Leveraging AI’s full potential doesn’t mean developing a pilot project in a vacuum with a handful of experts – which, ironically, is often called accelerator project. Companies need a tangible idea as to how artificial intelligence can benefit them in their day-to-day operations.
For this tohappen, one has to understand how these new AI ‘colleagues’ work and what theyneed to successfully do their jobs.
An examplefor why this understanding is so crucial is lead management in sales. Insteadof sales team wasting their time on someone who will never buy anything, AI issupposed to determine which leads are promising and at what moment salespeoplecan make their move to close the contract. CEOs are usually very taken withthat idea, sales staff not so much.
Experiencedsalespeople know that it’s not that easy. It’s not only the hard facts likename, address, industry or phone number that are important. Human sales peopleconsider many different factors, such as relationships, past conversations, customersatisfaction, experience with products, the current market situation, and more.
Make nomistake: if the data are available in a set framework, AI will also leveragethem, searching for patterns, calculating behavior scores and match scores, andfinally indicating if the lead is promising or not. They can make sense of thedata, but they will never see more than them.
The realchallenge with AI are therefore the data. Without data, artificial intelligencesolutions cannot learn. Data have to be collected and clearly structured to beusable in sales and service.
Without bigdata no AI
Withoutenough data to draw conclusions from, all decisions that AI makes will beunreliable at best. Meaning that in our example, there’s no AI without CRM. That’snot really new, I know. However, CRM systems now have to be interconnected withnumerous touchpoints (personal conversations, ERP, online shops, customerportal, website and others) to aggregate reliable customer data. Best case: allof this happens automatically. Entrusting a human with this task makescollecting data laborious, inconsistent and faulty.
To profitfrom AI, companies need to understand where it makes sense to implement it andhow they should train it. There’s one problem, however: the ‘thought patterns’of AI are often so complex and take so many different information and patternsinto consideration that one can’t understand why and how it made a decision.
Inconclusion, AI is not a universal remedy. It’s based on things we already know.Its recommendations and decisions are more error-prone than many would likethem to be. Right now, AI has more of a supporting role than an autonomous one.They can help us in our daily routine, take care of monotonous tasks, and letothers make the important decisions.
However, we shouldn’t underestimate AI either. In the future, it will gain importance as it grows more autonomous each day. Artificial intelligence often reaches its limits when interacting with humans. When interacting with other AI solutions in clearly defined frameworks, it can often already make the right decisions today.