Breaking the old models: How AI-enabled Apps are transforming businesses
By Stephanie AlKhafaji
In the past, AI-enabled apps were something that many businesses didn’t think was for them. It didn’t fit their current business models, they’d say. But now AI has firmly established itself as critical for the future of many businesses, which is leaving many of those same companies questioning whether or not their current business model is really “current” after all.
Why do so many humans still distrust AI-enabled predictions?
It could simply be the way we are wired. Or you could say it’s in our DNA. We as humans are cause-and-effect thinkers, whereas AI bases decisions solely on probability and communicates that through statistics and data. And to many people, it’s challenging to understand.
Also, it’s no secret that we are in a constant state of information and data overload from the almost the moment we wake up in the morning until we go to bed at night. So it can be a challenge understanding what information and data we NEED to listen to. And that will most certainly continue.
But probably the biggest challenge to overcome is reactive business models. Most businesses are reacting to the fast-paced world around them, constantly putting out fires and focusing only on the trees with very little vision of the entire forest. The big shift is getting companies to change their primary focus to the prioritization of future risk instead of day-to-day issues. That’s what AI-enabled Apps do.
How do you overcome these issues and help people become less AI-averse?
It may sound clichéd or simplistic, but it all starts with listening. As with any challenge you’re trying to overcome, sitting down and listening to the wants and needs is the only way to formulate a successful strategy for meeting them. But unfortunately many don’t know where to start.
So where do you start? You start with identifying the potential end users of the application. And, if you’re comfortable with it, creating personas describing who these people are in real terms to better understand what they need. For example, a dashboard for technicians would include things like equipment lists, recommended parts, upgrades and work order histories, whereas a dashboard for a manager in the same company, it might include machine hours, downtime and cost of operation.
Integration is where it’s at
The best way to successfully operationalize AI predictions is to automatically integrate them into the business’s existing systems. In that way, AI actually helps unify (or at least communicate with) disparate business systems running off of different platforms. And as they become more advanced with AI, many more aspects of a business’s operations can be integrated to create recommended actions.
Be sure to “loop” them in
Building closed-loop systems for continuous machine learning allows the machine learning module to improve future predictions. In essence, it becomes smarter and more intuitive – meaning its predictions get more precise and more accurate, with greater detail. This may seem simple but it’s often overlooked by businesses today, even though it’s critical to future growth.
Show them the value
It’s one thing to make grand promises about AI applications, but if you want to show their successes (or shortcomings) you need to establish Key Performance Indicators (KPI). With advance metrics in place you will be better suited to quantify the value of AI applications. This is critical as communicating machine learning concepts to business audiences can be difficult at times. Building value calculations into the application itself can go a long way to showing ongoing value.