28 Jun Why a company should think twice, before implementing AI

AI has become the latest craze among companies because the basic idea is to get AI to take over the mundane interactions with some existing application. Humans no longer have to click buttons, cut and paste values, or type data into fields. Instead, AI will do all that for the companies you and save you both money and time. Now that software can make intelligent decisions but judgment calls that previously only humans could manage. Now we need to make way for paperless offices and probably down the line also for humanless offices!!!

Think twice before implementing AI in place?

Although AI automates repetitive, rules-based processes that are usually performed by people sitting in front of computers by interacting with applications just as humans would. AI applications are capable of performing tasks that mimic human action like opening email attachments, completing e-forms and recording data. AI is useful especially when it interacts with older, legacy applications. They act as terrific tools as they breathe new life into legacy systems and create digital process flows. At one point where there was only spaghetti code, manual workarounds and swamps of data polluting the corporate underbelly, AI has made it easier and effortless now.

But there could be discrepancies – If there are any changes with the interface, the data, or any other aspect of the legacy application then faults are likely to arise because changing interfaces may add complexity to deployment. Because AI’s interaction with user interfaces is rather complex as minor changes to those interfaces may lead to a broken process. AI, after all, cannot adjust it’s behaviour the same way a human.

Another similar concern is whether changes are upstream and downstream, especially during bot configuration can delay bots being put into production significantly. For instance, a new regulation which requires just a few changes to an application form could delay months of work in the backend on a bot that was nearing completion. 

Companies often learn this the hard way. Companies immediately jump the gun without actually spending more time doing due diligence and speaking with the machine learning team, they simply want to plug into the AI and start right away, the results were wholly unsatisfying. And after the damage is done we try to address the problem with the sales team and they would have no idea what the tech did. This miscommunication between the sales team and the engineering team is a big issue with AI. The concerned people who are selling the solutions need to understand how the AI actually works.

AI technology is getting too much hype because of which many companies are spindling to AI without having any experience in the field. When you combine the complexity of this technology with the number of people getting on board it is definitely a recipe for disaster. It becomes hard to validate good performance as there aren’t many established benchmarks for AI confidence or accuracy across different verticals to authenticate their work and companies are taking advantage of the confusion. 

It’s great to see this industry grow, but can all those engineers actually write algorithms and train models? If not, then be very cautious.

There is no doubt that a lot of AI companies are doing groundbreaking things that can change the lives of companies and give them the necessary boost required. But every success story creates a dozen new “know-it-alls” in the field. You need to do your homework well so that you ask the right questions to ensure you’re signing up with the right people or in the worst case, you would at least be able to identify the right people from the lot. 

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