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Challenges faced in the industry

01/

Lack of real time operational visibility

Implementing IoT, sensors and other connected devices is one hurdle. Ensuring the data that’s being captured can be accessed, ordered, and delivers back valuable information is another.

02/

Data silos + AI readiness

Fragmented legacy systems trap critical data, preventing the unified “single source of truth” needed to train effective AI and optimise the value chain.

03/

Labour costs + workforce optimisation

Automation and AI-driven processes can be hindered by skills gaps and manual work dependency. The challenge lies in finding ways to simplify and automate manual work so your technicians, field staff, and frontline workers can deliver higher value.

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    Frontline worker enablement

    Empower workers with AI-enabled tools, real-time data, and digital workflows to boost safety, productivity, and expertise on the ground.

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    Manual + complex process automation

    Automate complex manufacturing processes with AI-enabled operations to reduce errors, maximise efficiency, and increase speed of delivery.

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    Cloud and data platform modernisation

    Build unified data platforms to enable AI adoption, and modernise legacy systems to achieve agility and scalability requirements.

Related Use Case

Field Operations Optimisation

For organisations with a large frontline worker or field technician employee base. Empower your field workers with summarisation tools to optimise site visit preparation with a single job overview.

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The ROI of Gen AI in Manufacturing and Automotive

The results are in: 72% of organizations are currently seeing ROI from their gen AI investments. Based on a global survey of 364 senior leaders of global enterprises ($10M+ revenue) the ROI of Gen AI report benchmarks the impact of Gen AI, and reveals how enterprises are achieving global success.