Process Automation in The Age Of AI: What It Means for Your Business

Artificial intelligence (AI) is moving from hype to boardroom reality. Across Australia, it’s on the executive agenda.

According to ADAPT ’s recent CIO Edge research, 70% of CIOs plan to increase investment in generative AI during 2025, yet only 25% of organisations have automated workflows in place – a striking gap between ambition and readiness.¹

This gap seems to reflect a wider truth: automation should no longer just be about efficiency. In the age of AI, automation appears to evolving into something far more powerful; adaptive, predictive, and insight-driven. For leaders navigating tighter margins, growing compliance obligations, and mounting customer expectations, there is a clear opportunity. But so too are the challenges.

From Rules to Intelligence

Traditional process automation is generally built on rules: defined workflows that minimise manual steps and errors. AI-enabled automation goes further. By applying machine learning, natural language processing, and predictive analytics, these systems don’t just follow instructions, they have the ability to learn, adapt, and improve over time.

Some of the most promising applications are already visible. Intelligent data capture now uses AI not only to extract information but also to validate it against other systems.² AI-powered chatbots are assisting employees with HR or policy queries and guiding customers through service workflows.³

In finance, predictive processes are being used to forecast cash flow and detect compliance anomalies. These aren’t abstract future possibilities – they are practical solutions we see already being piloted by organisations across sectors.

Where Businesses Are Gaining Ground

The recent ADAPT CIO Edge event in August showcased how some of Australia’s largest organisations are putting AI-enabled automation into practice. For instance, Coles introduced an “intelligent edge backbone” in its stores, using computer vision to trigger restocking decisions. At the same time, an internal AI bot now fields HR queries for more than 115,000 employees, significantly reducing call centre costs.¹

Another example showcased was the Department of Defence embracing AI in logistics planning and adversary detection, creating simplified systems that give personnel the tools to make faster and more confident decisions in complex environments. And as showcased, at Downer, the focus has been on structure and governance: every AI initiative begins with a clearly defined business problem, measurable goals, and the guardrails required to keep adoption on track.

Together, these examples showcased at the event reflect how AI-driven automation is already enabling fast customer service and improved employee productivity – even in complex environments.

Challenges To Overcome

Despite the momentum, most organisations are still in the early stages of their AI journey.

ADAPT’s research found that only 13% of CIOs currently consider their organisations successful with AI.¹

From their research, the reasons why are clear

One key barrier is likely to be adoption itself. Fewer than 30% of organisations significantly automate workflows today, which makes it difficult to scale AI beyond pilots, with data quality and governance also observed to be pressing issues.⁴ Research from MIT’s Center for Information Systems Research shows that more than half of companies globally remain in the first two stages of AI maturity, where pilots run but financial returns remain neutral.⁵ Therefore, we see that meaningful ROI seems to only emerges once AI is industrialised into platforms that can be reused across the enterprise.

Human factors also play a role. Without clear communication and strong leadership, employees may resist automation, fearing job loss or misunderstanding the role AI is meant to play. PwC’s Workforce Hopes and Fears Survey found that nearly one in three workers worry their job may be replaced by technology.⁶

And still, there’s the issue of legacy systems.

As Andrew Cresp, former CIO at Bendigo Bank, noted in ADAPT’s research, too many organisations “waste capacity supporting legacy systems instead of freeing up space for impactful automation”.⁴

In short, the hurdles are as much cultural and structural as they are technological.

Enablers of Success

So, what separates the 13% of successful adopters from the rest? Both ADAPT and McKinsey point to a common set of enablers:¹ʹ⁷

1. Establish a strategic framework early: define clear goals, governance structures, and ethical guidelines before implementing AI-enabled automation.
2. Prioritise high-value use cases: start with pilots that solve real business problems and deliver measurable outcomes, building trust and sponsorship.
3. Invest in people as much as technology: provide employees with the skills and confidence they need to use AI tools effectively.
4. Industrialise and scale: move beyond isolated pilots by embedding AI into platforms that can be reused and adapted across the enterprise.
5. Track value rigorously: measure ROI not only in cost savings but also in productivity, compliance, customer satisfaction, and employee wellbeing.

FUJIFILM Process Automation’s Perspective

At FUJIFILM Process Automation, we see AI as a powerful accelerator for process automation – but one that must be grounded in strategy, governance, and people.

Our automation ecosystem, spanning platforms such as Nintex , M-Files , and Esker , can help enable organisations to digitise processes, support compliance, and enhance customer and employee experiences.

By integrating AI capabilities into these workflows, we help leaders move from ambition to impact, aligning technology adoption with measurable business outcomes. We see AI not as a replacement for people, but as a tool that may help enhance their capacity – creating space for employees to innovate, problem-solve, and deliver better outcomes for customers.

“Instead of doing the work, [our staff] are now managing the automation and engaging with our clients – a much more valuable use of their time.” - Matt Kuc, Director, Austbrokers Countrywide .

It’s a clear example of how automation creates capacity for people to focus on what matters most. 

Automation in the age of AI is no longer a question of if but how. The organisations that act with clarity, discipline, and vision are likely to be the ones to realise its full potential.

References

1. ADAPT. (2025) How Leading Aussie CIOs Are Harnessing AI for Productivity and Cost-Savings. CIO Edge Research 2025. https://adapt.com.au/
2. Gartner. (2024). Market Guide for Intelligent Document Processing Solutions. https://www.hyperscience.ai/resource/gartner-2024-market-guide-for-intelligent-document-processing-solutions/
3. PwC UK. (2024) How AI is helping HR to retain, retrain and transform the workforce. https://www.pwc.co.uk/services/human-resource-services/insights/ai-helping-hr-retain-retrain-and-transform-workforce.html#:~:text=AI%20enables%20swift%20consolidation%20and,workforce%20planning%20and%20talent%20management.
4. ADAPT (2025). How Leading Aussie CIOs are Harnessing AI for Productivity and Cost-Savings https://research.adapt.com.au/data-ai/modelling-analytics-visualisation-and-intelligence/market-pulse/how-leading-aussie-cios-are-harnessing-ai-for-productivity-and-cost-savings
5. Weill, P. (2023). The Four Stages of AI Maturity. MIT Center for Information Systems Research (CISR). https://cisr.mit.edu/publication/2024_1201_EnterpriseAIMaturityModel_WeillWoernerSebastian
6. PwC (2023). Global Workforce Hopes and Fears Survey https://www.pwc.com/gx/en/news-room/press-releases/2023/pwc-global-workforce-hopes-and-fears-survey-2023.html
7. McKinsey & Company. (2023). The State of AI in 2023: Generative AI’s Breakout Year. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year