Sapna Gulati is a Head of Product Strategy and Design and is responsible for defining the product vision, strategy, and user experience of the company’s portfolio of products. She started her career as a developer and has worked at growth startups and Fortune 500 companies, where she held roles in various aspects of product development and management, go-to-market strategy, and streamlining execution processes.
In our conversation, Sapna discusses how GenAI is changing the game for product managers to focus more on strategic innovation and competitiveness. She talks about the pillars of prioritization, including company goals, resource availability, market demands, and customer needs. Sapna also emphasizes the importance of data quality because the outcomes you get from AI are only as good as the input you provide.
I believe that AI is becoming this transformative force that is going to reshape everyone’s lives. It’ll bring new opportunities with some challenges, so I think we need to embrace it with the right strategic mindset while keeping ethical considerations in mind. AI innovation, especially generative AI, is very exciting, but I would also say it’s creating a little bit of anxiety. It’s changing how we’ve done things before and where we’re headed in the future.
It’s certainly started to evolve my role as a product leader because I’m responsible for pushing for innovation in the companies I work with. Take data for instance — data has always been critical, but with the advent of AI, it’s becoming even more critical because it’s the central brain power that feeds into AI-powered applications.
Since many organizations are just starting to lean into the world of AI, I’m seeing that the role of product managers is becoming focused on being bridge builders along with being innovation drivers — ensuring alignment on how we’re going to use AI, where we’re heading, and how to bring it to reality.
Another important aspect to remember is that this technology is evolving rapidly. Whether you’re a product leader or working in technology, it’s crucial to keep learning and stay updated on these changes. AI is also reshaping our roles by automating many everyday tasks. This offers a great chance to streamline how we develop products, freeing up more time to concentrate on the strategic aspects of product management.
Data comes from various sources. When it comes to external sources, it can come from market research and prestigious sources like Gartner or other research companies that are constantly collecting market data. I subscribe to these resources and am continuously assessing the shift in market dynamics.
When it comes to internal resources, your internal teams, such as sales or customer success, your customers, your investors, your board members, etc., are great assets. They all are great sources of information for understanding and collecting the data and building your strategy centered around the company goals.
I think it’s really a multidimensional thing. It’s about aligning your product strategy with overall company objectives and ensuring that the products not only meet your customer needs and pain points but also contribute to the company’s growth and success. It is critical for any product leader to think holistically in terms of balancing your customer needs and market dynamics, but also ensuring that you’re constantly innovating and thinking about profitability.
Prioritization is a key aspect of any product role. You get requests from various sources, so how you prioritize is about making the right decisions and aligning them with company goals, market demands, customer needs, as well as resources availability. Those are all different pillars that you have to think about.
Once you have ensured that you are prioritizing all requests in alignment to overarching company goals and strategy, you prioritize based on the ROI of the features request and what outcomes it’s going to drive. There are various processes and tools that product teams use on a day-to-day basis for building business cases and stack-ranking the features and requests, and then making decisions that are data-driven and intentional.
It’s important to gather input from stakeholders and align that input with the product’s strategic goals. To do that, we use a combination of methods. The simplest is to use a value effort matrix to help quantify and compare requests.
We take the highest-value initiatives and do some analysis to understand the ROI of building them. We consider factors like user impact, business value, and feasibility and regularly revisit our priorities so we can adapt to changing circumstances and feedback.
The key is to not overdo it — you don’t want to make it a complex process, but you do want to be very transparent about how requests are coming in, how you organize them into different buckets, the value proposition, and the outcomes they’re supposed to drive. With all that on the table, we can make better decisions around prioritization.
There are a few important considerations. Of course, you need to be very clear about your vision and objectives and emphasize their strategic relevance. Engage your stakeholders early so you can foster a sense of ownership throughout the process — this makes it easier to gain their buy-in.
It’s also important to clearly articulate the value and opportunities. Value should not just be one-dimensional; you should also communicate how it’s going to benefit teams and overall company growth. You have to motivate people by conveying why the initiative really matters and reinforce your credibility with data-driven evidence.
I consider myself a leader who always has an open door. I’m easily accessible to my team and other teams that I may not be directly managing. I believe that ideas can come from everywhere, and I am always ears to talking to people, listening to their concerns and ideas and what they’re sharing. I think that builds some kind of trust in the relationship where you can discuss and analyze opportunities together instead of operating in a silo. That helps create that motivation and excitement, so when you come up with a proposal that has a strong vision and data backing, you can have an open and honest conversation about it because that’s your culture of active employee engagement.
Companies can start by setting clear goals for AI and understanding how it can benefit their business. For most AI projects, data needs to be well-organized and secure. It’s important to either hire people who know about AI or train their existing employees in AI skills. Companies can begin with small AI projects as proof of concepts to learn and iterate from them. There are many foundational tools and platforms available to leverage building and deploying your AI solutions. It is also very important to understand the risks and establish ethical guidelines for your organization to make sure AI is used responsibly.
A feasibility study is a good place to start. This will help you gauge the potential costs associated with a full-scale implementation and allow you to start prototyping and thoroughly testing your AI solutions.
One area where I think every company can apply generative AI right now is the user experience. The feedback loop is another area; you can establish mechanisms to gather feedback on your product’s performance and understand trends and sentiment analysis on that data.
There are many challenges and considerations but I think the key one is the quality of the data. Data quality and quantity both are important, and you need to make sure that you have large amounts of high-quality data.
Another point of consideration is your existing systems. You need to make sure that anything that you’re building, like your AI-powered applications, can integrate into your existing business processes and systems. That might take some cost and complexity because you might have to upgrade your legacy systems to ensure there’s compatibility.
A third and critical challenge is the expertise in the organization. There’s still a lack of AI-skilled workers, so the demand for skilled AI professionals is increasing. Hiring and retaining talent with that expertise is going to be a continued challenge.
There are also definitely cost challenges when it comes to implementing AI solutions because it might require investments in upgrading your infrastructure. You will need to subscribe and rely on cloud providers for advanced computing resources and data storage. Some key questions to ask yourself are: is there a clear return on investment? Is your organization culturally ready for the change? How are you going to address bias and privacy concerns? There are many factors for implementing AI.
It starts with a strong value proposition. What’s your return on investment, and is it worth doing the work and making a shift to AI? Many potential capabilities and benefits can come from implementing AI technology, but at the end of the day, if the cost of implementing something exceeds the value it’s going to return is higher, then you need to rethink your strategy.
Start with your ROI analysis, and not only consider short-term gains, but also long-term gains because eventually, that’s where the world is heading. Begin with some key use cases and then overtime, you’ll gain maturity in moving in this direction. It’s not something you can achieve overnight.
For all product and technology professionals, it’s important to embrace the learning curve of AI. Staying ahead of industry shifts and rapid advancements is critical because AI is evolving faster than anticipated.
I firmly believe in continuous learning, and I am personally dedicating a significant amount of time to grasp the evolution of AI technologies and machine learning. I strongly encourage every product professional to do the same because, as a key resource in product strategy, you are responsible for steering innovation. It’s essential to be well-informed and educated about the capabilities we can harness and the long-term value you can bring to your customers through your products.
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