Australian small and medium-sized businesses are using AI tools more widely than ever — but new industry data suggests most have yet to move beyond surface-level experimentation into genuine workflow transformation. For facilities managers and IT managers overseeing complex built environments, the gap between “AI-aware” and “AI-operational” is a useful lens through which to evaluate your own organisation’s position.
Adoption Is Up, But Transformation Is Rare
According to figures cited by Melbourne consultancy Appliers, drawing on data from the National AI Centre, 43% of Australian SMEs reported some level of AI adoption between December 2025 and February 2026. That sounds encouraging — until you look at the other side of the ledger. Deloitte Australia found only 12% of organisations said AI was genuinely transforming their business.
The picture that emerges is one of widespread but shallow adoption. Staff are using generative AI tools — ChatGPT, Claude and similar — to draft emails and summarise documents. But the underlying processes that drive sales, customer communication, reporting, and administration remain largely unchanged. Work is still being moved manually between systems. Spreadsheets are still being updated by hand. Follow-ups are still being chased by staff rather than automated.
Rob Pisano, co-founder of Appliers, put it bluntly: “Most businesses think AI adoption means someone writes emails faster. That is not transformation. Transformation is when work leaves the business completely.”
The Productivity Gap Is Real — and Measurable
KPMG research cited in the article found that Australian organisations lead the world in AI governance adoption, but lag global peers on AI-led productivity and workflow change. Only 35% of Australian organisations said they prioritised AI-driven productivity, compared with 42% globally.
That distinction matters. Governance frameworks — policies, acceptable-use guidelines, data handling rules — are necessary, but they do not reduce manual workload on their own. The operational gains only materialise when businesses actively redesign the processes that AI can automate or augment.
Deloitte Access Economics modelling estimated that increased SME AI adoption could add AUD $44 billion to the Australian economy. Yet only 5% of surveyed SMEs were considered fully AI-enabled. The implication is that the bulk of that economic potential remains unrealised — sitting in the gap between experimentation and genuine operational change.
The logistics sector is highlighted in the article as a useful test case. Appliers has been deploying AI systems in freight and logistics, automating internal reporting, customer communication, follow-ups and operational workflows. One Melbourne-area logistics business with 60 full-time staff reported improved productivity and staff satisfaction after embedding AI into routine processes — while continuing to hire.
What This Means for Facilities and Building Operations Teams
The dynamics described in this research are directly recognisable to anyone managing a multi-site facility, strata complex, or club operation. Building operations teams routinely handle high volumes of repeatable administrative work: contractor communications, access approvals, incident reporting, maintenance scheduling, compliance documentation and system health checks. These are exactly the categories of work that sit in the gap between “someone could use AI for that” and “AI is actually embedded in our process.”
Board and executive scrutiny is increasing, as the article notes. Directors are no longer just asking whether AI tools are in use — they are asking whether those tools are changing cost structures, service delivery and risk exposure. For strata committees and club boards, that translates into practical questions: Are your operational teams still manually compiling weekly security incident summaries? Are access event logs being reviewed by hand? Are maintenance contractors being chased by email?
Pisano’s observation that “the businesses winning with AI are not necessarily the biggest businesses — they are the fastest movers” applies equally to built-environment operations. Organisations that move deliberately from AI experimentation to AI-embedded workflows will carry a measurable operational advantage over those that remain in the browser-tab phase.
The Mallen Perspective
At Mallen Services, we see the same pattern in the electronic security and BMS space. The technology to automate routine monitoring tasks, flag anomalies, and generate structured reporting already exists — but it only delivers value when it is genuinely embedded into operational workflows rather than left as an optional tool for individual technicians or managers. That is why how Mallen uses AI is built around a model where AI-assisted analysis is paired with technician review, rather than replacing human judgement entirely.
The research also reinforces the value of getting baseline documentation right before layering automation on top. AI systems are only as useful as the data and processes they connect to. For facilities managers considering where to start, a structured site audit — covering network topology, device registers, access control configurations, and CCTV coverage — provides the foundation that makes meaningful automation possible later.
The conversation about AI and workflow change is accelerating. The organisations that treat governance as the destination, rather than the starting point, are the ones most likely to find themselves behind when peers begin realising measurable productivity gains.
Original source: https://securitybrief.com.au/story/australian-smes-adopt-ai-but-lag-on-workflow-change