In a recent episode of the “AI in Business” podcast, Shahar Chen, CEO and co-founder of Aquant, joined Matthew DeMello, senior editor at Emerge Technology Research, to share his perspectives on the rapidly transforming landscape of AI in manufacturing and its broader implications across various sectors.
Here is a recap of the main themes and takeaways from the podcast.
The Manufacturing Industry is Finally Embracing AI (A Long Time Coming)
Traditionally viewed as slow adopters of AI, manufacturing sectors are now at the forefront of AI integration. This shift is driven by the need to stay competitive—especially in an environment where product differentiation is minimal and margins are tight, notes Shahar. AI emerged as a critical tool in enhancing post-sale support, service, and customer satisfaction.
Bridging the Talent Gap with AI
A significant challenge in today’s dynamic service workforce is the retirement of seasoned professionals and the transient nature of the incoming–and oftentimes younger—workforce. This reality has led to a significant gap in expertise and experience. AI technologies step in as a “co-pilot,” supplementing human skills and ensuring that the depth of knowledge remains within the organization.
Considering AI a Co-Pilot, Not a Replacement
A crucial aspect of AI adoption is recognizing its role as an enhancer of human capabilities–not a replacement. This perspective helps shift from fear to acceptance, paving the way for the integration of AI and effective utilization. In service, AI acts as a co-pilot, guiding and assisting users throughout challenges—but leaving the control in human hands.
Democratizing Expertise through AI
Generative AI tools bring about the democratization of expertise, making organizational knowledge more accessible. This advancement means that the absence of an expert does not hinder problem-solving. AI can provide the necessary guidance and information.
AI Cuts Through the Noise
Many service organizations will claim their data is “garbage” or useless. However, Shahar points out that it’s not garbage at all—it’s actually noise.
“Every company has noise in their data,” explains Shahar. “The key to overcoming the noise is by pulling the insights of experts with decades of experience into a co-pilot. This approach significantly shortens the learning curve for new technicians, leading to highly accurate results. For instance, a technician who joined just six months ago can tackle complex problems, such as fixing an MRI at a hospital, in just 10 minutes using a co-pilot. This level of proficiency traditionally would take 30 years to develop–that’s a remarkable advancement.”
AI’s Direct Impact on Service Workflows
AI is helping service organizations make sense of their data—while profoundly transforming field service workflows—in several key ways:
AI is a real-time co-pilot, guiding even novice technicians through intricate problem-solving, boosting service quality and operational efficiency. These AI applications streamline processes and ensure consistent and high-quality outcomes in field services.
AI facilitates intelligent troubleshooting, swiftly diagnosing and resolving breakdowns—and minimizing operational disruptions.
Using predictive maintenance, AI forecasts equipment failures before they happen, revolutionizing preventive strategies and drastically cutting downtime and costs.
Once seen as slow in AI adoption, the manufacturing sector is now recognizing its critical role in this evolving AI ecosystem. As Shahar eloquently put it, AI represents a paradigm shift in industrial operations. At Aquant, we’re leading this transformation, leveraging AI to redefine customer service and field workflows, positioning it as an essential growth partner in various industries.
Listen to Shahar’s full conversation with AI in Business here or speak with us directly by signing up for a demo.
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