AI ACADEMY / AI WORKFLOWS
Build a process you can use again
Make a useful AI result easier to repeat. Learn to turn preparation, drafting and checking into a clear workflow, with the right information at each step and human review where it matters.
Illustrative stock image · Patrick Perkins / Unsplash
LEARN → PRACTISE → CHECK
Turn a result into a repeatable method.
Start with a specific task. Make the method visible. Keep the final judgement with a person.
01 / THE CHALLENGE
Make progress on a real working problem.
A good one-off result is difficult to repeat when its instructions, source material and review steps are scattered.
WHAT WE’LL SHAPE WITH YOU
- A workflow map for a recurring task
- Reusable instructions for each stage
- A review checklist and clear handover points
Make a useful result easier to repeat
DEEPER PRACTICE / AI WORKFLOWS
A workflow connects preparation, AI assistance and human review into a process you can explain. This introductory programme focuses on recurring work, with scope agreed before booking. Suggested modules and exercises are illustrative rather than a promise of a particular finished system.
Explore the detail · See the practice method · Questions before booking
Begin with a result you can recognise
01 / PRACTICAL DETAIL
A successful one-off prompt is difficult to repeat if nobody remembers the input, the instruction or the changes that made it work. Workflow design captures those decisions. It begins with the result: what must be included, what must be excluded and who will use it.
Choose a recurring task with a manageable boundary. Preparing a draft announcement from an approved brief is clearer than asking AI to run marketing. A comparison based on supplied criteria is clearer than asking it to choose a supplier. Smaller scope makes quality checks and ownership easier to define.
Task checklist
- State the expected result.
- Identify the source material.
- Define checks before drafting.
- Name the reviewer and decision owner.
Suggested curriculum: build the sequence
02 / PRACTICAL DETAIL
The first module defines the input and acceptance criteria. The second maps preparation, drafting, checking, revision and approval. Each stage should have a purpose and an output that the next stage can use. The third explores exceptions: missing facts, conflicting sources and answers that do not satisfy the brief.
The final module documents the process so another person can follow it. This includes instruction templates, source references, review questions and a route for asking for help. A workflow can remain completely human operated; automation is a later decision, not a requirement for making work repeatable.
Learning checkpoints
- Inputs and acceptance criteria
- Step-by-step process mapping
- Missing information and exception routes
- Reusable instructions and handover
- Comparison across repeated examples
Illustrative exercise: a theatre announcement
03 / PRACTICAL DETAIL
Use a fictional community theatre’s approved project brief. First extract the supplied facts and unanswered questions. Then create an outline, draft an announcement and compare the draft with the fact list. Finish with a human approval checklist rather than publishing the result.
Acceptance criteria include factual consistency, the requested structure, a clear audience and no invented dates, prices or claims. Deliberately remove one important detail from the next brief. A useful workflow should pause that part of the draft or ask a question, making the gap easy for the reviewer to see.
Example checks
- Each factual claim traces to the brief.
- Unanswered questions stay separate from confirmed facts.
- The draft follows the agreed format.
- Publication remains a distinct approval step.
Illustrative exercise: compare fictional proposals
04 / PRACTICAL DETAIL
Prepare two invented supplier proposals and an agreed table of criteria. Ask the assistant to organise the supplied information, keeping missing details blank or explicitly unknown. Review whether it changed units, confused terms or introduced a preference without supporting evidence.
The output should help a person assess options. It should not hide uncertainty behind a single score or treat an incomplete proposal as a complete one. This exercise develops a useful habit: separate information organisation from the human decision that follows.
Review criteria
- Use the same criteria for each option.
- Preserve units and qualifications.
- Mark missing information visibly.
- Keep recommendations distinct from extracted facts.
05 / PRACTICAL DETAIL
Preparation, fit and handover
This pathway suits people who already use AI and want a clearer process for recurring work. It is less suited to someone seeking an immediately deployed autonomous agent or a production integration. SearchScale’s Agents and Business Builder offers remain separate conversations with their own scope.
Prepare a task description, the output format and the checks currently performed. Use synthetic or approved non-sensitive examples. Useful learning artefacts may include a process map, staged instructions and a review checklist; exact content is agreed before booking. A handover should also state when to stop, ask for clarification or return to manual work.
To assess the method afterwards, try a second example and compare it with the same acceptance criteria. Record which stage needed correction. Improving the weakest stage is often more useful than rewriting the entire process.
Preparation checklist
- Know who supplies and reviews inputs.
- Capture exceptions alongside the normal path.
- Keep original sources accessible.
- Distinguish practice, implementation and live use.
The method matters as much as the output.
A VISUAL PRACTICE MAP
Use this four-stage map to organise the exercises on this page. It describes a learning method, rather than a deployed system or client result.
Workflow exception test
Illustrative input: A fictional theatre brief includes a production name and venue but omits the performance date and booking link.
Try this instruction: “First extract confirmed facts and missing information. Then outline an announcement using confirmed facts only. End with a review checklist and unresolved questions.”
What to check: The sequence keeps extraction separate from drafting, makes the missing details visible and leaves publication as an explicit human decision.
01 / PREPARE
Validate the input
Identify facts, constraints and unresolved questions.
02 / DRAFT
Use staged instructions
Create the agreed output from approved source material.
03 / REVIEW
Check the result
Trace claims and handle exceptions before handover.
04 / APPROVE
Keep a decision point
A person decides whether the result is ready to use.
PREPARE → DRAFT → REVIEW → APPROVE
QUESTIONS & SCOPE
Make an informed choice.
Does a workflow require programming?
No. A documented sequence can be run manually. Practical scope depends on the task and is agreed before booking.
Can it send messages automatically?
The learning pathway does not itself authorise external actions. Any live implementation needs a separately agreed scope and approval arrangement.
What if outputs vary?
Assess each output against consistent criteria and record where variation affects usefulness or accuracy.
How is this different from Foundations?
Foundations develops individual instructions and checks. Workflows connects those skills into a repeatable sequence with ownership and exceptions.
Make your first enquiry useful.
Share the points below in your enquiry. SearchScale will discuss suitability and the programme scope before you commit. Dates, format, fees and required accounts are confirmed directly.
- One recurring task
- Its current inputs and outputs
- Your existing review process
- Who owns the final decision
- Your current AI experience
- Any implementation needs to discuss separately
Practical experience. Transparent learning.
SearchScale Coaching is founded by Chris Matthews, an entrepreneur and SEO practitioner since 2011. The Academy applies a practical business perspective: start with a real task, understand the method and check the result. Explore our approach and the separate Business Builder programme for more about SearchScale’s wider work.
Worked example: turn a draft into a workflow
Start with a fictional meeting note containing three agreed actions and one unresolved question. Ask for an action table with task, owner and due date; require “not specified” for missing information. Check each row against the original note. Save the prompt, input format and review checklist together so another person can repeat the task without guessing.
Illustrative learning example. These exercises use fictional inputs. They are not client case studies, measured business results or a promise of a particular outcome.
Learn from original sources
Tool behaviour changes. Read the current documentation alongside any exercise: OpenAI’s prompt engineering guide and Anthropic’s prompting documentation. These are independent reference sources; linking to them does not imply endorsement, certification or partnership.
Quality checks that matter
Check factual claims against their source. Keep confidential information out of practice exercises. Identify missing inputs instead of filling them with guesses. Retain a human decision before publishing, sending or acting on an output. For an automated process, define permissions, failure handling and a way to stop it.
A clear scope before you commit
All five Academy programmes are available now. Contact SearchScale to agree scope, dates and fees. Before booking, agree the audience, starting level, learning goals, delivery format, included resources and fees with SearchScale. Confirm any required tool accounts or separate subscriptions. The examples on this page explain the practical focus; your agreed programme scope determines the exercises and deliverables.
Have a question or spotted an error? Contact SearchScale with the page and the point you would like clarified. This Academy content was updated on 2 October 2026. The guides use AI-assisted drafting, original fictional exercises and linked primary documentation. Illustrations show the learning method; they do not document client results. Report a factual correction through the contact link above.
PERSONALISED GUIDANCE
Make the workflow fit the work.
Bring a task your team repeats and the constraints that matter. Chris’s SME-focused AI consulting perspective helps ground the conversation in practical needs. Agree the workflow, review points and learning goals before choosing the support.
Meet Chris Matthews and explore the approach →
Start with a free 20-minute discovery call. Receive a report outlining proposed deliverables afterwards. For focused advice, choose a one-hour consultation at US$150, with written follow-up.
LET’S FIND YOUR STARTING POINT
Bring the task.
We’ll help shape the training.
Tell us what you want to improve and who will take part. We’ll agree the programme scope, relevant exercises and resources, delivery arrangements and fees.
Opens your email app. Include your goal and experience level; leave confidential records out of the enquiry.