From Junk Work to Judgment Work: What AI Should Actually Change | AI Experts Blog

← Back to all posts<br>July 29, 2026<br>From Junk Work to Judgment Work: What AI Should Actually Change

Lucas Erb (and agents)<br>Founder of AI Experts

The attention allocation problem<br>Professional firms do not have a shortage of expertise. They have an attention-allocation problem.<br>Accountants, advisers, investors, analysts, and other specialists spend too much of the day finding information, copying it between systems, reconciling versions, formatting deliverables, chasing inputs, and correcting avoidable mistakes.<br>The client is paying for judgment. The workflow keeps consuming it.<br>AI can change that equation, but only when the workflow changes with it. Controlled studies show that generative AI can improve speed and quality on suitable tasks. The same research also shows a sharp boundary: when a task falls outside the model's capabilities, AI can make knowledgeable people confidently wrong.<br>The goal is therefore specific. Use AI to compress the work around judgment while giving people better evidence, and more time to question, interpret, advise, decide, and own the outcome.<br>Expensive expertise has become the integration layer<br>A senior professional opens a data room. Thirty minutes later, they are still naming files.<br>Then they copy numbers into a spreadsheet, chase a missing answer, reconcile two contradictory decks, reformat the summary, and send it up the chain. The judgment the client is paying for begins somewhere after lunch.<br>This is common in firms that sell expertise. Their workflows grew one request, spreadsheet, review step, inbox, and workaround at a time. Eventually the most capable people become the glue connecting all of it.<br>We call the result "junk work".<br>The label does not mean the task is unnecessary. To call this work "junk" would be a rude misnomer. Rather, work becomes "junk" when it repeatedly consumes expert attention without requiring expert judgment. Documents still need to be found, numbers reconciled, and deliverables checked. The question is whether the firm needs its scarcest people doing that work by default.<br>There is evidence that the burden is substantial. In a 2023 survey commissioned by Asana and conducted by GlobalWebIndex, 9,615 knowledge workers across six countries reported spending 58% of the day on "work about work", meaning coordination rather than skilled, strategic work. It is a vendor-sponsored, self-reported survey, not direct observation. The number should not be treated as a law of nature. It does capture a problem most professional teams recognize immediately.<br>The important question is not how busy the firm is. It is how much expert attention reaches the work that requires expertise.<br>The evidence is promising, and inconvenient<br>Generative AI is good at compressing certain kinds of knowledge work.<br>A peer-reviewed experiment published in Science assigned 453 college-educated professionals to complete writing tasks with or without ChatGPT. Participants with access to ChatGPT finished 40% faster on average, while evaluators rated their output quality 18% higher. The tasks were bounded writing assignments, so the findings do not prove that AI improves every professional workflow. They do show that meaningful gains are possible when the task fits the tool.<br>A larger field experiment with 758 consultants found a similar pattern. On 18 realistic tasks considered to be inside the model's capability frontier, people with GPT-4 completed 12.2% more tasks and worked 25.1% faster, with significantly improved quality.<br>The same study found the reverse on a complex managerial task outside the model's capability frontier: participants using AI were 19% less likely to reach the correct solution. Faster production is not a gain if the workflow cannot detect when the task exceeds the model's competence.<br>A 2025 survey of 319 knowledge workers found that higher confidence in generative AI was associated with less reported critical-thinking effort. Participants' 936 first-hand examples also suggested that critical thinking shifts toward information verification, response integration, and task stewardship. These findings are based on associations and self-reports, not direct measures of cognitive decline.<br>Therefore, the shift in workflow is not inherently bad. Verification of AI output is valuable when reviewers receive usable evidence with enough time to examine consequential claims.<br>The time equation a professional firm should change<br>Many firms approach AI workflow redesign as a list of use cases. A better starting point is a map of human attention.

Today, expert time is split across two very different kinds of work:<br>Junk work: find, copy, format, reconcile, chase, and rework.<br>Judgment work: interpret, decide, advise, and own the outcome.<br>After a well-designed reinvention, the division of labor becomes clearer:<br>AI-supported work: find, extract, compare, draft, and flag.<br>Expert work: question, interpret,...