BACKGROUND
Generation X (Gen X) is my generation. We were born between 1965 and 1980, and many grew up as latchkey kids (I know I did). Our moms kicked us out of the house all day to ride bikes and drink from the garden hose. We came of age with the rise of MTV. I can still remember the first time I watched "Video Killed the Radio Star" by the Buggles.
In 1991, Douglas Coupland published "Generation X: Tales for an Accelerated Culture," a defining book for that generation. It gave a name and identity to this group and brought to light how the emerging digital and information age was shaping society and individual identities. This group entered the workforce as personal computing started changing the nature of work in millions of companies. We saw the rise of the personal computer, email, local area networks, the internet, mobile, social, and so much more. We aren't the digital natives that future generations were, but we adapted and adopted quickly.
Why am I highlighting this group? It's not to pit one generation against another but to highlight an observation I've seen play out over the last year regarding AI adoption. The people who are getting the most significant impact out of using generative AI tools (like ChatGPT, Claude, and Gemini) are people with specific domain expertise. They have expertise in their industries, functions, companies, and markets. People who are part of Gen X are now at the age where they are often in leadership roles and have built that expertise across companies over the past 25 years.
The biggest challenge Gen Xers have right now in maximizing the value of these tools is time—the time to experiment and learn what they can do. Most of us run from meeting to meeting all day and have significant family commitments when not at work. Younger employees (say, first five years out of college) have much more time to learn and experiment with these tools. They are adopting the models in their daily workflow – but I'm not seeing them get as much value out of the models as they could – because they lack the requisite domain expertise to ask/frame the right questions/issues.
Gen X has the domain expertise that allows them to ask the "right questions" to identify and solve the "right problems." Data from a generative AI tool alone isn't helpful if it doesn't tell a story and provide high ROI next steps.
The most significant opportunity I see for companies right now to get the most value out of these generative AI tools is to provide detailed training (with specific use cases) to the domain experts in their organization (they can certainly be young, too – this truly isn't just Gen X). Provide the ongoing training and insights they need to maximize their value from these tools to help unlock productivity and shared learning across the organization.
Let me give you a detailed example from work I've done recently.
CASE STUDY: PIPELINE COVERAGE AND CONVERSION
A B2B SaaS company was struggling with pipeline coverage and conversion ratios. One of the sales ops analysts offered to dig in and use ChatGPT for analysis. They downloaded a large set of salesforce data for the last two years into a spreadsheet and uploaded it to ChatGPT. The data includes all sorts of information about the deals (source, segment, time in stage, BDR, account executive, size, stage, etc.).
The analyst uploaded the file into ChatGPT and entered the prompt, "Analyze this file and create some charts for me based on trends you see in the data." ChatGPT dutifully crunched away, identified a few trends, and created half a dozen charts. The analyst asked for more charts and tables on a few things, then threw them all into a PowerPoint deck and shared it back with management.
While the 20-page deck had many interesting-looking charts and tables, it needed more insight. Why was pipeline coverage a problem? Were all pipeline conversion ratios bad, or just some? While the analyst was really adept with ChatGPT, he needed to gain business understanding to ask the right questions (hence, this was a learning opportunity).
I told him to first divide this problem into two parts: pipeline coverage and pipeline conversion. Let's take each part in turn.
For pipeline coverage, we need to understand where the pipeline is coming from, the trends in pipeline generation, and what coverage looks like across the team. I suggested a better prompt to start with might look something like this: "This file has two years of pipeline for our B2B SaaS firm. We are looking to understand what's happening in pipeline coverage ratios. Analyze this data and look for trends and insights into pipeline coverage ratios. Specifically, analyze the pipeline by source and calculate if it is increasing, decreasing, or staying the same. Analyze the pipeline by source and account executive for trends, and analyze the pipeline by source and segment. Look for any changes in how the pipeline was allocated by the source and account executive. Identify the top 10 most critical insights based on this analysis and create charts with summary text highlighting the insights."
That type of analysis would allow us to drill down and look for meaningful changes and then ask "why" and "how" things changed. When you spend many years in a field, you learn to ask the critical questions. Now, let's turn to pipeline conversion. There wasn't much in the analyst's initial analysis. I would suggest a prompt that looks something like this:
"Calculate the pipeline conversion for each quarter based on the create date. Create a table showing each quarter's conversion rate based on ARR. As a reminder, the conversion rate is calculated for a given quarter as the total sum of ARR closed won from the deals created in a given quarter (through today) divided by the total ARR of all of the deals created in that quarter. Create a quarterly table to show the trend in conversion rate. Now create additional tables and do that analysis by segment, by source, and by segment and source. Now do a pipeline conversion table for each account executive over those same set of quarters in the data. Once those tasks are complete, do the same analysis but instead of ARR use the # of deals as the metric."
This type of prompt provides insight into the trends regarding what's happening and where we see issues. It will allow us to quickly identify the areas to focus on and determine what's going wrong. Combining those two types of analysis will give the team a great starting point.
WHY GEN X
I've seen examples like this play out across different RevOps teams and in legal, marketing, finance, product development, and more. Organizations that have taken the time to get their domain experts up to speed are getting far more value from these tools than those companies taking a slapshot or haphazard approach. Combining deep expertise with these emerging technologies will allow companies to unlock significant productivity gains across the organization.
Gen X was initially known for having a cynical or skeptical worldview, but that has evolved (like most things), and the group is now known for its independence, adaptability, and strong work ethic. This group has survived and thrived in every technology evolution in the past 30 years and has built the expert knowledge that makes teams and organizations successful.
Gen X has the best opportunity to implement AI and create a massive upside. We know how our businesses work and can apply AI in a way that genuinely creates efficiency and optimization in high-value places. While Gen Z and Millennials may know how to "use" AI, Gen X will make AI essential.
HOW TO GET STARTED
The best way I've seen folks get started is by an immersive experience showing the power of what these tools can do—creating a training experience with real-world and relevant use cases that are relevant to them specifically. A training session that shows how many activities people are doing today can be done much more quickly, accurately, and, in many cases, with much higher degrees of creativity and professionalism. Expose them to the emerging capabilities of what the best models can do today and share where the technology is going. Once people see what can be done and learn the basics of using these tools, I see people developing incredible use cases across their organizations.
I recently had the opportunity to present to several hundred lawyers who were part of the Beverly Hills Bar Association (BHBA) here in the LA area. Over an hour, I went through a large number of use cases specific to the legal industry (and also covered things like which tools are best at different things, prompt engineering, how to address privacy issues, etc.). I am also doing customized training and research for a number of firms that want to go deeper and train their entire organizations on how they can use these tools to transform their organizations today.
GET ON BOARD
Please reach out if you are interested in highly practical, use-case-driven AI training sessions for your teams or executive staff. As always, I'm ending with photos of Ollie. We had some friends visiting from the East Coast this past week, and they had some photos of Ollie from a trip a long time ago that I hadn't seen in years and years. I'm sharing these memories here.
Best,
Steve
Steve - this is a great article and exactly where my head is at too. There is a lot of sparkle around AI and ChatGPT but a chasm to cross in knowing the basic tools and prompts to use to get started. Would be great to connect with you as a fellow Gen Xer!