Key Takeaways
- Impinj is reframing its value around Physical Intelligence, emphasizing trusted, real-time item-level data as the missing link between physical operations and enterprise AI.
- The company argues that RAIN RFID, combined with partner-led solutions and next-generation innovations like Gen2X and machine learning, can help enterprises scale automation and AI with greater confidence.
Impinj has introduced a new vision called Physical Intelligence, defined as trusted, real-time data about individual items in the physical world, data that AI systems need to make decisions and take action.
The launch changes how Impinj describes its value. The company has long led with its technology: its ICs, readers and RAIN RFID platform. Physical Intelligence leads with business outcomes instead, such as reducing waste, improving inventory accuracy and automating workflows. That shift reflects a growing problem for enterprise buyers. Many companies have invested heavily in AI but can’t see much of what happens in their physical operations. Gartner found that only 22% of enterprises have scaled AI across business units. Branda argues that missing real-world data is a major reason why.
In the Q&A below, Matt Branda, Impinj’s Vice President, Product and Solution Marketing, answers questions about the company’s strategy, its partner ecosystem in more than 115 countries and its plans for emerging markets.
RFID Journal: Launching a new vision and a new logo signals a significant pivot. What do you most want customers, partners and investors to understand about who Impinj is becoming?
Matt Branda: The new vision and logo does signal a pivot in how we tell our story and articulate our value to the industry, but Physical Intelligence is also a natural evolution of what we have been building at Impinj for the last 25 years. We were early pioneers of RAIN RFID technology and helped build the commercial ecosystem that now connects tens of billions of physical items to the digital world. That foundation remains the same, and our partner ecosystem continues to play a critical role in our strategy and success.
Historically, we described Impinj largely through the lens of our technology – our ICs, readers and RAIN RFID platform. While that remains an important part of our story, Physical Intelligence allows us to speak more directly to enterprise buyers about the business challenges we help solve and the outcomes we enable. It’s a shift from leading with how our technology works to leading with the business outcomes it makes possible.
For these enterprise buyers, Physical Intelligence represents an opportunity to address meaningful operational challenges with trusted, real-time item-level data that helps them visualize everything, waste nothing and act instantly. For our partners, it creates new opportunities to collaborate and deliver solutions that address these enterprise needs more broadly. And for investors, it reinforces Impinj’s role at the intersection of the physical and AI-powered digital worlds.
So yes, our vision and logo are new, and we’re expanding how we engage enterprise customers, but our foundation remains the same: Our technology, platform and partner ecosystem are essential to helping enterprises connect the physical and digital worlds.
RFID Journal: You draw a clear line between Physical Intelligence and Physical AI. Can you explain that distinction in practical terms, and why you believe the market needs a separate category for what Impinj does?
Branda: Physical Intelligence is trusted data about individual items within the real world. Physical AI enables autonomous systems such as agentic AI, robots, industrial equipment and vehicles to act in the physical world. Physical AI consumes Physical Intelligence to drive actions.
Put another way, Physical AI acts; Physical Intelligence gives AI – physical and otherwise – the context to know what to act on.
This category matters because the data layer behind autonomous decisions is too often overlooked. Much of the physical world remains digitally invisible, leaving even advanced AI systems with an incomplete picture. Without hard, trusted data, AI is just guessing, and building a business on guesswork is a recipe for disaster. Physical Intelligence closes that gap by reliably connecting physical items to digital systems and turning item-level events into actionable context.
Why Trusted Real-World Data Determines Enterprise AI Success
RFID Journal: Physical Intelligence is described as the “eyes and ears” of enterprise AI. What convinced you that the biggest barrier to AI success isn’t smarter models, but better real-world data?
Branda: By listening to enterprises. We’ve seen visionary companies like Inditex and UPS adopt RAIN RFID technology and increasingly integrate physical intelligence with their AI systems. At the same time, we’ve heard from enterprises that have invested heavily in AI but are struggling to realize its full potential because they lack trusted, real-world data.
We know from experience across retail, logistics, supply chain and other industries how much operational value depends on reliable information about individual products, packages and assets. AI systems cannot respond to a misplaced product, a misloaded package or an expiring item unless that event is captured in the first place. AI has become smart enough to make good decisions when it’s well-informed, but it struggles when it lacks visibility into what’s happening in the physical world.
That’s why reliable, real-world data is so critical. The quality of that data frequently determines whether AI can improve physical operations or is left working from an incomplete picture. As an example, UPS describes RFID as the “eyes and ears” of its network, generating data from billions of package movements, while AI serves as the brain that turns that information into decisions, predictions and actions. UPS reports that RFID has helped reduce misloads by nearly 70%.
RFID Journal: RAIN RFID is the backbone of Physical Intelligence today. Why does a passive, battery-free technology remain the right foundation as enterprises push toward fully autonomous operations?
Branda: Accurate real-time item-level data is essential for enterprises to understand how billions of everyday products, packages, and assets move through supply chains and retail environments. RAIN RFID makes this possible by giving individual items a unique digital identity that can be tracked automatically at key points throughout their journey, without batteries or line of sight. With more than 160 billion items already connected using Impinj technology, RAIN RFID is proven at enterprise scale and continues to become more capable.
RAIN RFID is uniquely suited to delivering item-level visibility and traceability at scale, given its low cost, battery-free operation, and ability to identify hundreds of items simultaneously. Other technologies, including computer vision, Bluetooth Low Energy (BLE), and sensors, can complement RAIN RFID by capturing additional information about physical environments, assets, and operations. Together, these technologies can contribute to a richer layer of real-world data that enables AI systems to make more informed decisions and helps enterprises automate and optimize their operations.
RFID Journal: You describe the Impinj Platform as an “enterprise solutions flywheel.” How does the value compound for a customer once all four stages are running together?
Branda: The real value comes when enterprises can move beyond simply knowing what items they have to understanding how those items move through their operations and using that information to make better decisions and automate workflows.
That’s what our Platform enables through four interconnected stages: Identify, Track, Analyze and Automate. We start by giving individual items a unique digital identity, allowing enterprises to know what they have. We then capture when and where those items are moving, turning that information into actionable insights that can improve business operations. Ultimately, those insights can power AI-driven workflows that help enterprises automate processes and continuously improve how they operate.
Take a retailer, for example. They might start by using RAIN RFID to improve inventory accuracy across a handful of stores. As they expand their deployment and connect item-level data across the enterprise, they can begin improving replenishment, fulfillment, loss prevention and the customer experience. At scale, that same foundation can enable more sophisticated capabilities, such as reliable omnichannel fulfillment across multiple locations.
That’s the flywheel effect: The more item-level data enterprises capture and put to work across their operations, the more opportunities they unlock to improve efficiency, automate workflows and drive greater business value.
How Gen2X, Machine Learning and Privacy Protections Advance RAIN RFID
RFID Journal: Gen2X and machine learning are central to your next-gen roadmap. What specific problems do these innovations solve that earlier RAIN RFID couldn’t?
Branda: As enterprises expand RAIN RFID into new products, environments and use cases, they need more reliable item-level visibility with less complexity.
Gen2X improves tag readability and read range, especially for small, low-cost labels on densely packed items. These advancements help expand item connectivity beyond traditional retail apparel into new product categories and applications.
Machine learning addresses a different challenge: making RFID reading solutions easier to deploy and scale. By identifying moving items and confining read zones, machine learning enables more reliable, repeatable and scalable autonomous reading solutions while reducing the RF complexity traditionally associated with RFID deployments.
Together, these innovations make RAIN RFID more capable, easier to deploy and better suited to supporting item-level visibility and automation across increasingly complex enterprise operations.
RFID Journal: You’ve committed to protecting item and consumer data as connectivity expands. How do you balance broader item visibility with privacy and confidentiality concerns?
Branda: RAIN RFID continues to move closer to consumer-facing applications, and enterprises need to balance consumer privacy regulation compliance with maintaining operational efficiency and flexibility. We have worked closely with the RAIN Alliance and other industry leaders to drive solutions to protecting data, privacy and trust, including tag locking, user-controlled privacy commands, and cryptographic authentication.
At the same time, we are innovating beyond the standard with Gen2X Protection to deliver advanced innovations for protecting privacy, data, and trust. We developed Impinj Protected Mode to enable authorized systems to make tags invisible to readers using a secure password, while preserving the ability to restore reading functionality for legitimate processes such as returns. Impinj Integra prevents memory and write errors by self-checking tag memory during normal operation, checking signal strength during encoding and verifying writes. We also developed Impinj Endpoint IC Verification, a Gen2X innovation that allows RAIN readers and printers to verify, during normal inventory and with no speed penalty, whether a tag contains an authentic Impinj endpoint IC. This capability helps inhibit fraudulent tags and items, including those used for fraudulent returns and counterfeiting.
Taken together, these innovations help systems control when and where tagged items are read, protect tag data, and increase consumer confidence – both in the items they purchase, and that their tags aren’t being read indiscriminately.
RFID Journal: You sell to, with and through a partner ecosystem spanning more than 115 countries. How does the Physical Intelligence vision change what you ask of your partners?
Branda: Our partners have always played a critical role in bringing Impinj technology to enterprises, and that isn’t changing. Impinj provides the Physical Intelligence that makes these solutions possible – the underlying technology and the trusted, real-world data it delivers. Our partners bring that technology to life through complete solutions that address specific enterprise needs.
That includes everything from RFID labels, readers and other hardware to enterprise software, systems integration, installation and ongoing support. This allows us to combine our technology leadership with the industry expertise and capabilities of our global partner ecosystem to solve enterprise pain points.
With Physical Intelligence, we’re building on that foundation. As enterprises look to connect more of the physical world to their digital systems and AI investments, we’re working with partners to develop new solutions, expand into additional use cases and demonstrate measurable business value.
We’re also expanding our partner ecosystem as RAIN RFID adoption grows beyond traditional markets into new industries and applications. Ultimately, our partners are essential to translating the Physical Intelligence vision into real-world solutions that help enterprises improve their operations.
How Impinj Plans to Win in Emerging Markets and Help Enterprises Scale AI
RFID Journal: The addressable market you outline spans food packaging, auto parts, parcels and beyond. As you move into these activation and emerging markets, who do you see as your primary competition, and how do you plan to win?
Branda: In many emerging markets, our biggest competition isn’t another company or technology. It’s the status quo – disconnected systems and manual processes that leave enterprises without reliable, real-time information about their physical operations. Many businesses still rely on periodic inventory counts and incomplete data, making it difficult to know exactly what they have and where items are moving. As enterprises invest more heavily in AI and automation, that lack of trusted, real-world data becomes an even greater challenge.
We also recognize that RAIN RFID is part of a broader Physical Intelligence ecosystem. Technologies such as computer vision, Bluetooth Low Energy (BLE) and sensors can complement RAIN RFID by capturing additional information about physical environments and operations. But RAIN RFID uniquely enables scalable, item-level visibility and traceability, providing a foundation for connecting individual items to enterprise systems.
Our strategy is to combine that capability with Gen2X performance enhancements, enterprise solutions and our global partner ecosystem to make more items visible and traceable at scale. By expanding into new products, industries and use cases, we can help enterprises reduce waste, improve inventory availability, minimize shipping errors and automate workflows.
RFID Journal: UPS credits RFID with cutting misloads by nearly 70%. Gartner found only 22% of enterprises have scaled AI across business units. When you talk with prospective customers, which RFID industry success stories resonate most, and why? What role does missing physical world data play in those stalled deployments?
Branda: The relevance of any given RFID success story depends on the industry and key operational challenges an enterprise is trying to solve. Inditex offers strong evidence of what real-time item data can do for replenishment and inventory performance – a leading fashion retailer reducing unsold inventory from the industry standard 10-20% to only 0.57%. Chipotle shows the value of traceability when a business needs to respond within hours to a food recall, enabling rapid identification, removal, and replacement of affected jalapeño shipments. UPS provides a clear illustration of the critical role physical world data now has for enterprise AI systems, linking data from billions of package movements to decisions, predictions and actions that – as you noted – decreased misloads by nearly 70%.
Many enterprises are investing heavily in AI while their physical operations remain only partially visible to digital systems. A system cannot address a misloaded package, stockout or expiring product without timely information that the event occurred. Accurate, continuously available operational data helps connect AI investment to specific workflows and measurable outcomes. When businesses can visualize every physical item, and their AI systems begin to have the trusted, real-time data they need to take actions and inform optimizations across business units – that’s when expanding AI deployments across the entire enterprise makes sense.
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