The Future of Physical Operations

Senior Executives at Samsara are forecasting trends in physical operations for next year and beyond.

Philip van der Wilt (pictured), SVP and GM EMEA of Samsara says, “physical operations will continue to be challenged by the uncertainty surrounding fleet electrification and the need to double down on fuel efficiency. Businesses are waking up to the fact that it’s not petrol, diesel or electricity that powers fleets — it’s data.

“Those who have already invested in technology and IoT platforms to manage their fleets are already better off. Fleets that have already invested in connected data platforms are better able to identify which routes, vehicles, and tasks are best suited to the electrification of their fleets.

“They’re also using these same fuel-agnostic systems to identify other technologies that will lead to fleet decarbonisation. It’s now up to the rest of the industry to play catch-up or risk being hit with a double whammy — falling behind on electrification plans while being unable to manage sprawling fuel costs.”

Stephen Franchetti, CIO, Samsara, added: “As the AI explosion continues, an organization’s ability to stay competitive and innovate will come down to their enterprise data strategy. Over the past year and a half, there’s been a significant explosion of ‘ready for prime time’ generative AI, opening opportunities for enterprises to benefit from intelligent automation. There’s no denying that AI will continue to increase efficiency, accuracy, and overall business agility in 2024.

“With this, we’ll start to see an increased need for a robust foundation of reliable and well-governed enterprise data. Utilizing the power of this data is paramount for training precise machine learning models, deriving insightful analytics, and enabling intelligent decision-making. As AI technologies continue to evolve, the quality and accessibility of enterprise data could significantly impact an organization’s ability to assess large datasets in real-time, stay competitive, eliminate bias, and free up more time for innovation.

“Expect to see an increase in vertical use cases for AI and a tight race between incumbents and emerging vendors to solve more nuanced, complex problems for these users.

“There’s already a race for incumbent players to infuse AI into every facet of their platforms. At the same time, we’re seeing several new emerging apps coming onto the scene that are purpose-built for vertical use cases within the business – like Sales, Marketing, Legal, and IT. As AI models become more robust and sophisticated, they will be able to handle the nuanced and complex tasks needed for these vertical teams. This will ultimately enable better integration between systems and processes and lead to improved operational efficiencies, as well as cost savings.

“Amidst emerging threats, increased regulation and data privacy laws, organizations will lean on technology for management and protection. With a global focus on data privacy, organizations must leverage technology to identify and mitigate risks quickly and effectively. In 2024, leaders will invest in AI-driven security to monitor network behavior, detect anomalies, and protect against potential threats – all in real time. This proactive approach will allow organizations to enhance their ability to safeguard data and operations.

“This technology, however, is only effective when coupled with a robust data strategy that leverages a zero-trust model. In the new year, more leaders will adopt this approach, which requires verification at every step of the data access and transfer process, significantly reducing the potential for breaches.”

Finally, Evan Welbourne, Head of AI and Data for Samsara, says, “explainable AI will play a key role in the broader acceptance and trust of AI systems as adoption continues to increase.

“The next frontier in AI for physical operations lies in the synergy between AI, IoT, and real-time insights across a diversity of data. In 2024, we’ll see substantial advancements in predictive maintenance, real-time monitoring, and workflow automation. We may also begin to see multimodal foundation models that combine not just text and images, but equipment diagnostics, sensor data, and other sources from the field. As leaders seek new ways to gain deeper insights into model predictions and modernize their tech stack, I expect organizations to become more interested in explainable AI (XAI).

“XAI is essential for earning trust among AI users – it sheds light on the black-box nature of AI systems by providing deeper insights into model predictions and it will afford users a better understanding of how their AI systems are interacting with their data. Ultimately, this will foster a greater sense of reliability and predictability. In the context of AI Assistants, XAI will reveal more of the decision-making process and empower users to better steer the Assistant toward desired behaviors. In the new year, I anticipate XAI will advance both the functionality of AI Assistant and the trust of AI systems.

“The evolution of generative AI across industries will focus on advancements in domain-specific knowledge and expertise, making specialized talent increasingly competitive.

“The advent of ChatGPT this past year showcased the potency of large language models (LLMs) in understanding and generating human-like text, which has accelerated investments and innovations in generative AI. Moving into 2024, I anticipate a continuous maturation of generative AI technologies, particularly emphasizing domain-specific knowledge and real-time adaptation to evolving scenarios. This convergence of generative AI with domain expertise will facilitate more nuanced and valuable insights, making AI a quintessential partner in decision-making processes across industries.

“With this, the demand for AI and machine learning talent will continue to surge in 2024, as businesses increasingly integrate AI not just into their products, but into their operational frameworks. Apart from foundational skills in machine learning, statistics, and programming, I expect to see an increased demand for expertise in domain-specific AI applications and AI governance.”