Written by Sanjay Nayar
The best kind of artificial intelligence built into consumer applications are ones that seamlessly fit into our lives to offer up improvements or suggestions.
These algorithms leverage the power of technology to do things that the human mind simply can’t (or doesn’t have the time to) accomplish by finding options that we otherwise might not have even considered.
For consumer applications, this is something that we have come to expect in our day to day applications that we use, whether it be Netflix’s suggestions based on what you have previously watched or Amazon’s suggestions of other products that might interest you based on your purchases or searches.
The algorithmically driven suggestions are done in a way to be helpful but not prescriptive in nature. They manifest themselves as an assistant to lean on when you need help or to chime in when you didn’t even know you needed the help.
Perhaps the best example of advanced algorithms in our consumer applications is driving directions and suggested routes. We all remember (well, maybe not all of us…) the days of opening a physical map to figure out the best route from one location to another.
Mapping software today goes far beyond this capability to provide improved direction suggestions by taking into consideration many different ever-changing factors including having the most up to date maps, current traffic, and road closures and construction.
Not only would it be impossible for a human brain to synthesize the data, it would be out of date as soon as that foolhardy analysis was completed.
Aside from simply suggesting an optimal route, these applications can tailor suggestions based on the desired outcome of the user.
Maybe I’m looking for the fastest route to get from Point A to Point B. Or perhaps the route that might take a little longer but is the cheapest (avoids tolls). Or perhaps I’m in no hurry at all and want a suggested route that would be the most scenic and take me near to known attractions.
Each of these routes has a different focus as well as pros/cons but it’s up to the user to determine what’s best for them given the information presented to them.
One final benefit that navigation applications provide is an ability to immediately respond to changes.
If there’s an accident, it will suggest reroutes that get us to our destination by changing course. And if I want to go it myself for whatever reason, I can do that and simply ignore the constant and perceived irritation from the GPS telling me its “Recalculating” every turn I’ve gone rogue.
So why am I talking so much about these helpful algorithms in consumer navigation applications?
Because we should expect our Enterprise PPM applications to work in the exact same way.
Algorithms and AI should work in tandem with the user to be able to look around corners and suggest opportunities for improvement that that organizations may have never been considered.
Just like with consumer navigation applications, organizations have a vision of where they want to go but there can be many different paths to be able to reach that destination.
Why shouldn’t a planning application be able to provide different “routes” with visualized trade-offs to understand the pros/cons using the best data that is available?
How might I get to my strategic destination if, for example, I want to:
Why isn’t my organizational GPS constantly recalculating for me to help suggest paths for funding and timing to meet these objectives?
And just as navigation software reacts to real-time changes to my environment (traffic) or my requirements (detouring to fill my gas tank), PPM software should do the same to adapt to new disruptors suggesting different course of action to minimize the impact on my current plan.
We should expect the same intelligence from our PPM as we do in the apps that we use in our day to day lives to help navigate us in an ever-changing world.
And that’s exactly what the next generation of Strategic PPM tools are starting to provide.
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