AI is Nothing Without Humans

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 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. Experienced salespeople know that it’s not that easy. It’s not only the hard facts likename, address, industry or phone number that are important.

It is not only the hard facts such as name, address, industry and telephone number that count. Human colleagues take into account, sometimes consciously, sometimes unconsciously, many other factors, relationship networks, previous contacts, current satisfaction with the service, experience with the products, the competitive situation, and so on.

The AI also accesses the relevant data, provided it is available. The more granular, the better. It searches for patterns, calculates the "behavior score" and "match score" and shows whether the investment in the contact is worthwhile or not. To do this, it also needs a framework within which to operate. The AI is therefore not too different from a human colleague, but its perceptions are limited to the pure data level.

The real challenge is therefore not so much the AI itself, but the data without which it cannot learn. It must be collected in a consistent and structured manner and then used in sales and service. But this requires enough of them—without big data, there is no AI, because without differentiated patterns, there are no reliable conclusions. But this also means that without CRM as a basis, nothing works in our example. Surprise, it's not all that new!

Today, however, the CRM system must be networked in order to aggregate customer-related data from personal contacts, ERP, web store, customer portal, website and various other contact points, the so-called touchpoints. Automatically?

Ideally yes. Because as soon as an employee is responsible for recording data in full, it becomes time-consuming and gaps must be foreseen. In order to hire an AI, you first need to understand what it can be used for and how to train it. But that's when the problems start: the AI's "thought patterns" are usually so complex and involve so much data and patterns that it is almost impossible to understand how the decisions are made. So if the sales department is also told why the AI made the decision it did: jackpot! However, this usually remains a mystery to human colleagues.

Artificial intelligence is therefore not a miracle cure either, but is based on things that we have known and know for a long time. Their recommendations are more human and error-prone than often assumed or hoped for. As things stand today, AIs offer assisted rather than autonomous driving. They can be our co-bots that support us and that we can activate when necessary. They help with everyday tasks, take over tedious jobs and then hand them over to the real professionals to make decisions.

However, they have previously defined exactly what they want from the AI and can assess its impulses. But we should not underestimate these CoBots: In the future, they will also gain further autonomy in companies. This is because AIs reach their limits as long as they do not act directly among themselves. Wherever their algorithms can connect directly with each other, they can make valid decisions under clear framework conditions.