AI has come a long way in recent months. Powered by widespread usage and stronger datasets, the capabilities are expected to keep growing exponentially.Product managers sit at the forefront of adopting AI and it has become a widely expected fact that sprinkling a bit of AI onto any digital product can make it better: enhance the user experience, remove friction points, increase engagement, and ultimately drive more value for the user.
In this blog post, we'll explore 9 ways AI can be used to supercharge your product.
1. Predict user needs with recommendation engines
One of the most impactful ways that AI can improve digital products is through smart recommendations. By analyzing user data and behavior, AI algorithms can tailor recommendations, content, and interfaces to each individual user, resulting in a more personalized experience. This can increase engagement, reduce churn, and drive conversions. Youtube, for example, uses AI to recommend videos based on the user's preferences (65% of watch time on Youtube is generated by AI-based recommendations), while Spotify uses it to suggest playlists based on the user's listening habits. TikTok is another example of a great recommendation system. They leveraged it to completely remove the signup and onboarding flow from their app - a significant friction point in the user journey. Instead of asking the user for interests, the app takes the user straight to the first video (straight to value) and starts the process of testing and learning the preferences.
2. Recommended best actions (for SaaS)
Recommendation systems with AI predictions can be utilized to recommend actions that are most likely to lead to successful outcomes. AI is much better at understanding large data sets and creating valuable insights that might not be (easily) visible to humans. For example, a CRM system could recommend hot prospects that are most likely to buy, in the same way, that modern ad systems are optimizing audiences based on campaign goals. Marketing automation tools could also recommend running specific kinds of content experiments (or even do it automatically for you).
3. Make things faster & remove high-friction steps
Every time a product has to ask a user to do something, they risk asking too much and losing the user forever. This is especially challenging for products where it’s not really possible to provide the user most of the value without doing some work first. For example, think of an app designed to help you lose weight by measuring your daily calorie intake. Inputting data manually feels like a lot of work, especially if you have to do it five times a day. Now imagine if instead the user can snap a picture of the meal and the algorithm takes care of the rest. AI can help complete a lot of these low-value necessary tasks with significantly less effort by using computer vision, natural language, and predictive algorithms.
4. Make things bite-sized by summarizing content
The general attention span of people is shrinking rapidly and anything that takes more than a few minutes feels like a lot of work in today's life. AI can help products with large-form content tackle this issue by creating AI-generated summaries for podcasts, movies, books, or news articles. This will also save a lot of time and effort for product teams that are currently summarizing things manually.
5. Offer shortcuts to UX
ChatGPT like Large Language Models can offer great shortcuts for situations where users know exactly what they want to get done. In shopping, for example, instead of browsing through products and categories in a quick commerce app, the user could just say “I want to buy size 5 baby joy diapers” and be taken straight to the checkout page with said diapers in the basket.
6. Explain difficult things in natural language
Any user opening Google Analytics for the first time will feel overwhelmed with all the data on the dashboard. What's important? Where should I pay attention? What should I do based on this information? LLMs could greatly improve the user experience and lower the learning curve by simply explaining all this in layman's terms.
7. Help your users create
Generative AI applications have lowered the barrier to creating content like text, images, videos, or even music. Making it possible to help your users get things done or even do it for them. For example, Genius AI helps UI designers finish their designs in Figma (you’ll actually see an extra mouse cursor finishing the screen you started to create) and GitHub is helping developers code faster with co-pilot, which finished lines of code for them. While a dating app might help the user write a profile text that stands out. As a rule of thumb, If you are working on a product where the user interface has text input fields - AI can help make it better.
8. Do things for your user (Automate things)
If the user can easily describe what they want to get done, there’s no reason to actually make them do it. Why would you still have to add Google Tag Manager tags to every button on your website when it’s a task easily done by a machine? Galileo AI is able to create UI designs based on text prompts, while Tome crafts narrative-driven presentations with one prompt.
To conclude, AI has the potential to significantly enhance digital products, providing personalized experiences, automating repetitive tasks, leveraging predictive analytics, and improving user interface design. By incorporating AI into your product strategy, you can create more valuable and engaging products for your customers and stay ahead of the competition.