5 business priorities will power AI adoption in Australia
With 2026 now underway, I’d describe AI adoption in Australia as ‘patchy and promising’.
There are a handful of organisations that are genuinely advanced – starting to move from isolated pilots to AI-native patterns where AI is embedded into core workflows, not just sitting on the side as a chatbot.
Then there’s a big middle group doing experiments in pockets, and a long tail that’s still at the ‘we set up a task-force and did a slide deck’ stage.
Devesh Maheshwari (Lendi Group)
Most boards have accepted that AI isn’t optional anymore. That sentiment is playing out in Moxie Research – AI Outlook: Australia 2026.
Based on the data, 93% of leadership teams in Australia are in favour of AI yet only 62% are deliberate in driving organisational change from the top.
When asked how effectively their leadership team supports AI initiatives, local businesses answered:
Champions AI initiatives, actively drives AI strategy: 62%
Interested in AI but provides limited support: 31%
Little to no engagement with AI initiatives: 7%
The gap is execution.
Many organisations still treat AI as a project, not a capability. They’ve stood up a couple of Generative AI (GenAI) use cases but they haven’t done the harder work around data foundations, operating model and governance.
So yes, adoption is ‘high’ on paper but true maturity – where AI changes how the business runs day to day – is still emerging.
Over the next 12–24 months, I see five big business AI priorities emerging:
Productivity and cost-out, without killing the customer experience. Most organisations are under margin pressure, so they’re starting with obvious things like contact centres, document-heavy workflows and internal knowledge retrieval. The driver is simple: do more with the same or fewer people, but don’t degrade service.
Revenue protection and uplift. There’s a quiet fear that competitors will use AI to compress costs and improve experience faster. So, you’re seeing interest in AI for personalised offers, next-best-action, churn prediction and smarter pricing. This is less mature than productivity plays, but it’s growing fast.
Reinventing key journeys, not just tasks. The more progressive players are asking, ‘what does the home loan journey, the insurance claim, the retail experience look like in an AI-native world?’ That’s a different question from, ‘where can I bolt a model on?’ It’s driven by a belief that AI will reshape entire value chains, not just automate steps.
Risk and compliance modernisation. In regulated sectors especially, there’s a push to use AI to reduce manual compliance overhead, improve quality and get better line of sight on obligations. At the same time, there’s a need to manage AI risk itself, so risk teams are getting pulled into the conversation earlier.
Talent leverage, not just headcount reduction. The smarter organisations aren’t trying to replace experts. They’re trying to amplify them. That means AI co-pilots for frontline staff, product managers, engineers, underwriters and sales. The driver here is scarcity of specialised skills and the need to move faster with the people you already have.
The common drivers across all of that are compressed economic conditions, rising customer expectations and the growing realisation that AI will become part of the baseline, not a differentiator, pretty quickly.
The differentiation will come from how businesses architect it and how fast they learn.
It’s all about the use cases
At this stage of the maturity curve, most organisations have stood up select GenAI use cases but have yet to tackle more complex work such as data foundations, operating models and governance frameworks.
According to Moxie Research, the business units that champion the full-scale use of AI the most can be ranked as:
Business Operations
Finance and Accounting
Product Development
Sales and Marketing
Customer Service
Human Resources
Frontline Workers
Within that context, three domains are proving consistently impactful:
Business Automation
Decision Intelligence
Experience
Right now, the common deployed use cases look like this:
Customer service augmentation: AI agents for L1 support, smart FAQs, call summarisation and agent assist in contact centres.
Document-heavy workflows: Things like loan processing, claims, onboarding, KYC/AML checks and contract review, where you have lots of semi-structured documents and repetitive interpretation.
Knowledge retrieval and internal co-pilots: Enterprise search over policies, procedures, product information and technical documentation. This is big because it cuts down time-to-answer for staff across the board.
Marketing and content generation: Drafting campaigns, personalising content and assisting marketing teams with ideation and refinement.
Analytics and decision support: Forecasting, anomaly detection, risk scoring and recommendation systems that are now being enhanced or wrapped with generative interfaces.
The next wave that’s starting to break is more agentic use cases, where AI doesn’t just answer a question but takes a series of steps, calls internal systems and orchestrates an outcome end-to-end.
That’s where we’ll see real step changes in experience and efficiency.
According to Moxie Research, the key technology pillars underpinning AI strategies in Australia are:
Cyber Security: 57%
Data Strategy and Infrastructure: 47%
Performance Measurement and Optimisation: 44%
Governance and Ethical AI: 42%
Operational Integration: 40%
Talent and Skill Development: 37%
Strategic Alignment with Business Goals: 36%
Technology and Tools: 32%
AI Outlook: Australia 2026
On the technology side, I see four major priorities:
Establishing a secure, scalable AI platform. Organisations are trying to move away from ad hoc calls to public models towards a more controlled platform – model routing, prompt management, observability, guardrails, and policy baked in. The driver is security, consistency and cost control.
Moving from single models to multi-model, agentic architectures. The days of ‘we picked a single LLM and that’s our strategy’ are numbered. Teams are starting to use different models for different tasks and are experimenting with agents that can plan, call tools and collaborate. The driver is performance, resilience and the need to handle more complex workflows.
Tight integration with core systems and data. This means connectors, event-driven architectures and well-defined APIs so AI isn’t living in a silo. Organisations want AI that can actually take action – update records, trigger workflows, personalise experiences in real time.
MLOps / LLMOps and governance as first-class concerns. Model monitoring, feedback loops, evaluation frameworks and policy enforcement will be big investment areas. The driver is simple, you can’t scale AI responsibly without treating it as a product lifecycle, not a one-off build.
Underpinning all of this is a renewed focus on data quality and semantics, because the best model in the world can’t fix broken source systems or unclear definitions.
Challenge lies in the use case, not the tech
The technology is not the hard part anymore. The hard part is making a use case viable and scalable.
Also, I’d call out a few specific challenges:
Lack of problem clarity. A lot of AI work starts from ‘we should use AI’ instead of ‘this is a high-value problem worth solving’. Teams jump to models before they’ve defined the business outcome, constraints and success metrics. That’s how you end up with impressive demos that never get productionised.
Fragile foundations. Many organisations don’t have production-grade data pipelines, observability or identity/permission models that support AI at scale. So even when a model works in a sandbox, it’s hard to plug into real workflows safely.
Ownership and operating model. Where does AI ‘live’? Is it in IT, digital, data or the business? Without a clear owner and an agreed operating model, AI becomes everybody’s job and nobody’s accountability. You get duplicated experiments and no shared platform.
Trust, risk and ‘model fear’. There’s still a lot of concern about hallucinations, bias, data leakage and regulatory unknowns. Some of that is legitimate, some of it is an excuse to delay decisions. But unless you define a clear risk appetite and guardrails, your lawyers and risk teams will quietly slow everything down.
Change and adoption. You can build the world’s best AI tool but if you don’t change workflows, KPIs and incentives, nobody will use it properly. This is where most AI programs stumble. They underestimate the behavioural and process change required.
According to Moxie Research, AI has accelerated into the centre of business and consumer life but its meteoric rise has exposed deep and unresolved issues in privacy and security.
These two risk domains are becoming the defining barriers to trust, adoption and enterprise-grade readiness.
Going further, I’d put AI risks in a few buckets:
Data security and privacy. What data is leaving your environment? Are you exposing customer or confidential information to third-party models? Do you have proper red-teaming and data classification in place? This is a core concern for Australian organisations, especially post major breaches.
Compliance and explainability. If an AI system makes or supports a decision, can you explain it in a way a regulator, auditor or customer will accept? That’s essential in financial services, health and government. You can’t outsource accountability to a model.
Model misuse and over-trust. There’s a real risk that staff over-trust AI suggestions, especially from conversational interfaces that sound confident. Guardrails, human-in-the-loop design and strong UX patterns matter here.
Reputation and consumer expectations. One bad AI-driven decision, or one clumsy customer-facing bot, can erode trust quickly. Organisations need a clear view of where it’s acceptable to use AI directly with customers versus where it should stay behind the scenes.
Strategic risk of doing nothing. The less obvious risk is inertia. Playing it too safe and treating AI purely as a threat creates a different kind of exposure, where your offerings and cost base lag the market over the next three to five years.
The mitigation story is not ‘slow down’ but ‘industrialise’ – put in place an AI risk framework, standard patterns and an agentic architecture that lets you move fast safely, instead of re-litigating the same issues on every use case.
Partnering in the age of ecosystem
I see partnerships becoming more ecosystem-driven and less transactional.
The days of buying a monolithic ‘AI solution’ from a single vendor are over. We’re moving to a world where you combine hyperscalers, model providers, specialist tools and your own in-house capabilities into an architecture that suits your domain.
According to Moxie Research, Australian businesses are taking a balanced approach to maximising the benefits of AI, leveraging both in-house and outsourcing functions:
Building internal skills and increasing in-house capabilities: 48%
Increasing partnerships with specialist AI vendors / partners: 42%
Acquiring companies with proven AI expertise: 10%
By order of priority, the most in-demand technology outsourcing partners in Australia are:
Specialist AI Vendors: 46%
Business Advisory / Consultancy Firms: 42%
Data Specialists: 37%
Independent Software Vendors (ISV): 35%
Software Developers: 34%
For a CTO, that means:
Strategic partners, not just suppliers. I’m looking for partners who’ll co-design roadmaps, share risk and help us build capability, not lock us into their product for the next decade.
Openness and interoperability as non-negotiables. I care a lot about APIs, data portability and standards. If a partner makes it hard for my agents to call their tools or for my data to move, that’s a problem.
Co-innovation around agentic patterns. The interesting work will be done with partners who understand agentic architectures, tool calling and orchestration, and are willing to test new patterns in production with us.
Focus on enablement. The best partners won’t just deliver features. They’ll help uplift our teams, from engineering and data through to risk, legal and operations.
As the landscape matures, I expect fewer but deeper partnerships. The organisations that win will be those that can plug those partners into a coherent AI-native architecture, not those that just collect logos.
Devesh Maheshwari is CTO at Lendi Group. As part of Moxie Top Minds, Devesh contributed to AI Outlook: Australia 2026 by Moxie Insights. Download the report here.
