OKRs Make AI Implementations Realistic
Undoubtedly, AI is changing and will continue to change how we interact with digital products and services. Unlike other tech fads over the past few years, AI is already changing how we do every part of product development, business, customer service, and beyond. It is the new “shiny object” executives are chasing.
Asking the Right Questions
If it hasn’t happened already, your boss will come to you and ask you to integrate AI into your product. Your job is to remind them that AI is a feature and to ask, “What problem are we trying to solve with AI?” You should also ask, “How will we know the AI integration is helping the customer experience?” And finally, “What customer experience are we NOT going to do while we work on implementing, fine-tuning, and maintaining the AI feature?”
These are the same questions you’d ask of any feature. If our purpose is to make our customers successful and our leaders are demanding specific implementation of features, how do we reconcile our OKR goals in the face of this AI-driven demand?
If an idea, regardless of how shiny, new, or powerful, isn’t going to make your customers more successful, wait. Wait until you have a better sense of where AI can help solve real problems in your product. Ensure that the effort you put in yields a better user experience for your customers and better business results for your company. Otherwise, we’re ignoring the core value of OKRs and filling our product with bloated software that we’ll have to maintain forever.
AI is a Feature, Not a Strategy
To be clear, AI is an output. It’s a feature of your product or service. It’s something you “make” that you hope will drive new and better customer behavior. Regardless of executive directive, AI integration into your product offering should, first and foremost, solve or improve an existing problem or way of working for your users.
The interesting part of adding AI to your product is that it’s a technology that allows you to reinvent your user experience completely. While, in many cases, it can make an existing workflow easier, more efficient, or unnecessary, there will certainly be opportunities to completely rethink how your customers work with your product. In all cases, though, incremental improvement or total redesign, the customer and their needs must be at the center of your discussions.
Here’s an example from my daily routine. I use an email product that I love called Spark. It’s made my email life easier, more collaborative, and convenient. I’m a paying customer. Over the past few months, the Spark team has added “AI” to the product.
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An example of that AI integration is when I’m responding to an email that’s prepping me for an upcoming podcast interview and has made some requests from me. Step 1 is to hit reply as usual. Then I’m presented with the option to have the AI feature generate a reply.
Next, it prompts me to add context to the response. In other words, I have to start writing the response myself to “help” the AI know how to respond “automatically.”
Finally, it generates a response that I now have to read, review, edit, and then send.
The integration of AI in this workflow saves me nothing. I still have to prompt the AI to respond in a specific way, review that it wrote something I agree with, make the proper edits, and only then send the email. In this case, I would’ve sent all of the required materials along with this email to save another back-and-forth. I would also have edited this to sound like me and, well, not a bot. (I never say “Best”).
What OKR is this feature helping the Spark team achieve? Less time spent answering emails? That’s not the case for me, at least. A higher number of emails disposed of in a similar amount of usage time? Again, not for me. The reality is the Spark team added this because they, like many other companies, see AI as an arms race they have to stay competitive in when, in reality, I’m guessing it does nothing for their download rates, account setup percentages, usage rates, and retention (all OKRs).
Chasing the Shiny Object vs. Solving Real Problems
It can be tempting to chase the shiny object. Instead, consider the goals you have for your product. Boiled down, they’re likely to be some variation of Acquisition, Activation, Retention, Revenue, and Referral (the infamous Pirate Metrics). What attracts your customers, gets them to use the product, and keeps them coming back? Innovation is important and critical to the success of your product, but not at the expense of basic usability, performance, and stability.
Getting the fundamentals right first will help you achieve your core OKRs. Once the fundamentals are in place, you identify where and how to innovate and improve the service. There will likely be use cases where AI makes a ton of sense. The question to ask, though, is in the service of what? How will AI make the user experience better, faster, more accurate, more efficient, etc.?
Once the AI integration is deployed, measure those user behaviors (aka Key Results). If they haven’t changed, find out why. For example, if the Spark team asked me, I’d tell them the AI feature was useless to me. I may be an outlier, but I’d guess there are many like me who’d prefer to write their own email responses in their own voice.
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