You’re inundated with AI success stories, and as a business or technology leader, you want your organization to benefit too. But where do you start? With so many possibilities, it’s easy to try to boil the ocean, overplan and overthink yourself into inaction.
Instead, try choosing a single use case. Just one. Something that your staff or customers will thank you for, because it immediately saves them time or helps them get to faster or better outcomes. Then, keep building on that success.
Here’s a real world example…
Launching Our First AI Use Case
At Plative, we’re a professional services firm, and naturally, our consultants spend a lot of time meeting with clients and partners. After every meeting, some poor soul has to take the time to write up meeting notes to capture the essence of the conversation. This isn’t the glamorous part of consulting, but it’s time consuming and oh so necessary.
So, that’s the first task we gave to generative AI: After every client meeting, automatically read through the meeting transcript and publish a draft meeting notes document – professionally formatted and templatized to include the information and insights we and our clients care about.
We built, beta tested, and deployed this process to production in just 30 days. And it was a certified hit with our consultants! They no longer had to write notes from scratch, but rather just fine tune an AI draft that’s already at least 80% of the way there. This saves each of our team members hours each week that they can now spend on more value-added work.
But That Was Just The Beginning
Once our AI pipeline started producing meeting notes, we quickly realized we could also have it analyze and assign a client sentiment and risk score to each meeting. If a risk score trips a certain threshold, we automatically push alerts into account team and leadership slack channels so our people can swarm and support each other and our clients more proactively. No more waiting for weekly status meetings to elevate risks. Now, we can monitor risk at scale across the entire portfolio in real time.
Another Light Bulb Moment
Now that we had all this wonderful real-time data on customer sentiment and risk scores, we then decided to load it into our BI platform and see what macro story it tells us. Again, a huge hit…
For the first time, our account managers and delivery leaders had a real time visual heatmap of customer sentiment and risk across each of their books of business, accompanied by an AI synthesis and commentary, enabling them to spot trends and outliers that they can take action on.
Continuous Innovation
With all that goodness under our belt, the next evolution is even more exciting. We’re now in the process of training an AI model on our sentiment and risk scores, coupled with other key business metrics. This model will help us predict the likelihood of account renewal, AR risk and churn risk, so our account team can take preemptive action.
This is already next level stuff, and there’s still more great ideas in the backlog (e.g. automated project status reporting, sentiment-driven marketing journeys). But none of it would be possible if we hadn’t tested the waters with that initial meeting notes use case.
So how do you eat an AI elephant? One use case at a time of course.
If you’re contemplating how to get started on your AI journey or to drive greater business impact from your AI investments, sign up for our complementary AI workshop.
We routinely help clients assess AI opportunities across their value chain and tailor innovation roadmaps that can deliver value in as little as 30 days and then continually build from there.