Manufacturing has entered a new phase in its digital transformation, with Industrial AI moving beyond pilot programs into core operational systems.
Vendors and manufacturers alike are increasingly focused on measurable outcomes – such as uptime, efficiency and supply chain performance – rather than experimentation, reflecting a broader shift in how technology investments are assessed across the sector.
Shift from AI experimentation to scaled deployment
Manufacturing companies are accelerating the adoption of Industrial AI as the technology transitions from experimentation to scaled deployment, according to new results from IFS and insights from its regional leadership.
The company reported strong financial performance for FY2025, with annual recurring revenue rising 23 per cent year-on-year and cloud revenue increasing 30 per cent, as more manufacturers embed AI into core operations rather than treating it as a standalone innovation initiative.
In an exclusive interview with Australian Manufacturing, Paul Butterworth, Managing Director for Australia and New Zealand at IFS, said the shift reflects growing pressure on manufacturers to demonstrate tangible results from AI investments.
“Over the past few years manufacturers have been experimenting with AI in some form or another. What changed last year was the level of scrutiny and expectation around outcomes,” Butterworth said. “The conversation shifted from innovation to operational performance.”
IFS said its Industrial AI platform is now being deployed at scale across manufacturing, asset maintenance and supply chain environments, with customers focusing on measurable operational gains such as improved uptime, scheduling and asset availability.
Operational outcomes drive adoption
For manufacturers, particularly in asset-intensive industries, confidence in how AI operates has become a key factor in adoption.
“AI must be explainable, governed, safe, and operating on trusted system data,” Butterworth said, adding that once organisations see the technology working reliably in production, “scaling becomes a disciplined expansion exercise rather than a leap of faith.”
The company’s results show a net retention rate of 114 per cent, indicating that existing customers are expanding their use of the platform. Butterworth said this growth is partly driven by manufacturers extending initial deployments across multiple sites after achieving early success.
“In most cases, manufacturers begin with a targeted deployment focused on a specific operational challenge within one facility,” he said. “Once they see measurable improvements, such as reduced backlog or improved schedule performance, the internal case for broader rollout becomes much stronger.”
IFS also highlighted use cases delivering faster returns, including predictive maintenance and production planning, which help reduce unplanned downtime and improve throughput without requiring new capital investment.
“Reducing unplanned downtime on critical equipment has a direct and measurable financial impact,” Butterworth said. “Even modest improvements in asset availability can significantly improve margins.”
Embedded AI reshapes manufacturing systems
IFS argues that Industrial AI differs from more general enterprise AI tools in how it is integrated into manufacturing environments.
“Manufacturing operates in the physical world, where decisions directly affect safety, uptime and cost,” Butterworth said. “Industrial AI is built to function inside the systems that run the business, rather than sitting alongside them.”
The company’s FY2025 performance was also supported by acquisitions aimed at expanding capabilities across supply chains and warehouse operations, alongside partnerships with technology providers including Microsoft and Siemens.
Industry analysts say this reflects a broader shift in the market. IDC Group Vice-President Micky North Rizza said the sector is reaching an inflection point, where organisations are prioritising purpose-built AI platforms that deliver measurable outcomes over more generic tools.
Challenges and regional outlook
Despite the growth, challenges remain for manufacturers earlier in their AI adoption journey. Butterworth identified fragmented data systems as a common barrier, noting that asset, engineering and supply chain information is often siloed.
“A realistic path begins with a clearly defined operational problem rather than a broad transformation agenda,” he said. “Organisations that approach AI incrementally tend to move faster and more sustainably.”
In Australia and New Zealand, manufacturers are taking a cautious but pragmatic approach, with a strong focus on return on investment and risk mitigation.
“There is healthy skepticism toward technology that promises transformation without clear operational benefit,” Butterworth said. “Adoption may begin cautiously, but once AI demonstrates value in live production, scaling tends to proceed with confidence.”
Looking ahead to 2026, IFS expects Industrial AI to become more deeply embedded in day-to-day manufacturing operations, supporting real-time decision-making across planning, maintenance and logistics.
“Workforces will be augmented rather than replaced,” Butterworth said. “The focus is not automation for its own sake, but measurable improvements in performance and reliability.”
This article contains information provided by IFS and is intended for general use only. It does not take into account your personal, professional, or business circumstances.
