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The landscape of smart data capture software is undergoing a significant transformation, with advancements that can help businesses build long-term resilience against disruptions like trade tariffs, labor shortages, and volatile demand.

No longer confined to handheld computers and mobile devices, the technology is embracing a new batch of hybrid data capture methods that include fixed cameras, drones, and wearables.

If you weren’t familiar with smart data capture, it is the ability to capture data intelligently from barcodes, text, IDs, and objects. It enables real-time decision-making, engagement, and workflow automation at scale across industries such as retail, supply chain, logistics, travel, and healthcare.

The advancements it’s currently experiencing are beyond technological novelties; they are further redefining how businesses operate, driving ROI, enhancing customer experience, and streamlining operational workflows. Let’s explore how:

More than just smartphones

Traditionally, smart data capture relied heavily on smartphones and handheld computers, devices that both captured data and facilitated user action. With advancements in technology, the device landscape is expanding. Wearables like smart glasses and headsets, fixed cameras, drones, and even robots are becoming more commonplace, each with its own value.

This diversification leads to the distinction of devices that purely ‘capture’ data versus those that can ‘act’ on it too. For example, stationary cameras or drones capture data from the real world and then feed it into a system of record to be aggregated with other data.

Other devices — often mobile or wearable — can capture data and empower users to act on that information instantly, such as a store associate who scans a shelf and can instantly be informed of a pricing error on a particular item. Depending on factors such as the frequency of data collected, these devices can allow enterprises to tailor a data capture strategy to their needs.

Practical innovations with real ROI

In a market saturated with emerging technologies, it's easy to get caught up in the hype of the next big thing. However, not all innovations are ready for prime time, and many fail to deliver a tangible return on investment, especially at scale. The key for businesses is to focus on practical, easy-to-implement solutions that enhance workflows rather than disrupt them by leveraging existing technologies and IT infrastructure.

An illustrative example of this evolution is the increasing use of fixed cameras in conjunction with mobile devices for shelf auditing and monitoring in retail environments. Retailers are deploying mobile devices and fixed cameras to monitor shelves in near real-time and identify out-of-stock items, pricing errors, and planogram discrepancies, freeing up store associates’ time and increasing revenue — game-changing capabilities in the current volatile trade environment, which triggers frequent price changes and inventory challenges.

This hybrid shelf management approach allows businesses to scale operations no matter the store format: retailers can easily pilot the solution using their existing mobile devices with minimal upfront investment and assess all the expected ROI and benefits before committing to full-scale implementation.

The combination also enables further operational efficiency, with fixed cameras providing continuous and fully automated shelf monitoring in high-footfall areas, while mobile devices can handle lower-frequency monitoring in less-frequented aisles.

This is how a leading European grocery chain increased revenue by 2% in just six months — an enormous uplift in a tight-margin vertical like grocery.

Multi-device and multi-signal systems

An important aspect of this data capture evolution is the seamless integration of all these various devices and technologies. User interfaces are being developed to facilitate multi-device interactions, ensuring that data captured by one system can be acted upon through another.

For example, fixed cameras might continuously monitor inventory levels, with alerts to replenish specific low-stock items sent directly to a worker's wearable device for immediate and hands-free action.

And speaking of hands-free operation: gesture recognition and voice input are also becoming increasingly important, especially for wearable devices lacking traditional touchscreens. Advancing these technologies would enable workers to interact with items naturally and efficiently.

Adaptive user interfaces also play a vital role, ensuring consistent experiences across different devices and form factors. Whether using a smartphone, tablet, or digital eyewear, the user interface should adapt to provide the necessary functionality without a steep learning curve; otherwise, it may negatively impact the adoption rate of the data capture solution.

Recognizing the benefits, a large US grocer implemented a pre-built adaptive UI to enable top-performing scanning capabilities on existing apps to 100 stores in just 90 days.

The co-pilot system

As the volume of data increases, so does the potential for information overload. In some cases, systems can generate thousands of alerts daily, overwhelming staff and hindering productivity. To combat this, businesses are adopting so-called co-pilot systems — a combination of devices and advanced smart data capture that can guide workers to prioritize ROI-optimizing tasks.

This combination leverages machine learning to analyze sales numbers, inventory levels, and other critical metrics, providing frontline workers with actionable insights. By focusing on high-priority tasks, employees can work more efficiently without sifting through endless lists of alerts.

Preparing for the future

As the smart data capture landscape continues to evolve and disruption becomes the “new normal”, businesses must ensure their technology stacks are flexible, adaptable, and scalable.

Supporting various devices, integrating multiple data signals, and providing clear task prioritization are essential for staying competitive in an increasingly complex, changeable and data-driven market.

By embracing hybrid smart data capture device strategies, businesses can optimize processes, enhance user experiences, and make informed decisions based on real-time data.

The convergence of mobile devices, fixed cameras, wearables, drones, and advanced user interfaces represents not just an evolution in technology but a revolution in how businesses operate. And in a world where data is king, those who capture it effectively — and act on it intelligently — will lock in higher margins today and lead the way tomorrow.

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Google Gemini introduced a new feature aimed at education called Guided Learning this month. The idea is to teach you something through question-centered conversation instead of a lecture.

When you ask it to teach you something, it breaks the topic down and starts asking you questions about it. Based on your answers, it explains more details and asks another question. The feature provides visuals, quizzes, and even embeds YouTube videos to help you absorb knowledge.

As a test, I asked Gemini's Socratic tutor to teach me all about cheese. It started by asking me about what I think is in cheese, clarifying my somewhat vague answer with more details, and then asking if I knew how those ingredients become cheese. Soon, I was in a full-blown cheese seminar. For every answer I gave, Gemini came back with more details or, in a gentle way, told me I was wrong.

The AI then got into cheese history. It framed the history as a story of traveling herders, clay pots, ancient salt, and Egyptian tombs with cheese residue. It showed a visual timeline and said, “Which of these surprises you most?” I said the tombs did, and it said, “Right? They found cheese in a tomb and it had survived.” Which is horrifying and also makes me respect cheese on a deeper level.

In about 15 minutes, I knew all about curds and whey, the history of a few regional cheese traditions, and even how to pick out the best examples of different cheeses. I could see photos in some cases and a video tour of a cellar full of expensive wheels of cheese in France. The AI quizzed me when I asked it to make sure I was getting it, and I scored a ten out of ten.

(Image credit: Gemini screenshots)Cheesemonger AI

It didn’t feel like studying, exactly. More like falling into a conversation where the other person knows everything about dairy and is excited to bring you along for the ride. After learning about casein micelles. starter cultures, and cutting the curd, Gemini asked me if I wanted to learn how to make cheese.

I said sure, and it guided me through the process of making ricotta, including pictures to help show what it should look like at each step.

(Image credit: Gemini screenshots)

By the time I was done with that part of the conversation, I felt like I’d taken a mini‑course in cheesemaking. I'm not sure I am ready to fill an entire cheeseboard or age a wheel of gruyère in my basement.

Still, I think making ricotta or maybe paneer would be a fun activity in the next few weeks. And I can show off a mild, wobbly ball of dairy pride thanks to learning from questioning, and, as it were, being guided to an education.

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As AI tools become more and more embedded in our everyday work, new research claims the challenge of not getting the best out of them may not lie solely with the technology.

A report from Multiverse has identified thirteen core human skillsets which could determine whether companies fully realize AI’s potential.

The study warns without deliberate attention to these capabilities, investment in AI writer systems, LLM applications, and other AI tools could fall short of expectations.

Critical thinking under pressure

The Multiverse study draws from observation of AI users at varying experience levels, from beginners to experts, employing methods such as the Think Aloud Protocol Analysis.

Participants verbalised their thought processes while using AI to complete real-world tasks.

From this, researchers built a framework grouping the identified skills into four categories: cognitive skills, responsible AI skills, self-management, and communication skills.

Among the cognitive abilities, analytical reasoning, creativity, and systems thinking were found to be essential for evaluating AI outputs, pushing innovation, and predicting AI responses.

Responsible AI skills included ethics, such as spotting bias in outputs, and cultural sensitivity to address geographic or social context gaps.

Self-management covered adaptability, curiosity, detail orientation, and determination, traits that influence how people refine their AI interactions.

Communication skills included tailoring AI-generated outputs for audience expectations, engaging empathetically with AI as a thought partner, and exchanging feedback to improve performance.

Reports from academic institutions, including MIT, have raised concerns reliance on generative AI can reduce critical thinking, a phenomenon linked to “cognitive offloading.”

This is the process where people delegate mental effort to machines, risking erosion of analytical habits.

While AI tools can process vast amounts of information at speed, the research suggests they cannot replace the nuanced reasoning and ethical judgement that humans contribute.

The Multiverse researchers note that companies focusing solely on technical training may overlook the “soft skills” required for effective collaboration with AI.

Leaders may assume their AI tool investments address a technology gap when in reality, they face a combined human-technology challenge.

The study refrains from claiming AI inevitably weakens human cognition, but instead it argues the nature of cognitive work is shifting, with less emphasis on memorising facts and more on knowing how to access, interpret, and verify information.

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While the new ‘Liquid Glass’ look and a way more powerful Spotlight might be the leading features of macOS Tahoe 26, I’ve found that bringing over a much-loved iPhone feature has proven to be the highlight after weeks of testing.

Live Activities steal the show on the iPhone, thanks to their glanceability and effortless way of highlighting key info, whether it’s from a first or third-party app. Some of my favorites are:

• Flighty displays flight tracking details in real-time, for myself, family, or friends
• Airlines like United show my seat, a countdown for boarding, or even baggage claim
• Rideshare apps tell you what kind of car you're driving is arriving in
• Apple Sports displays your favorite teams' live scores in real-time with the game

Now, all of this is arriving on the Mac – right at the top navigation bar, near the right-hand side. They appear when your iPhone is nearby, signed into the same Apple Account, and mirror the same Live Activities you’d see on your phone. It’s a simple but powerful addition.

Considering Apple brought iPhone Mirroring to the Mac in 2024, this 2025 follow-up isn’t surprising. But it’s exactly the kind of small feature that makes a big difference. I’ve loved being able to check a score, track a flight, or see my live position on a plane – without fishing for my phone.

(Image credit: Future/Jacob Krol)

I’ve used it plenty at my desk, but to me, it truly shines in Economy class. If you’ve ever tried balancing an iPhone and a MacBook Pro – or even a MacBook Air – on a tray table, you know the awkward overlap. I usually end up propping the iPhone against my screen, hanging it off the palm rest, or just tossing it in my lap. With Live Activities on the Mac, I can stick to one device and keep the tray table clutter-free.

Considering notifications already sync, iPhone Mirroring arrived last year, Live Activities were ultimately the missing piece. On macOS Tahoe, they sit neatly collapsed in the menu bar, just like the Dynamic Island on iPhone, and you can click on one to expand and see the full Live Activity. Another click on any of these Live Activities quickly opens the app on your iPhone via the Mirroring app – it all works together pretty seamlessly.

(Image credit: Future/Jacob Krol)

You can also easily dismiss them, as I have found they automatically expand for major updates, saving screen real estate on your Mac. If you already have a Live Activity that you really enjoy on your iPhone, there’s really no extra work needed from the developer, as these will automatically repeat.

All-in-all, it’s a small but super helpful tool that really excels in cramped spaces. So, if you’ve ever struggled with the same balancing act as I have with a tray table, your iPhone, and a MacBook, know that relief is on the way.

It's arriving in the Fall (September or October) with the release of macOS Tahoe 26. If you want it sooner, the public beta of macOS Tahoe 26 is out now, but you'll need to be okay with some bugs and slowdowns.

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• Huawei makes its CANN AI GPU toolkit open source to challenge Nvidia’s proprietary CUDA platform
• CUDA’s near 20-year dominance has locked developers into Nvidia’s hardware ecosystem exclusively
• CANN provides multi-layer programming interfaces for AI applications on Huawei’s Ascend AI GPUs

Huawei has announced plans to make its CANN software toolkit for Ascend AI GPUs open source, a move aimed squarely at challenging Nvidia’s long-standing CUDA dominance.

CUDA, often described as a closed-off “moat” or “swamp,” has been viewed as a barrier for developers seeking cross-platform compatibility by some for years.

Its tight integration with Nvidia hardware has locked developers into a single vendor ecosystem for nearly two decades, with all efforts to bring CUDA functionality to other GPU architectures through translation layers blocked by the company.

Opening up CANN to developers

CANN, short for Compute Architecture for Neural Networks, is Huawei’s heterogeneous computing framework designed to help developers create AI applications for its Ascend AI GPUs.

The architecture offers multiple programming layers, giving developers options for building both high-level and performance-intensive applications.

In many ways, it is Huawei’s equivalent to CUDA, but the decision to open its source code signals an intent to grow an alternative ecosystem without the restrictions of a proprietary model.

Huawei has reportedly already begun discussions with major Chinese AI players, universities, research institutions, and business partners about contributing to an open-sourced Ascend development community.

This outreach could help accelerate the creation of optimized tools, libraries, and AI frameworks for Huawei’s GPUs, potentially making them more attractive to developers who currently rely on Nvidia hardware.

Huawei’s AI hardware performance has been improving steadily, with claims that certain Ascend chips can outperform Nvidia processors under specific conditions.

Reports such as CloudMatrix 384’s benchmark results against Nvidia running DeepSeek R1 suggest that Huawei’s performance trajectory is closing the gap.

However, raw performance alone will not guarantee developer migration without equivalent software stability and support.

While open-sourcing CANN could be exciting for developers, its ecosystem is in its early stages and may not be anything close to CUDA, which has been refined for nearly 20 years.

Even with open-source status, adoption may depend on how well CANN supports existing AI frameworks, particularly for emerging workloads in large language models (LLM) and AI writer tools.

Huawei’s decision could have broader implications beyond developer convenience, as open-sourcing CANN aligns with China’s broader push for technological self-sufficiency in AI computing, reducing dependence on Western chipmakers.

In the current environment, where U.S. restrictions target Huawei’s hardware exports, building a robust domestic software stack for AI tools becomes as critical as improving chip performance.

If Huawei can successfully foster a vibrant open-source community around CANN, it could present the first serious alternative to CUDA in years.

Still, the challenge lies not just in code availability, but in building trust, documentation, and compatibility at the scale Nvidia has achieved.

Via Toms Hardware

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