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Let’s talk AI and the 9 golden rules for getting it right from the start.

We all have to start somewhere. By now, most of us have already dabbled with AI in some form to generate content or images or find information – and have already seen it impact our lives.

But when you’re looking to use AI in your business – the how, where, and why matter more than you’d think. In the rush to adopt AI, too many businesses are still struggling to generate a return on their investment – making it a luxury, not an operational asset. 

Poorly implemented AI is becoming the new money pit, with businesses spending on tools they don’t fully use, don’t fully understand, and can’t yet measure.

So, what do you need to consider before you roll it out at large? What are the dos and don’ts that are better to know now than later?

 
1. Do start with the problems you want to solve.

One of the most common mistakes we see is businesses starting their AI adoption by asking, “How can we use AI?” 

It’s the wrong question, and it leads to the wrong decisions.

The problem is that everyone can use AI, but not everyone will get value from it. Without a clear purpose, you end up paying for licences that sit underused, producing outputs nobody trusts, for workflows that didn’t really need changing in the first place. The tool doesn’t disappear – just the money invested.

The better question is: “What problem are we actually trying to solve?”

For most businesses, AI’s biggest benefit is productivity. It excels at automating repetitive tasks, cutting through administrative bottlenecks, speeding up decision-making, and taking the load off customer service teams.

Golden rule: If you can’t explain the business value, don’t start the project. 

 
2. Do put some serious thought into who really needs it.

Instead of rolling out, say, ChatGPT to everyone, identify who in the business should have priority. For example, these roles often see immediate productivity gains when empowered by AI:

  • Executives and business owners
  • Sales teams
  • Marketing and content creators
  • Project managers
  • Business analysts
  • Consultants and professional services staff
  • Account managers
  • Customer service teams
  • HR and recruitment
  • Finance teams that do reporting and analysis

Start with the people who spend most of their day working with information – creating, analysing, summarising, communicating, or searching. That’s where AI delivers the fastest, most measurable return.

These roles can also benefit, but tend to need more specialised tools and tighter controls around how data and code are handled.

  • Technical specialists
  • Engineers
  • Developers
  • IT administrators
  • Operations managers

Giving AI to staff in purely transactional roles (for example, people who rarely work with large volumes of information) is unlikely to deliver a return. AI delivers the most value in information-rich roles – and the more information-heavy the role, the more it has to offer.

Golden rule: Choose your users carefully. AI is a business tool and needs to deliver efficiency and outcomes to justify its investment.

 
3. Do be (very) mindful of where you share your data.

AI is only as safe as the data you give it – and public AI isn’t safe at all. Before your employees start using any AI tools (sanctioned or not), establish clear rules around:

  • Customer data
  • Financial information
  • Intellectual property
  • Personal or sensitive information
  • Confidential business documents

Never ever share any of this sensitive data on a public AI platform. If you wouldn’t publish it on your website or in a press release or on LinkedIn, then it doesn’t belong in the public arena. While your employees may have started off experimenting with free AI tools to ‘get the feel of what it can do’, that ‘free’ rapidly becomes expensive when your data is exposed.

Examples include your staff potentially:

  • Copying your sales proposals and pricing into public AI tools
  • Uploading spreadsheets containing personal payroll information
  • Sharing confidential strategy documents or meeting notes for summarisation
  • Entering source code or commercial agreements

In Australia, the Privacy Act and the Australian Privacy Principles (APPs) set clear obligations around how personal information is handled. Using public AI tools to process personal data may breach the Act. It’s not just an IT risk; it’s a legal one.

Golden rule: Be aware that your employees may be using unauthorised AI tools (aka ‘shadow AI’) which can pose a huge organisational risk. Ensure you can see all tools and enforce the use of authorised tools only through monitoring, staff education, and clear business guidelines.

 
4. Do make sure you have a good handle on AI-enabled decision-making.

The government has recently updated the Australian Privacy Laws. One update specifically covers how you use the personal information you collect. If your organisation uses AI, automation, decision-support software, or other forms of automated decision-making to influence decisions about people, you need to update your privacy policy (by 10 December 2026).

You’ll need to make sure you can explain:

  • The personal information these systems use
  • The decisions that are made entirely by automated systems
  • The decisions that are heavily influenced by automated systems, even if human input is involved

Golden rule: Do your homework. Make sure you understand the legal requirements for transparency and update your policy to reflect it. (Especially as the Office of the Australian Information Commissioner is conducting privacy compliance checks and compliance is now an enforcement priority.)

 
5. Do create rules from the start.

You don’t need a 50-page AI policy, but you do need guardrails (aka governance). To start off with, you’ll need to decide:

  • Which AI tools are approved
  • Who is responsible for their oversight
  • What employees can and can’t use AI for
  • When human review is required

The most successful organisations build governance early rather than trying to bolt it on later.

Golden rule: Without governance, AI can create risk much faster than it creates value. Look to purpose-designed frameworks for SMBs like SMB1001 for guidance on AI policies.

 
6. Don’t feed garbage into your AI.

Garbage in, garbage out. It has been true in technology for decades, and AI is no different.

If your documents, CRM records, processes, or knowledge base are outdated, incomplete, or inconsistent, AI will simply produce faster versions of poor outcomes. And it will do it confidently and at scale, which makes it worse.

In our experience, data quality is one of the biggest barriers businesses hit when they start their AI journey.

Golden rule: Clean up your data before expecting great results from AI.

 
7. Don’t expect AI to replace your people.

The businesses that struggle most with AI are usually the ones that approached it as a way to reduce headcount.

Rather, the strongest AI outcomes typically come from reducing low-value admin work. Less time spent compiling reports, chasing information, and reformatting documents means more time for customer conversations, strategic thinking, and decision-making.

The businesses that get this wrong don’t just see poor AI outcomes. They see disengaged staff, internal resistance, and stalled adoption. Employees are far more likely to adopt tools they understand and trust. Bringing your team along isn’t optional; it’s an important part of your AI strategy.

Training and change management matter as much as the technology itself. Deploy AI tools without the groundwork and user adoption stalls fast.

Golden rule: AI should support human judgement, not substitute for it.

 
8. Do measure what matters.

Before you roll out any AI tool, define what success looks like. If you can’t measure it, you can’t manage it, and you definitely can’t justify the spend.

Simple metrics to start with:

  • Time saved per task or per role
  • Reduction in manual processing errors
  • Volume of output produced (content, reports, responses)
  • Staff satisfaction and adoption rates

These don’t need to be complex. A simple before-and-after comparison on a single workflow is enough to start building the business case.

Golden rule: If you can’t measure the outcome, you can’t prove the value.

 
9. Do bring in the experts.

AI is a challenge to keep pace with for both businesses and IT providers like A Corp. However, that’s our job. We are on a continual learning curve, focused on keeping up with AI, so you don’t have to.

Getting AI right is less about speed and more about intent. The businesses that see the strongest results started with a clearly defined problem, chose the right tools, and got expert input before committing to a direction.

So, if you’re not sure where to start, that’s completely normal. Pick one contained, well-defined project, establish your guardrails, and let what you learn inform the next step. A focused first project will tell you more about what your business actually needs than any amount of planning in isolation.

At A Corp, we work with businesses to figure out where AI genuinely fits, build solutions around how they actually operate, and make sure governance is in place to do it safely. We’re not here to sell you a tool. We’re here to make sure the right decision gets made.

Golden rule: The right guidance at the start costs far less than fixing a poorly planned rollout later.

 

Ready to start the conversation? Let’s talk AI.