As AI promises to automate 90% of your administrative tasks, are you ready to bet your brand’s future on the remaining 10%: Can’t high-value human judgment machines replicate themselves?
With enterprise AI adoption maturing from mass experimentation to results-oriented, with marketing leadership being asked to demonstrate ROI, marketing organizations are facing what might be called the second-order risks of rapid scale. Most serious for many is the phenomenon of worklop, which is the low-quality output generated by employees pressured to deliver huge amounts of AI-generated content without enough time for quality checks.
While AI can automate the vast majority of repetitive administrative tasks, a counterintuitive and growing need for marketing leaders is now placing an emphasis on human empathy, creativity and strategic judgment. To win, leaders must treat AI as a collaborator that questions strategy rather than an autopilot that dilutes brand integrity, all while respecting the value of human judgment.
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It takes humans to define AI slop
It’s hard to avoid AI slowdown these days, which comes from giving marketing teams the wrong incentives to hit increasingly aggressive output targets. While much of the initial conversation around AI has focused on investment and upside potential, all content created comes at a cost, much of which is bad for the brand.
Workslop, which you’ve no doubt experienced as a consumer or employee, is the proliferation of generic, low-quality output that occurs when marketing teams are forced to use artificial intelligence to deliver more volume with less time spent on quality control and critical thinking.
The expectation that AI will act as a silver bullet has created operating conditions that impose unrealistic performance pressures. Instead of increasing productivity, these pressures can silently erode results by flooding channels with mediocrity.
Speeding up broken processes is also counterproductive. Inserting generative AI into broken workflows will only deliver the same poor results, faster. Real ROI will come from building workflows from scratch rather than creating flashy demos that (almost always) lack substance or can’t be applied long-term.
However, identifying what is a failure and what is a truly valuable work outcome still requires humans, even though giving these humans the wrong incentives and KPIs to measure success can cloud judgment and generate the wrong results. This becomes a trap where massive efficiency gains must be balanced against the negative repercussions of producing poor quality work for both internal and external audiences.
Where automation ends and judgment begins
To avoid this trap, leaders must clearly delineate between executable tasks and judgment-based strategy.
Bain & Company research estimates that functions such as merchandising can automate 70% to 90% of administrative tasks, such as tender execution or specification management. This massive unlocking of capacity effectively commodifies administrative work.
As production costs decrease due to artificial intelligence, the value of selection increases. This same study shows that the competitive prize now shifts to that other 10% of work: value-creating judgments, new product development, and emotional connection.
AI will be able to anticipate how you will behave, but it will not build trust through empathy. Leaders will need to determine which trade-offs are off the table. The ones where doing something faster and cheaper can’t come at the expense of your brand or the trust of your customers.
Teams incentivized to simply automate and accelerate without the critical aspect of judgment are doing themselves and the brand a disservice. Marketing leadership benefits when teams with better insights can figure out which tasks can be automated and which still need a human touch.
Creating an AI-enhanced operating model
Treat AI as a collaborator that accelerates research and prototyping, investing heavily in human judgment for selection and implementation. Innovation should be powered by artificial intelligence, not simply automated.
Instead of letting AI manage strategy through a series of well-crafted instructions, use AI to ask questions about strategic choices. This creates dialogue and transparency in the process, where you can learn from the AI and vice versa.
AI tools can identify deviations from strategy, inconsistencies or biases by examining results and decision patterns. We end up with a virtuous cycle where humans possess the intent and vision, and AI is our partner that can enhance our intuition, but is constrained by our values.
Brands that blindly chase automation will face premature AI layoffs. In these situations, employees are cut before the AI is ready. Institutional knowledge is lost and costly rehiring processes occur in the future. While there is always pressure (sometimes immense) to save money and be efficient where possible, leaders should strongly resist headcount reductions based on hypothetical efficiency before it is actually achieved and proven stable.
Leaders can evaluate and make recommendations for many of these types of decisions on their own. However, it is much better for them to foster better analytical thinking and judgment in the teams most directly responsible for the work. Being able to rely on teams to understand and make difficult decisions will allow leaders to think further ahead and care for their team and brand in more concrete ways.
Protect human judgment in the circuit
Efficiency improvements resulting from AI should not be limited to the bottom line. Reinvest it in the workforce to prevent burnout and work slowdown. Using technology to make work easier and more rewarding strengthens employee confidence and increases the quality of the result.
This approach, however, requires knowledge and experience. The benchmark for marketing leadership has changed. Five years ago, digital literacy was a differentiator for CMOs, but today it’s table stakes. The new standard is AI-savvy leadership, capable of understanding generative AI, agent systems and robotics.
Recent analysis suggests that while most companies qualify as digitally literate, only 26% of large companies currently meet AI proficiency requirements. However, this skill is essential to prevent the job trap we talked about here and many other issues.
This shifts a key responsibility to the leaders of today and tomorrow: hiring employees with a learning mindset and reskilling employees to become powerful collaborators with AI. Top-performing companies are investing heavily in reskilling their workforce to ensure that key employees (not just third-party vendors) can deliver the next wave of change.
This approach goes well beyond familiarity with AI tools to a deeper understanding of what makes a good outcome versus AI failure, as well as what work is worth fully automating and what work needs a human in the loop.
Leaders who understand this nuance and develop the capabilities of their teams will see growth beyond initial productivity gains, with more sustainable, long-term innovation and growth stemming from an often overlooked and undervalued trait: judgment.
Finding balance
When content is infinite and cheap, quality and curation become scarce and expensive. The organizations that thrive will be those that refuse to let AI dictate the standard of quality. They will use automation to take work off their teams’ plates, freeing humans and allowing them to focus on the creativity, empathy, and judgment that machines can’t simulate.
Leaders must identify common sense in their teams and cultivate it over time. This is a key role that humans will continue to play and one of the primary values that they will continue to bring to the table.
