Unlocking Asset Value with AI
What's next for asset leaders
Executive Roundtable Series: Insights
Forward
For over two decades, I’ve worked in and around asset management, and if there’s one truth that continues to emerge - it’s that trust is the currency of transformation. Trust in data. Trust in the algorithms. Trust in the partners guiding you through this journey. AI has incredible potential in our sector, but many organisations are rightfully cautious.
Their job isn’t to build AI solutions - it’s to deliver critical services: to mine ore, power cities, run public infrastructure. That’s why AI must serve those missions, not distract from them.
At COSOL, we believe AI should never be a solution looking for a problem. It should start with solving a discrete, meaningful challenge - like poor data quality, inefficient maintenance, or safety risks.
Introduction
Artificial Intelligence (AI) is rapidly transforming asset-intensive industries. From mining and manufacturing to transport and utilities, asset leaders are beginning to explore and adopt AI-driven solutions that improve operational efficiency, reduce downtime, and enable predictive decision-making. Yet despite this momentum, many leaders remain uncertain about where to begin and how to extract real value from AI investments.
To answer these questions, COSOL hosted a series of executive roundtables across Australia in 2025, bringing together a curated group of asset management professionals.
These events aimed to explore the practical applications of AI and to help organisations approach AI adoption at their own pace.
This paper distills key learnings from those events, supported by real-world case studies, survey feedback, and expert insights from Anthony Cipolla. It offers a roadmap for moving beyond the hype, and into tangible outcomes.
Contents
Cutting Through
The AI Noise
Cutting Through The AI Noise
What We Heard From Our Customers
The Global Context
While local insights matter, it’s equally important to understand the broader landscape. Recent global research confirms what many are starting to see first-hand: AI adoption is no longer an experiment - it’s a strategic imperative.
Executives Are Getting Serious About AI
A 2025 global survey found that 89% of executives view AI as the most transformational technology of our time. Notably, 61% flagged generative AI as particularly impactful.
(LinkedIn Survey)
Organisations Are Centralising AI Strategy
Data governance and risk are increasingly managed through centralised Centres of Excellence (CoEs) - ensuring structured, scalable, and compliant AI integration.
(McKinsey - The State of AI)
AI Adoption Is Delivering ROI
34% of industrial manufacturers report returns from multiple AI use cases. Among heavy equipment manufacturers, 67% are using AI to enhance efficiency, and 74% see competitive advantages as a result.
(KPMG Assets)
Additionally, in reference to a new KPMG study revealing only 42% of Australian workers believe AI will improve their job, and just one in three trust their organisation’s AI use,
Scott shares, “Trust is earned when AI systems are transparent, ethical and consistently deliver value.”
You can read the full article on CRN (global media source for IT channel news, analysis and insight).
Why this paper, and Why now?
AI isn’t a trend - it’s a capability. And like any capability, its success depends on preparation, experimentation, and scaling. This paper outlines a practical framework for asset leaders to move from AI ambition to AI action, using a “Walk, Jog, Run” model supported by real-world examples and lessons learned.
Whether you’re still getting your data in order, testing your first AI use case, or ready to embed AI across the enterprise, you’ll find insight and guidance here - delivered with the voice of those who’ve already begun the journey.
The AI Readiness Journey: Walk, Jog, Run
Chapter 01
Walk
Build the Foundation
Trust, The Ultimate Foundation
Trust is the quiet force that underpins every successful AI initiative. Yet in asset management, it’s hard-won - especially when it comes to unfamiliar technologies. As one COSOL client put it, it’s tough being the “early adopter” of technologies in asset-intensive industries, even though everyone wants to be the “fast follower.”
No one wants to fail in a close-knit sector. That’s why leaders must trust that AI is secure, that algorithms are transparent and fair, and that the data driving decisions is reliable. But trust doesn’t appear overnight. It’s built through real use, clear governance, and repeatable outcomes. If teams can’t explain what the AI is doing or why, confidence falters.
Data First
The truth is that “Without data, there is no AI.” Even so, the inconvenient truth is that “big data doesn’t mean good data.” There is a misconception that the more data you have the better your results.
But data quality beats data volume hands down. Therefore, the first step is ensuring a reliable subset of clean, accurate, and complete data on which AI models can operate. Don’t hold off on AI until all your data is clean - your delay will cost you a market advantage. Simply choose your data carefully, improve it for AI, and add to it later.
Platform Readiness
Modern EAM systems like IBM Maximo come with built-in AI features - such as Work Order Intelligence and FMEA Builder - so organisations don’t need to build AI from scratch.
These tools improve data quality and align maintenance with business goals, helping fast-track AI adoption and build internal trust. While Maximo makes this significantly easier, success ultimately depends on ensuring that whichever platform you are using can scale AI effectively without reinventing the wheel.
Responsible AI Use
A responsible AI use policy should be considered right at the start of your journey. Risk and governance aren’t afterthoughts - they’re foundational. Embedding clear principles for ethical use, oversight, and accountability from the outset helps build trust and supports long-term scalability.
In the foreword of the Australian Institute of Company Directors (AICD) and Human Technology Institute’s A Directors Guide to AI Governance it mentions, ”Currently,
research suggests that there is generally limited board oversight of AI use, with AI application often subject to inadequate controls and risk oversight. In many cases,
directors and senior executives are unaware of where within the organisation’s value chain AI is being used, and how.”
Their guide provides all leaders with a suite of resources to apply their eight elements of safe and responsible AI governance.
Strategic Partnerships
Most organisations don’t have the resources to build a fully staffed AI team - Harvard Business Review also notes that “re-skilling programs” in the age of AI require a major investment of time, and learning through trial and error can be costly and slow.
By partnering with experienced AI and asset management experts like COSOL, organisations can bridge internal skill gaps, avoid missteps, and accelerate the integration of AI into operational workflows and change management. The right partner doesn’t just bring technical and asset management expertise - they bring a clear roadmap to value.
Case Study
Brisbane Airport
Challenge
COSOL customer, Brisbane Airport’s Asset Optimisation Team were keen to identify how IBM Maximo Application Suite could harness the data already being collected by their existing PLC/SCADA devices as the foundation of a predictive maintenance practice.
Although a variety of data streams were available, analysis was completed in many different toolsets, resulting in a fragmented picture of overall asset health, and acting on any insight generated required manual intervention.
Solution
Established the Proof of Concept environment using MAS Manage 8.2, Health 8.4, and Monitor 8.6.
The team were able to connect to the airports existing IBM Maximo database and were able to assess the health of each specific asset based on their asset age, belt condition, and structural condition.
Outcomes
The project showed how IBM MAS Health and Monitor could consolidate data, linking work orders and cost history into a unified system, providing a comprehensive view of asset performance.
This expanded Maximo’s value by allowing operations teams to monitor performance, supply data for replacement planning, and driving the evolution of maintenance and SAMP.
David Small, Principal Sales Leader - Automation ANZ IBM Australia, shares the AI direction that IBM advises asset-centric organisations, which is to look for business use cases that offer a value add and business improvement in their asset management journey. Some specific AI examples include:
- Help maintenance supervisors and asset managers review and prioritise work given their resources and goals - quickly compile insights on assets and maintenances, to better plan, schedule, and assign work.
- Assist technicians and field workers find the right information to diagnose and fix problems - search from and summarise various documentations and perform step-by-step diagnosis.
- Help reliability engineers and maintenance supervisors automate condition-based work creation - recognise anomalies and create work from alerts, service requests, and performance indicators.
- Reduce creation time of asset FMEA (Failure Mode and Effect Analysis) from 100s of hours to minutes.
Some Potential 'Walk' Use-Cases
Gen-AI Productivity Wins
- AI Assisted Search
- Digital Assistants (i.e. Co-Pilot)
- ‘Talk to your data’ Agents
- Coding Assistants
- Content Generation & Review
- Knowledge Summarisation
Machine Learning Low Hanging Fruit
- Sentiment Analysis
- Classification
- Forecasting
- Exception/Outliers
- Segmentation
Case Study
IBM Toyota
Challenge
Toyota Finance aimed to enhance operational efficiency and customer service across its financial services. Traditional digital tools struggled with complex tasks like generating FAQs and ensuring regulatory compliance in communications. Additionally, creating and maintaining comprehensive FAQs for call centers demanded significant human resources, highlighting the need for more advanced, scalable solutions.
Solution
Collaborating with IBM, Toyota Finance established a sandbox environment on IBM Cloud, integrating OpenShift and watsonx.ai. Over 10 months, they co-developed six generative AI pilot projects, including tools for email proofreading and automated FAQ creation. This approach enabled iterative development and evaluation, fostering continuous improvement in operational processes.
Outcomes
The generative AI initiatives led to a 10% reduction in time spent checking emails and app notifications, enhancing accuracy and quality. The email proofreading tool was deployed in November 2024, while the FAQ generation tool is slated for production in February 2025. These advancements mark a significant step toward Toyota Finance’s digital transformation goals.
10%
REDUCTION IN TIME
Chapter 02
Jog
Prove the Value with Practical Pilots
Audit Your Processes - Identify The Right AI Pilot:
Don’t implement AI for its own sake - start with a clear business problem. Focus on high-effort, low-value tasks where productivity gains are measurable and meaningful.
For example, enabling users to access scattered data sources through natural language search is a powerful, practical use case. “Productivity gains will be the first cab off the rank for most organisations,” according to the COSOL roundtable moderator, who told attendees to lead with value, not tech. Ultimately, choosing the right pilot will deliver results you can quantify and build on.
Run A Proof Of Concept (PoC):
Choose a task where you can clearly demonstrate time savings or improved accuracy - this builds the business case for future AI investment.
Content retrieval is a great starting point: AI can extract insights from unstructured data using everyday language, helping teams find what they need faster. Embedding tools like ChatGPT or Copilot into secure environments increases workforce productivity without added risk.
Partner For Speed and Success
Leverage AI and asset management experts to design and deliver your pilot efficiently. They know what works, how to avoid pitfalls, and how to show value quickly. And they can train your team to move forward with confidence.
Case Study: Exploring Agentic AI at COSOL
COSOL recently put agentic AI to the test in our own operations - and the results have been remarkable. We developed a research model capable of interacting with multiple internal databases, retrieving deeply buried information across systems with unprecedented speed and accuracy.
CEO, Scott McGowan explains, “Agentic AI is a self-reflective orchestrator. It can take a prompt, access tools and resources, and use what seem like cognitive and critical reasoning skills to develop a plan, execute it, and deliver actionable outcomes.” When the team posed a complex question, the AI didn’t just return data - it produced a coherent plan and a detailed report. COSOL’s internal success demonstrates the immense potential of applying agentic AI to streamline and enhance complex workflows - starting with a well-chosen proof of concept.
FMEA Builder
A heavy asset operator can reduce Failure Mode and Effects Analysis (FMEA) documentation time by 90% using IBM Maximo’s AI-powered FMEA Builder, enabling faster prioritisation of maintenance strategies and enhancing safety protocols.
Case Study
National Capital Region
Transport Corporation
Challenge
The National Capital Region Transport Corporation (NCRTC) faced the complex task of managing assets across the expansive Regional Rapid Transit System (RRTS) project. Traditional asset management methods were insufficient for ensuring real-time visibility, predictive maintenance, and operational efficiency required for such a large-scale infrastructure initiative.
Solution
NCRTC partnered with IBM to implement the IBM Maximo Application Suite, integrating AI, IoT, and analytics.
This enabled real-time asset monitoring, predictive maintenance, and streamlined operations. The solution provided a unified platform for managing assets, enhancing decision-making, and ensuring safety and reliability across the RRTS project.
Outcomes
The deployment of IBM Maximo led to significant improvements in operational efficiency and asset reliability for NCRTC. The organisation achieved enhanced real-time visibility into asset conditions, reduced response times, and established a scalable framework for future infrastructure projects, setting a new standard for sustainable transit management in India’s National Capital Region.
Chapter 03
Run
Scale With Confidence And Strategy
Expand Use Cases
You can leverage AI to summarise and calculate key insights from large volumes of asset-specific content, enabling faster and more informed decisions. Or you can use AI to automate repetitive tasks and workflows, guiding users through next steps based on their input. Doing this reduces manual effort and improves consistency across operations.
Engage Key Stakeholders
Align key functions - Operations, Asset Management, and IT - from the outset. Keep your implementation team lean to stay agile, and appoint internal change champions who can advocate for adoption. Continuing to work with expert partners, like COSOL, in this stage will help with broadly upskilling teams and accelerating organisation-wide deployment.
Define A Clear Roadmap
Set specific priorities and smart goals that align with your organisational strategy. Then commit! But leaders agreed: If a goal is not achievable within a certain time frame or too complex, it’s very discouraging. So, be realistic. Support your AI vision with consistent investment and a structured execution plan that allows for iteration and learning along the way. Toustone | COSOL’s Craig Lefoe agrees: “Success with AI is a journey, not a one-off project. It needs to be constantly reviewed, refined, and maintained. It should keep learning, keep aligning, and keep delivering value.”
Enable the Workforce
Focusing on educating your workforce in AI literacy and responsible AI is imperative for building internal capability - as is empowering service teams to act on AI-driven insights. For example, enabling maintenance teams in IBM MAS’s Health module to dynamically adjust maintenance schedules based on asset risk. Finally, when you have gained a level of AI maturity and confidence, you can implement governance frameworks that embed AI outputs into operational decision-making and ensure accountability.
Case Study
Australian Train Operator
Challenge
One of Australia’s largest rail operators lacked a unified, structured database, making it difficult to accurately forecast energy consumption. With contractual obligations to both government and private stakeholders, including penalties for inaccurate forecasts, they needed a reliable, data-driven solution. The operator recognised that integrating more data sources would improve prediction accuracy and sought Toustone | COSOL, to build a robust data foundation and deliver an advanced energy measurement and forecasting system.
Solution
Toustone, a COSOL Company, worked with the transport operator to automate data collection from complex sources like smart meters, capturing energy usage in five-second intervals. Using its Sustainability Suite, they developed an AI-powered forecasting tool achieving over 90% accuracy. The system factors in variables like train schedules, weather, and passenger loads to predict energy consumption. Interactive dashboards let users monitor ESG metrics in real time, down to individual meters - identifying anomalies such as a faulty elevator.
Outcomes
The operator now forecasts energy use 6–12 months ahead, enabling cost-efficient purchasing and strategic planning. A trial train speed adjustment showed instant energy savings via the dashboard. The system helps compare asset designs, identify high-consumption areas, and forecast carbon emissions, without dedicating manual resources. Within just 6–8 weeks, they delivered a scalable solution that boosts both sustainability performance and bottom-line results.
Practical Use Cases Across The AI Maturity Curve
Case Study
Sund & Bælt
Challenge
Sund & Bælt faced challenges in inspecting and maintaining vast infrastructures like the Great Belt Fixed Link. Traditional methods involved manual inspections, often requiring mountaineers to scale structures, making the process time-consuming, costly, and risky. With over 300,000 square meters of concrete needing inspection every six years, the company sought a more efficient, safer, and cost-effective approach to infrastructure maintenance.
Solution
Collaborating with IBM, Sund & Bælt implemented the IBM Maximo for Civil Infrastructure solution. This integrated AI, IoT, and mobile technologies to automate inspections using drones and sensors. The system consolidated data from various sources, including maintenance records and 3D models, enabling real-time analysis of structural conditions. Digital twins were developed to simulate and predict infrastructure performance, enhancing proactive maintenance strategies.
Outcomes
The adoption of the IBM Maximo solution led to significant improvements: inspections became safer and more efficient, maintenance planning was optimised, and the projected lifespan of the Great Belt bridge extended by 100 years. Additionally, the company anticipates a reduction of 750,000 tons in CO2 emissions, aligning with sustainability goals and demonstrating the value of proactive, technology-driven infrastructure management.
12 Inconvenient Truths About AI
Throughout the series our attendees surmised that to successfully adopt AI in asset-centric environments, it’s essential to take a strategic, phased approach - one that aligns with your operational realities and builds momentum through clear wins.
The journey isn’t about jumping to the most advanced technology - it’s about choosing the right starting point, scaling with purpose, and empowering your people along the way.
The following list summarises the critical actions that will help you move from AI ambition to meaningful, measurable outcomes:
No Innovation Without Investment
AI doesn't fund itself.
Upfront spend is unavoidable.
Expect ROI To Lag Behind The Hype
The value comes, but not always immediately.
Stop Hunting For Reasons To Use AI
You already know your biggest problems. Start there.
“Show And Tell” Sells The Vision
Early wins, well marketed, build buy-in fast.
AI Isn’t Just IT’s Job
It’s a people transformation and process rethink.
Workforce Disruption Is Inevitable
Start reimagining roles now, not later.
Dream Big,
Act Small
Narrow your focus to what’s achievable first.
POCs Are Fine, But Foundations Matter
Without structure, it won’t stick or scale.
Good Data
> Big Data
More isn’t better. Better is better.
Imperfect Data Still Has Value
Don’t wait for perfection to get started
Small, Lean Teams Run Fast
Big teams slow you down. Stay agile.
Start With A Lean Roadmap
Massive strategies stall. Focus wins.
Behind Closed Doors:
What Roundtable Attendees Are Saying About AI
Closing Thoughts
AI’s role in asset management is no longer optional; it’s an enabler of future-ready operations. COSOL, in collaboration with IBM, provides a flexible framework and the practical guidance needed to adopt AI at a pace that suits each organisation. Whether you’re walking, jogging, or running, the path forward is clear: focus on data, pick the right use case, and build on each success.
For more information or to begin your AI journey, contact COSOL’s AI & Asset Management team.
Recommended:
Our Top COSOL AI Reads
Our Top AI Reads
Media And Public Exposure
Modern Aussie Asset Management In The Age Of AI
Featured in AusBiz, COSOL’s Scott McGowan, Managing Director and CEO of COSOL, shares what modern asset management really means in the age of AI. From tackling legacy data to embedding trust in decision-making, the piece explores how Australian industries can unlock measurable value through responsible, long-term transformation.
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About
COSOL is built on one belief: in asset-intensive industries, reliability is everything.
We’re a trusted, data-led asset management partner for organisations around the world who can’t afford to fail. With over 25 years of experience, we combine deep industry expertise, dependable delivery, and powerful digital capabilities - including AI, data, and proprietary software - to help our clients reduce downtime, improve performance, and optimise the full asset-lifecycle with confidence.
From mining and energy to infrastructure, transport and government to defence, our clients trust us to work as part of their team - responsive, easy to work with, and focused on outcomes that matter.
COSOL's AI-enabled solutions:
- OnPlan Digital Asset Maintenance
- EAM Intelligence
- Enterprise Intelligence, including Transport Suite