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Six AI industry trends we’re tracking in 2024 (and beyond)

Christian Pedersen Profile picture for user Christian Pedersen May 2, 2024
Summary:
IFS is keeping a close eye on the adoption of AI across asset-intensive industries - from manufacturing to construction to telecom. Inspired by AI project lessons, Christian Pederson highlights six industry trends to watch.

Technology eye watching future concept © agsandrew - Canva.com
(© agsandrew - Canva.com)

In 2024, Artificial Intelligence (AI) continues to be a transformative force across industries. Beyond the headlines of the “AI race” between leading players including Microsoft, AWS, Google and Open AI, the adoption of AI across industries has quietly accelerated over the past year, and it is only set to increase.

By 2040, the adoption of AI is projected to reach 34.8%, with over 1.3 million businesses utilizing it to drive innovation. McKinsey’s Global Survey on Artificial Intelligence has highlighted the adoption of AI has more than doubled since 2017. PWC affirms that AI is already having a widespread effect on innovation, with the potential to contribute around $15.7 trillion to the global economy by 2030. 

The implementation of AI has provided organizations with new opportunities to streamline processes and optimize efficiency through machine learning, automation, and anomaly detection. With industries continuing to invest in advanced technology like AI, they will be able to optimize and automate industry-specific processes, ultimately boosting the productivity of their people, assets, and services throughout their ecosystem.

AI will be the driving force for telecom operators to enhance operational efficiency

AI-powered automation is already playing a key role for telecom operators to optimize various tasks and processes such as network management, service provisioning, fault detection, resolution, security, billing, and customer support. The adoption of this technology is only set to increase in 2024 and is poised to improve network availability, performance, and operational efficiency.

By leveraging AI and automation, telecom operators can create self-optimizing networks (SONs) that can adjust network parameters based on real-time data and feedback or automate customer service interactions using chatbots or voice assistants. Furthermore, the harmonization of data across various solutions such as EAM, FSM, APM, and ERP creates a world of possibilities for cross-data intelligence – automating the delivery of resources, anticipating asset health, and real-time scheduling and dispatching that automates the delivery of resources at the right place and the right time.

Dynamic planning is driving enhanced performance for modern manufacturers

Digitally mature manufacturers are enhancing their existing systems by incorporating AI to drive performance. In just a few years, the acceleration of AI spending in IT will rise to 40% and manufacturers will prioritize the use of AI to increase revenue and decrease costs. In fact, McKinsey has highlighted that adopting AI pattern recognition tools can lead to a 4% increase in revenue, up to 20% reduction in inventory, and a decrease in supply chain costs by up to 10%.

With ongoing supply chain challenges to overcome, manufacturers can leverage AI within their existing ERP and EAM systems to optimize their inventory and resources with real-time machine data. For example, using AI embedded within ERP systems, manufacturers will be able to swiftly adapt to unexpected raw material changes, predicting potential supplier delays. By doing so, manufacturers can enhance their adaptability, reduce lead time, and minimize the impact of supply chain disruption for efficient production.

The EUR industry is using AI and automation to accelerate its evolution towards a composable environment

Digital transformation continues to be a driving force for innovation in the Energy, Utilities, and Resources sectors. Industry leaders are prioritizing the adoption of AI, automation and IoT to drive innovation and new use cases. By implementing intelligent, AI-powered systems, the EUR industry will be able to rapidly identify, plan and address disruptive events (outages, sick leave, supply chain issues, etc.) before productivity is impacted.

For instance, an intelligent system typically uses data from an asset performance management (APM) solution to flag that an asset is likely to fail within the next three months. In addition to providing an advanced warning, the AI-powered system will collect and utilize data from other modules, such as ERP and scheduling, to make recommendations that enhance uptime and reduce costs.

Utilizing AI will alleviate long-standing challenges for Construction and Engineering companies

While traditionally resistant to embracing change, the construction and engineering industry is now increasingly turning to advanced technology to combat persistent challenges like productivity and the labor shortage. AI and automation are at the forefront of this shift.

The advent of robust AI-powered data analytics is enhancing the way organizations collect and evaluate data from multiple sources, like ERP systems. By adopting this approach, construction and engineering companies are able to gain precise and actionable insights for multiple projects and accurately forecast margins and performance indicators.

Moreover, with more advances in intelligent planning and scheduling, construction companies have the ability to enhance performance in terms of productivity and improve project and maintenance service delivery. This impacts all types of resources, including labor, equipment, materials, and subcontracts.

Asset-centric service providers are using AI to transform fleet management

Increasingly, fleet managers will use AI-powered computer vision to interpret streamed video data. For example, wind turbines are routinely equipped with IoT remote sensing, providing operators with real-time operational data and digital twin models. Integrating AI intelligence from cameras, sensors, service records, and digital twin models can give teams managing fleets and other asset collections such as wind farms ultimate visibility.

By rapidly comparing specifications, AI can identify underperforming wind farms, generate sales opportunities for manufacturers, and offer recalibration services to deliver a guaranteed uplift in power generation efficiency. This entire cycle will be driven by AI, moving from transaction-based sales to a consultative, fact-based, value-added partnership.

The increasing adoption of AI forces cyber-security to up its game for defense companies

Beyond the benefits of AI adoption, the AI boom has brought its own cyber threats, including AI-enabled hackers. AI can accelerate malware and change codes, making it harder for cybersecurity infrastructure to detect threats. The heightened risk has created an impetus for the deployment of robust AI-enabled defense technology.

As a result, defense forces are turning to autonomous cyber defense tools with intelligent agents to monitor network activity and trigger immediate action when anomalous behavior is detected. By leveraging machine learning (ML) to boost threat detection accuracy and automate responses to cyberattacks, cybersecurity teams can stay one step ahead of hackers.

In 2023 we saw that the industries that have already heavily invested in digitalization will be able to leverage AI at speed and deliver peak operational performance in 2024, and beyond. Across industries, enterprises are enhancing their resilience to respond quickly, effectively, and accurately to changing market demands through adopting AI.

As IFS’s latest research reveals, executive and board level expectations for AI value are high, but lack of organizational readiness may hinder progress and scaling. Therefore, advancing digital transformation – together with adopting a true industrial AI strategy – remains a top priority for these enterprises, enabling them to develop composable and resilient operations. Achieving this requires innovation and the right technology mix to optimize agility within their business. The integration of AI into organizations has become essential for companies that seek to expedite their digital transformation strategies. This is a critical moment for them to fully embrace embedded innovation and leverage AI's capabilities to enhance their operations, processes, and customer experiences.

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