identifying risks, and facilitating communication and collaboration among team members. Moreover, AI automates and streamlines development processes such as coding, testing, debugging, and deployment, expediting product delivery. Additionally, AI facilitates seamless integration with other systems and platforms through APIs, SDKs, and connectors, ensuring interoperability and scalability ??.

- AI to audit product development practices, such as writing user stories and drafting or reviewing product requirement documents, can significantly enhance quality and expedite time-to-market, particularly for large teams. I have utilized AI to ensure that compliance guidelines are adhered to by my team, and where gaps are identified, AI has enabled me to provide timely feedback or commendations to team members. - Gen AI to create flow diagrams and UMLs using D3.js, which has sped up the process and enhanced the product development guidelines. -Regarding API integration, reviewing documentation, and quickly creating and validating requests and responses with tools like Postman has empowered my team to write more precise requirements.

AI has really helped me with the product development process. The biggest lift has been moving past the blank page and not starting from scratch. An example of this is for PRDs. I can use a prompt to get the process started, identify the key requirements, and then work with AI to flesh out the PRD. This helps me get it to a good spot before taking it to others for additional feedback. I also leverage AI as a partner and have conversations to refine my thinking around a topic. This helped me build my own LLM and productionize it in GCP without being an engineer or programmer.

One of the areas where AI has been very helpful to me throughout the product development cycle is Product Launch. For our Product Launch, we use AI to prepare Product/ Feature presentation decks that are helpful for our sales/ solutioning teams. We also use AI to help our content writers to prepare content for our blogs, website, etc for the Product Launch.

Following are some areas in which AI can be leveraged for am efficient product launch. 1. Personalised marketing campaigns: AI can really help us identify the different user types (relevant to the product being launched), and hence can also be used to create personalised marketing campaigns which resonate for the identified user types. 2. Post launch analysis: Once we have a robust telemetry, metrics tracking in place, we would be able to gather the relevant adoption and other useage data. AI can then be used for making sense of all the data we gather post launch and hence identify the issues being faced by users, fix them promptly and make the experience better for users while making the product launch even more successful.

Generative AI has been a 10x multiplier in planning product and initiative launches. While one still needs to be a fact checker and editor, it excels at; ? Generating rough outlines from gathered context ? Converting output formats easily between bullets, tables, and csv ? Creating first drafts of kickoff and status emails ? Proposing schedules and timelines based on constraints

AI plays a pivotal role in orchestrating a successful product launch, enhancing marketing strategies and performance metrics ??. Leveraging NLP and ML, AI assists in creating and optimizing various promotional materials, including landing pages, ads, emails, social media posts, and content. It personalizes copy, images, videos, and headlines to resonate with target audiences, maximizing engagement and conversions. Additionally, AI enables continuous measurement and optimization of launch performance through analytics, dashboards, and reports. Tracking key performance indicators (KPIs) such as conversions, retention, revenue, and growth, AI empowers product managers to refine strategies and drive sustained success ??.

Launching a product is a critical moment, and AI can help ensure that it's a success. By analyzing market trends, competitor activity, and customer engagement data, AI can help determine the best time, price, and promotional strategies for a product launch. It can also predict which channels will be most effective for reaching the target audience, optimizing marketing spend and increasing the likelihood of a successful launch.

Applying AI to product development supercharges product iterations in 3 areas: 1. Data Analysis: Machine learning models have the capacity to analyze massive amounts of data, from user feedback to market trends, empowering product managers with AI-driven insights for more informed decision-making. 2. Predictive Analytics: AI's ability to forecast user behavior based on historical data enables product managers to stay ahead, anticipating user needs and crafting features before users even realize they want them! 3. AI-driven Experimentation: Product managers thrive on experimentation. AI can amplify this by facilitating targeted A/B tests and rapid feature iterations, therefore accelerating the discovery of customer-centric solutions.

Using AI for product analytics has been of great value for my team. AI driven insights into product performance, adoption or unmet user needs offers excellent cues for product improvement and optimization !

Leveraging AI is paramount when managing a high volume of user feedback—like the 10,000 comments we receive weekly. While it’s challenging to manually sift through such a vast amount of data, this feedback is a treasure trove of insights, revealing opportunities for product enhancement and NPS score improvement. By employing a Python script alongside a generative AI API, we’ve been able to contextually categorize all customer feedback efficiently. Once categorized, we feed these remarks back into the API to extract areas for improvement. This process provided high-level recommendations to our product design and engineering teams.

Empowering Continuous Improvement with AI ?? ?? User Engagement Analytics: Dive deep into user engagement metrics with AI to understand what keeps users coming back. ??? Voice of Customer: Use NLP to aggregate and analyze customer feedback across platforms for comprehensive insights. ??? Feature Impact Analysis: Leverage AI to evaluate the impact of new features on user satisfaction and engagement. ?? UI/UX Optimization: Utilize AI to continuously refine UI/UX based on user interactions and feedback. ?? Performance Benchmarking: Use AI to benchmark your product against competitors and industry standards continuously.

AI helps me reveal hidden insights, especially in large-scale B2B products. By analyzing support tickets and user data across multiple touchpoints and over long periods of time, AI identifies recurring patterns and pain points I’d never catch manually and would spend days synthetizing with traditional ways or waiting on the Data Team to provide a report. Instead of just gathering feedback, AI helps me prioritize what’s critical for customers, and analyze data easily to guide decisions for product updates.

AI can certainly help with improving the product management deliverables as well as overall product delivery. It can help you with research, competitive landscaping, document standardisation (epic, features, user stories etc), use case generation, programming, automating deployment pipeline etc. There are some very impressive plug-n-play AI products available for each of these. AI provides useful helping hand as long as one can ensure the security considerations are not compromised. Fair warning that manual intervention at each stage is a must!

AI is a double-edged sword - a powerful tool that can revolutionize product management when used correctly, but can lead to catastrophic outcomes if misapplied. As someone deeply invested in the field, I am firmly in favor of embracing the opportunities AI presents. However, with this enthusiasm comes a responsibility to approach AI implementation thoughtfully and ethically. We must recognize its potential to revolutionize product management, but also acknowledge the importance of navigating its benefits and risks judiciously. By doing so, we can harness AI's transformative power while safeguarding against its potential pitfalls, ultimately driving innovation and value creation in a responsible manner.

As a GPM, yes it’s great to use AI to help with “on the job tasks”, but I believe there is also space to utilize AI to help us learn, grow and develop as humans, outside of work. Often time we forget that we are more than our job title yet who we are outside of our job title plays a huge role on the way we do our job. Personally, while I use AI at work with PM tasks, I actually use AI more to help me become a better human outside of work, which in return helps me become better at my job.

The revolutionary movement towards AI is not just in the technicalities of product development; it’s a paradigm shift in the mindset of PMs. Effective product management is to think of AI as a)Tool to help develop and ship products b)Capability that can be built into products to create great value and capture those key performance indicators with ease.

AI is definitely a boon for any product manager, be is effective customer research, summarize findings, deriving insights from data as well as create multiple scenarios by training the AI on your customer set to accurately predict their response to your product. However, one of the best Use case that I foresee for AI is its ability to take Agile to new heights, be it a digital scrum master, managing sprint backlogs, user stories, everything in the most efficient manner.