Help with the decision behind the question
AI tools can already summarise text and organise information. Google’s AI Overviews, for example, provide summaries with links to supporting information. How these capabilities change your own customers’ reading habits will vary.
Some readers may use AI for an initial overview, then visit your website with a more specific question. They may understand what a service does while still needing help deciding whether it suits their circumstances.
As summaries become easier to produce, familiar advice alone may offer less differentiation. General explanations still help customers understand your services. Their usefulness grows when you explain what affects a choice, what the evidence shows and where an approach falls short.
Actual business experience can provide that detail. Having done the work is only the starting point: readers need enough context and reasoning to use what you learnt.
Experience needs context and judgement
Consider three levels of explanation:
“We tried it.” This establishes experience, but gives readers little help assessing whether your preference applies to them.
“We recorded how it worked under particular conditions.” Tasks, setup, constraints and observations let readers assess how closely the situation matches their own.
“Here is what that means for your choice.” A recommendation connects the observations to a decision, including trade-offs and conditions where another option may suit.
“Easy to use” becomes more meaningful when you explain who used it, which task they completed and how much preparation was needed. AI can summarise these accounts too; their value comes from useful evidence and the decisions it supports.
Five useful sources of content
Look for material in work you already do. Keep a short record while the details are fresh.
Direct comparisons and experiments. Give each option the same tasks and assessment criteria where practical. Record dates, settings, preparation, staff effort and problems. Explain any differences in the conditions that affect the comparison.
Methods you chose, and why. Record the alternatives, the constraint that mattered most and the reason for your choice. Explain what would have led you to choose differently.
Recurring customer questions. Keep the wording customers use and the situation that triggers the question. “Can we change this ourselves after launch?” provides a more specific starting point than website management.
Failures and unsuitable conditions. Record the intended outcome, what went wrong, what changed and what remains uncertain. Separate a confirmed cause from a possible explanation. Show where the approach becomes unsuitable.
Follow-up observations. Return after the approach has been used in normal work. Compare the initial expectation with ongoing effort, maintenance and recurring problems. Note changes in staff, tools or workload that could affect your advice.
Turn the work into an article in six steps
Choose a customer question. Identify the intended reader and one decision the article should support. Start with enquiries, project discussions or support questions. Keep the scope narrow enough to answer with your evidence.
Record the situation and constraints. Describe the task, available skills and systems, timing, budget and capacity. Include conditions that could change the recommendation and define what a useful result would look like.
Gather the evidence. Collect dated notes, screenshots, comparison records, specifications or measurements. State what was counted and over which period. Keep direct observations separate from assumptions and mark gaps for checking.
Explain the judgement. Show how the evidence led to the choice. Identify which factors mattered most, the alternatives considered and why another approach was less suitable under those conditions.
Present the result and limits. Describe what happened, what remained difficult and what is unknown. Keep conclusions within the observation period. Several changes made together do not establish which one caused an improvement.
Give the reader a next action. Suggest a specific check, comparison or small test. “List the booking changes your team handles manually” gives someone a practical starting point.
Before and after: an online booking article
Hypothetical example only. The business, comparison and findings below are illustrative.
Before: general advice
Online booking software saves time and makes appointments easier. Choose a system with reminders, calendar integration and a simple interface.
After: advice connected to a decision
For a mobile service business that checks travel time and job requirements before accepting work, a booking request may be more practical than instant confirmation.
In this hypothetical comparison, the business tests both workflows with a nearby appointment, a distant appointment, a booking change and a job needing a quote. The instant-confirmation workflow accepts a time before travel is checked; the request workflow keeps it pending for review.
That supports a request process where each job needs assessment. Instant confirmation may suit services with fixed durations and reliable availability. Before choosing, run those scenarios through your own workflow. Check what the customer sees and what staff must adjust.
The second version gives the reader context, observations, reasons and a useful next step. For a real article, use verified records from work you have actually completed.
Use AI to organise, then check the reasoning
AI can group notes, suggest a structure and prepare a first draft. Supply the customer question and supporting material first. A useful instruction is:
Use only the supplied material. Separate observations from interpretation. Retain a reference to the supporting note for each factual claim. Mark missing evidence for checking. Do not invent details, results, sources or quotations.
Review the draft in two passes. Check the facts: verify dates, product versions, numbers and descriptions against original records. Open cited sources and check whether they support the sentence. Keep hypothetical examples clearly labelled.
Then check the judgement: does the recommendation follow from the evidence? Look for missing conditions, overlooked alternatives or conclusions that became broader during drafting. Fewer manual corrections in one trial should not become a promise that every business will save time.
Have someone familiar with the work check both the account and the recommendation. Start with one customer question and the records behind the answer, using the brief below.
Copy-and-use content brief
Copy these prompts into your next content brief and fill in the details before drafting.
Working title: [Name the decision this article helps someone make.]
Reader and question: [Who is asking, and what do they need to decide?]
Situation and constraints: [Relevant budget, time, team capacity, tools and conditions.]
Experience to draw on: [What did we do, compare or observe, and when?]
Evidence available: [Notes, screenshots, measurements, source links and dated follow-up observations.]
Decision and reasons: [What did we choose? What alternatives were considered, and why?]
Results and limits: [What happened? What remains uncertain? When would this advice not apply?]
Reader’s next action: [One specific check, comparison or small test.]
Review before publication: [Who will verify facts against sources, check the reasoning and resolve missing evidence?]
Next review: [Set a date or trigger for checking whether the advice still holds.]
Source
The AI Overviews reference supports the description of an existing feature. The possible changes in reader behaviour and the writing process above are editorial guidance.