One of the great things about AI is its ability to handle much of the grunt work of marketing. The downside is that grunt work is also a key early learning experience.
Consider data hygiene, one of those pieces of grunt work. Automating it will doubtless increase efficiency and accuracy. It will also deprive people of an experience that teaches how to quickly assess if AI output makes sense or not — as well as see other potential issues with the data. This is just one example of the way this type of work develops skills and insights we use throughout our careers.
The lack of these learning opportunities is going to lead to a shortage of talented young marketers.
So, how are we to address this forthcoming talent development problem? It seems cruel and inefficient to haze new practitioners with mere busy work once AI can handle it. Just because we walked to school in the snow barefoot uphill (both ways), doesn’t mean we should make our successors do that merely to build character.
Looking at this from a broader perspective, this isn’t necessarily a new problem. As technology has advanced in many realms, people no longer have had to learn more fundamental skills.
An imperfect example is learning to drive a car with a stick shift. Learning to drive manually can help a person better understand how a car operates and functions and can help inform their driving even in automatic vehicles. For a long time now, most people have learned to drive automatic vehicles and likely will never have to drive manual. Life hasn’t ended due to this shift. Besides, there have always been bad and impatient drivers!
So, in addition to traditional professional development tactics — how will we provide people with these formative and essential experiences?
Fortunately, people are asking these questions in other fields. Recruiters are especially concerned as they are trying to fill positions that require experience provided by vanishing entry-level positions.
“When you do away with the entry-level roles, someone has to do the training, or eventually you run out of trained people,” writes HR expert Suzanne Lucas. “This can be done through on-the-job training or increased education, but the difference is in who pays. On-the-job training comes at the company’s expense, while education usually comes at the employee’s expense, with no job guarantee.”
Dig deeper: If every company is now a technology company, how should they hire tech talent?
Employers need seriously to consider this issue. Will they handle some of the training or offload it to job applicants and employees? In the first case, you know that training is specifically what you need. In the second case, it may or may not be, but you aren’t paying for it.
If the issue isn’t dealt with, staffing levels will hit crisis levels in marketing technology and operations. If you doubt this, take a look at the shortage of nurses in the U.S., which is particularly severe in the high-skill areas like intensive care.
The pandemic was incredibly hard on all medical professionals. Nurses in particular faced the horrible experience of seeing so many patients die, mandatory longer hours and little to no increase in pay. Is it any wonder that an increasing number of nurses are retiring early or moving to low-pressure jobs? Now we have less experienced nurses providing more acute care, which leads to more mistakes.
While the stakes are far lower in marketing, the lack of experienced marketing technologists will mean less effective campaigns and more operational struggles.
One solution is ensuring discipline-focused certification includes curricula related to the underlying problems that AI solves. For instance, case studies and projects could include some data hygiene work. Covering scenarios about how data can get garbled and duplicate records are created can help practitioners understand such problems.
Another topic worth covering during certificate programs is a basic understanding of how AI works. While practitioners may no longer need to perform rudimentary or intermediary tasks, it is important that they understand what’s going on in the background.
Additionally, while studying marketing or IT in college isn’t required to work as a martech or MOps practitioner, institutions that teach the subjects should teach about fundamental issues that AI tools handle.
Further, when vendors add AI features to their products, they need to detail the underlying tasks each AI feature handles. This can easily be done without divulging any proprietary information.
Another place that can help revisit fundamental issues are conferences – like the MarTech Conference. Some workshops and sessions should focus on these issues to help newer practitioners understand them.
No matter how much grunt work we can turn over to AI, challenges will remain. That’s why it is critical for our field to figure out how to prepare new practitioners to understand how to evaluate and assist with AI output.
Further, when we automate learning experiences, new challenges will also present themselves. We ignore that fact to our own peril.
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Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.
