While AI copilots — which work alongside humans to speed up their workflows — currently comprise 90% of company activity, the tech will serve as a stepping stone to more autonomous solutions in the coming years. Eventually, AI agents could manage entire industrial processes, shifting human roles from operational tasks to strategic oversight.

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By 2029, SDVs could account for as much as 90% of auto production, up from just 3% in 2021, per Morgan Stanley. However, the transition comes with steep challenges, such as the technical complexity involved and the need for strong cybersecurity measures to protect connected vehicles.

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This spending is creating opportunities for growth across the AI data center value chain.

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Nvidia has used its experience developing graphics processing units (GPUs) — which were originally developed for applications like gaming but turned out to be rather good at AI tasks — to become a dominant force in AI infrastructure and applications. 

But a host of established companies and startups are investing heavily to capture a share of the growing AI computing market. This includes building new types of chips specifically for training and running AI models, evolving traditional processors like CPUs and GPUs to better support AI workloads, and exploring novel information processing technologies such as quantum and neuromorphic computing. 

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Humans will still play a crucial role — but instead of assembling parts or operating machinery, they will maintain robots and keep them running.

In these future factories, robots coordinate in unison, completing work automatically — without breaks, every hour of the day — to get products out the door. 

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While Nvidia initially developed its graphics processing units (GPUs) for gaming, these chips turned out to be ideal for powering AI tasks. Now, the company is focusing its efforts on providing the computing hardware — notably its A100 and H100 GPUs — and the software infrastructure required for developing generative AI applications.

Amid the generative AI rush, Nvidia has grown rapidly. In fact, it recently surpassed Microsoft and Apple to become the world’s most valuable company. To bolster its leadership position and keep ahead of AI computing competitors like AMD and Intel, Nvidia has forged relationships with companies across the AI landscape.

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While most genAI initiatives are still in early stages of development, incumbents and startups are betting heavily on its potential.

ABB, for instance, has identified over 100 internal applications for the tech, from robotics to predictive maintenance. Retrocausal, which develops a computer vision-enabled AI copilot for the assembly line, estimates that genAI can reduce manual assembly time by a third.

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Intelligent, interconnected machines are driving the rise of smart factories and “Industry 4.0“, the latest wave of manufacturing digitization. This new era is seeing humans collaborate more closely with robots and advanced algorithms being deployed to monitor all aspects of manufacturing operations. By enhancing operational efficiency and productivity, this movement has also revitalized manufacturing in high-cost regions like the US and Europe, enabling companies to produce goods closer to their end users.

The automation technologies behind Industry 4.0 take a number of forms. For example, mobile robots are being developed that autonomously move parts across the factory floor. Systems that automate product inspection help remove human error from the quality control process. Additionally, advanced analytics are helping manufacturers automate data transformation and key processes. This helps manufacturers boost operational efficiency and accuracy, maximize output, and minimize environmental impact.

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It has backed 25+ tech startups via its venture arm, Caterpillar Ventures, since 2015. Additionally, its internal innovation team actively employs new technologies, such as autonomous heavy equipment and additive manufacturing.

Caterpillar maintains a particular focus on boosting the sustainability and connectivity of its machinery. The company has set ambitious goals to reduce operational greenhouse gas emissions and enhance the energy efficiency of its products. For instance, Caterpillar has developed hybrid and electric machines that produce fewer emissions and consume less fuel than traditional equipment.

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This system not only enables industrial companies to collect data but also helps them analyze it to boost efficiency and sustainability. These functionalities are particularly notable for manufacturing plants given that vast quantities of their data typically go unused. 

In the market map below, we identify 112 IIoT vendors operating across 11 categories.

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Companies offering weather risk intelligence solutions help organizations across industries — such as aerospace, agriculture, construction, government, insurance, and logistics — better assess and manage the risk of these weather events to ensure operational continuity and protect their assets. These solutions make use of AI, advanced analytics, and forecasting models, and they’re catching the eyes of investors.

Tomorrow.io recently raised an $87M Series E round after launching its second weather satellite into space. Five other weather intelligence companies — ClimateAi, Reask, Truweather Solutions, Salient, and PlanetIQ — have also raised funding in 2023 so far.

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The company built its business by empowering sales professionals with its flagship customer relationship management (CRM) product. But it has also expanded beyond the CRM through strategic M&A and venture capital investing.

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