Today, the gap between AI marketing claims and real-world capabilities can feel wide enough to drive an entire fleet of autonomous vehicles through. No surprise then, that AI misuse topped the Q1 Reputational Risks Index for 2025.
Barcode scanning is no exception. In this blog, I’ll cut through the noise and highlight what really matters when evaluating AI in barcode scanning — so you can make informed, strategic decisions.
While all modern barcode scanning software is “AI” in the sense that it relies on machine learning algorithms, that doesn’t guarantee meaningful business impact. The real question is: does it improve critical metrics like performance, process efficiency, and profitability?
Understanding the levels of AI in barcode scanning helps you separate incremental improvements from truly transformative solutions. These distinctions will help ensure you’re investing in technology that delivers real operational and financial outcomes.
The three levels of AI in barcode scanning
At Scandit, we view AI capabilities in barcode technology across three distinct levels.
Level 1 — AI in name only
At this entry level, “AI” is often just a marketing label applied to basic computer vision. Solutions here enable camera-equipped smart devices to read common barcodes — but only under ideal conditions.
It’s important to acknowledge that even this level represents a major leap from 15 years ago, when Scandit was founded. Back then, barcode scanning required specialist hardware like laser scanners.
Today, barcode scanning functionality can be added to virtually every smartphone or smart device, making it the baseline expectation rather than a differentiator.
If your scanning tasks are low-volume and straightforward, solutions positioned at this level can still help you to upgrade your devices, and potentially save on hardware costs. But they won’t move the needle on productivity or operational performance.
Level 2 — Tackling real-world complexity
Solutions at this middle level don’t just try to read barcodes. They also apply algorithms to reconstruct them if necessary — for example, if a label is damaged.
In other words, Level 2 scanning moves beyond perfect conditions and starts addressing real-world performance and operational challenges. Smart devices running solutions at this level can handle:
These capabilities translate into smoother user experiences and measurable ROI improvements — as many Scandit SDK users can attest. But it’s important to recognize that this is still evolution, not revolution.
Level 2 solutions deliver incremental improvements in scanning accuracy and efficiency, but they’re still focused on what is being scanned — not the why or the broader context in which the scan happens. Solutions are often hard-coded for specific scenarios—for example, optimized to scan EAN-13 barcodes on shiny, curved surfaces.
Level 3 — Context-aware Scanning
This is where AI becomes a true game-changer. Context-aware scanning introduces contextual intelligence, enabling barcode scanners to understand not just the data they’re capturing — but also the environment and intent behind each scan.
Why does this matter? Because in the real world, a barcode is never just a barcode. Context-aware scanning accounts for:
Cluttered, fast-moving environments
Scanning while in motion
Complex product labels with multiple barcodes and accompanying text
With the release of Scandit SDK 7.0 and beyond, Level 3 capabilities became a reality through new, patented capabilities such as:
Smart Scan Intention: Uses contextual clues to automatically identify which barcode the user intends to scan, even when multiple barcodes are present. This eliminates the need for manual input.
Smart Label Capture: Understands the relationship between barcodes and printed text on complex product labels, ensuring that only the relevant data is captured and passed to users and IT systems.
Text recognition (OCR) as a backup: Automatically identifies when a barcode is present but unscannable (for example, if it’s damaged), and uses text recognition to read the numbers printed under the barcode instead.
At this level, AI systems analyze multiple signals — environmental factors, user behavior, label complexity — to create a comprehensive understanding of what’s being scanned, why it’s being scanned, and what data actually matters.
Users get the data they need on the first try, no matter the environment. This removes frustrating mis-scans and eliminates the need for repeated scans or manual data entry.
How AI transforms barcode scanning processes, not just improves them
Level 3 AI doesn’t just make existing processes slightly better — it’s the foundation for reimagining workflows, simplifying multi-step tasks, and unlocking efficiencies that directly impact revenue and customer satisfaction.
Consider a retail example: Instead of manually entering unit prices or weights, a single scan can automate it all. One US retailer using Scandit’s Smart Label Capture reduced data entry errors and saved $1.3 million annually through this functionality alone.
This isn’t just about better scanning — it’s about enabling strategic business outcomes. When we reach Level 4 of AI in barcode scanning, we'll witness another leap forward in algorithmic intelligence. Solutions will not only decode barcodes efficiently but also intelligently infer relationships and context. Is the barcode part of a product label, or printed directly on a shipping box? Are there duplicates, or multiple instances of the same barcode present?
This enhanced contextual understanding will dramatically accelerate processes within businesses, enabling entirely new use-cases and opportunities that we can barely imagine today. At Scandit, we're already pioneering advancements to make this vision a reality.
Take Staples Canada: their outdated scanning technology delayed digital transformation efforts and led to poor price accuracy. With a new Scandit-powered app, they achieved 100% price compliance, directly impacting customer satisfaction and financial performance.
On the consumer side, apps like Yuka serve over 60 million users who rely on barcode scanning to access expert product information on the nutritional value of food products. Scandit’s Smart Scan Intention eliminates scanning errors, delivering a smooth, intuitive experience.
As a consumer-facing app with a community of over 60 million users, an optimal, intuitive, and friction-free scanning app is essential for Yuka. That frictionless UX is often threatened by unintentional scans, a result of the environments Yuka users scan products in, such as the store, pantry, or fridge, where barcodes are heavily prevalent. With its advanced algorithms, Scandit’s Smart Scan Intention helps eliminate this friction by understanding which barcode the user intends to scan, leading to fewer errors and more accurate scanning.
When scanning technology fails, the impact goes beyond missed barcodes. It means lost revenue, broken customer trust, and competitive disadvantage.
Getting started with AI-enhanced barcode scanning
With this information, you’re uniquely positioned to ask the right questions when evaluating scanning technology:
Does the solution adapt to different contexts without requiring manual configuration for every edge case?
Can it combine barcode and text recognition with true semantic understanding?
Is processing performed on-device to ensure real-time responsiveness? (This is applicable to all levels.)
If the answer to any of these is no, you’re likely looking at basic or advanced AI — not the level 3 context-based AI needed to transform your workflows.
Why AI barcode scanning matters for your competitive edge
While many organizations settle for small, incremental improvements, understanding the true capabilities of AI in barcode scanning positions you to make smarter, future-proof technology decisions.
Whether your priority is reducing operational friction today or enabling new workflows tomorrow, having a clear view of what’s possible with AI in barcode scanning helps ensure that your technology choices deliver value — both now and as your business evolves.
:format(jpeg))
:format(jpeg))
:format(jpeg))
:format(jpeg))