Why is your ERP useless without AI?
Why is a legacy ERP becoming irrelevant in an AI era, and what can you do about it?
Let’s face it. In a few years, we will lose the battle against the terminator. Even without the help of a computer scientist, a sales manager recognizes that the near future will bring considerable changes in his profession – triggered by artificial intelligence (AI).
Soon machines will take over the job of a sales manager. There will be artificial thinking selling-machine more efficient than salespeople will ever be, thanks to artificial intelligence. The more data they gather, the better they get. Machines will soon take over. Brace yourself.
Now, back to 2021. Let’s bring this fantastic technology into context and avoid the well-selling drama. Artificial intelligence and machine learning technology have made notable improvements in the last couple of years. Nonetheless, we still do not see Terminator doing business in sales or working as a sales controller (yet?).
When we refer to artificial intelligence for sales controlling or planning, we are broadly describing predictive analytics applications, powered by machine learning algorithms, something we have been writing about in our blog. In this context, practitioners understand sales planning and controlling as an application of computer science commonly known as narrow artificial intelligence. The “narrow” is the keyword.
“The more we digitalise, the more our prosperity is secured.” Wolfgang Wahlster – Leader of the German Research Center for Artificial Intelligence (DFKI).
Predictive analytics and machine learning are making inroads into every field of technology. The Guardian listed last year all the areas where the machines are rising and beating humans (link below). Similarly, artificial intelligence is systematically making some systems obsolete. Just think of the lost battle of Yahoo vs Google. One was an application powered by humans, one by computer science. Guess who won?
Artificial intelligence is not going to replace a sales management job anytime soon. AI replaces skills and tasks, not salespeople. Machine learning in ERP systems brings enormous benefits, and thriving companies are providing their sales managers with this innovation.
Why is a legacy ERP becoming irrelevant in an AI era?
In this brave new world, a non-intelligent ERP is increasingly becoming pointless. Why? For two reasons. First, because although data is precious, the analysis and interpretation of the data are where real value lays. Second, because most ERP systems still lack the information that matters most, such as customer interactions, behaviour and relationship management.
Nowadays, Business-To-Business (B2B) sales leaders can gather more data from the indeed more complicated sales process. However, the ultimate decisions, the kind of actions that the sales team undertakes and its timing, are the only thing that will have a significant impact on successful sales operations. Sales data per se has no value unless it is used to alter behaviour or to help a sales team prioritise actions.
“Sales data has no value unless used to change salespeople’s behaviour and priorities.”
Unfortunately, most B2B companies are still lacking an ERP system that can access and analyse additional pieces of relevant sales data, such as opportunity management and sales planning. Moreover, even if they could access and integrate the data, a sales manager still has only 24 hrs during the day to visit customers, coach her team and reach her sales target. Do they have the time to write an ad-hoc Multivariate Adaptive unsupervised Regression for machine learning? I don’t think so.
ERP + artificial intelligence: What can a Sales Manager do?
In this article, we would like to provide with four specific pieces of advice for sales leaders in B2B. There are fundamentally four things they can do not to be left behind without artificial intelligence. Of course, the first option would be doing nothing and moving to a desert island.
Second, a sales leader can implement machine learning and predictive analytics in its current ERP system. Third, they can build their customised algorithms using ERP and CRM data. Last but not least, a sales leader in B2B can opt to enlarge their existing CRM and ERP with external intelligence. Let’s review each of these options with pros and cons.
Use Case: How cross- and up-selling based on Qymatix Algorithms is helping a medical components manufacturer to sell more.
Option One: Moving to a desert island
|Pros (+)||Cons (-)|
|No more quota setting and end-of-quarter stress||No paycheck and no coffee, among others|
The first action that sales managers in B2B can undertake is moving to a desert island, away from chances and job-stealing algorithms. Who hasn’t dreamed of quitting his sales job to escape the winds of change? Renegade of technological advances. Moving to a depopulated island is an attractive alternative. It is unfortunately not a genuinely realistic option for most salespeople (especially if you enjoy good coffee).
Our phones are smart; our cars are self-driving; even our refrigerators are “capable of thinking”. Why shouldn’t our ERPs be brilliant too? How about our CRM?
Shall sales with artificial intelligence be too much for a sales leader to stomach, then moving somewhere where no smartphones, cars, fridges or microwaves are on-sight is the perfect place to live. Which island to move? We leave that up to them.
“Artificial intelligence is not going to replace a sales management job anytime soon.”
Option Two: Implementing predictive analytics and machine learning in the existing ERP (and CRM)
|Pros (+)||Cons (-)|
|Customized solution. Uses existing solutions in ERP suite||Usually done by external integrators. Depends on the ERP.|
If moving to an island is not an option, a second alternative is to implement predictive analytics and machine learning within the existing sales systems. If your company is using any major ERP suite, the chances are that they are already offering an extension for machine learning. In this case, the IT department should specify the requirements together with sales and contact their ERP system integrator.
This kind of development can offer a flexible solution without adding an extra tool to the sales department. Nonetheless, not every ERP suite provides advanced machine learning capabilities, and an external integrator with specific knowledge in the field is required.
Option Three: Building your own customized algorithms using ERP and CRM data
|Pros (+)||Cons (-)|
|Builds in-house expertise.||Long-term development. Limited data set for machine learning.|
Another possible way to tackle ERP and CRM artificial intelligence is to develop proprietary data analysis models and customized algorithms in-house. Although this might represent a significant delay for a sales leader, building its own data science capabilities is the preferred way taken by big organizations.
We have provided sales managers with several ideas to get started.
If the company can afford it, building a top-notch data science expertise can provide a competitive advantage in additional fields. This data-based advantage can go well over the scope of predictive sales analytics.
Option Four: enlarging the current CRM and ERP with external intelligence
|Pros (+)||Cons (-)|
|Best ROI for medium-size companies. Quick implementation.||Might add a new system to sales operations.|
If all previous three options are unattractive and the company still wants to quickly profit from machine learning and artificial intelligence in its ERP system, this last option will do.
Expanding its CRM and ERP with integrated artificial intelligence means connecting their existing systems to external cloud applications and letting them do the machine learning and data mining.
This is probably the best option for sales leaders of B2B medium-size companies and can provide an interesting return on investment . It does not require an expensive data analysis team nor a disruptive implementation. Since this is what our tool does, we like number four.
Conclusion: Artificial intelligence and Machine Learning for ERP
Artificial intelligence (AI) and machine learning (ML) are radically changing B2B sales. Doing nothing about it is not an option.
In particular, AI and ML are shifting the ERP software landscape and are transforming sales planning and operations. However, when it comes to B2B, we can conclude that the artificial sales manager is still a few years away.
The data-mining capabilities of even the most advanced self-learning artificial intelligence system lacks the human genius for inspiration, innovation, and personal interaction.
Nevertheless, if you want to save money, free some time for customers communications and increase revenues, it might be just about time to pimp your ERP with some artificial intelligence, machine learning and predictive sales analytics.
Use Case: Predictive Analytics reduced customer churn while increasing sales team satisfaction and engagement.
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