Practical AI articles and decision guides
Find a useful starting point for the decision in front of you. These articles explain tasks, trade-offs and ways to test a proposal before committing time or money.
Start with the decision you need to make
- Choose a first trial: work through a bounded document task, with approved inputs, a reviewer and a clear stop decision.
- Review existing spending: measure the value of AI licences, separating usable capacity from money the business actually saves.
- Evaluate a delivery proposal: check what an AI and Technology Partner should deliver, including acceptance evidence, ownership and exit arrangements.
How to use the examples
The proposed workflows and illustrative calculations are starting points for your own tests. They are not reports of completed customer projects or proof of measured savings. Check the assumptions, source links and conditions before applying an example to your business.
For a practical reading note, record the task, permitted inputs, expected output, reviewer and failure conditions. Add your current effort and correction costs. This makes it easier to compare two approaches using the same evidence, rather than the strength of their sales claims.
Browse all 47 articles
Search titles and descriptions, choose a topic, or combine both. Clear the filters to return to the full list. For terms used in an article, use the glossary; for product comparisons, browse the technology guides.
Review AI-assisted legal work before it leaves the firm
Define what a lawyer must check in AI-assisted work. Use a matter chronology example, source checks and current Australian professional and court guidance.
Read the article
Check what your AI assistant can actually access
Test AI connector permissions with named users, allowed documents, blocked records and revoked access. Keep read access separate from permission to act.
Connect your tools around a clear business handover
Map one handover between business tools. Define record ownership, matching, retries and reconciliation before deciding whether AI belongs in the workflow.
Define who owns the technology work before choosing a hire
Compare an internal technology lead, external partner and combined arrangement by decisions, delivery, support, handover and clearly scoped costs.
Where should your business start with AI?
Choose a first AI workflow by measuring the current task, testing useful output and counting review time. A worked example shows how to compare options.
Make a technology roadmap that survives the next vendor pitch
Turn overlapping software and unfinished integrations into a practical roadmap. Map dependencies, ownership, migration checks and the order of work.
Map where your AI workflow sends business data
Compare cloud, private and local AI by tracing prompts, retrieved text, logs and support access. Deployment labels alone do not establish data control.
Choose an AI model by testing your actual task
Compare AI models on supported answers, critical errors, review effort, cost and latency. Keep a repeatable test set before committing or switching.
Measure sales administration before promising time back
Run a practical sales task audit that includes review and exceptions. Use a transparent workload calculation without treating every saved hour as revenue.
Keep consulting research traceable from source to draft
Design an AI research pilot with source records, claim checks and conflicting evidence. Count review effort before treating a faster draft as a better result.
Choose sales AI with a task and an acceptance test
Compare sales AI using real workflow requirements, difficult test cases, review effort and data controls. Includes a follow-up drafting evaluation brief.
Fund the AI responsibilities and evidence your business needs
Build an AI department proposal around responsibilities, pilot evidence and operating costs. Use staged funding decisions for a multi-site business.
Assess law-firm AI readiness through five matter-work checks
Review intake, file retrieval, collation, drafting and supervision before a legal AI pilot. Use evidence from the firm’s work instead of adoption pressure.
What needs maintaining after an AI launch?
An AI maintenance plan needs owners, change triggers, quality checks and recovery steps. Use a changed order form to see what the plan must cover.
Turn an unclear AI idea into a small testable brief
Plan a first AI trial without assuming you need a new hire or custom build. Use a bounded document example, approved inputs and a clear stop decision.
Trace AI answers across documents and systems
Use identifiers and source evidence to connect an answer across records. See when database queries, document retrieval or a graph can help, and what to test.
Write an AI agent role that people can enforce
Define an AI agent through its task, permitted records, allowed actions, escalation rules and human owner, then test the permissions behind that description.
Capture the reason for a decision, and when it stops applying
Create a usable decision record with evidence, alternatives, authority and review triggers. Keep a past exception separate from a rule for future AI answers.
Give your AI owner authority, time and clear boundaries
Define an AI owner’s decisions, support roles and escalation path. Use a practical role charter without assuming one person must build and approve everything.
Give the first AI decision an owner and useful evidence
Choose the first AI project with a business owner, process evidence and clear decision rights. Includes a quote-delay example and a brief for outside help.
Measure the queue before speeding up the task
Use a worked quote-queue example to separate faster drafting from completed work. Measure waiting, review capacity, corrections and the whole turnaround time.
Give business context an owner, an identity and an expiry rule
Build an AI context register for a field-service task, with stable asset identity, current records, access rules and tests for changed or withdrawn information.
Redesign the dependencies, then test the workflow
Separate necessary checks from avoidable handovers. Use a quote example to decide which steps can run together and which must wait for verified inputs.
Define what happens after the AI draft
Follow a customer change request from draft to recorded outcome. Compare existing workflow tools and custom work against the same completion requirements.
Measure what a multi-agent workflow actually delivers
Compare agent designs by accepted outcomes, review effort and total cost. Use a worked example to decide whether parallel agents justify their added overhead.
Make the AI workflow usable on an ordinary working day
Move a support-answer pilot into daily use with a clear entry point, ownership, current sources, exception handling and a tested fallback when it fails.
Reduce avoidable interruptions without hiding urgent work
Map why a coordinator gets interrupted, then test shared status, batched updates or assisted summaries against missed urgent work and total review effort.
Budget for the whole service when running AI in-house
Estimate local AI costs with a transparent worked example. Check capacity, review effort, support and recovery before comparing hardware with hosted use.
Check the meaning of an AI output before acting on it
Turn a free-text order into a checked proposal. Separate valid structure from intent, catalogue values, current stock and permission to place the order.
Make AI supervision a task people can actually perform
Design staff training around realistic AI failures, evidence checks and recovery. Plan who reviews work and how the team continues when the tool is unavailable.
Assess the tasks before deciding how to fill the role
Compare a proposed hire with process changes and AI assistance using a workload example. Include review, exceptions, coverage and staff consultation.
What to expect from an AI and Technology Partner
Define the work, records and responsibilities an AI and Technology Partner should deliver. Compare monthly scope, acceptance, support and handover.
Measure AI licence value through accepted work
Assess AI licences with task evidence, complete costs and a clear distinction between capacity and cash savings. Includes an illustrative monthly calculation.
Define what you control before deciding to own your AI
Separate model licences, business data and application ownership. Use an exit rehearsal to test whether an AI service can move between operators.
Turn agent feedback into a tested improvement
Use observed failures, repeatable cases and a release comparison to improve an AI agent without mistaking a shorter prompt or cheaper run for better work.
Make Australian AI adoption support useful at the workbench
What should useful AI adoption support give an Australian small business? Separate current policy developments from practical help, evidence and obligations.
Measure how your business uses AI before claiming an impact
Separate AI access, operational use and measured outcomes. Build a practical use register without treating employment statistics as proof of business adoption.
Turn a sovereign AI requirement into verifiable controls
Map AI processing, administrator access, backups and support before choosing a deployment. Includes a practical data-control example for a research firm.
Use AI hiring research to ask better workforce questions
Read the Ramp and Revelio hiring findings within their limits. Then assess demand, capacity, review skills and service quality in your own workforce plan.
Diagnose a wrong AI report before adding another connector
Trace an incorrect AI report through its source records, metric definition, retrieval and calculation. Use a delivery example to identify the actual failure.
Find the task behind unapproved AI use
Investigate unapproved AI use without assuming it proves performance. Separate possible data exposure from workflow needs and give staff a clear approval route.
How to prove an AI agent completed the right action
Follow a delivery-address change from request to confirmed update. Check authority, current order state, duplicate requests and uncertain outcomes.
Check an AI security patch before it reaches production
Separate AI code repair from device patching and security monitoring. Use a customer-portal example to assess findings, tests, release and recovery.
Use AI to prepare construction records that people can verify
Assess AI for construction document control using an RFI and variation example. Check revisions, evidence, approvals and payment requirements before release.
Give an AI coding agent a task you can verify and maintain
Scope an AI-assisted software change with clear permissions, acceptance tests and handover. Includes a duplicate-invoice import example for an internal tool.
Test whether AI catches the cases that matter
Use a worked support-ticket example to measure missed issues, false alarms and review effort, then turn the results into a clear AI deployment decision.
Evaluate open AI models by licence, workload and support
Compare downloadable AI models using the exact licence, task performance, deployment needs and support. Includes a supplier evaluation example for SMEs.