Stop Measuring AI Activity: Prevent Workslop and Protect Real Productivity

AI can make a team look incredibly productive. Reports are drafted faster, meeting notes appear in seconds, and proposals that once took hours can be generated with a few prompts. But more output does not automatically mean more value. This is where workslop comes in: polished-looking AI output that lacks the context, accuracy or substance needed to move work forward. Instead of removing work, it often pushes the effort downstream—someone still has to fact-check the report, rewrite the generic proposal or clarify an answer that missed the client's actual needs.

The result can be surprisingly counterproductive. An employee may save 30 minutes by generating a first draft with AI, only for a colleague to spend an hour reviewing and correcting it. The organisation has created more content, but not necessarily more progress. Real productivity is not about producing more—it is about reducing unnecessary work while improving the quality of the outcome.

When More AI Output Creates More Work

The appeal of generative AI is clear: it can make repetitive tasks and content creation much faster. But speed becomes misleading when output volume is treated as productivity. Producing twice as many reports means little if half require heavy correction; generating more proposals means little if managers spend hours fixing inaccurate claims; and detailed AI meeting summaries are not useful if teams still need another meeting to clarify what actually matters.

The key is to distinguish more output from more useful output. AI can make it easy to produce something quickly, but the real measure of productivity is whether that output is accurate, relevant and good enough to move the work forward.

Why Workslop Happens

Workslop is rarely caused by the AI tool itself. It happens when organisations introduce AI without clearly redefining what good work and accountability look like. Employees may be told to “use AI more” without clear quality standards, while managers focus on faster turnaround or higher output without considering the review and correction that follow.

Under pressure, it is also easy to let AI take over the thinking rather than simply assisting with the task, the output looks finished, but finished does not always mean ready to use.

This matters even more when teams are already stretched. Microsoft's 2025 Work Trend Index found that 53% of leaders wanted productivity to increase, while 80% of workers reported lacking enough time or energy to get their work done. In that environment, pushing employees to generate more AI-assisted content can simply create more downstream work. The better approach is to use AI to remove low-value effort while keeping human attention on the parts that require context, judgement and accountability.

Stop Asking "How Much AI Are We Using?"

AI usage itself is not a productivity metric. An employee using AI 20 times a day is not necessarily more productive than someone who uses it twice. The better question is: what actually improved because AI was used? Maybe customer service teams respond faster without sacrificing accuracy, analysts spend less time cleaning repetitive data, or sales teams spend more time understanding clients instead of formatting proposals. These are meaningful gains because they improve the work—not simply increase the amount of content produced.

A useful way to think about it is useful outcome ÷ effort required.

Organisations should focus less on prompt counts, AI-generated documents or how often employees use a tool, and more on whether AI helps people reach better outcomes with less unnecessary effort. The goal is not maximum AI usage. It is better work with less wasted effort.

Give Every AI-Assisted Task a Clear Purpose

Before using AI, employees should know what they are trying to achieve, who the output is for, what decision it supports, what information it should use, and what good looks like. This prevents unnecessary content generation just because AI can produce it quickly. For example, instead of asking AI to write a client proposal, give it the client context, business priorities, approved information and any points that need verification. This gives AI a clear task and gives the employee a clear standard for reviewing the output.

The principle is simple: AI should support the work, not define it. The goal is not to create more content, but to use AI where it can genuinely move the task forward.

Build a Simple Quality Gate

Not every AI output needs the same level of review. A quick email draft may only need a basic check, while a hiring recommendation, financial analysis or customer commitment needs much stronger oversight. A simple quality gate can help employees make that distinction: Is it accurate? Is it relevant to the task or decision? Is it backed by reliable evidence? Is it safe to act on?

These checks do not need to slow work down. Routine, low-risk tasks can have a lighter review, while higher-impact work should require deeper human oversight. The key is making sure employees know when a quick check is enough and when an AI output needs to be challenged, verified or escalated before it moves forward.

Human Accountability Still Matters

AI can draft, summarise, analyse information and suggest possible approaches. But someone still needs to own the final result. This matters most when AI is used for hiring recommendations, employee relations, financial decisions or customer commitments, where a confident-sounding output can still be wrong or incomplete.

Every high-impact AI-assisted task should have a clear human owner who understands the information behind the output, the assumptions involved and when further review is needed. The person responsible should be able to challenge the AI rather than simply pass its answer forward.

This aligns with Singapore’s Model AI Governance Framework, which emphasises risk assessment, meaningful human accountability and appropriate testing. The goal is not to remove humans from AI-enabled work, but to make sure human judgement remains where the consequences matter.

A Practical Example: AI-Generated Client Proposals

Imagine a business-services firm introducing generative AI to help account managers prepare client proposals. At first, the results look impressive.

The team can produce proposals 40% faster, and the number of drafts going through the pipeline increases significantly. But reviewers start noticing problems.

Some proposals contain inaccurate industry claims. Others repeat the same recommendations across different clients. Some include pricing assumptions based on unsuitable previous cases. The team is producing more.

But reviewers are also spending more time fixing the work. Instead of celebrating proposal volume, the firm changes its approach. Before an AI-generated proposal is created, the account manager completes a short proposal brief covering the client's actual decision, relevant business context, verified sources, commercial constraints and required evidence.

AI can then be used to create the first draft. But the account manager remains responsible for the final output. Before submitting it for review, they must add several client-specific insights, validate factual claims and confirm that confidential information has not been carried across from another account. The organisation then samples a set number of proposals each month.

Instead of only tracking how many proposals were produced, it looks at:

• How much reviewer time was required

• What types of corrections were needed

• How often clients asked for clarification

• Whether recommendations were relevant to the client's situation

• Whether the proposals supported better commercial outcomes

Now the organisation has a much clearer picture of productivity. The goal is no longer to produce more proposals. It is to produce more decision-ready proposals with less downstream correction.

Change What Managers Reward

HR and business leaders can shape AI use through what they choose to measure and reward. If employees are judged by the number of reports, documents or AI-assisted tasks they produce, AI will naturally be used to maximise output. Instead, performance measures should focus on what the work actually improved, less rework, fewer hand-offs, faster response times, better customer outcomes and stronger judgement.

The shift is simple: ask How much unnecessary work did AI remove? rather than how often it was used. Ask Did it improve the quality or speed of the outcome? rather than how much content was produced. This encourages employees to use AI where it genuinely adds value, instead of using it simply because they are expected to.

From AI Activity to Real Productivity

Generative AI can change how work gets done, but faster content creation only matters when it leads to better outcomes. If AI simply creates more documents that others have to review, correct and interpret, the work has not become more productive, it has simply moved downstream. The better approach is to define what good work looks like, set clear expectations, build the right quality checks and keep human judgement where it matters. The goal is not more AI output, but less wasted effort and better work.

As an HR consulting partner, Cadence helps organisations navigate these changes by connecting people, performance and business needs. From workforce capability and performance management to workplace learning and AI-readiness, the focus is on building practical people strategies that support how work is actually evolving.