Give users a reason to be excited about AI in your product
We are in an age I like to call the “Great AI Race.” Leaders are looking for ways to quickly leverage AI and add it to their products and not lose this race.
Evidence of the AI race is all around us. AI seems to be in everything. Some ideas are good. Some aren’t. Some are even wacky enough to be on par with a Hanna-Barbera episode.
One day I stumbled on a beta AI feature in a product we use all the time. Otter.ai is a transcription tool we’ve been using for a while. Then I discovered I could have a conversation with Otter about the meetings I had recorded. Honestly, I was ecstatic. Here’s why:
- Otter’s AI feature allowed me to directly ask it questions about conversations. I could ask questions about a single conversation or ask it to look across multiple conversations and tell me about commonalities.
- I ended up learning to mostly trust the product because I tested the accuracy of its answers and it did pretty well. (But of course, you can’t fully trust everything AI says… yet.)
The trouble is, I’ve seen a lot of products released with AI features that seem… well… stupid. Or they don’t work well or the way I would want them to. It’s not a mystery why the feature doesn’t land. In order for users to love your new AI feature, they need two key things:
- They need to value the feature.
- They need to trust the AI.
Rather than add features just because they have AI, think about what would provide the most value to your users. Start with user research to understand what problems your customers are trying to solve. Then, think about unique ways you as a product leader can leverage AI to help them.
Here are a few ideas for how to leverage AI that goes beyond just generating content.
Intelligent automation for workflows
What can you do to help users automate their workflows? Is there a way to take away the repetitiveness of tasks so users can focus on more high-level thinking?
AI could predict and suggest workflows for users of SaaS products. This can help them streamline their processes and be more efficient.
Examples of intelligent automation
Monday.com
Here’s an example: the project management tool Monday.com can help users automate task assignments. It leverages AI by analyzing patterns in how users have both used the product and interacted with their team. Then it can predict which team members might be best for specific tasks. It can also automate reminders about due dates.
Zapier
Here’s another example: Zapier lets users to automate repetitive tasks by creating workflows that connect apps and services. AI powers its automation engine by learning from users’ workflows to offer suggestions for automating processes. For example, Zapier might recommend specific triggers based on past behaviors. That helps users automate tasks like updating spreadsheets, sending emails, or posting to social media.
Users don’t love repetitive tasks. They’re boring. Take a look at the areas in your product where you can remove some of their burden and improve the experience.
How this feature benefits product leaders: user satisfaction
Automating repetitive tasks frees up your users so they can focus on higher-level thinking activities. They can be more productive and ultimately feel more satisfied with their experience in the product.
A note of caution
Some researchers caution that some roles that over-rely on automation can lead to severe consequences. For example: if auditor gets used to automating work, they may not see what they need to see.
As a product leader, be aware of how too much automation could cause issues for your users and nudge them to not fully rely on tech for every task.
Smarter pricing models
Are you losing out on customers because users think they are paying for more than they need? Or are potential customers passing up purchasing because your pricing model doesn’t match their view of value?
As a product leader, you can leverage AI to help analyze customer usage data, and then optimize pricing models. When you use dynamic pricing strategies, the AI can help you adapt to customer needs and offer flexibility which customers — especially enterprise customers — value.
Examples of smart pricing
HubSpot
HubSpot uses AI-driven pricing models. It evaluates customer usage data to figure out the best pricing tier for individual users. The AI analyzes the data to identify customer needs and consumption trends. Then it offers personalized plans that match the value customers are getting from the platform.
Airbnb
Airbnb uses dynamic pricing algorithms to adjust rental prices based on supply, demand, local events, and historical booking patterns. Airbnb’s Smart Pricing uses AI to recommend pricing adjustments for hosts so they can price their listings competitively without manual intervention.
How this AI feature benefits product leaders: customer retention
Here’s what can happen when you lever AI for this type of need — adaptive pricing can increase revenue while reducing churn because customers stay with the product longer.
AI can be trained to analyze use and engagement data, and then it dynamically adjusts pricing. For example, if certain features are under-used, AI might suggest a lower-tier pricing plan to the user.
Or if a customer is nearing the limits of their plan, the AI could prompt an upsell. This way the pricing reflects actual value.
Another cautionary tale
Be careful how you implement AI, or you could make your customers mad. Companies like Uber and Ticketmaster use surge pricing to increase prices when demand is high. But in early September 2024, thousands of Oasis fans who were waiting in online queues found the concert ticket prices had increased beyond the advertised rate. They ended up paying double and were not happy.
Personalized user experiences
As humans, we tend to create emotional bonds with things that matter to us. And when the things we use feel more personal, it can help people form attachments and create those bonds.
So what can you do to personalize your users’ experiences?
AI can analyze your users’ behaviors, preferences, and usage patterns and then make personalized recommendations or present personalized content. When users feel like the product knows them, they tend to stay engaged and continue with your product.
Examples of personalized user experiences
Salesforce
Salesforce uses AI — specifically its Einstein AI — to personalize dashboards and reports for each user. It analyzes data from customer interactions and automates predictions based on user behavior. This helps sales teams focus on high-value prospects.
Netflix
Another example is Netflix. The streaming service uses AI to analyze viewing habits, genre preferences, and even the time spent watching content so that it can recommend more programs to users. Netflix uses machine learning models to adjust recommendations dynamically based on users’ interactions, so it delivers a customized experience for each individual user. This personalization even includes personalized thumbnails which are designed to appeal to individual users based on their preferences.
AI-driven product personalization leads to increased efficiency by showing relevant data at the right time, reducing the cognitive load on users.
How it benefits product leaders: product stickiness
It doesn’t matter if your product is B2B or B2C — humans drive the interactions with our products. When SaaS product leaders leverage AI and implement personalized workflows it improves user satisfaction but also increases product stickiness. Each user type sees a value proposition tailored just for them.
Here’s another cautionary tale
When you personalize experiences through AI, you also need to handle user data very carefully. AI systems can fall prey to data attacks just like any other tech. Before you implement AI, think through the risks to data.
While you’re at it, consider the downstream effects of personalized content. It’s well-documented how personalized social media fields reinforces confirmation bias and create “filter bubbles.” Be sure you aren’t creating a negative situation for users and take steps to avoid negative design consequences.
Let’s wrap up: ideas for you to leverage AI
You’re likely under a lot of pressure to leverage AI and make sure your product doesn’t miss out in the great AI race. But there’s more to implementing AI than brainstorming a few ideas.
Sure, you can come up with a ton of ideas on what to add AI to your product. But do users actually find it valuable? Does it help them solve a problem?
Implementing a feature poorly can do more harm than good. So be sure to interview users and use real data to decide what AI will best benefit them.
