As AI promises to automate 90% of your administrative tasks, are you ready to stake your brand’s future on the remaining 10% — the high-value human judgment machines can’t replicate?
With enterprise AI adoption maturing from mass experimentation to results-driven, with marketing leadership being asked to prove ROI, marketing organizations are encountering what could be called the second-order risks of rapid scaling. The biggest one for many is the phenomenon of workslop, or the low-quality output generated by employees pushed to deliver massive amounts of AI-generated content without enough time for quality checks.
While AI can automate a vast majority of repetitive administrative tasks, a counterintuitive and growing need for marketing leaders is now becoming an emphasis on human empathy, creativity and strategic judgment. To win, leaders must treat AI as a collaborator that interrogates 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 slop these days, and it stems from giving marketing teams the wrong incentives to meet increasingly aggressive output goals. While much of the initial conversation regarding AI focused on investment and upside potential, there’s a cost to all of the content created, much of which is detrimental to the brand.
Workslop, which you’ve no doubt experienced as either a consumer or an employee, is the proliferation of low-quality, generic output that occurs when marketing teams are pressured to use AI to deliver more volume with less time allocated to quality control and critical thinking.
The expectation that AI will act as a silver bullet has created operating conditions that impose unrealistic performance pressures. Rather than boosting productivity, these pressures can quietly erode results by flooding channels with mediocrity.
Speeding up broken processes is also counterproductive. Jamming generative AI into broken workflows will only provide the same subpar results, more quickly. Real ROI will come from building workflows from scratch rather than building flashy demos that (almost always) lack substance or can’t be applied long term.
Yet, to identify what is workslop and what is truly valuable work output still takes humans, though giving these humans the wrong incentives and KPIs to measure success can cloud judgment and generate the wrong outcomes. This becomes a trap in which 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 workslop trap, executives must clearly delineate between executable tasks and judgment-based strategy.
Research from Bain & Company estimates that functions like merchandising can automate 70% to 90% of administrative activities, such as running tenders or managing specifications. This massive unlocking of capacity effectively commoditizes administrative labor.
As production costs drop due to AI, the value of selection increases. This same study shows that the competitive premium now moves to that other 10% of work: judgment calls that create value, 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. Those where doing something faster and at lower cost can’t come at the expense of your brand or your customer’s trust.
Teams that are 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 understand which tasks can be automated and which still need a human touch.
Creating an AI-augmented operating model
Treat AI as a collaborator that accelerates search and prototyping, while investing heavily in human judgment for selection and implementation. Innovation should be augmented by AI, not simply automated.
Instead of letting AI run the strategy via a series of well-crafted prompts, use AI to interrogate strategic choices. This creates a dialogue and transparency in the process, where you can learn from AI and vice versa.
AI tools can identify deviations from strategy, inconsistencies or biases by looking at outcomes and decision patterns. We end up with a virtuous cycle where humans own the intent and vision, and AI is our partner that can supercharge our insight, but is bounded by our values.
Brands that chase automation blindly will face premature AI layoffs. In these situations, employees are cut before AI is ready. Institutional knowledge is lost, and expensive rehiring processes occur down the road. While there’s always pressure (sometimes immense pressure) to save money and be efficient wherever possible, leaders should strongly resist slashing headcount based on hypothetical efficiency before it’s actually achieved and proven stable.
Leaders can assess and make recommendations for many of these types of decisions on their own. Still, it is far better for them to foster better analytical thinking and judgment in the teams more directly responsible for the work. Being able to rely on teams to understand and make tough decisions will enable leaders to think further ahead and look out for their team and the brand in more substantial ways.
Protecting the human judgment in the loop
Efficiency gains from AI shouldn’t just flow to the bottom line. Reinvest it into the workforce to prevent burnout and workslop. Using tech to make work simpler and more rewarding strengthens employee trust and increases the quality of the output.
This approach, however, requires knowledge and experience. The benchmark for marketing leadership has shifted. Five years ago, digital literacy was a differentiator for CMOs, yet today, it is table stakes. The new standard is AI-savvy leadership, capable of understanding generative AI, agentic systems and robotics.
Recent analysis suggests that while a majority of companies qualify as digitally literate, only 26% of major companies currently meet the bar for AI savviness. Yet, this expertise is essential to prevent the workslop trap discussed here and many other issues.
This shifts a key responsibility to today and tomorrow’s leaders: hiring for a learning mindset and reskilling employees to be powerful coworkers with AI. Top-performing companies are investing heavily in reskilling their own workforces to ensure core 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 good output versus AI slop, as well as what work is worth automating completely and what work needs a human in the loop.
Leaders who understand this nuance and build the capability on their teams will see growth beyond initial productivity bumps, with a longer-term and more sustainable innovation and growth that comes from an often overlooked and undervalued characteristic: judgment.
Finding equilibrium
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 clear the workslop from their teams’ plates, freeing humans to focus on the creativity, empathy and judgment that machines cannot simulate.
Leaders must identify good judgment 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 they will continue to bring to the table.
