A troubling pattern is emerging across corporate America. Companies are mandating AI adoption. Employees are complying. Yet almost no one is seeing results.

According to a recent Harvard Business Review study, 95% of organizations see no measurable return on their AI investments. Even more concerning, researchers at BetterUp Labs and Stanford's Social Media Lab have identified a new phenomenon they call "workslop, "AI-generated content that looks professional but actually creates more work for colleagues who receive it.

The financial cost is high. For a company with 10,000 employees, workslop costs an estimated $9 million annually in lost productivity. But the interpersonal damage may be worse. Over half of employees who receive workslop report feeling annoyed, and 42% now view those colleagues as less trustworthy.

So why is this happening? And more importantly, how can organizations avoid it?

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The Problem with "AI Everywhere"

The root cause isn't AI itself—it's how companies are implementing it.

When leadership issues mandates "using AI everywhere," they model a dangerous lack of strategic thinking. Employees receive access to general-purpose AI tools like ChatGPT with little guidance on when, where, or how to use them effectively.

Workers then use AI indiscriminately, often in situations where it's poorly suited to the task. They copy and paste AI-generated content into documents without verifying accuracy or adding context. They treat AI as a shortcut rather than a tool that still requires human judgment and oversight.

As the HBR researchers note, "Gen AI gives us a new technology with which to lean into the same old bad habits—but now with the added cost of creating more work for our colleagues and undermining collaboration, at scale."

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The Alternative: Purpose-Built AI for Specific Outcomes

There's a better way to implement AI in your organization—one that delivers measurable results without generating workslop.

The key is moving from general-purpose tools to purpose-built AI solutions designed for specific, high-value use cases.

Purpose-built AI has three defining characteristics:

1. It solves a specific, well-defined problem. Instead of being a tool in search of a use case, purpose-built AI starts with a clear business challenge and designs the AI to address it.
2. It enhances rather than replaces human capability. The best AI applications amplify what humans do well while automating repetitive or time-consuming tasks.
3. It creates efficiency without shifting work downstream. Unlike workslop, which transfers effort from creator to receiver, purpose-built AI genuinely reduces the total work required.

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What This Looks Like in Practice

Consider the challenge of scaling professional development. Every HR leader knows that one-on-one coaching delivers the best results for skills like sales presentations, difficult conversations, or leadership communication. But it's simply not scalable. Most organizations can't afford to give every employee regular access to personalized coaching.

This is where purpose-built AI creates real value.

AI-powered avatar-based training platforms solve this specific problem by creating realistic, interactive practice environments. Employees can rehearse high-stakes scenarios with an AI avatar that responds naturally and provides personalized feedback.

This isn't AI generating generic content that someone else must review. It's AI enabling a specific capability that was previously impossible to scale.

The difference in outcomes is clear:

•  Employees practice real scenarios relevant to their actual work
• They receive immediate, actionable feedback on both content and delivery
• They can repeat scenarios until they build confidence and competence
• No human coach's time is required, freeing L&D teams for strategic work
• The organization gets measurable skill improvement and reduced training time

This is what strategic AI implementation looks like. It's targeted, outcome-focused, and genuinely efficient.

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How to Evaluate Your AI Investments

As you consider AI initiatives for your organization, ask these questions to distinguish value-creating solutions from potential workslop generators:

• Does it solve a specific, measurable problem? If the business case starts with "we should use AI because everyone else is," that's a red flag. Strong AI investments target clear pain points with defined success metrics.
• Does it enhance or replace human judgment? AI works best when it augments human capabilities, not when it tries to fully replace human thinking and context.
• Does it create genuine efficiency? Be honest about whether the AI reduces total work, or just shifts who does it. If someone downstream must interpret, verify, or redo the work, you're creating workslop, not efficiency.
• Can you measure the outcome? Purpose-built solutions deliver specific, quantifiable benefits—reduced training time, improved skill scores, higher completion rates, or cost savings. If you can't articulate what success looks like, the investment probably won't deliver ROI.
• Does it fit your strategic priorities? The best AI investments align with your organization's core goals and values, not just with what's technologically possible.

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The Path Forward

The HBR study reveals a critical inflection point. Organizations have enthusiastically adopted AI, but most are seeing their investments drain productivity rather than enhance it.

The solution isn't to abandon AI. Instead, focus on purpose-built solutions that address specific, high-value challenges. Invest in applications that enhance human capability rather than create more work. And hold every AI initiative to the same standard: does this deliver measurable value?

When AI is designed for a specific outcome—whether that's scaling coaching, automating data analysis, or streamlining workflows—it delivers results. When it's adopted indiscriminately, it creates workslop. Choose wisely.

Ready to see what purpose-built AI looks like in action? Learn how organizations are using avatar-based training to scale professional development without creating workslop. Explore Colleva's Learning & Development solution.

Sources:

Niederhoffer, Kate, Gabriella Rosen Kellerman, Angela Lee, Alex Liebscher, Kristina Rapuano, and Jeffrey T. Hancock. "AI-Generated 'Workslop' Is Destroying Productivity." Harvard Business Review, September 22, 2025. https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity