When one hears the term “artificial intelligence” or AI, the mind goes quickly to robots and talking computers that will one day take over the world. That’s great for Hollywood, and there is applicability in manufacturing and other areas of business where intelligent machines or robots can effectively automate what were once manual processes. But when it comes to the business software we use every day at work, the application is slightly less daunting but equally as powerful.
As a software salesman for over 30 years, I’m constantly crafting my elevator pitch, which is a statement that describes what my product can do in two minutes or less. The best description for business or ERP (enterprise resource planning) software is that of an application that can efficiently automate manual processes. On the low end, that can be something as simple as a spreadsheet, which automates minor calculations by inputting and copying formulas to multiple cells. A more advanced application might be a type of recruiting software that replaces the repetitive activities of collecting resumes and moving job candidates through the hiring process.
When Automation Fails
The biggest problem with automating a manual process occurs when that process is a bad one. A former boss of mine would draw the analogy for a manual process as someone walking toward a wall, whereby the automation of such would be putting that traveler into a car driving at high speed into the wall. By anyone’s account, that would not be considered an improvement. Obviously, the correct action would be to turn that traveler in the right direction before providing a ride.
A business equivalent of that scenario would be if the previously mentioned recruiting software were to track multiple instances of developer candidates, located somewhere specific, who were recruited through a particular job board and constantly failed a logic test after the fourth interview. In a manually constructed process, that anomaly would go unnoticed for multiple rounds before an HRIS (human resource information system) analyst printed a report and brought it to someone’s attention. Even when fortunate enough to have that analyst, the cost of man-hours spent interviewing and system uptime to that point would, in most instances, be considerable.
Where AI Comes In
This is where AI and machine learning platforms can provide such great value. The technology embeds the capability for software applications to correct bad processes within the program. If the above candidate scenario were run on an AI-based platform, after the scenario had been run a few times, the system would flag the process, adapt and start rerouting candidates from the particular job board and location, so they either don’t advance or more diligence is done at an earlier stage in the process.
Another example of what machine learning brings to the table is the ability to model scenarios based on prior data. Picture an organization that has 10 years of people data in its HRIS and has been experiencing 15% turnover every year. Using that historical data, an AI platform can automate the collection and weighting of key data points. Factors such as compensation, time in the same position, work-life balance, physical and mental stress can all be programmed into a system to provide retention index to determine if an individual is more likely to stay or leave at a certain moment in time.
One last and very prevalent use of AI in business software is in the realm of natural language processing (NLP). This is where rather than navigating through a series of complex menus, system users can get to the information or section of a program they need by typing a command in the manner they would speak it. Instead of stepping through a hierarchical software workflow, a user would simply type in something like “Where am I getting my best hires?” Then, just like typing a question into Google or any other browser, the answer or a ranking of answers would appear. As in the other AI scenarios mentioned, the system would get more proficient with each query in learning intent, nuance, similarities and many other factors.
So, don’t think that when your company invests in AI-driven solutions that there are suddenly going to be a bunch of Transformers in your office. It’s more likely that the software you use will become smarter, more efficient and user-friendly every time it’s used. Thus, you’ll find future use fraught with fewer manual mistakes that cost time and money.
[“source=gsmarena”]
