IN THIS ARTICLE
- Why IoT connectivity is becoming table stakes, and what separates OEMs who are winning from those who aren’t
- The organizational bottleneck that prevents most connected product programs from delivering full ROI
- How AI converts field data into predictive service, scaled expertise, and commercial revenue opportunities
- Why the infrastructure Mesh Systems builds is the foundation that makes AI intelligence possible
Connectivity used to be the differentiator. Five years ago, an industrial OEM that could offer customers real-time visibility into their equipment’s performance had something most competitors couldn’t match. The connected product program was the competitive advantage.
That window is closing. Connectivity is becoming table stakes. And OEMs who built their strategy around the value of the connection itself, rather than the intelligence it enables, are discovering that dashboards and telemetry streams aren’t enough to sustain a premium or deepen a customer relationship.
The OEMs winning today aren't just connecting products. They're using the data those products generate to deliver outcomes their customers couldn't achieve on their own. And the engine driving that shift is AI.
Mesh Systems has been building connected products for industrial OEMs for 20+ years. What we’re seeing now, across every market we serve, is that the companies making the biggest leap forward aren’t the ones with the most sensors or the most data. They’re the ones who’ve built the intelligence layer that converts field data into decisions, actions, and results.
The Bottleneck Nobody Talks About
Most OEMs with connected product programs have more data than they know what to do with. That’s not a criticism, it’s an accurate description of what happens when you successfully instrument a fleet of industrial equipment. Telemetry flows. Platforms fill up. Reports get generated.
And then the data sits.
Not because the organization doesn’t care. Because there are only so many engineers who can review dashboards, only so many service managers who can triage alerts, only so many analysts who can turn raw telemetry into recommendations. Human bandwidth on it’s own is insufficient relative to the volume of signals a connected asset fleet generates.
This is the bottleneck that prevents most connected product programs from delivering on their full potential. The signals are there: early signs of component fatigue, utilization patterns that point to upsell opportunities, service histories that could dramatically improve first-time fix rates. But extracting those signals consistently, at scale, across every asset in the fleet, requires a kind of continuous analytical attention that no team can sustain.
The companies winning with connected products aren't the ones with the most data. They're the ones who've eliminated the gap between data and action.
What AI Makes Possible, and What Mesh Systems Makes Real
The intelligence layer that Mesh Systems integrates into connected product platforms doesn’t add complexity to the IoT infrastructure, it unlocks the value that was always latent in it.
Here’s what that looks like across the dimensions that matter most to OEM business leaders:
Predictive Service as a Product
When AI monitors your entire installed base continuously, cross-referencing sensor data with service history, product configuration, warranty status, and fleet benchmarks, predictive maintenance stops being an aspiration and becomes a deliverable.
Your customers don’t experience downtime that was predictable. Your service teams don’t spend their time reviewing dashboards; they spend it executing high-value interventions. And the service contract that was once a cost center becomes a genuine value driver.
This is a product transformation, not just an operational improvement. OEMs who can credibly offer outcome-based service, guaranteed uptime, proactive maintenance, data-backed performance benchmarks, are selling something fundamentally different from OEMs who offer reactive support.
Scaled Expertise Across the Fleet
Every OEM has a handful of engineers or service specialists who carry deep institutional knowledge about equipment failure modes, edge cases, and optimization strategies. When a difficult case arises, those people are invaluable. But their knowledge doesn’t automatically reach every service interaction, especially as fleets grow and geographic footprints expand.
AI changes this dynamic by encoding expert knowledge into a system that applies it consistently across every asset. The analysis your best engineer would otherwise spend several focused minutes working through, once the right tools and views are pulled up and their attention is on the problem, the platform handles automatically, surfacing the relevant insight for the machine that needs it. Just as important, AI catches the cases that never make it onto anyone’s desk at all, because the team’s list of priorities is longer than the hours in the day. This is what it means to scale expertise, making your best people’s judgment available everywhere, all the time, including on the problems nobody had gotten to yet.
Commercial Intelligence Built on Real Data
The commercial opportunity embedded in a connected installed base is one of the most consistently underutilized assets in industrial business. Every asset in your fleet is generating a continuous record of how it’s being used, and that record is rich with signals that traditional sales processes would never surface.
AI identifies these signals and routes them to the right person at the right time. An asset running near capacity for an extended period is a data-backed case for an additional unit. A fleet showing high performance variance is a genuine conversation about service contract expansion.
A machine approaching the end of its recommended service life is a natural, timely discussion about replacement or upgrade. None of these are cold sales pitches. They’re outcome-based recommendations that make your sales team more valuable to the customer, and more effective for the business.
The Mesh Systems Difference: Infrastructure Built for Intelligence
There’s a reason Mesh Systems invests as much as we do in the foundational architecture of connected products: the firmware, the cloud platform design, the security model, the enterprise integrations. Because AI is only as good as the data it works with, and data is only as good as the infrastructure that collects, moves, and contextualizes it.
OEMs who try to layer AI onto poorly instrumented products, unreliable data pipelines, or siloed systems don’t get the outcomes they’re looking for. The intelligence layer requires a solid foundation. That foundation is what Mesh Systems excels at building.
When the infrastructure is right, when device data flows reliably into a cloud platform that’s properly integrated with CRM, ERP, and service management systems, AI has everything it needs to deliver. Predictive maintenance that’s actually predictive. Service prioritization that’s actually accurate. Revenue intelligence that’s actually actionable.
Where to Start
OEMs at different stages of their connected product journey enter this conversation from different places. Some are building their first connected product and want to design for AI from the beginning. Others have mature IoT deployments and want to understand what it would take to add the intelligence layer. Others are somewhere in between, connected, but not yet converting that connectivity into the business outcomes they expected.
Regardless of where you are, the path forward starts with the same question: what do you want your connected product program to deliver, not in terms of data, but in terms of outcomes for your customers and your business?
Mesh Systems helps OEMs answer that question and build the system, from device to cloud to AI, that makes the answer real.
Mobeen Khan is Chief Product Officer at Mesh Systems and a Strategy & Venture Partner at Momenta.
Mesh Systems’ latest ebook, The Last Mile of Connected Product ROI, picks up where this post leaves off, walking through where connected product programs typically stall after the data starts flowing and the practical framework for closing the gap between signal and outcome.