Why Robot Teams Need Software, Not Just Hardware

The global robotics market is projected to exceed $70 billion by 2028, according to the International Federation of Robotics (IFR). Capital is flowing into actuators, sensors, manipulators, and mobility platforms at an extraordinary pace. Boston Dynamics, Agility Robotics, Figure AI, and dozens of well-funded startups are pushing the boundaries of what physical robots can do. The hardware is getting very good, very fast.

And yet, most robotics programs still stall after pilot. Not because the robots cannot perform the task, but because the organization cannot operate the robots at scale. The gap is not mechanical. It is operational.

Robots may be the most visible part of a deployment, but they are not the whole operating system around the deployment. Teams also need software for rollout coordination across sites and environments, workflow automation that connects robot events to business processes, observability that gives operators and engineers a shared view of fleet health, integrations that pipe robot data into existing enterprise systems, and operational learning that turns field data into fleet-wide improvements. When any of those layers is missing or stitched together with ad hoc scripts, the deployment hits a ceiling. The robot works fine in isolation. The fleet does not work as a system.

This distinction matters because it reframes where the real investment needs to go. The industry has spent the last decade solving the hardware problem. The next decade will be defined by teams that solve the operations software problem.

There are two mental models for how organizations think about robots, and the distinction explains most of the friction in scaling deployments.

The first model is robot as appliance. In this view, the robot is a self-contained product. You buy it, plug it in, and it does its job. The vendor provides the software, the updates, and the support. The organization treats the robot like a piece of equipment: a forklift, a conveyor belt, a vending machine. This model works when you have one robot doing one thing in one place.

The second model is robot as system. In this view, the robot is a node in a larger operational architecture. It generates data, consumes instructions, interacts with other systems, requires coordination with other robots, and depends on human oversight for exception handling. The organization treats the robot like a piece of infrastructure that must be managed, monitored, and continuously improved. This model is what real-world deployments actually look like once you move past a single unit.

The problem is that most robotics vendors sell the appliance model, but most enterprise buyers need the system model. A warehouse deploying 30 autonomous mobile robots across three shifts does not need 30 appliances. It needs a fleet operations system that handles task allocation, traffic coordination, failure escalation, shift handoffs, software updates, and performance analytics. The robot is a component. The system is the product.

This mismatch is where operations software becomes critical. Without a software layer that treats the fleet as a system, every operational task becomes a manual, robot-by-robot effort. That approach does not scale past a handful of units, and it certainly does not scale across multiple sites.

Robotics is not the first industry to discover that hardware maturity creates a demand for platform software. The pattern has played out in at least three adjacent domains, and each one offers a useful parallel.

Smartphones. In 2007, the mobile industry was defined by hardware. Nokia, Motorola, and BlackBerry competed on form factor, radio quality, and battery life. Then the iPhone arrived, and within a few years, the value shifted decisively from the hardware to the app ecosystem and the platform. By 2012, the smartphone hardware was largely commoditized. What mattered was iOS vs. Android, the developer ecosystem, the app store, and the services layer. Today, Apple's hardware margins are impressive, but its services revenue is the fastest-growing segment of the business. The hardware became the substrate. The software became the product.

Cloud computing. In the early 2000s, running internet infrastructure meant buying servers, racking them in a data center, and managing the operating system, networking, and storage yourself. Amazon Web Services launched in 2006 with a simple insight: abstract the hardware, sell the platform. The progression from IaaS (Infrastructure as a Service) to PaaS (Platform as a Service) to managed services mirrors exactly what is happening in robotics. First, you sell the machine. Then you sell the layer that makes the machine manageable. Then you sell the layer that makes the machine programmable by non-specialists. Robotics is somewhere between the IaaS and PaaS phase today.

IoT. The Internet of Things went through a hardware-first phase in the early 2010s, when the focus was on getting devices connected. Within a few years, the industry realized that the hard problem was not connecting devices but managing them at scale: firmware updates, fleet segmentation, remote diagnostics, security patching, and data aggregation. Companies like Particle and Balena built entire businesses around device management platforms that sit above the hardware. The lesson was clear: connected devices without device management software are a liability, not an asset.

Robotics is following the same trajectory. The hardware is increasingly capable. The missing layer is the software platform that makes robot fleets operable, observable, and improvable. The companies that build that layer will capture a disproportionate share of the value, just as AWS, Android, and Balena did in their respective domains.

When we say "robot operations software," we mean something specific. It is not the robot's autonomy stack, the navigation planner, or the perception model. It is the layer above all of that, the layer that operations teams, site managers, and fleet engineers interact with every day. A mature robot operations platform typically covers six capabilities.

Fleet management. A centralized view of every robot across every site: identity, status, software version, configuration, environment, and mission state. This is the equivalent of a cloud provider's resource console. Without it, operators are blind to the fleet-level picture and resort to per-robot troubleshooting.

Deployment pipelines. The ability to push software updates, model updates, configuration changes, and behavior parameters to robots in a controlled way. This means staged rollouts, canary deployments, rollback capabilities, and version tracking. In traditional software, CI/CD is table stakes. In robotics, most teams still deploy by physically visiting the robot with a USB drive or running a fragile SSH script.

Observability. Real-time and historical telemetry, logs, events, and alerts. Observability is not just dashboards. It is the ability to answer questions like: "Why did Robot 14 at the Chicago site fail three missions in a row last Tuesday?" That requires structured data, searchable logs, correlated events, and alert routing.

Workflow automation. Event-driven logic that connects robot operations to business processes. When a robot encounters an error, the right person should be notified, the right ticket should be created, the right fallback should be triggered, and the right data should be captured for post-incident analysis. Without workflow automation, every exception is a manual fire drill.

Integrations. Robots do not operate in a vacuum. Their events need to flow into warehouse management systems, ERPs, ticketing platforms, communication tools, and analytics pipelines. The operations platform must provide the connective tissue: APIs, webhooks, message queues, and pre-built connectors for common enterprise systems.

Analytics. Aggregated metrics across the fleet that help teams identify patterns, track improvement, and make data-driven decisions. This includes mission success rates, intervention frequency, uptime, utilization, mean time to resolution, and environment-specific performance comparisons.

Individually, any of these capabilities can be built internally. Collectively, building and maintaining all six is a multi-year engineering effort that distracts from the team's actual mission: deploying robots that do useful work. That is the core argument for adopting a platform rather than building one from scratch.

Something meaningful has changed in how enterprises evaluate robotics investments. Five years ago, the procurement conversation was almost entirely about the robot: payload capacity, navigation accuracy, battery life, safety certifications, and unit cost. The operations software was an afterthought, something the vendor would provide "good enough" tooling for, or something the internal team would figure out later.

That is no longer the case. In 2025 and 2026, enterprise buyers are asking a new set of questions during the evaluation process:

• How do we manage this fleet alongside robots from other vendors? Heterogeneous fleets are becoming the norm, not the exception. A large logistics operator might run AMRs from one vendor, robotic arms from another, and inspection drones from a third. They need a single operational view.
• What happens when we expand to a second site? The pilot worked. Now the question is whether the operational model is repeatable. If every new site requires bespoke integration work, the scaling economics do not hold.
• How do we reduce the per-robot operational cost over time? The total cost of ownership is not just the hardware price. It includes the engineering hours spent on monitoring, updating, troubleshooting, and integrating. Operations software that reduces those hours directly improves the ROI.
• Who is on call when something goes wrong at 2 AM? Without automated alerting, workflow triggers, and remote diagnostics, the answer is "nobody effective." Enterprises with 24/7 operations need software that makes incident response systematic, not heroic.

This shift in buying behavior is creating a new category of enterprise software spend. According to a 2025 analysis by ABI Research, the robotics software platform market is growing at nearly twice the rate of the robotics hardware market. The reason is straightforward: hardware purchases are one-time capital expenditures, but operations software is recurring value. As fleets grow, the software becomes more valuable, not less.

For robotics vendors, this creates both a threat and an opportunity. Vendors that bundle strong operations software with their hardware will command premium pricing and higher retention. Vendors that ship robots without an operational software story will increasingly lose deals to competitors who offer the complete system, or they will find their hardware managed by a third-party platform that captures the ongoing operational relationship with the customer.

The recognition that robotics needs a dedicated operations software layer has given rise to a growing ecosystem of companies building platforms in this space. The approaches vary, but the thesis is shared: the value is shifting from the hardware to the software around it.

Viam has positioned itself as a general-purpose robotics development and fleet management platform. With a flexible hardware abstraction layer and a cloud-based management console, Viam allows teams to build, configure, and manage robots from a single platform regardless of the underlying hardware. Their approach is developer-first, targeting the robotics engineer who wants a modern development experience.

Formant focuses squarely on fleet monitoring, teleoperation, and data management for deployed robots. Their platform is used by teams in logistics, agriculture, and defense to monitor robot health, stream video, and manage incidents. Formant has been particularly strong in providing real-time observability for teams that need to keep eyes on robots operating in complex environments.

InOrbit takes a robot-agnostic approach to fleet management and operations, with a strong emphasis on analytics and interoperability. Their platform integrates with multiple robot vendors and provides a unified operational layer for heterogeneous fleets, a use case that is becoming increasingly common in large enterprise deployments.

Freedom Robotics (now part of Woven by Toyota) built one of the early cloud platforms for robot monitoring, remote control, and fleet management. Their work demonstrated the market demand for hardware-agnostic operational tooling and influenced how the industry thinks about the robot-to-cloud data pipeline.

SVT Robotics approaches the problem from the integration side, building a platform that connects robots and automation systems to enterprise software through a library of pre-built connectors. Their thesis is that integration complexity is the primary bottleneck in robotics deployments, and that a middleware layer can dramatically reduce the time and cost of connecting robots to business systems.

What all of these companies share is a recognition that the robot itself is only one part of a larger operational system, and that the software layer around the robot is where repeatable, scalable value is created. The market is still early. No single platform has emerged as the dominant standard, and the category boundaries are still being drawn. But the direction is clear: robot operations software is becoming a defined market category, not just a feature set bundled with hardware.

ROBOFLOW AI is built on a specific thesis: that the next wave of robotics adoption will be shaped not by better robots, but by better software around them. The hardware has reached a level of maturity where the limiting factor in most deployments is no longer the robot's capability. It is the organization's ability to operate, monitor, coordinate, and improve a fleet of robots across real-world environments.

Our platform is designed to be the operations layer that sits above existing robotics stacks. We are not replacing ROS 2, vendor SDKs, or autonomy pipelines. We are providing the fleet management, workflow automation, observability, integrations, and analytics that turn isolated robot deployments into managed, improvable systems.

This positioning reflects the lessons from every adjacent industry that has gone through the same hardware-to-platform transition. The smartphone industry proved that the platform captures the ecosystem. Cloud computing proved that abstracting infrastructure unlocks scale. IoT proved that connected devices without management software are a cost center, not a capability.

Robotics is at that inflection point now. The IFR projects that the number of operational service robots in commercial environments will more than triple between 2024 and 2030. That growth will not be sustained by better hardware alone. It will be sustained by software platforms that make it practical for organizations to deploy, operate, and improve robot fleets without building an internal robotics infrastructure team from scratch.

We believe the companies that build the operations software layer for robotics will be as consequential as the companies that built the cloud platforms for software infrastructure. The robot is the compute node. The operations platform is the operating system. And the organizations that adopt that platform early will have a structural advantage in how quickly and reliably they can scale their robotics programs.

ROBOFLOW AI is designed to be that platform: practical, hardware-agnostic, and focused on what happens after the robot is deployed. Because that is where the real work begins.