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The first thing an AI consultant should do after being hired is usually not build a chatbot, connect an API, or recommend an automation. It is to spend a full working day observing how the client actually operates. Thanh Pham describes arriving at an Austin health clinic with a small microphone and a notepad, asking permission to record, and then watching meetings, patient intake, scheduling, staff communication, and breakdowns without proposing solutions. That quiet first day exposes the repetitive work and informal workarounds that a conventional discovery call tends to miss. Once the day is transcribed, AI can help identify recurring patterns and rank practical automation opportunities. The result is a proposal grounded in observed behavior rather than assumptions.
What should an AI consultant do first after being hired?
Start with a permission-based observation day. Do not begin by building, pitching, or redesigning a process. Watch the client’s work in context, record it only with explicit consent, and collect the interruptions, handoffs, repeated questions, and manual steps that people have stopped noticing.
Pham summarizes the reason plainly: “The problem is that the problem description is almost always incomplete.” A client may accurately describe the outcome it wants while leaving out the informal steps that consume time. Observation gives the consultant evidence about those steps before an implementation plan locks in the wrong problem.
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- It is: a nonjudgmental look at meetings, handoffs, tools, exceptions, and routine work.
- It is not: a sales presentation, an automation workshop, or an attempt to optimize every activity immediately.
- The deliverable: a factual record of recurring workflows that can later be analyzed and prioritized.
Why can a discovery call miss the real automation opportunity?
People describe work as they believe it should happen, not every way it actually happens. A 45-minute conversation usually captures official responsibilities and memorable frustrations. It rarely captures the tiny workaround performed dozens of times, the question answered in a chat message, or the task that exists only because nobody has built a tracking loop.
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In the clinic example, a doctor assigned tasks verbally during a weekly meeting. There was no central task list, automated follow-up, or accountability loop, so action items remained in people’s memories. That arrangement had become routine; it was therefore easy to omit from a discovery conversation. Watching the meeting made the missing system visible.
| Consulting lens | What it reveals | Typical weakness |
|---|---|---|
| Self-reported problem | What staff remember as frustrating or important | Routine workarounds may be treated as normal and left out |
| Observed behavior | Actual handoffs, interruptions, duplicate entry, and exceptions | Requires time, access, and careful consent |
| Short discovery call | Goals, constraints, and desired outcomes | Provides limited evidence about frequency and sequence |
| Full-day transcript | Repeated language and processes across real situations | Needs privacy controls and human interpretation |
Observation does not make the client’s description irrelevant. It adds the operational detail needed to decide whether an AI solution is appropriate and where it would have the greatest practical effect.
What should you observe during the first working day?
Follow the work from beginning to end, including the moments employees normally skip when explaining it. Attend scheduled meetings, ask staff to demonstrate workflows in real time, and note what happens when information is missing or a request arrives unexpectedly.
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Meetings and task assignment
Record how decisions are made, who receives an action, where the action is written down, and how completion is checked. Listen for verbal commitments that never enter a shared system. In the clinic, this was the point at which the consultant saw that memory—not a tracker—was carrying the follow-up process.
Intake, scheduling, and handoffs
Watch the movement of a request between people and tools. Note duplicate data entry, copying and pasting, re-keying information from messages, and pauses while someone searches for context. Ask the person doing the work to narrate each step rather than summarize the entire process afterward.
Interruptions and exceptions
Capture staff questions, urgent requests, missing details, and the “what if” cases that force a manual detour. These moments often explain why a process feels difficult even when its official diagram looks simple.
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Manual repetition
Mark every step repeated daily or weekly: a recurring email thread, a three-step form routine, a report assembled from the same sources, or a status update requested in the same meeting. Frequency matters more than how impressive an automation would look in a demo.
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The described workflow uses a small microphone that can be worn or clipped to a surface. With the client’s permission, the consultant records the day’s meetings, workflow discussions, problem-solving, interruptions, staff questions, and process explanations. The recordings are converted into a transcript covering hours of real work, then analyzed with AI for recurring patterns and themes.
Use a clear consent checkpoint
Explain what will be recorded, who will access the files, how long they will be retained, and how the material will be used. Obtain approval before recording begins and pause or stop recording whenever a participant asks. A client can choose observation without audio, or restrict recording to specific meetings and workflows.
Separate raw audio from the working transcript
Store the original recordings and the transcript with access controls appropriate to the client’s information. Remove or redact details that are not needed for process analysis, especially patient, financial, employee, or customer information. Confirm the client’s retention and deletion requirements before sending material to any transcription or analysis service.
Let AI find patterns, not make the final decision
AI can surface repeated phrases, topics, and sequences in a long transcript far faster than a person reading it line by line. It can still misunderstand speakers, miss context, or treat an exception as a standard process. A consultant should verify each candidate workflow against the recording and with the people who perform it.
How do you find repetitive tasks clients no longer notice?
Look for recurrence, not drama. The strongest candidates are ordinary actions that happen every day or week and consume attention each time. Pham’s rule is to “automate the tasks that happen daily or weekly, not the rare impressive ones.”
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Signals in conversation
- “I always have to…” followed by the same sequence of clicks or messages.
- “Every Monday…” or another fixed cadence attached to an email, report, or reconciliation.
- “If that happens, I ask…” indicating a repeated clarification loop.
- “It is in my notes” or “I remember who owns it,” indicating a fragile memory-based process.
Signals in the workflow
- The same information is entered into more than one system.
- A person waits for a status update before the next step can begin.
- A recurring meeting exists mainly to restate assignments or check progress.
- An exception appears so often that it is effectively part of the normal process.
Write down the task, its trigger, the people and tools involved, the approximate cadence observed, and the consequence of delay. Do not convert an observation into an automation requirement until the owner confirms that the description is accurate.
What is a practical step-by-step observation framework?
The following sequence turns a day of shadowing into evidence that can support an implementation proposal.
- Request permission before the first session. Explain that the goal is to understand real work rather than pitch a solution. Identify participants, sensitive conversations, recording boundaries, storage, access, and deletion expectations.
- Map the day’s work. Attend the relevant meetings and have staff demonstrate workflows while they perform them. Keep a time-stamped note of triggers, handoffs, tools, interruptions, and workarounds.
- Mark repeated manual steps. Flag actions that recur daily or weekly, including email chains, copy-and-paste work, recurring searches, and verbal assignments with no shared record.
- Transcribe the permitted recordings. Check the transcript for missing speakers, unintelligible sections, and sensitive data. Treat the transcript as working evidence, not an unquestionable log.
- Ask AI to rank candidates. A useful starting prompt is:
Identify the 5 most time-consuming repetitive tasks in this transcript, ranked by frequency. - Validate the ranking with staff. Ask the people who perform each task whether the sequence, frequency, and pain point are represented correctly. Correct misheard or exceptional cases.
- Build the proposal from evidence. Describe the observed workflow, the repeated friction, the information required, the proposed intervention, and the human checks that remain necessary. Rank items by frequency and plausible time savings rather than novelty.
How should automation opportunities be prioritized?
Use a simple evidence-first ranking. A task that occurs frequently and follows a stable pattern normally deserves attention before a spectacular but rare use case. Frequency is not the only criterion, however: risk, data sensitivity, error cost, and the client’s ability to maintain the solution also matter.
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| Field | Question to answer |
|---|---|
| Trigger | What starts the task? |
| Cadence | Does it occur daily, weekly, or only occasionally? |
| Steps | Which actions are repeated, copied, searched, or re-entered? |
| Owners | Who performs the work and who receives the result? |
| Inputs | What documents, messages, fields, or decisions are required? |
| Exceptions | When must a person override or review the normal path? |
| Risk | What could go wrong if an automated step misreads or omits information? |
| Expected benefit | What recurring effort or delay could plausibly be reduced? |
This record keeps a proposal specific without pretending that observation produced measured savings. The article describing the method reports no named statistics, study sample, measured time reduction, or numeric approval-rate result; it is a qualitative case example and guide.
What did the health-clinic example reveal?
The clinic’s weekly meeting exposed a gap between assigning work and tracking it. The doctor spoke the assignments, but there was no central system, automatic follow-up, or accountability loop. Staff relied on memory, so a process that looked ordinary in conversation carried a hidden risk of missed action items.
The lesson is broader than healthcare. Any organization can normalize a verbal handoff, a recurring message thread, or a personal spreadsheet until nobody thinks to name it as a process. Observation makes the invisible structure discussable. It also prevents the consultant from proposing an elaborate AI feature when a simpler shared task record may address the actual bottleneck.
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What should the final AI consulting proposal contain?
Start each recommendation with what was observed, not with a product category. A reader should be able to trace the proposal back to a real workflow.
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Describe the trigger, each handoff, the tools used, the repeated manual work, and the exception points. Quote or paraphrase process language only when it clarifies the sequence, and remove unnecessary personal or sensitive details.
Define the intervention narrowly
Specify whether the first step is classification, drafting, extraction, routing, reminders, or another bounded capability. State what the system will not do. Keep a human approval point where an error could affect a patient, customer, employee, payment, or legal obligation.
Set an evaluation plan without invented promises
Choose measures the client can collect after implementation, such as completion time, number of handoffs, missed follow-ups, or manual entries. Do not claim a percentage improvement before the client has baseline data and a defined test period.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What mistakes undermine an observation-first engagement?
Several shortcuts recreate the exact blind spots the method is designed to remove.
- Building on day one: A quick prototype can anchor everyone to an unverified assumption.
- Recording without clear permission: Even a useful transcript is unacceptable if participants did not understand the recording and its use.
- Observing only managers: Leaders explain intended workflows; frontline staff encounter the exceptions and workarounds.
- Chasing novelty: A rare, impressive demo may deliver less value than a dull weekly task.
- Trusting the transcript blindly: Misattributed speakers or missing context can produce a false pattern.
- Automating a broken handoff: If ownership is unclear, adding AI may accelerate confusion rather than resolve it.
- Ignoring maintenance: A workflow that depends on an owner, review queue, or changing source data needs an operating plan.
What should a client expect from the first day?
Expect questions, note-taking, and periods in which the consultant does not offer an opinion. Staff may be asked to perform ordinary work slowly enough to explain it, while the consultant records the sequence and asks what happens when the normal path fails.
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By the end of the observation period, the client should have a shared list of recurring workflows and an agreed process for checking the transcript-derived findings. The consultant should be able to explain why each recommended opportunity was selected, what evidence supports it, and where human judgment remains essential.
Why is observation a better starting point than an impressive AI demo?
An impressive demo answers whether a capability exists. Observation answers whether that capability belongs in this client’s work, at this point in the process, with these people and constraints. Pham’s formulation captures the distinction: “You don’t find those by asking. You find them by watching.”
A proposal built from observed behavior can be more specific than one built from a short discovery call because it names the actual trigger, repetition, handoff, and exception. It still requires validation, privacy safeguards, and a measured rollout, but it starts with a problem the organization can recognize in its own day.
Frequently Asked Questions
How long should an AI consultant observe a client before proposing automation?
The described method uses a full working day so the consultant can see meetings, routine work, interruptions, and exceptions in context. The right duration depends on the client’s operating cycle; a process that happens weekly may require observing the relevant weekly event rather than compressing it into a short interview.
Can observation be done without recording audio?
Yes. A consultant can take structured notes, observe screens or handoffs with permission, and ask staff to demonstrate the workflow. Audio recording creates a richer transcript, but it is optional and should never override a participant’s privacy or the client’s data-handling rules.
Who should review an AI-generated list of repetitive tasks?
The people who perform the work should review it, along with the process owner responsible for the outcome. Their review can distinguish a genuinely frequent task from a one-off exception and can catch transcription errors before a recommendation becomes a build requirement.
What if the most frequent task is not suitable for AI?
Frequency is a prioritization signal, not an instruction to automate. A shared checklist, clearer ownership, a form change, or a simple tracking system may solve the observed friction more safely than an AI component.
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