Buy, Build or Partner: How Australian Boards Should Frame Their AI Strategy

Eighteen months ago, the dominant boardroom conversation about artificial intelligence was about whether to engage with it at all. That conversation has moved on. AI adoption in Australian professional services and financial services has reached levels that would have seemed optimistic in earlier forecasts, and the productivity outcomes – reduced document processing cycles, faster analytical turnaround, improved customer interaction quality – are increasingly demonstrated by real operational data rather than vendor projections.[1]

At the global transaction level, the shift is equally pronounced. Approximately one-third of the 100 largest M&A deals completed in 2025 cited AI as part of the strategic rationale – not as a descriptive tag but as a genuine driver of the transaction thesis. Acquirers are pursuing AI-related targets to access proprietary data sets, to acquire teams that would take years to recruit organically, to capture workflow tools embedded in customer operations, and to accelerate digital transformation timelines that their own organisations cannot meet through internal development alone.[2]

For Australian boards, this global context creates a clear imperative but not a clear answer. The fact that AI matters strategically does not resolve the more difficult question of how a specific company, in a specific competitive position, with a specific capital base and time horizon, should approach it. That is the question the buy/build/partner framework is designed to answer – and it is one that requires honest strategic analysis rather than a default to whichever option appears most immediately available.

The three pathways are distinct in their capital intensity, risk profile, speed of execution, and the type of competitive advantage they are capable of generating – and understanding those distinctions is the starting point for any serious strategic analysis.

Acquiring an AI-capable business or technology asset provides the fastest path to operational capability, particularly where the target has proprietary training data, a functioning product with demonstrated customer adoption, or a team of specialists who would be difficult and slow to hire individually. The trade-offs are valuation risk – AI assets are being priced at premiums that require rigorous scrutiny of the underlying earnings quality and defensibility – and integration risk, which in AI acquisitions often manifests as key-person dependency and cultural friction between technical and commercial teams.

Building AI capability internally offers the highest long-term control and customisation, but it is the slowest and most uncertain path. Recruiting machine learning engineers, data scientists, and AI product managers in the current Australian market is genuinely competitive, with demand from global technology firms, local banks, and the superannuation industry all pulling from the same limited talent pool. Internal builds also require sustained investment over multi-year horizons before they generate meaningful competitive returns – a commitment that boards need to be clear-eyed about before committing.[3]

Partnering – through licensing agreements, API integrations, joint ventures, or commercial relationships with AI platform providers – offers the most capital-efficient access to capability in the near term. The trade-off is dependency: a partnership does not create proprietary advantage and can be replicated by competitors who sign similar agreements.

The global acceleration in AI-driven M&A has been real and substantial, but it has also produced a set of cautionary examples that Australian boards should study carefully. Acquirers who have paid high multiples for AI businesses on the basis of forward capability projections – rather than demonstrated commercial traction – have frequently found that the technology did not translate into the revenue and margin outcomes the transaction thesis assumed. The 2025 Deloitte M&A Generative AI Study found that while AI adoption in M&A processes more than doubled over the year, the proportion of transactions that cited AI-specific diligence frameworks remained low – meaning many acquirers are buying AI capability without the analytical tools to assess what they are actually acquiring.[4]

For Australian companies considering AI acquisitions, the discipline required is not fundamentally different from any other transaction: a clear thesis about what the asset provides that cannot be replicated through other means, a realistic view of integration complexity, and a valuation anchored in current earnings rather than speculative projections. What makes AI acquisitions distinctive is the degree to which value is concentrated in people and proprietary data rather than physical assets or contractual revenue streams – both of which are more fragile and harder to retain through a change of ownership.

Acquisition makes most sense when proprietary data is the moat. A business that has accumulated years of structured training data specific to a domain – clinical outcomes in healthcare, credit behaviour in financial services, operational patterns in industrial settings – has built something that cannot be replicated quickly through a partnership or an internal build. Buying access to that data, along with the team and platform that operationalises it, can create genuine and durable competitive advantage. Without that proprietary data dimension, the case for acquisition over partnership weakens considerably.[5]

Internal capability building is the right answer for organisations that have a long enough time horizon, a sufficiently large and structured proprietary data asset, and the organisational culture to sustain multi-year investment in a capability that will not produce immediate commercial returns. For larger Australian companies – major banks, healthcare systems, large professional services firms, and established technology businesses – those conditions can exist. For most mid-market businesses, they are aspirational rather than realistic.

The talent constraint is the most immediate practical barrier. The pipeline of AI and machine learning specialists emerging from Australian universities remains smaller than demand, and compensation expectations for experienced practitioners are set by global technology companies rather than the local mid-market. A realistic internal build programme for a mid-market business requires accepting that the capability will be narrower and more slowly developed than a vendor or acquisition alternative would provide – and pricing that trade-off honestly in the strategic assessment.

Where internal builds are genuinely appropriate is in the development of AI applications that are deeply specific to a company’s own processes and data – workflow automation tools, internal knowledge management systems, or customer interaction models trained on proprietary interaction history. These are situations where off-the-shelf solutions or partner-provided tools will not capture the specific context that generates the competitive advantage. In those cases, investing in internal development is not a luxury but a strategic necessity, and the capital commitment should be treated with the same rigour as any other long-term capital investment.[6]

The framing of partnership as the default option for businesses that cannot afford to buy or build understates both its strategic logic and its practical demands. For most Australian mid-market companies, a well-structured AI partnership is not a fallback – it is the most commercially rational answer to the question of how to access capability at pace without deploying capital that could generate better risk-adjusted returns elsewhere.

The AI platform landscape has matured rapidly. Foundation model providers, specialist application vendors, and sector-specific AI tools now offer levels of capability that would have required substantial internal investment to replicate just two or three years ago. For businesses in sectors like financial services, healthcare, logistics, and professional services, commercially available AI tools – properly configured and integrated into existing workflows – can generate meaningful productivity and quality improvements without the capital intensity or execution risk of an acquisition or build programme.

The discipline required to make partnerships work strategically is different from acquisition discipline but equally rigorous. It involves assessing the long-term commercial terms of platform dependency, understanding what proprietary advantage is retained when a tool is available to competitors on the same terms, and building the internal capability to configure, monitor, and extract value from external AI tools – which is itself a form of organisational capability that takes time and investment to develop. Boards that treat partnership as a passive decision rather than an active strategic commitment will find that the expected competitive benefits are slow to materialise.

The buy/build/partner framework is not a formula that produces a single correct answer – it is a structure for asking the right questions with the rigour that a significant capital allocation decision deserves. For Australian boards, the most important shift is treating AI strategy as an integral part of corporate strategy and capital planning, rather than delegating it to a technology function and reviewing progress periodically. The companies that will build durable AI-driven competitive advantage over the next five years are not necessarily those that moved fastest or spent the most – they are those that were clearest about what advantage they were trying to create, and disciplined about which pathway was genuinely capable of creating it.