As AI promises to automate 90% of your administrative tasks, are you ready to risk the future of your brand on the remaining 10% – high-value human judgment machines can’t replicate?
As business AI adoption grows from more experimental to result-driven, when marketing leadership is asked to prove ROI, marketing organizations are experiencing what can be called second-order risks of rapid scaling. Biggest for many is the occurrence of workslop, or low-quality output produced by workers who are pushed to deliver large amounts of AI-generated content without enough time for quality testing.
While AI can perform many repetitive management tasks, the opposing and growing demand for marketing leaders now emphasizes human empathy, intelligence and strategic judgment. To win, leaders must treat AI as a strategic partner instead of an autopilot that extends product integrity, while respecting the value of human judgment.
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Need people to explain AI slop
It’s hard to avoid the downside of AI these days, and it comes from giving marketing teams the wrong incentives to meet aggressive exit targets. While much of the initial discussion around AI has focused on investment and massive potential, there are costs to all content created, much of which is detrimental to productivity.
Workslop, which you’ve no doubt experienced as a buyer or employee, is the increase in low-quality output, which typically occurs when marketing teams are pressured to use AI to deliver more volume in less time allotted for quality control and critical thinking.
Expecting AI to act as a silver bullet has created working conditions that place unreasonable performance pressures. Rather than increasing productivity, these pressures can destroy results by overfilling channels.
Speeding up broken processes doesn’t help either. Coupling productive AI into a broken workflow will only provide the same suboptimal results, much faster. The real ROI will come from building workflows from scratch rather than building shiny demos that (almost always) lack something or can’t be used for long.
However, identifying what is workslop and what is really useful work still takes people, although giving these people the wrong incentives and KPIs to measure success can cloud judgment and produce wrong results. This becomes a trap where greater efficiency benefits must be balanced against the negative effects of producing poor quality work for internal and external audiences.
When the automatic ends, and the judgment begins
To avoid this performance trap, managers must clearly distinguish between operational and judgment-based strategies.
Research from Bain & Company estimates that functions such as sales can automate 70% to 90% of administrative tasks, such as running tenders or managing data. This great openness of power helps the management staff effectively.
As the cost of production decreases due to AI, the value of choice increases. This same study shows that the competitive premium is now moving 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 empathic trust. Leaders will need to decide which trade-offs are off the table. Those who do something quickly and at low cost cannot come at the cost of your product or your customer’s trust.
Teams are motivated to automate and accelerate without the critical element of judgment they create for themselves and the product. Marketing leadership benefits when better informed teams are able to understand which tasks can be automated and which still require human intervention.
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 powered by AI, not automated.
Instead of letting AI drive strategy through a series of well-designed instructions, use AI to investigate strategic options. This creates dialogue and transparency in the process, where you can learn from AI and vice versa.
AI tools can identify strategic deviations, inconsistencies or biases by looking at outcomes and patterns of decisions. We end up with a virtuous cycle where humans own the purpose and vision, and AI is a partner that can cost us a lot of our understanding, but is bound by our values.
Brands that blindly chase automation will face early AI layoffs. In these cases, workers are cut before the AI is ready. Institutional knowledge is lost, and costly re-hire processes occur down the road. While there is always pressure (sometimes too much pressure) to save money and be efficient where possible, leaders must fight hard against cutbacks based on perceived efficiency before it is achieved and proven to be sustainable.
Leaders can evaluate and make many recommendations for these types of decisions on their own. However, it is best for them to encourage better analytical thinking and judgment in the teams directly dealing with the work. Being able to rely on teams to understand and make difficult decisions will enable leaders to think ahead and look at their team and product in much bigger ways.
Protecting human judgment from the loop
The operational benefits from AI shouldn’t just hit the bottom line. Reintroduce it to employees to prevent burnout and death. Using technology to make work easier and more rewarding strengthens employee confidence and increases output quality.
This method, however, requires knowledge and experience. The benchmark for marketing leadership has changed. Five years ago, digital literacy was a differentiator for CMOs, yet today, it’s table stakes. The new standard is AI-savvy leadership, capable of understanding generative AI, agent systems and robots.
A recent analysis suggests that while most companies qualify for digital literacy, only 26% of large companies meet the criteria for AI savviness. However, this technology is essential to prevent the worklop trap discussed here and many other issues.
This shifts an important responsibility to the leaders of today and tomorrow: hiring a way to learn and re-educate employees to be powerful partners with AI. High-performing companies invest heavily in reskilling their workforce to ensure that key employees (not just third-party vendors) can deliver the next changes.
This approach goes beyond familiarity with AI tools to a deeper understanding of what makes a good output versus AI slop, and what work should be completely automated and what work requires a human in the loop.
Leaders who understand this diversity and build capacity in their teams will see growth beyond initial productivity hiccups, and long-lasting and sustainable innovation and growth from an often overlooked and undervalued factor: judgment.
Finding balance
If content is endless and cheap, quality and editing are rare and expensive. Successful organizations will be those that refuse to let AI dictate the quality standard. They will use automation to clear workflow from their team’s plates, freeing people to focus on creativity, empathy and judgment that machines cannot imitate.
Leaders must see good judgment in their teams and cultivate it over time. This is an important role that people will continue to play and one of the main values that they will continue to bring to the table.
