Improved decision making through AI gives your business a real shot in the arm, but it’s easy to jump to the wrong conclusions and not recognise the full value it offers.
Some people assume that AI-powered prescriptive analytics have little to offer them, because they can already manually solve business problems like rostering. Sure, it’s true that a person can usually come up with a workable rostering solution in a spreadsheet. Yet it’s unlikely to be the optimal solution to best-meet all your business needs – meaning you’re leaving value on the table.
People are very good at solving problems, especially the same problem over and over again. They quickly learn the easiest way to get a workable solution – given the tools they have available – so they can get the job done and move on. They’re not looking for the optimal solution.
As a result, for any problem of even modest complexity, you’re not getting the full business benefit when you tackle it manually, rather than taking advantage of prescriptive analytics.
The next misconception is that it’s a question of all or nothing – you either make all of your decisions manually or else hand over control and rely on full automation.
That’s not the case. You can easily choose to only optimise or automate parts of a process, using the technology for decision support while retaining full control. This makes it much easier to handle change management while quickly reaping the benefits.
People also assume they need vast amounts of business data in order to obtain meaningful results. Not so, as prescriptive analytics is very different to other types of analytics when it comes to data requirements.
For example, vast amounts of historical data isn’t required for optimisation tasks such as rostering. Instead, an accurate representation of the problem is the key to success.
That said, there’s also a misconception that these tools are highly dependent on fully capturing every single business rule involved in making decisions. That sounds like a long and arduous task, but thankfully it’s not necessary.
Rather than attempt to anticipate every single outcome, however unlikely, users can intervene in the rarest of edge cases.
Finally, there’s a misconception that prescriptive analytics is prohibitively expensive, with long implementation times. That’s no longer the case, with off-the-shelf solutions from the likes of Daitum allowing businesses to get up and running within weeks – with a typical Return on Investment of less than six months.
Making the most of prescriptive analytics doesn’t require giving up control, gathering vast amounts of data or capturing every single business rule. Nor do you need to invest large amounts of time and money. This means it’s easy for businesses of all sizes to put prescriptive analytics to work, offering decision support to quickly unlock business value.