GUEST RESEARCH: SAP research reveals that AI adoption is siloed across lines of business, with each department facing unique challenges in realising value.
The business functions with the greatest potential to unlock enterprise-wide AI value may also be creating its biggest constraints, according to a new analysis of global research released today at SAP NOW in Sydney.
The new research found that, while enthusiasm for enterprise AI is driving both investment and ROI, siloed adoption has created five enterprise fault lines that threaten to mitigate potential gains. In particular, Finance teams are forging ahead in a silo, Legal teams are struggling with governance gaps, shadow AI is rife in customer-facing teams, HR is managing a data deficit and Procurement teams are dealing with innovation outrunning its foundations.
“Organisations aren’t missing out on value from AI because they lack ambition or investment. Instead, the dollars are falling through the cracks between functions,” said Rachel Hunter, Head of AI, SAP Australia and New Zealand. “The organisations that make the most of AI will be those who remove AI sprawl and connect the discipline of Finance, the oversight of Legal, the pace of Sales and Marketing, Procurement’s innovation and the workforce focus of HR around trusted data and shared outcomes.”
Inconsistent AI sprawl across functions has enterprise-wide consequences for outcomes
The new insights have been sourced from the SAP Value of AI 2026 report, which spoke to 2,600 business leaders across 13 countries to explore how business invest, implement, and drive value from AI. The new analysis has identified key challenges across five critical business functions.
Finance is mature, but its silos could slow everyone else.
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Finance respondents noted they had invested more than any other function, leading to 65% noting they were scaling or leading in AI automation and 52% scaling or leading in generative AI.
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Yet only 16 per cent of its AI is currently deployed across functions, though that’s expected to rise to 45 per cent in just two years.
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Only 34 per cent of Finance respondents consider themselves data ready for AI, and integration or interoperability is its largest agentic AI barrier. Given Finance touches all points of the enterprise, keeping its AI capability siloed may limit far more than Finance’s own returns.
Legal is least ready to govern the AI it is most aggressively deploying.
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Legal respondents apply AI to more tasks than any other function at 33 per cent today, which is expected to reach 50 per cent in two years.
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Yet 49 per cent say AI is not delivering its full potential, the highest of any function.
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While Legal respondents were most likely to note a defined AI strategy at the leadership level (82%), it also reported the lowest governance readiness for AI processes and frameworks at 26 per cent. The function intended to provide the guardrails is itself most in need of stronger foundations.
Customer-facing teams are moving fast and creating governance exposure.
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Sales and Marketing respondents have the second highest data readiness at 63 per cent, trailing only Operations, and ranked the highest for piloting agentic use cases (68%).
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Yet more Sales and Marketing respondents said st shadow AI was the most material AI risk to them at 80 per cent, while 73 per cent reported shadow AI use at least occasionally. The function may be moving faster than its data readiness or governance today.
HR could lead workforce transformation if its data can earn trust.
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HR respondents emerged as one of the front leaders in Generative AI maturity, with 24 per cent leading in implementations despite respondents having one of the lowest average AI spending levels at US$25.1 million.
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Yet data readiness stands at just 37 per cent. In a function responsible for the enterprise’s most sensitive workforce decisions, that data gap could undermine the transformation HR is best placed to lead.
Procurement’s innovation is outrunning its foundations.
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Procurement respondents are ahead of most other functions in leading agentic AI maturity and cross-functional AI deployments, despite lower spending than some functions like Finance.
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Its respondents are also strategically aware, being the most likely to agree (78%) that delivering ROI from AI requires data readiness, integration and use-case readiness.
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Yet 86 per cent report incomplete or inconsistent data – the highest of any function. Procurement’s ambition is clear, but without stronger data foundations it risks creating isolated breakthroughs rather than scalable enterprise value.
Strategic adoption, not investment, now determines value
For leaders, the next phase of AI is not about adding more isolated use cases. It is about joining the strengths already present across the business to extend Finance’s discipline beyond its boundaries, equipping Legal to govern at the pace of adoption, bringing shadow AI into trusted environments, giving HR the data quality needed to transform work responsibly, and connecting Procurement’s innovation to dependable data and enterprise workflows.
“The organisations pulling ahead will treat AI as an enterprise operating model, not a series of departmental technology projects,” said Hunter. “That means shared data, connected processes, clear governance and a workforce equipped to use AI with confidence. Investment matters, but integration is what turns it into value.”
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