Artificial intelligence in B2B Sales. New Challenges – New Opportunities.

One interesting read about the use of artificial intelligence in B2B sales.

Artificial intelligence (AI) is gaining relevance in Business-to-Business (B2B) sales. Research shows that investment in the development and integration of AI and, in particular, machine learning, technology is continuing to rise.

More money pours today into AI enterprise projects than ever before. Companies should try to avoid vagueness and lack of focus on their aims and expectations, to prevent the costly failure of their AI developments – too often a cause of failure.

The use of artificial intelligence in sales and controlling offers an exciting field of application, for there companies can effectively expand existing CRM and ERP systems. Most if not all sales teams in B2B are in possession of relevant sales data that AI-based solutions can crunch.

Nonetheless, implementing AI in an ERP or a CRM system can be a minefield. There are several obstacles to overcome that ensure the success of the integration of AI into sales systems. Let’s review together some of these challenges.

What Does Artificial Intelligence for an ERP System Mean?

Let’s briefly define what is artificial intelligence from a sales perspective.

Researchers talk of General Artificial Intelligence (General AI) and Weak or Narrow Artificial Intelligence (Weak AI).

General Artificial Intelligence aims to develop machines that behave “as if they possessed intelligence”. We usually associate general AI with Terminator or Hal9000: a non-human entity that can replace a human with a combination of mechatronics and software.

On the other hand, artificial intelligence in an ERP system is an example of Narrow Artificial Intelligence.

Narrow AI works with specific problems for which software performs better than humans. Machine learning is one example of Weak AI: the artificial generation of knowledge based on ERP sales transactions and CRM sales data.

The Increasing Relevance of AI in Sales Management

The implementation of AI in Sales has a very attractive ROI. AI enables to distribute limited sales budgets based on the marginal earnings of targeted sales activities. That’s why large companies like Zalando and Otto are implementing artificial intelligence in their operations.

Both companies are building their platforms to centralise access to customers. Otto and Zalando serve mainly the B2C market, yet one can also find examples in B2B.

Building an entire AI or data science department might make sense for big corporations, but not for every sales department. Those aiming to keep up with their competition and remaining relevant must be able to implement AI – quickly. We wrote more about building or buying your own intelligent sales software here.

Artificial Intelligence in an ERP System – What Can a Sales Manager Do?

AI will redefine management. Sales cannot escape this disruptive trend. Advances in AI, machine learning and sales automation are replacing many of the time-consuming tasks of sales teams. It is time to equip your ERP-System with artificial intelligence, machine learning and predictive analytics.

There are mostly three ways in which managers can implement artificial intelligence in their current sales operations.

First, sales leaders can implement machine learning and predictive analytics in their current ERP system. Second, they can build their customised algorithms using ERP and CRM data. Finally, they can enlarge their existing CRM and ERP with external intelligence.

Sales leaders can use artificial intelligence to automatically find cross-selling opportunities, reduce customer churn and effectively perform pricing analytics. These are the activities with the highest ROI.

There are, however, three main reasons why implementing AI in sales can be hard. Without proper training, AI can become a non-actionable, non-explainable “Blackbox” and can undermine your sales reps’ confidence.

If AI is not Explainable, It Will Be Hard to Adopt.

“If you can’t explain it simply, you don’t understand it well enough.” (misattributed to Einstein) is especially true for artificial intelligence in B2B sales.

Sales teams want and need to understand how AI works. Otherwise, AI solutions risk becoming obscure and not trust-worthy. Sales leaders championing AI in their organisations should assume a coaching role and discuss together with their sales team how AI works. We wrote about it here.

Being able to explain how artificial intelligence works does not mean describing the data mining results using your pocket calculator, no. It means being able to define and illustrate the fundamental principles of data mining models behind the AI solution.

Offers your AI Solution Actionable Insights?

There are three types of discoveries or insights that sales executives gain from using AI in an ERP system. We discussed the obscure ones above.

Further to unexplainable sales insights, sales teams receive evident or obvious suggestions. For example, “this customer will not buy” when you know the customer to be insolvent.

Arriving at obvious conclusions is reasonable. An AI system usually works with incomplete datasets and does not possess the experience and wisdom of a key account manager. However, if something is obvious and well-known, it offers no value besides maybe reinforcing held beliefs.

The most valuable insights are the actionable ones. These are discoveries that a sales team can act upon, for example by prioritising and selectively targeting customers.

Key account managers will quickly adopt any AI solution that saves them time and helps them to sell more. Using AI, they can find cross-selling opportunities, avoid customers from churning and focus on customers with the highest likelihood of accepting new offers.

Most sales teams are in possession of relevant sales data that AI-based solutions can crunch.

Sales Attitude – What has AI in There for Me?

Many IT projects in sales fail because they do not manage to convince enough internal adopters. And if a company invest in an AI solution no key account manager uses, the business will suffer.

The successful use of artificial intelligence in sales depends on the underlying attitude of the salespeople. If they see AI as a colleague, not a competitor, they will become more productive. If they run against the machine, everybody loses.

Artificial intelligence in an ERP System usually relies on the form of human loop or feedback. It still needs salespeople to feedback to learn. There are other examples of weak AI that need human interaction to learn, such as monitored text translators and image recognition.

Seeing an algorithm as your sales controller or your direct competitors might be understandable but is detrimental to the success of an AI implementation. Artificial intelligence cannot replace sales management; it can only augment their analytical skills.

Artificial Intelligence in B2B Sales. New Challenges – New Opportunities. Summary.

Artificial intelligence is a game-changer for B2B sales. It can offer double-digit ROI in short periods of time. ERP sales data is usually the first data set that feeds the AI solution.

Nevertheless, implementing AI in an ERP system can be problematic. It needs to gain the support of the sales team. Key account managers are the ones to win the most of them if they are ready for a change in the way they work.

AI only works if it can offer actionable insights. In other words, the robot should learn and discover pieces of hidden information that the sales team can use.

Part of the problem comes from understanding how AI works, what algorithms underpin its calculations and how it can support sales planning and controlling.

Lastly, if an artificial intelligence solution and is not explainable and its benefits not transparent, the sales team will have a hard time adopting it, and the implementation effort will fail.

Note: This article was corrected on 12/16/2022 to remove outdated information about Zalando.


Further Read:

5 Key Artificial Intelligence Predictions For 2018: How Machine Learning Will Change Everything – Forbes

Change Management: Grundlagen und Erfolgsfaktoren – Springer Gabler

Harvard Business Manager 6/2018: Change Management – von manager magazin Verlagsgesellschaft mbH

Finlay, Steven. Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies

Stuart Russell und Peter Norvig – Artificial Intelligence: A Modern Approach, Global Edition

Tarun Khanna. When Technology Gets Ahead of Society. Harvard Business Review.

Forbes – Think You Know How Disruptive Artificial Intelligence Is? Think Again.