CGI: How to Secure Investment in AI-Powered Optimisation

Manufacturing managers navigate a complex landscape of competing investment priorities, where tangible projects such as production line upgrades often overshadow less visible but equally critical initiatives.

Asset optimisation powered by artificial intelligence represents one such challenge, promising substantial returns yet struggling to compete for budget allocation against more immediately comprehensible proposals.

Marcel Mourits, Vice President at CGI, has witnessed this challenge first hand across multiple manufacturing sectors. His insights reveal the obstacles manufacturers face in securing funding for asset maintenance improvements and the strategic approaches that can overcome these hurdles.

The diverse landscape of asset optimisation

Asset optimisation initiatives vary significantly across industries, each tailored to specific equipment types and business objectives. 

In polyethylene production, for example, manufacturers grapple with optimising wear patterns on knife edges used to cut endless PE strands into small granules. 

"The challenge here is striking the right balance,” Marcel explains. “Repair too early and the costs increase unnecessarily; repair too late and the product quality suffers due to degraded cutting precision.”

National railway operators are incorporating predictive features into their asset monitoring systems. Marcel says that deeper insights into performance trends and potential failure scenarios can help organisations "plan maintenance more effectively, reduce unexpected repairs and ultimately extend the Mean Time Between Repairs (MTBR), while optimising costs and improving safety".

In foam manufacturing, when heating pads fail, the issue is not immediately visible, yet product quality deteriorates rapidly. 

"Monitoring the heating pads' health in real time requires sensors and asset monitoring technology," Marcel adds. “Furthermore, incorporating predictive analytics can help estimate the components' RUL (remaining useful life), allowing for more timely repairs."

The investment optimisation challenge

The complexity manufacturers face when optimising investment portfolios has become increasingly pronounced. 

According to the latest CGI Voice of Our Clients research, around two-thirds (38%) of manufacturing executives identify optimising investments and operations as a significant challenge in achieving better outcomes.

This complexity stems largely from the rapidly evolving technology landscape.

"The rise of cloud technology has made solutions from hyperscalers viable alternatives to traditional manufacturing execution systems (MES) and computerised maintenance management systems (CMMS), provided organisations select providers that align with their data sovereignty and regulatory requirements" says Marcel. 

Data and analytics platforms offer robust functionality that allow manufacturers to harness data in real time, while self-service and low-code solutions enable tailored workflows without extensive technical expertise.

"Traditional MES vendors are broadening their offerings to encompass the entire range of MOMS with integrated capabilities that now feature AI tools customised for specific use cases," Marcel adds. 

Perhaps most significantly, AI is transitioning from hype to reality, with manufacturers prioritising tangible, use-case-driven deployments that deliver measurable improvements.

Real-world implementation barriers

Despite these technological advances, CMMS and Enterprise Asset Management (EAM) investments frequently compete with projects offering quicker returns. 

Marcel acknowledges that, while these alternatives may seem more beneficial short-term, effective CMMS/EAM implementation ultimately enhances asset availability, production quality, operational efficiency and long-term profitability.

"Additionally, the total cost of ownership can increase notably due to customisations, system upgrades and the incorporation of hardware such as sensors and IoT devices," Marcel highlights.

Integration complexity poses another challenge, while the skills gap compounds technical obstacles: “Operating modern CMMS/EAM solutions requires expertise in areas like data analytics, IoT and IT management, which manufacturers currently lack.” 

Maintenance teams accustomed to traditional methods may resist digital systems, necessitating investments in change management and training.

What’s more, data quality issues can severely limit system effectiveness and cybersecurity concerns loom large. 

Perhaps most critically for securing investment, companies face difficulty demonstrating ROI quickly. Marcel notes that organisations "often face a learning curve and need time to fully realise the benefits of these systems, making it harder to quantify ROI quickly”.

Securing investment

Marcel advocates for a strategic approach that aligns asset optimisation efforts with broader organisational goals. 

His first strategy centres on seeking synergies with other digital transformation initiatives: “When executives prioritise investment proposals, they typically align them with key company drivers or strategic objectives.

“While this alone is usually sufficient to justify a healthy ROI, securing the necessary budget often requires demonstrating additional, broader benefits.”

Marcel recommends identifying synergies between asset management and strategic objectives ranging from sustainability to supply chain agility, production quality and EHS compliance. This holistic approach can drive greater returns on investment.

The second strategy involves demonstrating benefits beyond asset availability, Marcel says: “Beyond maintenance savings, asset optimisation drives significant reductions in production costs. With improved machine reliability and more predictable availability, manufacturers can schedule production more efficiently and achieve higher overall equipment effectiveness (OEE)."

Asset optimisation also reduces waste through proactive monitoring, minimises product loss and maximises yield. By ensuring machines operate at peak performance, manufacturers achieve tighter tolerances and more consistent product quality.

Competitive advantage

Securing investment for asset maintenance and optimisation can be challenging, but Marcel's experience demonstrates it is far from impossible. 

By integrating proposals with larger digital initiatives and quantifying broader business outcomes, manufacturing managers can present compelling cases for their asset maintenance strategies.

Marcel concludes: "Such a strategic approach that aligns your goals with the business's priorities will ensure your plant's assets are running at peak efficiency while also securing the necessary resources to drive long-term success.”

As manufacturing continues its digital transformation, leaders who successfully articulate the comprehensive value of AI-powered asset optimisation – from improved safety and quality to enhanced sustainability and operational agility – will position their organisations for sustained competitive advantage.