Source: e3zine.com
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 to happen, one has to understand how these new AI ‘colleagues’ work and what they need to successfully do their jobs.
An example for why this understanding is so crucial is lead management in sales. Instead of sales team wasting their time on someone who will never buy anything, AI is supposed to determine which leads are promising and at what moment salespeople can make their move to close the contract. CEOs are usually very taken with that idea, sales staff not so much.
Experienced salespeople know that it’s not that easy. It’s not only the hard facts like name, address, industry or phone number that are important. Human sales people consider many different factors, such as relationships, past conversations, customer satisfaction, experience with products, the current market situation, and more.
Make no mistake: if the data are available in a set framework, AI will also leverage them, searching for patterns, calculating behavior scores and match scores, and finally indicating if the lead is promising or not. They can make sense of the data, but they will never see more than them.
The real challenge with AI are therefore the data. Without data, artificial intelligence solutions cannot learn. Data have to be collected and clearly structured to be usable in sales and service.
Without big data no AI
Without enough data to draw conclusions from, all decisions that AI makes will be unreliable at best. Meaning that in our example, there’s no AI without CRM. That’s not really new, I know. However, CRM systems now have to be interconnected with numerous touchpoints (personal conversations, ERP, online shops, customer portal, website and others) to aggregate reliable customer data. Best case: all of this happens automatically. Entrusting a human with this task makes collecting data laborious, inconsistent and faulty.
To profit from AI, companies need to understand where it makes sense to implement it and how 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 patterns into consideration that one can’t understand why and how it made a decision.
In conclusion, 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 like them 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 let others 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.