There is an emerging trend in what AI workflow advisors are telling teams: record someone completing a task, let AI analyse the footage, then use what it finds to improve or automate the process.
AI product specialist Adam Leon highlights what this advice is missing: the goal is getting lost.
Screen recordings can reveal the reality of work in remarkable detail. They capture every search, pause, correction, handoff, and workaround. Yet greater visibility into a process does not tell us what the process should achieve.
Without a clear outcome, teams risk using AI to make the existing workflow faster while preserving the very problems they need to solve.
A recording captures the work people rarely see
Most process documents describe a clean sequence of events. Real work is rarely so orderly.
Consider a creative director reviewing a campaign. The documented process may be simple: read the brief, review the creative, approve the work.
A screen recording could reveal something very different. The creative director searches for the latest brand guidance, checks whether a product claim is current, compares the work with a previous campaign, asks a colleague to clarify the audience, and transfers feedback between several systems.
This evidence is useful. It can expose:
- Scattered knowledge
- Repeated data entry
- Unclear ownership
- Delayed decisions
- Avoidable tool switching
- Gaps between the documented process and daily practice
It can also reveal the invisible work employees do to protect the organisation. A repeated check may prevent an inaccurate claim. A message to a colleague may resolve an ambiguity before it reaches the customer.
The recording shows the action. It needs context to explain its value.
Visible activity can become a distraction
AI analysis naturally gravitates towards what it can measure: clicks, time, repetition, and movement between systems.
The most important parts of the work may be harder to see. Creative judgment, customer understanding, brand consistency, and confidence in a decision do not always leave an obvious trail on screen.
An AI advisor might suggest removing an approval because it slows the process. That approval may protect the brand from a costly error.
It might recommend automating local campaign adaptations. If the automation draws from outdated guidance, inconsistency will spread more quickly across locations.
It might identify three reference checks as unnecessary duplication. Those checks could reveal a deeper problem: the team has no single source of truth it trusts.
A screen recording can show where effort goes. The desired outcome determines what that effort is worth.
Define what better means first
Before recording a task, the team needs to agree on the result it wants.
“Speed up campaign approvals” gives everyone a direction, but it leaves quality, consistency, and customer impact undefined.
A clearer outcome might be:
Approve accurate, on-brand campaign materials within two working days, with clear ownership and fewer avoidable revisions.
This gives AI meaningful criteria for its analysis:
- How quickly should the work move?
- What must be accurate?
- Which brand standards must be protected?
- Who has authority to make each decision?
- Where is effort being wasted?
- What should improve for employees and customers?
The outcome helps teams distinguish between friction that creates no value and careful work that protects the experience.
Branding defines what people expect. Every internal workflow contributes to whether the organisation keeps that promise.
Employees provide the context AI cannot see
Recording someone’s screen can feel uncomfortably close.
People may worry that every pause will be judged, private information will be captured, or footage will be used in a performance review. Those concerns deserve clear answers before recording begins.
Teams should explain:
- Why the recording is needed
- What will and will not be captured
- Who can access it
- How long it will be retained
- How sensitive information will be protected
- How the findings will be used
The employee should also have the opportunity to explain their decisions. Ask where they lacked information, which sources they trusted, and when experience mattered more than a documented rule.
Employees live with the consequences of unclear standards and unreliable systems. Their input turns observed behaviour into practical understanding. It also helps ensure that improvement feels collaborative rather than imposed.
Put the outcome before the footage
A responsible improvement process follows a clear sequence:
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Define the desired outcome. Include speed, quality, risk, employee effort, brand consistency, and customer impact.
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Record representative work. Capture routine tasks, difficult cases, and exceptions.
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Invite employee commentary. Let people explain decisions, workarounds, and moments requiring judgment.
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Identify the cause behind each action. Repeated effort may point to missing information, unclear ownership, weak systems, or a necessary safeguard.
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Redesign before automating. Resolve gaps in knowledge and responsibility before asking AI to accelerate the process.
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Measure the complete result. Track time alongside errors, revisions, escalations, employee experience, and customer outcomes.
This order matters. When AI receives footage without a defined outcome, it can only infer what improvement means. The result will often favour efficiency because efficiency is easy to measure.
Keep asking where the work is meant to lead
Adam Leon sees genuine value in using screen recordings to understand workflows. They offer an honest view of the effort hidden behind formal processes, especially across teams using scattered tools and guidance.
His concern is what happens when the recording becomes the starting point and the destination.
Before asking AI how to improve a task, ask:
What should become better for the customer, the employee, and the organisation when this work is complete?
Define that outcome, then use the recording to find the gap between the current process and the result you want.
Otherwise, AI may help you complete every step more efficiently while the real goal quietly slips out of view.