Ilan Gluck, Head of Go-to-Market, North America at Digital Matter, is an experienced technology and commercial leader with a background spanning the Internet of Things (IoT), artificial intelligence, supply chain technology, data analytics, and enterprise strategy. Before joining Digital Matter, he spent more than five years with Cox 2M and GearTrack, serving as General Manager and Chief Revenue Officer and helping advance real-time asset tracking and supply chain visibility solutions. Earlier, Gluck was General Manager at Wasteless AI, where he worked on AI-powered dynamic pricing technology designed to help food retailers reduce waste, and held senior product and commercial roles at WeissBeerger, an IoT and data analytics company acquired by AB InBev. He began his career in strategy and operations at Deloitte Consulting, giving him a combination of product, commercialization, and strategic experience across emerging technologies.
Invisible Technologies is a business process automation company that blends advanced technology with human expertise to help organizations scale efficiently. Rather than replacing humans with automation, Invisible creates custom workflows where digital workers (software) and human operators collaborate seamlessly. The company offers services across areas like data enrichment, lead generation, customer support, and back-office operations—enabling clients to delegate complex, repetitive tasks and focus on core strategic goals. Invisible’s unique “work-as-a-service” model provides enterprises with scalable, transparent, and cost-effective operational support.
Before joining Digital Matter, you worked across GearTrack, Wasteless AI, and WeissBeerger, where real-time operational data was used to improve supply chains, reduce waste, and create better commercial decisions. How has that experience shaped your view of IoT as a foundation for enterprise AI?
Across GearTrack, Wasteless AI, and WeissBeerger, I saw firsthand that the best technology is only as good as the data behind it.
Each company was solving a different problem, but the common thread was using real-time information from the physical world to make better decisions. That might mean understanding where inventory was, identifying waste before it happened, or helping a business respond faster to what was changing on the ground.
Those experiences really shaped how I think about IoT and AI. A lot of valuable operational information still isn’t captured in traditional business systems. It exists across vehicles, equipment, inventory, facilities, and supply chains. IoT is what turns that physical activity into usable data.
For me, that makes IoT a critical foundation for enterprise AI. It gives AI a much clearer and more current picture of how a business is actually operating. Instead of only looking backward at historical reports, companies can start identifying patterns, anticipating issues, and making better decisions in real time.
Many companies are training AI models on the same public datasets as their competitors. Why do you believe proprietary operational data is becoming a more important source of AI differentiation?
Same data in, same insights out. As access to AI models becomes more widely available, the models themselves are increasingly becoming commodities, with the real differentiator being the quality, uniqueness, and relevance of the data feeding them.
If everyone is using the same tools trained on the same public, third-party, or historical datasets, they all arrive at roughly the same conclusions.
Take two competing retailers using the same AI platform, market forecasts, and historical demand data. If one of them has deployed IoT sensors across its warehouses, trailers, pallets, and equipment, it’s continuously feeding its AI ground truth data – real-time information about inventory movement, dwell times, asset utilization, bottlenecks, and operational disruptions. Its AI isn’t just learning from what happened last quarter. It’s continuously learning from what’s happening across the business right now.
Over time, that compounds into a real advantage. Its AI isn’t relying solely on historical trends or third-party benchmarks. It’s learning from the company’s own operations in real time, creating a source of intelligence that competitors can’t buy, scrape, or replicate.
When sensors are deployed across fleets, facilities, equipment, and supply chains, what types of operational intelligence can companies capture that they would otherwise miss?
Many organizations make critical decisions without continuous visibility into what’s happening across their physical operations. They may know how much inventory they have or when a shipment was scheduled to arrive, but they often lack insight into how assets, equipment, and workflows are actually performing day to day.
By deploying sensors across fleets, facilities, equipment, and supply chains, organizations can identify bottlenecks, track asset utilization, monitor inventory movement, and understand how real-world conditions are affecting performance.
For example, a retailer may know inventory arrived at a distribution center and was eventually delivered to a store, but not realize certain pallets consistently spend several extra days in a staging area before moving downstream. Without sensor data, that delay remains largely invisible. With IoT, it becomes measurable and actionable – allowing teams to identify the root cause, improve inventory flow, and reduce stock availability issues.
How does IoT-generated data differ from traditional enterprise data when it comes to training, fine-tuning, or grounding AI systems?
Traditional enterprise data is often transactional and historical, capturing events that have already happened, such as orders, invoices, inventory records, or completed business processes. While that information is valuable, it primarily reflects a snapshot of the past. And with markets moving faster than ever before, what happened last season may not tell you much about this one.
IoT-generated data is different because it captures what’s happening in real time. It provides continuous insight into asset location, movement, utilization, environmental conditions, and other operational signals that aren’t typically available in traditional enterprise systems.
For AI applications, that distinction is important. Historical data helps explain what happened, while IoT data helps establish what’s happening now. When organizations combine the two, they can ground AI systems in current operational realities, creating more accurate insights and enabling faster, more informed decision-making.
Digital Matter emphasizes low-power, durable devices built for long-term asset tracking and sensor monitoring. What are the biggest technical challenges in keeping IoT devices reliably producing data for years in complex environments?
IoT devices are often deployed in environments that are difficult to predict and even harder to control. A single device may spend years moving between facilities, vehicles, customer sites, remote locations, and areas with inconsistent connectivity.
One of the biggest challenges is balancing visibility with power consumption. The more frequently a device collects and transmits data, the more energy it consumes. Designing devices that can intelligently capture meaningful events, adapt to changing operating conditions, and maximize battery life over multiple years is critical to delivering reliable data at scale.
Durability is equally important. Devices must withstand vibration, impact, temperature fluctuations, moisture, and years of day-to-day use while continuing to perform consistently, with little to no maintenance. Even a small failure rate can become a significant operational challenge when deployments scale to thousands or tens of thousands of devices.
How can AI help companies move beyond simply tracking where an asset is, toward predicting failures, optimizing routes, reducing waste, or improving utilization?
Knowing where an asset is important, but location alone only tells part of the story. The real opportunity comes from understanding how assets are being used over time and identifying patterns that would be difficult to spot manually. When organizations combine IoT data with AI, they move beyond simply reacting to events and start anticipating them.
By analyzing trends in movement, utilization, dwell time, environmental conditions, and other operational data, AI can help identify inefficiencies, predict potential issues, and surface opportunities for improvement. That can lead to better asset utilization, more efficient operations, reduced waste, and faster decision-making. The value isn’t just knowing where an asset is today, but using the data it generates to make smarter decisions about what happens next.
In industries like logistics, cold chain, healthcare, construction, or equipment management, where do you see the strongest near-term opportunities for combining IoT data with AI?
The strongest opportunities are in industries managing huge volumes of distributed assets across complex operations, which is why logistics, cold chain, healthcare, construction, and equipment management all fit the profile.
And this isn’t theoretical. Large enterprises, from major retailers to automotive OEMs and heavy equipment manufacturers, are already executing on this strategy, which is exactly why they’re making significant IoT investments. Increasingly, their boards are no longer asking whether they’re using AI. They’re asking where the measurable business value is coming from, and the answers are operational: improved throughput, fewer quality incidents, better on-time delivery, and better customer experience.
In the near term, the biggest value will come from helping organizations make better operational decisions using the activity they already generate every day but have never been able to fully capture or leverage.
As companies deploy more sensors and connected devices, how should they think about data quality, signal reliability, security, and governance when treating IoT data as a strategic AI asset?
AI models are only as good as the data underneath them. Organizations tend to focus on collecting more data, but if that data is incomplete, inconsistent, or unreliable, the AI’s insights will be too. It’s the classic garbage in, garbage out problem, and at enterprise scale it becomes an expensive mistake.
That’s why data quality has to be a priority from day one. Organizations need confidence that data is being collected consistently, transmitted securely, and accurately reflects what’s happening in the physical world, along with clear governance around where data comes from, how it’s managed, and how it should be interpreted.
Ultimately, the value isn’t in generating more data. It’s in generating trusted, high-quality data that can serve as a reliable foundation for decision-making.
For companies that already have IoT deployments in place, what steps should they take to make that data more usable for AI applications?
The first step is to start with the business problem, not the AI. Once the objective is clear, whether that’s reducing dwell time, improving utilization, or cutting losses, it becomes much easier to identify the data, workflows, and AI capabilities needed to support it. Many organizations already collect enormous amounts of operational data, but it sits in disconnected systems and never makes its way into a decision.
The second step is the one most companies underestimate, and that’s change management. Scaled deployments rarely fail because of the technology. They fail when people don’t act on what the data is telling them. An alert that a staging area is understocked only creates value if someone actually moves the inventory, so building the processes and habits that connect insights to action is the hardest and most important part of the work.
Ultimately, the companies seeing the most success are the ones connecting operational data to measurable business outcomes and making sure their people use it.
Looking ahead, do you believe proprietary operational intelligence will become one of the biggest barriers separating AI leaders from companies that are simply using the same tools as everyone else?
Yes. As AI becomes more widely available, technology alone becomes less of a differentiator.
The organizations that stand out will be the ones generating unique operational intelligence from their own environments and using it to make better decisions. In many industries, some of the most valuable information still exists in the physical world, across assets, equipment, inventory, and supply chains – and it can’t be bought, scraped, or replicated by a competitor.
The companies that can consistently capture, understand, and act on that information will be in a much stronger position than those relying solely on the same tools and datasets as everyone else.
Thank you for the great interview, readers who wish to learn more should visit Digital Matter.
