Somewhere between the invention of sliced bread and Slack notifications, the working world figured out how to crank out more with less thought. Now, thanks to AI, we’ve accelerated that process into a perfect storm of productivity theater. We’re not really working smarter; we’re just working sloppier, faster, and with more enthusiasm for ticking boxes than for solving anything that matters.
This isn’t a crisis of technology. It’s a crisis of intention. And it’s hilarious, depressing, and deeply predictable all at once.
Work Has Always Been About Looking Busy
Long before algorithms joined the office, humans perfected the art of “seeming busy.”
- PowerPoint decks full of arrows pointing at other arrows.
- Meetings to discuss meetings.
- Reports no one ever read, filed neatly into oblivion.
- Email chains that were basically Victorian novels written by middle managers.
AI didn’t invent busywork. It just put the whole charade on steroids.
Enter the Robot Intern Nobody Asked For
AI tools are pitched as helpers, summarising, drafting, analysing, and polishing. Great. But the way most workplaces use them is less about saving time and more about producing a conveyor belt of half-digested sludge that has to go somewhere - usually onto another poor soul’s desk.
Instead of asking, “What problem are we solving?” the question becomes, “What can I offload to the machine so it looks like I’m doing something?”
Spoiler: it all ends up in the inbox of some human, who then has to sort, rewrite, reframe, or quietly delete the avalanche of robo-output.
The Rise of “Work Slop”
You know the stuff:
- AI-generated reports that read like a Wikipedia article written by a drunk committee.
- Brainstorming decks with ideas that were clearly spat out in 0.2 seconds and left unscrutinised.
- Customer “personalisation” emails that feel about as personal as a fortune cookie.
Slop is easy to make. Slop is easy to pass on. Slop looks like progress, which is why it thrives in organizations obsessed with task completion over outcomes.
The Task Trap
Most workplaces worship the holy checklist. Tasks equal progress, and more tasks equal more progress. Except… no.
Completion ≠ value. Finishing a task doesn’t mean the job is done; it just means the motion was performed. You can polish a turd into infinity, but it’s still a turd.
AI supercharges this problem: the faster you can create things, the more tasks appear. Work becomes an ouroboros of endless slop production.
Objective? What Objective?
Imagine if work was measured not by the number of things shipped but by the number of problems solved. Suddenly, half the AI-generated content pipeline would evaporate into thin air. That 45-slide deck? Gone. That “summary” no one asked for? Deleted. The fake personalisation campaign? Never launched.
We don’t have a productivity crisis. We have an objectives crisis.
Funny (and Painful) Examples of Work Slop
- The AI-Generated Blog Nobody Reads
Some poor intern schedules it on the site, marketing pretends it matters, and analytics confirm that zero humans clicked. But hey - it’s a deliverable. There is just so much content noise now. - The Deck of Eternal Slides
AI can churn out a hundred slides. Only two are relevant. Guess who has to figure out which ones? The human analyst, crying into their coffee. - The Slop-Forward Meeting Agenda
Someone asked AI to “make an agenda.” Suddenly, you’re staring at a ten-point plan that includes “align on synergies” and “explore innovation pathways.” Translation: “waste an hour.”
The Slop Economy
The tragedy is that AI could free people from garbage work. Instead, it’s being used to generate more garbage work. The world doesn’t need more words, more decks, or more summaries. It needs better judgment about what actually matters.
But judgment isn’t measurable on a dashboard. Task completion is. And so, the hamster wheel spins faster.
Who Pays the Price?
The receiver. Always the receiver.
- Writers editing AI mush.
- Analysts validating AI data dumps.
- Managers drowning in AI-crafted “insights.”
AI doesn’t eliminate labor. It redistributes it, downstream, disguised as “productivity.”
How We Break the Cycle
- Refuse to worship the checklist. Stop treating tasks as trophies.
- Ban slop. If it isn’t useful, don’t make it, don’t send it, don’t save it.
- Focus on objectives. Ask “What’s the outcome we actually want?” before pressing “generate.”
- Treat AI like a scalpel, not a fire hose. Precision over volume.
The Punchline
AI isn’t the villain - we are. We’ve taken a tool with enormous potential to solve problems and instead used it to accelerate our favorite pastime: looking busy.
Until organisations get serious about outcomes instead of output, we’ll all be knee-deep in work slop. And the saddest part? We’ll call it progress.
Closing Thought
The future of work doesn’t hinge on whether AI is good or bad. It hinges on whether people can stop confusing stuff with results. If not, bring your wellies - the work slop is rising.
If you haven’t been keeping up with current events, the next wave of digital disruption is upon us in the form of artificial intelligence (AI). For experienced leaders who’ve already weathered the disruptive impacts of Web 2.0, e-commerce and mobile technology, this may feel like yet another industry trend we need to come to terms with. But the truth is, artificial intelligence is a seismic shift that will completely transform how we both work and live.
From healthcare to transportation, AI will empower us to make more informed decisions at an unprecedented scale. If you’d asked us two years ago which sectors wouldn’t be severely impacted by AI, we would’ve mentioned STEM, the creative arts and empathy-based healthcare. Yet, in the past six months, we’ve seen all three experience the powerful effects of AI. On the surface, AI promises greater productivity, streamlined processes and limitless innovation. But this promise comes with caveats, as concerns emerge about potential job displacement, compelling organisations to adapt their structures and strategies. This article delves into the layers of AI’s impact in the workplace, exploring its influence on knowledge workers, organisational design and the pivotal role of the C-suite.
The AI-Driven Shift: A Complicated Transition
While AI’s transformative potential is undeniable, it brings with it many complexities that greatly impact the workforce. On the one hand, AI presents an enticing prospect of automation, alleviating the burden of mundane tasks and enabling employees to concentrate on high-value, strategic work. On the other hand, these advancements also give rise to real concerns about job displacement and the widening skills gap as the demand for AI expertise skyrockets. And the question of whether the number of new jobs AI creates will surpass those lost remains to be seen.
Beyond the individual level, organisations are grappling with the AI revolution, often finding themselves ill-prepared to navigate this transition. A lack of readiness, caused by either underestimating the reach of AI or struggling to keep pace with its rapid evolution, means a strategic re-evaluation among those in leadership positions is needed. We saw this denial during the previous wave of digital disruption, resulting in unprepared incumbent players displaced by new, more agile organisations that embraced or embodied the emerging technology.
Adding to the intricacies of this transition, governance, regulation and ethics present further challenges. As AI continues to permeate business operations, concerns about privacy, data security and ethical use of AI have taken centre stage, and regulators are scrambling to keep up. Many governments around the world are still struggling to grasp the very concept of TikTok, let alone comprehend the complexities of the impact of AI on the workplace and society. On the flipside, though, corporations find themselves in a unique position to self-govern, developing robust ethical frameworks that will guide their AI implementations now and into the future. C-suite executives, then, have an extraordinary opportunity to drive AI strategy and ensure it aligns with emerging regulations and ethical standards.
Navigating the Impact of AI: The Burning Question
Amidst these formidable challenges, a pivotal question arises: “How can C-suite executives effectively assess the impact of AI on the workforce?” Addressing this complex puzzle calls for more than a straightforward approach; it needs a comprehensive strategy that brings together the technological finesse of AI with the intricate dynamics of the human workforce.
Scenario Planning: Anticipating the Unknown
In our experience collaborating with clients, we’ve come across a diverse range of perspectives about the impact of AI on the workforce. While some perceive AI as a threat to their businesses, others recognise it as an incredible opportunity. We firmly believe that predicting the future is an impossible task, and it’s also unnecessary. The strategic tool of scenario planning is a much better way to navigate the uncertain terrain of AI transformation. By envisioning various outcomes and preparing suitable responses, organisations can address potential vulnerabilities and seize opportunities, whether by embracing automation or adopting AI-driven business models.
Typically, we develop three to five scenarios for each client, outlining the conditions needed for each to occur. Then we comprehensively assess the positive and negative impacts of each scenario on the business. In formulating responses, we often discover a common subset of actions applicable across all scenarios. The beauty of this approach is that it doesn’t require the ability to predict the future. In fact, these ‘no regret’ actions foster consensus within the executive team, as they establish strategic flexibility regardless of the scenario that ultimately unfolds.
Redesigning the Organisation for AI
In recent years, we’ve witnessed the convergence of strategy and organisation design to the point where they’re now almost indistinguishable. This has also corresponded with an excessive emphasis on talent. While talent is undoubtedly crucial, we firmly believe in the adage that “even a flawed system can outperform the most skilled individual”. As a result, we consider organisational design to be essential for all leaders striving to align their organisations with their strategic objectives. By investing in organisational design, they can create work environments that not only promote the growth and success of their talent but also reduce obstacles along the way, decreasing frustration.
When considering the impact of AI on an organisation, it’s crucial to acknowledge the necessary adjustments to its design. As AI will influence various aspects of organisation design, including ways of working, incentives, talent management and structure, integrating it into daily operations and strategies requires a comprehensive reassessment of capabilities, team dynamics, collaboration and decision-making processes.
To drive performance, leaders must identify any necessary roles and skills and determine the most suitable organisational model for managing operations integrated with AI. It’s also important to consider critical questions: How will decision-making authority be distributed among teams? What roles are required for data collection, analytics and operational execution? How can potential conflicts over resources, data ownership, skills and capabilities be avoided or resolved?
Unveiling these answers will shed light on the distinct challenges and opportunities that AI presents for each organisation. In reality, reshaping an organisation for AI is not a one-time endeavour; it requires an ongoing process of adaptation and evolution. Our approach has always encouraged leaders to treat organisation design like product design – managing ‘release cycles’ of iterative change rather than opting for a drastic overhaul. As AI technologies advance and mature, organisations must continuously adjust and refine their structures, processes and cultures. It’s undeniably a formidable task, but one that can yield substantial rewards for enhanced efficiency, innovation and competitiveness.
The Emergence of the AI Vanguard: Chief AI Officer and Chief Data Officer
As organisations grapple with the complexities of integrating AI, there’s a growing demand for specialised roles to address these challenges. The rise of Chief Data Officers (CDO) and Chief AI Officers (CAIO) within the C-suite reflects this need. The CAIO acts as a strategic compass, ensuring AI aligns with organisational objectives and overseeing implementation across functions. In contrast, the CDO manages data as a strategic asset, ensuring quality, compliance and accessibility for AI initiatives. Together, they lead the charge in navigating the transformation, and driving AI development and deployment.
The specific roles of these executives, however, vary based on company size, maturity level and strategic objectives. Both positions require a deep understanding of data science and AI technologies. Their emergence is driven by the vital role data plays in this transformative process. Data, often called the ‘new oil’, undeniably fuels AI. To be truly prepared, it’s essential that an organisation handles data intelligently. This involves understanding data origin, implementing effective cleaning and storage procedures, and employing efficient analysis methods. Developing robust data management and analytics capabilities is absolutely crucial when preparing for the advent of AI. In an era where many organisations can access sophisticated AI models, proprietary data sets have become a critical competitive differentiator and they are vitally important in this landscape.
In many respects, the positions of CDO and CAIO represent a convergence of traditional Clevel roles. They combine knowledge usually held by executives from IT, operations, analytics and finance. By creating these specialised positions within their executive teams, organisations can reap the rewards of AI while managing potential risks. This also allows companies to drive innovation and gain a competitive edge in the rapidly changing technological landscape.
The advent of the Chief AI Officer (CAIO) and Chief Data Officer (CDO) roles significantly impacts the role of the traditional Chief Information Officer (CIO). While the CIO has historically managed data and technology, the introduction of CAIO and CDO roles brings a more nuanced division of responsibilities. We expect the CIO will continue to oversee the technological infrastructure of the organisation, ensuring its strategic alignment with business goals. Meanwhile, the CAIO will focus on embedding AI technologies into business operations and the CDO will primarily manage data governance and strategy. Together, the CIO, CDO and CAIO form a triumvirate working in unison towards a common goal: leveraging data and AI to drive innovation, efficiency, and strategic advantage. This collaboration requires clearly defined roles and open communication channels to ensure seamless operational and strategic integration.
Charting the Future: Embracing AI Transformation in the C-Suite
The potential of AI in the business world is significant, but its realisation ultimately depends on the C-suite. As the decision-making core of any organisation, the C-suite has the power to drive AI adoption and integration. As a C-suite executive, you’re responsible for developing and executing AI strategy, aligning AI applications with business objectives, and cultivating an AI-ready workforce. These crucial components require the executive team’s vision, commitment and leadership. It’s your role to champion the cause, overcome resistance and propel the organisation into the AI age.
But for AI transformation to truly succeed, it requires more than just technological upgrades and talent acquisition. A fundamental redesign of an organisation is needed – one that remodels its structure and processes, morphing it into an environment that supports AI. Shaping the organisation’s design is a powerful tool for the C-suite. By ensuring that the design aligns with strategic goals, leaders can encourage a culture of innovation and agility – principles essential for navigating the dynamic AI landscape of the future. So, while the road to AI maturity may have its challenges, the rewards it promises undeniably make the journey worthwhile. The future is here… and it’s time for the C-suite to take action!
Humanly Agile is business design practice that helps senior executives bridge the gap between strategy design and execution. We create organisation designs that enhance agility, and human-centred change programs that accelerate transformation.
Jamie Pride is the Managing Partner of Humanly Agile and a recognised authority on organisation design. He has worked with many Fortune 500 companies to help them develop strategies, structures and cultures which are ready for the AI revolution. With over 25 years’ experience in strategy design, culture change and leadership development, Jamie helps organisations succeed in the era of disruptive technology.
Peter Ryan is an Executive Advisor with Humanly Agile and has over 30 years of Big 4 experience in technology advisory, implementation and managed services delivery in Australia, Asia, USA and UK. His work has focused on defining technology strategy, designing pragmatic operating models and implementing complex advanced technology enabled solutions across multiple industries.
