Leaning into the Future: Optimizing Human Talent in the Evolving AI Landscape
Leaning into the Future: Optimizing Human Talent in the Evolving AI Landscape
As Chief Marketing Officers (CMOs) and go-to-market (GTM) leaders grapple with the transformative potential of generative AI on marketing and sales roles, a pivotal strategic question looms: With tools like ChatGPT rapidly evolving, how can their teams create a substantial impact?
Sure, there’s the low-hanging fruit: the obvious, immediate benefit of harnessing generative AI tools in content creation workflows for tasks such as drafting blog outlines or ad copy. But once this initial boost in efficiency has been realized, where else can humans and generative AI partner?
To answer this question, we’ll delve into a specific use case: how marketing teams create a case study. Until recently, workflows for such tasks were seldom broken down into detailed, step-by-step actions. This was mainly because marketing content production, which is characterized by tailored messaging and design, wasn’t seen as a candidate for automation.
However, if we break the case study creation process into distinct stages requiring human intervention, we can identify six broad phases of content production:
Going a layer deeper to unpack Phase 1, we can see that the following steps are involved:
| Internal Team Planning and Conferencing | Action |
| Determining the need for a case study. | Judging |
| Creating the content structure. | Creating |
| Researching potential references or stories. | Analyzing |
| Scheduling a meeting with the Customer Success (CS) team. | Coordinating |
| Discussing potential stories with the CSM team. | Discussing |
| Generating a transcript of the meeting. | Creating |
| Summarizing the key points of the meeting. | Creating |
| Reviewing the transcript and meeting summary. | Analyzing |
| Selecting the reference customer. | Judging |
| Confirming the chosen story internally. | Coordinating |
Each phase involves a unique blend of decision-making, proactive actions, coordination, discussion, analysis, evaluation and creative output. After combining all six phases needed to develop a case study, we find ourselves with a staggering total of 51 distinct steps. It’s worth noting that roughly half of these steps could arguably be enhanced by current AI and automation tools (for a more in-depth analysis, feel free to get in touch).
This naturally begs the question: Among the workflow steps that are yet to be automated, is there a common thread? Unraveling this could expose the areas where marketing professionals could make significant contributions today and in the foreseeable future.
Because generative AI for text-based content requires text inputs, automating the entire workflow would require documenting all human conversations and thoughts involved in developing the case study. In other words, you would need to be able to feed text into the generative AI tool for each of the workflow steps.
This then suggests that workflow steps associated with human interaction are primed for automation. Because conversations can be readily documented with AI tools such as MS Teams, Fireflies.AI and so on, these steps hold immense automation potential. For example, marketing teams often determine which customers to engage for a case study by talking to customer success teams. Suppose that all of these discussions—both about the customer and with the customer—have been transcribed. In that case, they could feed a generative AI tool, which could then automate the selection process, eliminating the need for a team meeting. A generative AI prompt could read something like, “Review all internal and external meeting transcripts and identify the three customers with the highest CSAT who use our product to save time on X.”
While the vast majority of the content creation workflow for a case study can be automated, there are still some areas that require human thought, which is naturally much harder to document and automate. For now, humans—specifically product marketing or content specialists—will continue to play a crucial role in kicking off a project, conceptualizing the output, ensuring quality, and making final edits. These activities can be generalized to take the form of:
The traits required to do these activities well could form the foundation of a hiring framework for companies looking to optimally leverage human resources in our increasingly AI-dominated era. We can summarize this hiring framework with the following four tenets: (1) judgment, (2) creativity, (3) aptitude and (4) expertise.
As David Friedberg put it in an episode of his All-In Podcast focused on AI, “It feels like the pace of change is so high that you’re kind of in a dust storm now. You don’t really know where you’re going to end up.”
In this context, one might be tempted to envision a somewhat dismal future for content marketers. However, the true picture is far more optimistic. By leveraging generative AI, marketers can create content more efficiently and spend more time on inherently human-focused activities, such as webinars, roundtable discussions, and panelist appearances. As buyer behaviors inevitably shift toward self-service models for evaluating purchases, we may witness the emergence of new forms of interactive marketing content, including virtual reality experiences. This shift could fundamentally alter the sales process itself, potentially positioning marketing as the last touchpoint in the sales cycle, much like smaller B2C transactions. In this rapidly evolving landscape, content marketers face an unprecedented need to adapt and update their skill sets.
Generative AI paints a picture of significant disruption for B2B content marketing as we know it. Yet, the near-term marching orders for marketing teams are unmistakable: Embrace new generative AI tools for swift gains in quality and efficiency. Extend the application of these tools into fresh content creation arenas by transcribing all associated work and meetings to serve as inputs for generative AI. And, simultaneously, pivot teams toward tasks that demand a more intensive human touch.
Discover how you can harness the potential of generative AI, optimize your marketing talent, and redefine content creation in the AI-dominated era for more impactful marketing campaigns.
Adam Aftergut is the principal of West97 Marketing, which provides on-demand product marketing and sales enablement for industry-leading B2B SaaS enterprises and startups that market and sell their products to Citibank, Disney, General Motors, Kaiser Permanente and many other market leaders. Adam’s unique approach brings together value-based messaging and consultative sales methodologies with high-impact, enterprise-class deliverables tailored for each client’s needs, goals and strategic context.
