AI Won’t Automatically Make Your Business More Productive

Artificial intelligence (AI) can help small and medium-sized businesses improve productivity, but simply adding another tool to the technology stack will not deliver measurable results, especially if the information and processes it relies on aren’t ready.

The tension is becoming more important as Australia looks to AI to address one of its biggest economic challenges. A new parliamentary inquiry announced by Federal Treasurer, Jim Chalmers, will examine how AI and other technologies can lift productivity and competitiveness.

Yet only 7% of Australian businesses report using AI broadly across their operations, suggesting many are still working out where and how it can deliver value. 

For SMBs with limited technology resources and little time for trial and error, five foundational steps should come before implementing AI.

1. Start with the productivity problem, not the AI tool

The starting point for AI should not be a new product, a competitor’s investment or the fear of being left behind. It should be a clearly defined business problem.

Identify specific points of friction in the business. Look at how work gets done day to day and where people are losing time. Repetitive administration, searching for information or waiting for routine tasks to be completed can all point to areas where AI may be useful.

Once the problem is evident, it is easier to determine whether AI is the right solution and what capability is actually required.

Starting with an obvious business problem helps ensure you apply AI where it can meaningfully improve productivity.

2. Get your information ready

Once you’ve identified the problem, the next question is whether AI has the information it needs to solve it.

AI is only as useful as the information available to it. If critical business information is scattered across inboxes, shared drives, cloud applications and legacy systems – or is duplicated, outdated or difficult to find – introducing AI will not resolve those underlying problems.

Instead, it can reproduce them faster and at greater scale.

Before implementation, it is important to understand what information the business has, where it lives, whether it is accurate and current, and whether the right people and systems can access it.

That is why information readiness is becoming a key determinant of AI success. When employees spend less time searching for information, and AI can draw on accurate, accessible and trusted content, enterprises have a much stronger foundation for productivity.  

3. Fix the processes before automating them

Good information still needs to sit within a good process.

Before adding AI to an existing workflow, map how that workflow operates. Look for unnecessary approvals, duplicate work, manual hand-offs, repeated data entry and unclear responsibilities.

Automating a poorly designed process can simply allow a business to do an inefficient thing faster.

The greater opportunity is to use AI implementation as a reason to rethink how work gets done. Ask which steps are still necessary, which can be simplified, where AI can genuinely remove administrative effort and where human judgement continues to add value.

The objective should not be to automate as much as possible. It should be to create a better process.

4. Set the rules for information access

As AI becomes more useful, it also tends to become more connected.

An AI assistant drafting generic marketing copy presents a very different information risk from an AI system connected to customer records, contracts, employee information, financial data or intellectual property.

Before expanding access, businesses should decide what information an AI system needs to perform its task, who should use it, what actions it can take, and where human approval is required.

It is important to understand what happens to business information entered into third-party AI tools and to confirm it still meets existing privacy, security, and records management requirements.

The principle is simply to give AI access to information it needs to do the job, not the keys to the entire business.

Getting this right also supports productivity. Clear information ownership, appropriate access permissions and trusted information reduce the likelihood that employees will spend time correcting inaccurate outputs, investigating mistakes or managing problems created by inappropriate AI use.

5. Define productivity before measuring AI

Finally, decide what success looks like before commencing implementation.

The measure should not be how many employees use AI or how many AI tools you deploy. Those figures measure adoption, not productivity.

Instead, establish a baseline tied to the core business problem. Success might mean decreasing administrative hours, preparing quotes faster, shortening processing times, responding to customers faster or giving employees more time to focus on revenue-generating work.

With a baseline in place, determine whether AI is delivering meaningful return and make informed decisions about where it should, and should not be scaled.

Treat AI as an organisational change, not as an IT purchase

The national debate is becoming increasingly centred on whether AI can materially improve Australia’s productivity. For SMBs, the answer will depend less on having access to the latest AI model and more on how effectively they prepare the business around it.

It is not essential to become an expert before you start. Businesses need to understand the problem they are trying to solve, prepare the information AI will rely on, improve the processes around it, establish sensible guardrails, and determine how success will be measured.

Getting these foundations right can help turn AI from another expense into a tool that changes how work gets done.

Ultimately, Australia’s AI productivity opportunity will be shaped by the information foundations and business processes built around it.

George Harb is Vice President of Sales at OpenText

Last Updated on September 23, 2026 by George Harb