INBOX INSIGHTS, July 31, 2024: AI Adoption, Hiring Industries - Trust Insights Marketing Analytics Consulting

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How Do You Convince the Unconvinced?

AI isn’t for me. AI will take my job. I can do it better than AI.

Trust me. Most days I’m in the “unconvinced” category. However, when I come across a use case that resonates, I’m on team AI all the way.

In the 2024 State of Marketing AI Report from the Marketing AI Institute, they asked marketers about the barriers to AI adoption. The top responses were a lack of education and training (67%) and a lack of awareness or understanding (56%).

You can get your copy here.

Those are big numbers. For any given person, giving them more resources isn’t going to solve the problem. There are a lot of options to choose from when it comes to AI systems that marketers could use. It’s overwhelming.

In talking with other marketers, a lot of us (myself included) give up before we even get started. The report also talks about the lack of AI roadmaps and AI counsel. These deficiencies make it challenging for marketers to understand why and how they can use generative AI.

So, how do you convince the unconvinced?

There are a lot of misconceptions around the integration of AI. For example, generative AI is an all-or-nothing approach. It either does your job or doesn’t. It can do things better than you, so you are now irrelevant. It’s better, smarter, and faster than humans.

You can bust these myths for your team, but you might not like the answer. Sorry, not sorry. The answer is process documentation. It’s a necessary evil. Or in my case, it’s heaven. Good process documentation means you know what’s going on. You know how work is getting done. You know how long it’s taking. You know how repeatable and scalable it is. And you can figure out how much people love or loathe it.

A good framework to start with is the Trust Insights TRIPS Framework. In this simple framework, you can gather a lot of critical information. It’s an efficient way to prioritize your process documentation. The core elements are Time, Repeatability, Importance, Pleasantness, and Sufficient Data. You can set this up as a spreadsheet with numerical inputs. Your rankings will help you focus on the high-value and least-liked tasks.

From there, you can explore the task itself. How does it get done? How many people are completing the task? What are the steps?

Once you have the process documented, you can figure out where AI fits. By involving your team every step of the way, they have ownership and can give input. If there are tasks that take a longer time to complete but that people love, focus elsewhere first. Or you can figure out if there are parts of the task where generative AI can support but not take the whole thing.

To convince the unconvinced, you need to meet people where they are. You need to listen to them. They need to be part of the change, not have it thrust upon them. Generative AI is going to get smarter, faster, and better. Marketers need to understand how to work with it, not fight against it.

How are you integrating AI with your teams? Reach out and tell me, or come join the conversation in our Free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the limitations of generative analytics, particularly for casual users. Discover why subject matter expertise is still essential for accurate data analysis, even with these powerful tools. Learn practical strategies for determining if a task is suitable for generative AI and how to incorporate it effectively into your analytics workflow. Finally, understand the importance of process mapping in maximizing the benefits and mitigating the potential challenges of using generative AI for analytics.

Watch/listen to this episode of In-Ear Insights here »

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In this week’s Data Diaries, let’s expand our look at the employment market. Usually we just look at open marketing positions to get a sense of how the marketing industry is doing, but that neglects the many other industries out there. What’s happening right now in the overall economy? Using data from the St. Louis Federal Reserve Bank’s FRED system for open job positions in the USA, here’s what we can see.

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The vertical black line on the bar chart represents the pre-pandemic index from February 2020. Bars to the left of the black line are industries where there’s less hiring demand than before the pandemic. Bars to the right of the black line are industries where there’s more hiring demand than before the pandemic.

What we see is fascinating. The top five industries that have substantial hiring demand:

Therapy
Physicians and surgeons
Civil engineering
Personal care and home health
Veterinary

And the bottom five industries that have a sharp lack of demand:

Software development
Information design & documentation
Marketing
Media and communications
Mathematics

Take a close look. What do you see in common? The majority of the top jobs in demand are healthcare. The majority of the bottom jobs least in demand are knowledge work jobs. That’s an interesting observation. Why might this be happening?

Well, on the healthcare side, we know for sure the pandemic burned out a lot of professionals in the field. And the ongoing effects of COVID continue to create heavy healthcare burdens as the disease remains unchecked in most places.

And on the information side, it’s still too soon to say, but it is curious that the majority of jobs in general on the chart that are above the pre-pandemic index level are jobs that AI will affect less. Every industry will be impacted to some degree, but knowledge work jobs will be affected the most. In several different recent papers, from the NBER to McKinsey, on average software developers are at least 50% more productive with generative AI tools than without – and those papers are over a year old, well before today’s current models.

Making any role 50% more productive inherently reduces demand for more hiring. Why hire when people are getting twice as much work done as before in the same period of time?

It’s too soon to tell, but there’s a real possibility that knowledge work industries may continue to see depressed hiring as AI makes us more efficient. Rather than outright destroying jobs as many fear, it might have a more subtle impact. As Katie detailed in the opening, there’s still a strong need for basic adoption help, but as adoption increases, the need to hire as many knowledge workers may gradually decrease over time.

Where does that leave employment? Look at the top of the chart. Look at all the professions where AI is going to struggle, and where we have dire needs. We have no shortage of jobs right now in those categories, and there’s a good chance that situation will persist.

Here’s a roundup of who’s hiring, based on positions shared in the Analytics for Marketers Slack group and other communities.

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