You can see the potential of AI, but it is harder to see where it fits into your own business. A useful starting point is to identify one recurring problem, understand the work behind it and decide whether AI could make that work easier to do well.
For a small business, the challenge is often quite practical: too much time preparing proposals, searching for information, responding to similar enquiries or turning meeting notes into actions. These are places to investigate before committing to another subscription or a larger technology project.
You are not alone in working through this. The National AI Centre’s December 2025 to February 2026 research found that 43% of Australian SMEs reported some AI adoption, while confidence, clarity and trust remained barriers. That is a snapshot of a particular period, rather than a measure of what your business needs today.
Start with a business problem you can describe
Choose something the team already recognises. “We spend too long preparing the first draft of a proposal” is a better brief than “we need an AI strategy”. It gives you a task to observe, people to involve and an outcome to measure.
Ask the person doing the work to walk through it. Where does the information come from? What gets copied between systems? Which decisions need experience? Where do errors or delays occur? A short conversation with the people closest to the process can reveal more than a product demonstration.
Write a simple starting statement: We want to improve [task] for [person or team], measured by [time, quality or response outcome].
Look for repetitive work and information bottlenecks
Build a short list of tasks that happen regularly and have a clear output. These examples are possible uses to assess, rather than promised results:
- Proposal preparation: organise an approved brief into a first draft, with someone checking scope, exclusions and commercial details.
- Document review: extract requirements or compare versions, then verify findings against the source documents.
- Internal knowledge: help staff find an answer in approved procedures, with links back to the relevant information.
- Customer enquiries: prepare draft responses to recurring questions for staff to review before sending.
- Meeting follow-up: turn authorised notes into proposed actions, with participants confirming owners and deadlines.
- Reporting: draft an explanation of verified figures, with the calculations and interpretation checked separately.
Also consider whether the task needs AI at all. A better template, clearer process or a simple rule in an existing system may solve it more reliably. The useful outcome is less wasted effort and better work, whichever approach achieves it.
Understand what information and systems are involved
Before choosing a tool, identify the information it would need. Is it in email, shared folders, a customer system or a collection of spreadsheets? Is it current, complete and owned by someone who can confirm its accuracy?
A proposal assistant, for example, needs a trustworthy brief and approved reference material. If old pricing, superseded terms and current service descriptions sit together without clear ownership, you have an information problem to resolve before relying on generated drafts.
List the required inputs, expected output and person responsible for review. You do not need to connect every business system to learn whether one bounded task is useful.
Distinguish assistance from automation
There is a practical difference between asking an AI tool to draft something for you and allowing a connected system to act on your behalf.
An assistant might suggest a reply that you check and send. An automation follows configured steps, while an AI agent may select actions within the tools and permissions it has been given. Connecting those actions to customer records, outbound messages or other business systems adds requirements for access control, testing and approval.
For a first trial, keep the action boundary clear: the tool prepares an output; a named person reviews it; the existing business process remains responsible for what happens next.
Set a few clear boundaries before testing
AI output can be inaccurate, incomplete or confidently phrased despite missing evidence. Decide what must be checked and who is accountable for that check. Treat a convincing draft as work to review, rather than proof that the underlying facts are correct.
Agree which information may be used, which tools and accounts are approved, and who should have access. Check how the proposed service handles your information before entering confidential material. Use fictitious or suitably de-identified examples for early tests where appropriate. Recording or processing meeting content also needs the relevant permissions and participant agreement.
Keep these checks proportionate to the task. A broader review is needed before connecting AI to sensitive business information or allowing it to make consequential decisions. The National AI Centre’s AI screening questions provide a useful starting resource.
Choose one small pilot with a measurable result
Compare the opportunities against four questions: does the task matter, does it occur often enough, is the information usable, and can the result be checked easily? Start with an option that has a useful outcome and manageable consequences if it gets something wrong.
For example, trial a proposal-outline assistant on a small set of approved sample briefs. Ask it to organise requirements and flag missing information. Keep pricing, technical commitments and final approval with the responsible person. This is an illustrative pilot, not a claim about a completed All Digital client project.
Record the current effort before the trial. Then compare the complete process, including checking and correcting the output. If drafting is faster but review takes much longer, the saving may be smaller than it first appears.
- Time to produce a usable, reviewed result.
- Missing requirements, factual errors and rework.
- Consistency across different examples and users.
- Tool costs, setup effort and ongoing support.
- Whether the team finds the process practical enough to keep using.
Agree in advance what would justify continuing, changing or stopping the pilot. The National AI Centre’s implementation guidance includes setting acceptance criteria and testing against intended use and risk.
Turn the result into a practical next-step plan
After the trial, write down what worked, what failed and what needs to be in place before wider use. That might be cleaner reference documents, staff training, a permissions review or specialist integration advice. It may also be a decision to use a simpler solution.
A useful roadmap identifies the next task, its owner, the information required, the controls and the evidence needed to proceed. It does not need to commit the business to a large implementation before the first use case has demonstrated value.
How All Digital can help
All Digital helps businesses discover and assess practical AI and automation opportunities, understand the information and technology involved, and identify requirements and risks before committing to a solution.
Our focus is independent discovery and advice: clarifying the business problem, prioritising possible uses and establishing a sensible path forward. Where specialist platform configuration or integration is required, that expertise should be identified as part of the next-step plan.
Contact All Digital to discuss a task your team would like to improve, or explore our independent technology consulting services.