Automatic identification and data capture (AIDC) refers to methods that allow machines to identify objects, collect data about them, and enter that data directly into computer systems without human intervention. These methods include radio-frequency identification (RFID), barcodes, QR codes, biometrics, and more. Over recent years, AIDC has advanced significantly and is now at the forefront of digital transformation, enabling real-time visibility, traceability, and data-driven decision-making.
Kings Research estimates that the automatic identification and data capture market globally is set to generate a revenue of USD 165.08 billion by 2032, supported by rising adoption globally.
In this blog, we examine current global trends in AIDC, explore how technologies are evolving, and provide insight into its impact from a technology-led, systems-level viewpoint.
The Global Landscape of AIDC
Standardization and Governance
AIDC is not just about hardware and scanning; it is governed by important international standards. The ISO/IEC JTC 1/SC 31 committee, for instance, is dedicated to automatic identification and data capture techniques.
One key standard is ISO/IEC 20248, which defines how data in barcodes or RFID tags can be digitally signed using a “DigSig” structure. This specification makes it possible to verify that data stored on a physical tag has integrity, even in offline settings.
Such standardization brings trust, security, and interoperability to AIDC systems, especially in critical use cases like identity documents, certificates, or secure supply chains.
Key Technologies in AIDC
RFID (Radio-Frequency Identification)
RFID remains a foundational AIDC technology. There are two broad kinds of tags: passive (which do not have their own power source) and active (which do). (Source: pmc.ncbi.nlm.nih.gov)
Passive RFID tags have seen especially high adoption, and recent forecasts project 115 billion UHF (RAIN) RFID tag chips to be shipped by 2028.
These tags are used not just for tracking goods, but also for sensing and even environmental monitoring. For example, research has shown passive RFID being used to monitor earth surface processes (e.g., soil moisture, temperature) because these tags can cost far less and require much less maintenance than traditional wireless sensor networks. (Source: arxiv.org)
In more advanced applications, wearable RFID systems are being developed. One prototype embeds passive tags into clothing, pairs them with a reader, and uses radio-pattern recognition to detect human activities in real time, achieving accuracy as high as 93.6% in experiments.
Barcodes and 2D Codes
Barcodes (1D) have long been the workhorse of data capture, from retail checkouts to inventory management. However, 2D codes like QR codes are gaining ground because they can hold much richer data.
A recent development is the rectangular micro-QR code (rMQR), standardized as ISO/IEC 23941.
The rMQR code is designed for situations where space is constrained (for example, on narrow packaging) and can replace traditional barcodes while supporting more efficient data encoding.
Digital Signatures on Tags
The ISO/IEC 20248 “DigSig” structure allows a barcode or an RFID tag to carry a digitally signed data payload, adding a layer of security and authenticity.
This is particularly significant for verifying documents or objects in offline contexts, where a scanner or reader can check data integrity without needing always-on internet access.
Such verifiable identifiers support trust, especially for applications like digital identity, certificates, or document verification.
Global Adoption Trends
Public Sector and National Identity
Many governments are leveraging AIDC for national identity, access control, and public service infrastructure. RFID is often used in identity cards, passports, and transport fare-collection systems. In academic work, RFID-based national ID, traffic management, asset management, and document tracking are common use cases. (Source: www.scirp.org)
These broader public deployments help reinforce trust in AIDC not just for business operations, but also for citizen-facing systems.
Supply Chain and Traceability
AIDC is increasingly central to building traceability into supply chains. With global trade, the accurate, real-time capture of location data for products has become critical. RFID, barcodes, and 2D codes enable companies to know exactly where items are and to verify authenticity.
Academic research on RFID in the context of “Industry 4.0” (for example, in supply chain decision-support systems) highlights how AIDC technologies strengthen resilience and traceability.
In regulated sectors (e.g., pharmaceuticals), digital signature-enabled tagging (via ISO/IEC 20248) can help to prevent counterfeiting and ensure authenticity.
Environmental & Infrastructure Monitoring
As noted earlier, passive RFID is being repurposed for environmental sensing: for example, to monitor landslide displacement, snowpack, or sediment transport.
This trend reflects a deeper shift: AIDC is not just about identification, but about embedding data capture into physical processes. Passive tags act as low-cost, low-maintenance sensors, offering long-term monitoring capabilities.
Security and Anti-Counterfeiting
One powerful trend is the use of digitally signed barcodes or RFID tags to guarantee data integrity. The ISO/IEC 20248 DigSig structure provides a way to embed a cryptographic signature into a tag, making cloning or tampering more detectable.
Such secure identifiers are being used for document verification (e.g., diplomas, licenses) and for tracking valuable goods. They offer a means to verify authenticity even offline, thereby strengthening trust in critical systems.
Challenges and Considerations
- Scalability vs. Cost: While RFID adoption is growing, cost remains a consideration, especially for very high-volume, low-cost tags. The infrastructure (readers, middleware, integration) also represents a sizable investment. Moreover, to maintain interoperability and trust, organizations must align with international standards (like ISO/IEC 20248), which adds complexity.
- Data Integrity and Security: Embedding cryptographic signatures (DigSigs) in AIDC carries benefits, but also challenges. Tag memory is limited, and cryptographic operations must be carefully designed to fit within tight data and power constraints. The standard helps, but implementations must be robust.
- Privacy Considerations: With AIDC, especially RFID and wearable systems, privacy is a major concern. In large-scale identity or tracking deployments, questions about data collection, retention, and usage must be addressed through policy and design.
Adoption in public systems must balance utility with protecting individuals’ rights.
Technical Limitations
- Reading efficiency: In some environments (metal, liquids), RFID reads can be difficult.
- Tag collision: When many tags respond at once, systems must manage collisions so that none are missed.
- Offline verification: While DigSigs support offline verification, devices still need secure ways to store or fetch certificate data securely.
Innovations on the Horizon
- Edge Intelligence: One emerging pattern is combining AIDC with edge computing. Readers are no longer passive; they can preprocess data, do signature verification, and run local validation logic before forwarding only essential information. This reduces latency and bandwidth and improves responsiveness.
- Smart Tags with Sensing: RFID tags are increasingly “smart”: not just identifiers but sensors. Passive tags are being developed that sense variables like temperature, motion, or strain. They can be deployed in infrastructure, logistics, agriculture, or even environmental science to provide persistent data at scale.
- Federated Learning for Tag Security: Research is exploring ways to improve security for large-scale tag deployments. For example, a study used federated machine learning and data augmentation to detect cloned RFID tags. By training models across many devices without sharing raw data, the system significantly boosted clone-detection accuracy.
- Miniaturization and QR Variants: New barcode symbologies (like the rectangular micro-QR) are making it possible to place rich, encoded data even in very tight physical spaces.
Also, future tags may combine QR or Data Matrix codes with DigSigs for compact, secure, verifiable labeling.
Impact from a Global Technology Perspective
- Trust and traceability: By embedding cryptographic signatures in AIDC identifiers, stakeholders can ensure data authenticity, even in offline settings.
- Sustainability: Use of passive RFID for environmental monitoring helps build low-impact, long-term sensor networks without the cost and maintenance of conventional sensors.
- Efficiency and automation: Real-time identification and data capture reduce human error, speed up processes, and help organizations make data-informed decisions.
- Resilience: With edge intelligence and local verification, systems remain more resilient to network disruptions or cyber risks.
- Inclusivity: As AIDC matures, it may extend to public services (identity, certificates), helping governments worldwide to build more efficient and secure citizen-centric systems.
Conclusion
Automatic identification and data capture is undergoing a deep transformation. No longer limited to basic tracking, AIDC is becoming a secure, intelligent, and widely trusted data fabric.
Standards like ISO/IEC 20248 (DigSig) lay the foundation for trustworthy identifiers. RFID and barcode technologies are evolving to support not just identification but sensor-based insights. Research into federated security and low-cost sensing promises to make AIDC stronger and more pervasive.
On the global stage, these advances are enabling high-stakes use cases: verifying identity documents, monitoring critical infrastructure, ensuring product authenticity, and building long-term environmental sensing networks.
The future of AIDC is not just about labels and tags. It is about embedding trustworthy, structured, and verifiable data into the physical world and doing so at scale. As these trends mature, those who design and deploy AIDC systems will shape not just how we track things, but how we trust them.
