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Yes—AI can make drafting and task initiation faster while leaving people responsible for deciding whether the result is accurate, important, safe, strategically aligned, and ready to act on. That is the argument Pratima Arora makes in The AI Journal. Arora, Smartsheet’s Chief Product and Technology Officer, describes a shift rather than a proven universal law: as AI-assisted activity increases, review, prioritization, approval, and next-step decisions can become the slowest part of the workflow. The practical response is not to treat every output as progress. Organizations need to carry context with each item, identify which decisions require a person, protect time for those decisions, and measure useful outcomes instead of raw activity.
Is human judgment becoming the bottleneck as AI makes work faster?
It can, when the amount of AI-generated work grows faster than an organization’s ability to review it and decide what happens next. The bottleneck is therefore not “human judgment” in the abstract; it is the finite capacity to inspect evidence, apply business context, resolve trade-offs, approve actions, and remain accountable for consequences.
Generative systems can draft text, summarize material, classify requests, create analysis, and start follow-up tasks in seconds. Those capabilities reduce the time needed to produce a first version. They do not automatically answer whether the source information is reliable, whether the recommendation fits the organization’s strategy, whether a risk is acceptable, or which competing request deserves priority. Every additional output can create another item that someone must understand and disposition.
Arora captures the distinction in one sentence: “AI can draft, summarise, and start the next task. But people still decide what matters.” Her thesis is a warning about workflow design, not evidence that every company has already reached a measurable judgment limit.
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What do the available figures actually show?
The strongest numerical claims in Arora’s article are company-reported figures from Smartsheet, not an independently published benchmark of decision capacity.
| Reported measure | Figure | What the source says | Important limit |
|---|---|---|---|
| Automation intensity per enterprise account | Up 55% year over year | Smartsheet says its analysis covered 1.4 million active enterprise projects. | The article does not state the measurement period, define the intensity metric, or publish a methodology. |
| Overall activity | Up 46% year over year | The same Smartsheet analysis reports higher activity across those projects. | The source does not establish that automation caused the increase or compare it with human review capacity. |
These numbers indicate more activity and automation in the dataset described by Smartsheet. They do not prove that judgment capacity rose more slowly, nor do they provide a ratio between project activity and available reviewers. Treating 55% and 46% as a universal measure of an AI-created bottleneck would go beyond the evidence.
How does an AI-driven bottleneck form?
The mechanism is easiest to see as a sequence. AI compresses the early production steps, but the later control steps still require information, authority, and time.
1. Generation becomes cheap
A team can produce several draft proposals, summaries, designs, or analyses where it previously produced one. The lower cost of trying an idea encourages more attempts and more parallel work.
2. The queue of candidates expands
Each draft needs a destination: approve, revise, combine, defer, reject, or investigate. If the organization has no explicit intake and triage rules, all of those candidates arrive in the same review queue.
3. Review requires context that the draft may not contain
A reviewer may need the original customer request, the assumptions behind a calculation, the policy that governs an exception, the latest project status, or the owner who can authorize a change. Retrieving that context can take longer than producing the AI-assisted draft.
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4. Decisions create coordination work
Approval may trigger dependencies across legal, security, finance, operations, or customer teams. A decision that is technically correct but poorly communicated can generate rework, so reviewers must often explain the rationale and record conditions for execution.
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5. Accountability remains human
Organizations still need a person or defined role to accept responsibility for a consequential action. Automation can recommend or route a case, but responsibility for an exception, an inaccurate statement, or a missed risk cannot be delegated merely because a model produced the initial content.
This sequence explains why counting drafts or completed automated steps can make a workflow look faster while the organization’s real constraint moves to validation and prioritization.
Does the workplace evidence show that AI intensifies work?
A separate account in Harvard Business Review describes an eight-month, in-progress study of about 200 employees at one U.S. technology company. Aruna Ranganathan and Xingqi Maggie Ye report that, after AI tools were offered, employees worked at a faster pace, took on a broader scope of tasks, and extended work across more hours.
The authors characterize their finding this way: “In our in-progress research, we discovered that AI tools didn’t reduce work, they consistently intensified it.” The qualifier matters. This was one company, a limited group, and an ongoing study; it is a bounded example of work intensification rather than proof that every organization experiences the same effect.
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Even so, the example supplies a plausible explanation for a judgment queue. If employees can accept more assignments and produce more intermediate outputs, managers and specialists may receive more material to inspect. Without explicit stopping rules, faster production can increase the number of unresolved choices instead of reducing total effort.
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Which tasks still need human judgment?
Human involvement is most important where the answer depends on organizational intent, incomplete information, or consequences that must be owned by a person.
- Accuracy and evidence: checking whether a statement, number, citation, or extracted requirement is supported by the underlying material.
- Strategic fit: deciding whether a proposed action advances the current objective rather than merely satisfying the wording of a request.
- Risk and ethics: assessing privacy, safety, legal exposure, fairness, security, and reputational impact.
- Prioritization: choosing among several plausible tasks when resources, deadlines, or customer needs conflict.
- Exceptions: determining when a standard rule should not apply and documenting who approved the deviation.
- Commitment: authorizing a message, release, purchase, policy change, or external promise that creates an obligation.
Not every generated item requires the same level of scrutiny. A low-risk internal formatting task may need a quick check, while a customer-facing claim or production change may require specialist review and a recorded approval. The key design question is to classify work by consequence and uncertainty, not to label everything “AI-generated” and apply one blanket review rule.
How can an organization keep decisions from piling up?
Arora’s proposed response is operational: preserve context, make the human-versus-automated boundary explicit, and give decision-makers enough information and time to act.
Keep context attached to the work
Store the request, source documents, assumptions, model output, revisions, owner, deadline, and decision history with the work item. A reviewer should not have to search several chat threads or drives to reconstruct why an output exists. When a model summarizes a document, retain a link to the original and identify the document’s version or date.
Define decision rights before automation expands
For each workflow, specify what the system may perform automatically, what requires a named approver, and what must be escalated. A useful rule can include the action’s risk, audience, reversibility, and financial or operational impact. Write the rule where people can see it; an undocumented expectation will not scale with volume.
Use triage instead of a single review queue
Route items by urgency, risk, confidence, and required expertise. Fast-track routine, reversible work; send ambiguous or high-impact cases to a specialist; and return incomplete requests to the requester rather than asking a reviewer to infer missing details. Triage reduces the chance that a large number of easy drafts hides a small number of dangerous ones.
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Centralize the information reviewers need
A connected record should show status, dependencies, ownership, source material, and the latest decision. Centralization is valuable because it reduces context retrieval, not because one database makes judgment automatic. Access controls and retention rules still need to match the sensitivity of the information.
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If reviewers are expected to make careful choices between meetings and production tasks, the queue will expand. Reserve scheduled review periods, set service-level expectations for different risk tiers, and limit the number of simultaneous approvals assigned to one person. The aim is focused capacity, not simply more alerts.
Make bottlenecks visible and escalatable
Track items waiting for a decision, their age, assigned role, risk tier, and stated blocker. Define an escalation path when an item exceeds its target age or when the assigned reviewer lacks authority. Escalation should move a decision to the right level; it should not silently convert an unresolved question into an automatic approval.
What should leaders measure instead of output volume?
Output counts are useful for understanding demand, but they do not show whether work produced a sound and timely result. A balanced view combines production, decision flow, and outcome quality.
| Dimension | Questions to ask | Possible signal |
|---|---|---|
| Demand | How many AI-assisted items enter each workflow, and how many are duplicates or abandoned? | New items by risk tier and percentage closed without action. |
| Decision flow | Where do items wait, and which roles carry the longest queue? | Median and oldest decision age by queue, owner, and risk class. |
| Context quality | Can a reviewer find the source, assumptions, owner, and prior decisions? | Share of items returned for missing information or unclear authority. |
| Decision quality | Are approvals later reversed, escalated, or associated with preventable rework? | Reversal, exception, defect, and rework rates, interpreted with their causes. |
| Outcomes | Did the completed work achieve the customer, operational, or strategic objective? | Outcome measures chosen for the specific workflow, rather than a universal score. |
Arora recommends judging success by whether work moves through an organization with clear, informed decisions and useful outcomes. That is a management principle, not a validated universal metric. Teams should establish a baseline, segment results by workflow and risk, and review whether a faster queue is actually producing better decisions.
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What are the limits of the bottleneck thesis?
The available evidence does not establish an enterprise-wide limit for human judgment, a causal estimate linking AI deployment to decision quality, or a public method behind Smartsheet’s reported project figures. It also does not show that every increase in activity is harmful. More activity can represent valuable experimentation, and automation may free experts for difficult cases when routing and context are designed well.
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There are other possible constraints: poor data, unclear ownership, slow procurement, limited specialist skills, security controls, or a lack of authority to act. Calling judgment the bottleneck too quickly can lead leaders to hire reviewers when the real problem is incomplete requests or an approval policy that sends trivial cases to executives.
Use the thesis as a diagnostic question: after AI speeds up creation, which step now determines how long it takes to deliver a trustworthy result? Measure that step directly before changing staffing, controls, or technology.
How can a team implement this approach without a major reorganization?
A small pilot can reveal whether the queue is a judgment problem and which intervention helps.
- Select one workflow. Choose a process with visible AI-assisted output and a clear completion event, such as a support response, compliance review, or internal proposal.
- Map the path. Record where an item is generated, what information a reviewer needs, who can approve it, and where it waits.
- Classify risk. Create a small set of categories based on consequence and reversibility. Assign an approval rule to each category.
- Attach the evidence. Require the source, assumptions, model output, owner, and deadline to travel with the item.
- Set queue targets. Define expected decision times by category and an escalation route for overdue or authority-limited cases.
- Review outcomes. Compare waiting time, rework, reversals, and completed-work quality with the baseline. Investigate exceptions rather than rewarding speed alone.
- Adjust the boundary. Automate more only where the evidence shows low risk and reliable context; add human review where errors or ambiguity remain consequential.
This process treats automation as a change to the whole system. It prevents a faster drafting step from becoming an excuse to hide unresolved decisions in an unowned queue.
Frequently Asked Questions
Does a human have to review every piece of AI-generated content?
No. Review depth should match the item’s risk, audience, uncertainty, and reversibility. Routine internal work can use lightweight checks, while consequential or externally visible actions need a named approver and an auditable rationale.
How can a small team protect review time when it cannot hire more people?
Reduce avoidable demand first: deduplicate requests, require essential information at intake, and route low-risk cases with simple rules. Then reserve recurring blocks for decisions and publish an escalation rule so urgent work does not interrupt every review session.
Can the judgment bottleneck be measured with one universal KPI?
Not credibly from the evidence available. Queue age, rework, reversals, missing-context returns, and outcome-specific measures are more informative when segmented by workflow and risk than a single enterprise score.
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The record should include the original request, source material and version, assumptions, generated output, owner, deadline, dependencies, and prior approvals. A reviewer should be able to understand both where the answer came from and what authority exists to act on it.
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