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