Use AI for “Dirty Jobs” and Be Employees’ BFF


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There are plenty of tasks in a company that are simple and repetitive, or in the words of Vinnie Mirchandani, dull, dangerous, and dirty. These tasks still need to get done although hardly anyone wants to do them. Often they are actually not done at all.

You are asking what this has to do with this column? Let me do a little detour to explain.

Earlier this year, during some vacation time in beautiful Bali I had a conversation with the Chef de Village of the Club Med Resort, Jeremie Gonzalez. Naturally, we came to the topics of customer experience and to how deliver a great one.

This culminated in the 1 million dollar question.

Who is more important: The customer or the employee?

Jeremie’s answer was instantaneous.

The customer!

Why? Was my obvious next question.

His answer came equally quick: “We deliver happiness, it is what we do”.

To enable this, he made it his personal mission is to keep his team happy.

Because only happy employees create happy customers.

The employee is a means to an end, an important one, but still a means. The end is the happy customer.

Not much to say against it, following his philosophy – his team looked genuinely happy, actually radiated happiness. And the team delivered on the promise.

Abstracting away from this example of the hospitality industry this raises an interesting question:

What does it take to make – and keep – an employee happy?

Of course, this is a multi faceted and difficult question, but there are some constants in there that work across industries, including:

  • Employees, people, need to be treated with respect. This is one of the reasons why I do not like the term “human resource”. A resource is in my book something that gets used, consumed – and disposed of, if no longer needed.
  • Employees need to have a meaningful job – meaningful to them
  • Most people want values and adherence to them. Corporate values do not only need to be written down, but also lived, especially by upper management
  • Employees need to be equipped with the authority and tools that enable them to get their job done effectively and efficiently, even in an enjoyable way.

I could add a number of points but you get the drift.

Meaningful Work

At the end of the day customer engagement and customer experience on one hand and employee engagement and employee experience on the other hand are not that different from each other.

The links lie in a “meaningful job” and getting “their job done effectively and efficiently, even in an enjoyable way”.


The simplified Maslov pyramid that I used before and that describes the hierarchy of customer expectations is equally valid for employee experience. Employees can and will only consistently provide customers with an efficient, or even joyful experience, if they are enabled to do so. This means that they, themselves, need to find themselves on the higher levels of the expectation pyramid.

And here we are back to AI and machine learning. Machines are good at simple and repetitive, whereas humans excel at doing the complex and complicated tasks.

At the same time numerous studies show that an outside-in focus on customer experience is good for the company bottom line.

Combined with the view that every employee supports the customer experience, there are multiple opportunities for leaders who embrace customer experience to improve their business.

Help with “Dirty Jobs”

To start with, it is necessary to find out the tasks and activities that take up employees’ time (and morale), therefore preventing them from engaging in a way that can result in an exceptional experience. On a side note: please read Paul Greenberg’s great article on how customer engagement and customer experience differ from each other. They do!

This exercise also sets a focus on how the machine can serve the human.

From there on it is pretty easy. I have written about it a number of times in this column, covering sales, service, field service, marketing, or e-commerce.

The same applies to finance departments, HR, or general administration. Let me expand on a few examples.

Employees, as well as customers, have questions. Similar to customer inquiries these could be answered by a bot, instead of being exposed by an FAQ, or making employees call someone in the corresponding department. Virtually every service platform can deliver this; just look at Zendesk, or Freshworks, or one of the many other companies.

Salesforce Einstein allows service technicians to snap a picture of a part to get it identified. This makes them more efficient by saving them the time to look it up. SAP offers Service Ticket Intelligence to automate the classification and routing of incoming incidents in order to speed up resolution processes by reducing the time service agents spend on these tedious tasks.

Another example is accounting. New Zealand based Xero applied machine learning to accounting by analyzing how business owners/book keepers apply account codes and how their accountants correct these. As a result the book keeping process is now far easier and less error prone for Xero customers. SAP, again, applied machine learning to the matching of invoices and payments. This improved the automation rate of payment clearing at BASF from 70 per cent to about 94 per cent, thus freeing up finance personnel to improve service levels.

Systems like Outlier or others can relieve employees from constantly monitoring dashboards, by providing alerts that are caused by anomalies in the company’s data, be it financial, or other types of data. Specialist like Splunk can do the same for security relevant data. Startup Taskpace helps with the prioritization of tasks with the goal of having the people work on what matters instead of taking care of the routine.

The result is in all cases that the alert or priority is brought to the user instead of the user continuously scanning for it.

A company’s back end systems can deliver queries using conversational UIs, powered by machine learning. Meet Unit4’s Wanda, ‘she’ is doing exactly this. Think of scenarios like time sheeting, planning and scheduling meetings, or getting an overview about a project or financial data. This enterprise digital assistant is connecting to the business application and to existing communication applications, like Slack, Skype, etc. to execute tasks and to deliver answers in a way humans are used to operate. This is also something that SAP Leonardo targets at, albeit more on the framework level.

AI can be the employee’s best friend. The secret to getting there lies in thinking of AI as an enabler of people, not as their replacement.

Thomas Wieberneit

Thomas helps organisations of different industries and sizes to unlock their potential through digital transformation initiatives using a Think Big - Act Small approach. He is a long standing CRM practitioner, covering sales, marketing, service, collaboration, customer engagement and -experience. Coming from the technology side Thomas has the ability to translate business needs into technology solutions that add value. In his successful leadership positions and consulting engagements he has initiated, designed and implemented transformational change and delivered mission critical systems.


  1. Thomas, I really like your angle of making life better for employees. As a support leader I’ve often wished I could wave a magic wand to make pain points go away, and while relief usually comes for some things, it’s never as fast as I wish. Thanks for these great examples you’ve provided!


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