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Yes—but it is a shift in where work queues form, not proof that every organization is already failing. Generative AI can draft, summarize and launch tasks quickly. People still have to determine whether the result is accurate, strategically appropriate, safe to use and worth acting on. When AI-assisted output grows faster than an organization’s ability to review context and make decisions, the constraint can move from production to judgment.
Pratima Arora, Smartsheet’s Chief Product and Technology Officer, makes that argument in The AI Journal. The practical question for leaders is not whether to slow AI down, but how to ensure that decisions keep pace with the work AI starts.
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Is human judgment becoming the bottleneck as AI makes work faster?
It can be, especially when an organization measures activity more carefully than it measures informed decisions. AI reduces the time needed to produce a first draft, summarize material or begin a workflow. It does not automatically supply the institutional context, accountability or authority needed to approve the next move.
Arora describes the distinction plainly: “AI can draft, summarise, and start the next task. But people still decide what matters.” Her thesis is about organizational flow. If more items enter a review, prioritization or approval queue while the number of people with the right context stays fixed, that queue becomes the new bottleneck.
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That does not mean human judgment is inefficient or that automation has failed. Judgment is the control layer that prevents a high-volume system from producing high-volume mistakes. The issue is whether a company has designed enough capacity, information and escalation paths for that control layer.
What do the reported AI activity figures actually show?
Smartsheet reported a sharp increase in automation and activity across its customer base, but the figures do not measure the capacity or quality of human decisions. They should be read as vendor-reported indicators of more work moving through enterprise projects, not as a proven ratio between AI output and judgment capacity.
| Measure | Reported result | What is known | Important limit |
|---|---|---|---|
| Automation intensity per enterprise account | Up 55% year over year | Smartsheet says the figure came from an analysis of 1.4 million active enterprise projects. | The August 31, 2026 article does not define “automation intensity,” identify the measurement period or publish a methodology. |
| Overall activity | Up 46% year over year | The same Smartsheet analysis covered the same 1.4 million active enterprise projects. | The report does not establish that automation caused the increase or compare it with human review capacity. |
Because the source does not provide a public method, the figures cannot show that a particular percentage of work is waiting for approval, nor can they prove that decision quality has declined. Arora is a company executive, so Smartsheet’s commercial relationship to the data is relevant context. The numbers support the observation that more activity is being generated; they do not establish a universal law about every workplace.
Why can faster generation create slower decisions?
A typical AI-assisted workflow has several stages, and only some are automatable. A model may create a draft in seconds, while people still need to retrieve background information, check claims, reconcile competing priorities, obtain consent and decide who owns the next action.
1. Production accelerates first
Drafts, summaries, classifications and proposed actions become cheap to generate. Teams can open more projects, test more options and send more material into shared systems. The visible output count rises even if the underlying goals have not changed.
2. Review work expands with the queue
Every additional output creates a potential verification task. Someone must ask whether the source is reliable, whether important details were omitted, whether a recommendation conflicts with policy and whether the item is ready for an external audience. A short AI draft can therefore create a longer human review trail.
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Reviewers often spend more time finding the decision history than reading the new draft. If assumptions, customer constraints, prior approvals and relevant data live in separate tools, the reviewer has to reconstruct the case before making a judgment. This is slower than editing the text itself.
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4. Prioritization becomes harder
When many items look complete, leaders must decide which deserve attention first. A team can be busy approving low-impact work while a consequential decision waits for the one person who understands its risks. The organization appears productive while its most important queue grows.
5. Accountability remains human
Automation can recommend an action, but a person or governed process usually remains responsible for accepting its consequences. That responsibility creates a natural checkpoint even when generation is nearly instantaneous.
This mechanism is plausible wherever output volume rises faster than review capacity, but it is not guaranteed. A team with clear decision rights, connected context and deliberate limits on intake may increase throughput without creating a damaging queue.
What does the Harvard Business Review study add to the picture?
An account in Harvard Business Review provides a bounded example of what can happen after AI tools are introduced. Aruna Ranganathan and Xingqi Maggie Ye describe an in-progress, eight-month study of about 200 employees at one U.S. technology company.
The authors report that employees worked at a faster pace, took on a broader scope of tasks and extended work into more hours after the tools became available. Their characterization is direct: “In our in-progress research, we discovered that AI tools didn’t reduce work, they consistently intensified it.”
The study does not show that every company will experience the same pattern. It involves one company, a limited number of employees and an investigation that was still in progress when the account was published on February 9, 2026. Its value here is as an example of work intensification: faster production can encourage organizations to raise expectations, add tasks and widen scope instead of reducing workload.
Which decisions still require a person?
The most useful distinction is not “AI work” versus “human work.” It is whether a task requires authority, context or consequences that a model cannot own.
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Accuracy and evidence
A reviewer must verify material claims, calculations, citations and interpretations against authoritative sources. This is especially important when an error would affect safety, finances, legal exposure or a customer commitment.
Strategic fit
A technically correct output can still point in the wrong direction. People who understand the organization’s goals have to judge whether a proposal supports the current strategy, serves the intended audience and fits the resources available.
Risk and values
Models do not carry an organization’s risk appetite or ethical obligations. Human owners must decide what is acceptable, what needs a specialist review and what should not proceed.
Priority and sequencing
“What should happen next?” is a decision about trade-offs. It depends on deadlines, dependencies, customer impact and opportunity cost, not only on the content of the generated item.
Commitment and accountability
Approving a budget, publishing a statement, changing a service or contacting a customer creates an obligation. The person with the authority to make that commitment should be explicit, even when AI prepared the underlying materials.
How should organizations redesign workflows around the judgment bottleneck?
Arora’s proposed response is operational: preserve context, identify where judgment is required and give decision-makers enough time and information to use it well.
Keep context attached to the work
Store the goal, owner, assumptions, source material, decision history and current status alongside the AI-generated output. A reviewer should not have to search several systems to discover why the item exists or what constraints apply. Centralizing this information also makes handoffs less dependent on one person’s memory.
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Separate automated steps from human decisions
For each workflow, label what AI may do without approval, what requires a routine check and what requires a named decision-maker. A useful policy can specify thresholds such as financial impact, external publication, sensitive data or a change to customer-facing behavior. The point is not to require approval for every sentence; it is to reserve human attention for consequential choices.
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If review is treated as spare capacity, it will be displaced by new production work. Set recurring blocks for approvals, provide realistic service levels for different risk categories and avoid filling every available hour with additional generation tasks.
Make bottlenecks visible and escalate them
Track where items wait, how long they wait and which roles are repeatedly overloaded. Escalation should be a designed path: a backup approver, a specialist queue or a decision to pause intake. Without that path, teams often respond to a growing queue by working longer hours, the pattern highlighted in the HBR account.
What should leaders measure besides the number of AI outputs?
Output volume is an input to the system, not its final result. A more useful scorecard combines throughput with decision quality, timeliness and the cost of review.
| Question | Useful signal | Why it matters |
|---|---|---|
| Are items moving? | Median time from AI-generated draft to approved or rejected decision | Shows whether the review queue is becoming a constraint. |
| Is review effective? | Rate of material corrections, reversals or escalations after approval | Reveals whether speed is being purchased with avoidable errors. |
| Is context sufficient? | How often reviewers request missing background before deciding | Identifies fragmented information and weak handoffs. |
| Is capacity sustainable? | Review hours, after-hours work and concentration of approvals in a few people | Shows whether the process depends on burnout or a single expert. |
| Did the decision help? | Outcome measures tied to the original goal, such as resolved cases or completed milestones | Connects activity to useful results rather than counting drafts. |
These are management measures, not a validated universal standard. Arora’s recommendation is to judge whether work moves through an organization with clear, informed decisions and useful outcomes, rather than treating activity alone as success.
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A small implementation can expose the queue before an organization attempts a broad policy change.
- Inventory AI-assisted workflows. List where models draft, summarize, classify or recommend actions. Record the owner, expected outcome and current approval step.
- Map waiting points. For two weeks, capture when an item enters review, when a person starts it and when a decision is recorded. Separate waiting for context from waiting for a person.
- Attach a minimum context package. Require the objective, source links, key assumptions, risk category, owner and requested decision to travel with each item.
- Set decision thresholds. Define which low-risk actions can proceed automatically, which need a routine check and which require a named senior or specialist approver.
- Reserve review capacity. Put approval blocks on calendars and set service targets by risk level. Do not promise instant decisions for work that requires investigation.
- Test escalation. Create a backup approver and a rule for pausing new intake when a queue exceeds its agreed limit.
- Review outcomes weekly. Examine delays, corrections, reversals and after-hours work. Change the workflow or staffing when the evidence shows a recurring constraint.
What failure modes make AI-related judgment queues worse?
- “Human in the loop” without a named owner: Everyone is expected to check the output, so no one is accountable. Assign one decision-maker and a backup.
- Approval of formatting instead of substance: Reviewers polish language while missing unsupported claims or strategic conflicts. Use a checklist that starts with evidence, risk and purpose.
- Context stored in private messages: The next reviewer cannot reconstruct why a decision was made. Put assumptions and approvals in the shared work record.
- Identical review for every risk level: Low-impact drafts consume the same scarce expertise as consequential actions. Tier the process by impact.
- More automation used to hide a staffing problem: Generating additional items does not solve a queue caused by too few qualified reviewers. Adjust intake, authority or capacity.
- Success defined as activity: A rising count of drafts can conceal slower decisions and more rework. Pair activity with outcome and quality measures.
Is the bottleneck claim universal?
No. The available evidence supports a credible pattern and a useful warning, not a universal measurement of human capacity. Smartsheet’s figures are company-reported, lack a public methodology and do not compare activity with decision quality. The HBR example is limited to about 200 employees at one U.S. technology company and was still in progress.
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There is also no directly comparable, independently measured enterprise-wide limit for human judgment in the cited material. It would be incorrect to turn the 55% and 46% figures into a ratio, or to claim that AI caused every increase in activity. Organizations should examine their own queues, error rates, review hours and outcomes.
What can an individual knowledge worker do?
Individuals cannot redesign an entire workflow, but they can make each handoff easier to judge.
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- Include source links, assumptions, deadlines and known uncertainties with an AI-generated draft.
- Mark what the model produced and what you have personally verified.
- Flag consequences that require a specialist or accountable approver.
- Record the final decision and its rationale so the next reviewer does not restart the investigation.
These habits reduce the time a reviewer spends reconstructing context and make it less likely that a polished draft will be mistaken for a settled decision.
What is the practical takeaway?
AI can increase the amount of work an organization starts without increasing the number of people able to decide what matters. Treat judgment as a designed capability: carry context with every item, distinguish routine automation from consequential approval, protect review time and measure whether decisions produce useful outcomes. The goal is not fewer AI outputs; it is work that reaches the right decision with enough evidence, at the right time.
Frequently Asked Questions
Does AI eliminate managerial work?
Usually it changes the work rather than removing it. Managers may spend less time requesting first drafts and more time setting priorities, resolving trade-offs, coaching reviewers and accepting accountability for consequential choices.
How can I tell whether my team has a growing review queue?
Compare the time an item waits before review with the time a reviewer actively spends on it. A widening gap, rising after-hours approvals or repeated requests for missing context are stronger warning signs than a simple increase in task count.
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Should every AI-generated item require human approval?
No. Approval rules should reflect impact and reversibility. Low-risk, reversible actions can follow automated checks, while external commitments, sensitive decisions and changes with durable consequences need an identified human owner.
What information should accompany an AI-generated draft?
Include the intended outcome, source materials, assumptions, model or tool used, generation date, unresolved uncertainties and the exact decision requested. Keeping that audit trail with the item helps a later reviewer verify both content and provenance.
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