IDC

Customer relationships shift across moments, usage, roles, and goals, often in ways that challenge traditional thinking. It’s no longer sufficient for brands to predict what someone might do next. Instead, they must also understand why customers behave as they do and act while the engagement window remains open. Today’s customer dynamics demand systems that can read intent and purpose in real-time, explain decision logic transparently, and trigger contextually appropriate responses. This requires predictive AI models augmented with generative AI capabilities and AI agents designed to analyze patterns, operationalize insights, make decisions, execute interventions, and learn from outcomes continuously. Brands need to understand that customer intent or behavior shifts do not wait until the next daily or weekly campaign planning and execution cycles. They need to synthesize intent signals, build accurate AI models and put them to work before they become irrelevant. Data foundation reality check Organizations rushing to augment their predictive AI systems with generative AI and AI agents often discover that their data architecture cannot support the complexities required to transform raw data and context into AI-ready inputs. This is not a minor issue. According to Future Enterprise Resiliency Survey, Wave 1, Feb. 2025, 38% of the respondents identified data management as one of their top priorities for AI strategy in 2025. The challenge isn’t just traditional data quality – it’s creating a unified data environment where structured customer transactions, unstructured behavioral signals, social interactions, and external market indicators can be processed collectively. When data sources remain siloed or poorly integrated, AI agents make decisions based on incomplete context, generative AI produces responses that ignore critical customer history, and predictive models optimize for patterns that no longer reflect current customer reality. Industry-specific requirements Organizations often overlook customer data characteristics and AI model needs by industry, even within context of marketing and CX use cases. In travel and hospitality, the emphasis might be on seasonal demand patterns, loyalty program activity, and booking lead times, whereas in fashion retail, it could center on style preferences, return behavior, and fast-moving trend adoption. These variations shape not just the data collected, but also how it’s processed, modeled, and translated into timely marketing actions. Best-fit customer analytics applications embed industry-specific frameworks, data models, and campaign templates. Prebuilt workflows and segmentation logic grounded in industry IP reduce customization effort, accelerate time-to-value, and ensure that marketing teams can act on insights in ways that resonate with their customers’ actual behaviors. The autonomous future The promise of autonomous customer analytics lies in its ability to analyze vast streams of customer data, make decisions and take actions at scale, and learn from the results to improve future performance. When built on a solid foundation, these systems don’t just respond to customer behavior, they adapt continuously, refining rules, models, and strategies based on what works and what doesn’t. Achieving this requires more than deploying an advanced AI model. It requires continuous learning architecture that captures outcomes, detects drift in data, model, or customer patterns, and adjusts actions accordingly. Without these capabilities in place, moving too quickly to autonomous AI decision-making can be risky. Weak data quality, insufficient governance, and lack of monitoring can allow small errors to accumulate rapidly, resulting in inconsistent actions. Value measurement systems Organizations struggle to measure the ROI of traditional predictive AI. Even in batch-driven models, linking predictions to business impact can be challenging with unclear baselines and inconsistent attribution. If it’s difficult to quantify the value of a churn prediction or a propensity score today, the challenge grows when moving to generative AI and AI agents. In fact, according to Future Enterprise Resiliency Survey, Wave 1, Feb. 2025, 34% of the respondents mentioned that demonstrable ROI is key consideration when they are evaluating agentic AI solution for marketing and sales. Successful organizations will be those that build value measurement into their customer analytics applications. This means not only track the business impact from predictive AI use case but also show the direct link between model outputs, actions taken, and outcomes achieved. By establishing this closed loop, organizations lay the groundwork for measuring GenAI and AI agent performance, where the same approach must scale and provide continuous feedback for improvements. Practical readiness Successful customer analytics transformation requires organizations to start with a fundamentally different question: not “what insights can we generate?” but “what customer behaviors can we influence, and what organizational capabilities do we need to influence them effectively?” Selecting the right use case (e.g., customer segmentation, propensity, personalization, journeys, digital experience, next best recommendations, etc.), strengthening the data foundation, pairing predictive AI with generative AI, piloting a bounded AI agent, governance, and establishing AI operationalization framework is critical to deliver consistent, measurable improvements in customer engagement and outcomes. IDC MarketScape Customer Analytics Applications To bring clarity to this rapidly evolving market and for buyers, IDC has released 2025 MarketScape report for Customer Analytics Application that help you understand and evaluate how these vendors stack up against capabilities and strategy criteria. Clients can access the report here: IDC MarketScape: Worldwide Customer Analytics Applications 2025 Vendor Assessment To learn more about our research findings, CDP vendors, and IDC’s marketing and CX technology best practices, feel free to schedule an inquiry or briefing request. Please reach out directly ([email protected]) or fill out a briefing request form. source

Direct to satellite communications won’t be a big money spinner straight off Smartphone to satellite direct communications becomes a commercial service in the US on 23rd July with the launch of the ‘T-Satellite’ service on T-Mobile via SpaceX Starlink. It is both a technological achievement and rather an underwhelming event following the hype that has gone into the subject over the last two years. D2D is not going to meet some of the overheated expectations of the space industry as its next great white hope. It will not produce billions of new short term revenues. What it will do to begin with is provide a very useful emergency and texting service. More will come later. Route one – need new phones D2D has already evolved a long way over a short time. As it started off, it was a deal between phone makers and existing satellite operators, notably the fist-generation LEO systems Iridium and Globalstar. With new chips in smartphones which could work on these operators’ frequencies, D2D service could commence, and a couple of years ago the big smartphone chipmakers began to look into tieups to make this work. This was a buzz theme at Mobile World Congress in 2023. Qualcomm had just signed up to work on D2D with Iridium, and Samsung and Mediatek were also looking at deals with smartphone makers. Only one of those deals stuck – Apple and Globalstar. That deal, in which all iPhones produced from the 14 series onwards work on Globalstar frequencies as well as the usual cellular ones, has subsequently been enlarged and Apple is now the primary backer of the next generation of Globalstar satellites. The Apple service at the moment is free. The other putative tieups however came to nothing because monetising service via this route would have been very complex. Route two – needing new satellites Instead D2D is moving emphatically towards service via a second option – using terrestrial cellular operator frequency. In theory, this means that all phones should be able to use the service – a TAM of seven billion or so of phones. Because of that, it was always the most logical way forward, but it did imply that investment needed to be made in new satellites which could work on those cellular frequencies. Starlink had the inside track for this because it could adapt satellites it already planned to launch. This move made the business model much more simple: service would be sold by the mobile operator and the satellite system would be like tower cellular infrastructure. A question of politics as well as investment Starlink however means the divisive figure of Elon Musk and there has been a lot of reticence in the telecoms industry to the emerging D2D industry being dominated by a satellite system he controls. This reticence has helped American rivals AST SpaceMobile and Lynk Global find backing. Both are startups and ambitious and needs lots of capital. AST in particular looks set to find the resources to launch a global system of broadband D2D capable of much more than emergency messaging. It has found backing from T-Mobile’s US mobile industry rivals AT&T and Verizon, and Google among others. Vodafone has done a joint venture deal with AST SpaceMobile to run service in Europe. The move to cellular operator frequency moves the D2D business squarely into the mobile operators’ court. Service can only work through them. Unsurprisingly they want to integrate D2D service into their existing tariff offerings. At present users are restricted to low rate messaging and testing on Starlink so far has shown that only some smartphones work well on the service. However technology is moving fast. AST is aiming at broadband communications, and Vodafone aims to have its JV service up and running in Europe in 2026. More in the works Meanwhile SpaceX, ever ambitious and trying to push the boundaries, wants to orbit some of its forthcoming satellites closer to the Earth. It also wants to increase their power output. That would bring its D2D performance to rival that of AST. And Starlink launches frequently and can put capacity into space more quickly. The latest and forthcoming work of standards body 3GPP will improve the reception and signal performance with satellites of new smartphone chipsets. Further out D2D may spread out into further services such as vehicle communications. So while the first steps in D2D have been smaller than the technology’s first boosters claimed, longer term it has substantial potential. Hence the big mobile players have made or are considering substantial investments which go well beyond the payback from messaging service. Already though the launch of emergency satellite messaging is a major technological milestone in global communications. Getting the small antenna on a smartphone to communicate with a satellite moving across the skey hundreds of kilometres above it sounds a long shot, and it is. To paraphrase the first man on the moon, Neil Armstrong, one small step for the telecoms industry, but one great leap for mankind. source

Customer relationships shift across moments, usage, roles, and goals, often in ways that challenge traditional thinking. It’s no longer sufficient for brands to predict what someone might do next. Instead, they must also understand why customers behave as they do and act while the engagement window remains open. Today’s customer dynamics demand systems that can read intent and purpose in real-time, explain decision logic transparently, and trigger contextually appropriate responses. This requires predictive AI models augmented with generative AI capabilities and AI agents designed to analyze patterns, operationalize insights, make decisions, execute interventions, and learn from outcomes continuously. Brands need to understand that customer intent or behavior shifts do not wait until the next daily or weekly campaign planning and execution cycles. They need to synthesize intent signals, build accurate AI models and put them to work before they become irrelevant. Data foundation reality check Organizations rushing to augment their predictive AI systems with generative AI and AI agents often discover that their data architecture cannot support the complexities required to transform raw data and context into AI-ready inputs. This is not a minor issue. According to Future Enterprise Resiliency Survey, Wave 1, Feb. 2025, 38% of the respondents identified data management as one of their top priorities for AI strategy in 2025. The challenge isn’t just traditional data quality – it’s creating a unified data environment where structured customer transactions, unstructured behavioral signals, social interactions, and external market indicators can be processed collectively. When data sources remain siloed or poorly integrated, AI agents make decisions based on incomplete context, generative AI produces responses that ignore critical customer history, and predictive models optimize for patterns that no longer reflect current customer reality. Industry-specific requirements Organizations often overlook customer data characteristics and AI model needs by industry, even within context of marketing and CX use cases. In travel and hospitality, the emphasis might be on seasonal demand patterns, loyalty program activity, and booking lead times, whereas in fashion retail, it could center on style preferences, return behavior, and fast-moving trend adoption. These variations shape not just the data collected, but also how it’s processed, modeled, and translated into timely marketing actions. Best-fit customer analytics applications embed industry-specific frameworks, data models, and campaign templates. Prebuilt workflows and segmentation logic grounded in industry IP reduce customization effort, accelerate time-to-value, and ensure that marketing teams can act on insights in ways that resonate with their customers’ actual behaviors. The autonomous future The promise of autonomous customer analytics lies in its ability to analyze vast streams of customer data, make decisions and take actions at scale, and learn from the results to improve future performance. When built on a solid foundation, these systems don’t just respond to customer behavior, they adapt continuously, refining rules, models, and strategies based on what works and what doesn’t. Achieving this requires more than deploying an advanced AI model. It requires continuous learning architecture that captures outcomes, detects drift in data, model, or customer patterns, and adjusts actions accordingly. Without these capabilities in place, moving too quickly to autonomous AI decision-making can be risky. Weak data quality, insufficient governance, and lack of monitoring can allow small errors to accumulate rapidly, resulting in inconsistent actions. Value measurement systems Organizations struggle to measure the ROI of traditional predictive AI. Even in batch-driven models, linking predictions to business impact can be challenging with unclear baselines and inconsistent attribution. If it’s difficult to quantify the value of a churn prediction or a propensity score today, the challenge grows when moving to generative AI and AI agents. In fact, according to Future Enterprise Resiliency Survey, Wave 1, Feb. 2025, 34% of the respondents mentioned that demonstrable ROI is key consideration when they are evaluating agentic AI solution for marketing and sales. Successful organizations will be those that build value measurement into their customer analytics applications. This means not only track the business impact from predictive AI use case but also show the direct link between model outputs, actions taken, and outcomes achieved. By establishing this closed loop, organizations lay the groundwork for measuring GenAI and AI agent performance, where the same approach must scale and provide continuous feedback for improvements. Practical readiness Successful customer analytics transformation requires organizations to start with a fundamentally different question: not “what insights can we generate?” but “what customer behaviors can we influence, and what organizational capabilities do we need to influence them effectively?” Selecting the right use case (e.g., customer segmentation, propensity, personalization, journeys, digital experience, next best recommendations, etc.), strengthening the data foundation, pairing predictive AI with generative AI, piloting a bounded AI agent, governance, and establishing AI operationalization framework is critical to deliver consistent, measurable improvements in customer engagement and outcomes. IDC MarketScape Customer Analytics Applications To bring clarity to this rapidly evolving market and for buyers, IDC has released 2025 MarketScape report for Customer Analytics Application that help you understand and evaluate how these vendors stack up against capabilities and strategy criteria. Clients can access the report here: IDC MarketScape: Worldwide Customer Analytics Applications 2025 Vendor Assessment To learn more about our research findings, CDP vendors, and IDC’s marketing and CX technology best practices, feel free to schedule an inquiry or briefing request. Please reach out directly ([email protected]) or fill out a briefing request form. source

Direct to satellite communications won’t be a big money spinner straight off Smartphone to satellite direct communications becomes a commercial service in the US on 23rd July with the launch of the ‘T-Satellite’ service on T-Mobile via SpaceX Starlink. It is both a technological achievement and rather an underwhelming event following the hype that has gone into the subject over the last two years. D2D is not going to meet some of the overheated expectations of the space industry as its next great white hope. It will not produce billions of new short term revenues. What it will do to begin with is provide a very useful emergency and texting service. More will come later. Route one – need new phones D2D has already evolved a long way over a short time. As it started off, it was a deal between phone makers and existing satellite operators, notably the fist-generation LEO systems Iridium and Globalstar. With new chips in smartphones which could work on these operators’ frequencies, D2D service could commence, and a couple of years ago the big smartphone chipmakers began to look into tieups to make this work. This was a buzz theme at Mobile World Congress in 2023. Qualcomm had just signed up to work on D2D with Iridium, and Samsung and Mediatek were also looking at deals with smartphone makers. Only one of those deals stuck – Apple and Globalstar. That deal, in which all iPhones produced from the 14 series onwards work on Globalstar frequencies as well as the usual cellular ones, has subsequently been enlarged and Apple is now the primary backer of the next generation of Globalstar satellites. The Apple service at the moment is free. The other putative tieups however came to nothing because monetising service via this route would have been very complex. Route two – needing new satellites Instead D2D is moving emphatically towards service via a second option – using terrestrial cellular operator frequency. In theory, this means that all phones should be able to use the service – a TAM of seven billion or so of phones. Because of that, it was always the most logical way forward, but it did imply that investment needed to be made in new satellites which could work on those cellular frequencies. Starlink had the inside track for this because it could adapt satellites it already planned to launch. This move made the business model much more simple: service would be sold by the mobile operator and the satellite system would be like tower cellular infrastructure. A question of politics as well as investment Starlink however means the divisive figure of Elon Musk and there has been a lot of reticence in the telecoms industry to the emerging D2D industry being dominated by a satellite system he controls. This reticence has helped American rivals AST SpaceMobile and Lynk Global find backing. Both are startups and ambitious and needs lots of capital. AST in particular looks set to find the resources to launch a global system of broadband D2D capable of much more than emergency messaging. It has found backing from T-Mobile’s US mobile industry rivals AT&T and Verizon, and Google among others. Vodafone has done a joint venture deal with AST SpaceMobile to run service in Europe. The move to cellular operator frequency moves the D2D business squarely into the mobile operators’ court. Service can only work through them. Unsurprisingly they want to integrate D2D service into their existing tariff offerings. At present users are restricted to low rate messaging and testing on Starlink so far has shown that only some smartphones work well on the service. However technology is moving fast. AST is aiming at broadband communications, and Vodafone aims to have its JV service up and running in Europe in 2026. More in the works Meanwhile SpaceX, ever ambitious and trying to push the boundaries, wants to orbit some of its forthcoming satellites closer to the Earth. It also wants to increase their power output. That would bring its D2D performance to rival that of AST. And Starlink launches frequently and can put capacity into space more quickly. The latest and forthcoming work of standards body 3GPP will improve the reception and signal performance with satellites of new smartphone chipsets. Further out D2D may spread out into further services such as vehicle communications. So while the first steps in D2D have been smaller than the technology’s first boosters claimed, longer term it has substantial potential. Hence the big mobile players have made or are considering substantial investments which go well beyond the payback from messaging service. Already though the launch of emergency satellite messaging is a major technological milestone in global communications. Getting the small antenna on a smartphone to communicate with a satellite moving across the skey hundreds of kilometres above it sounds a long shot, and it is. To paraphrase the first man on the moon, Neil Armstrong, one small step for the telecoms industry, but one great leap for mankind. source

Over the past decade, I’ve watched healthcare providers invest in electronic health records (EHRs), revenue cycle management (RCM), and a broad range of health IT solution areas spanning clinical, operational, and administrative functions. Yet, one issue continues to drain resources and morale like no other: prior authorization, also known as “prior auth.” Despite being intended to ensure appropriate care and control costs, in my analysis, prior auth costs the U.S. healthcare system at least $41.4 billion to $55.8 billion annually, at least, depending on how you model and factor in labor costs, delays, and the downstream clinical impact. What is even more bothersome is that prior auth isn’t just an operational inefficiency but a symptom of a deeper failure to prioritize system redesign over entrenched inefficiencies, temporary workarounds, and conflicting incentives. Why Prior Auth Won’t Fix Itself What I’ve come to believe, contrary to prevailing narratives, is that the prior auth crisis is not a failure of process or technology, but people and mindset. For years, U.S. healthcare leaders, particularly within provider organizations, have largely abdicated meaningful engagement with system redesign or resistance to imposing external forces. Instead, they’ve defaulted to compliance despite any dysfunction, relying on short-term patches, manual workarounds, and narrowly scoped initiatives to ensure payment. Most efforts have been reactive, designed to navigate and endure the complexity than challenge it. Survey data from the American Medical Association (AMA) paints a stark picture. Physicians and their teams spend 13 hours a week handling an average of 39 prior authorizations per doctor, so burdensome that 40% of practices have staff solely dedicated to it. Nearly 9 in 10 physicians report it drives burnout and inflates healthcare utilization. Even more troubling, 94% say prior auth harms clinical outcomes, 93% say it causes care delays, 82% report it leads to treatment abandonment, and 29% cite serious adverse events as a direct result.[1] Underneath any cultural resignation is the sense that such administrative complexity is “simply healthcare,” and the sheer magnitude of it is “how U.S. healthcare works.” This has shaped decades of efforts and investments that have further baked such dysfunction into the very DNA of the system. By not challenging and, in turn, reengineering prior auth from the ground up, it was standardized. The problem has not only hardened but has also been operationalized and institutionalized, resulting in such friction and colossal costs. The problem with prior auth isn’t that it’s only expensive to do, but also that it’s resistant to change. Unlike RCM, which has evolved over the years toward better end-to-end, front-loaded models that begin well before the claim is filed or the patient is even seen, prior auth too often gets triggered late in the care episode, after key decisions have been made. It’s still mostly payer-facing and payer-driven in the continuum of care, with misaligned, frequently conflicting incentives and inconsistent criteria across the board, except in the case of “payviders” or integrated delivery networks, where the divide is less pronounced and may promote rather than block collaboration despite it needing to be more in the patient’s or member’s interest than the system for true value-based care delivery. U.S. healthcare providers have repeatedly mistaken digitization for modernization. Converting paper into PDFs instead of structured data, automating outdated steps, or adding a portal to a broken process. These were not transformative moves. At best, they converted paper files into EHRs or manual billing into RCM, without questioning the process design behind them. Does it drive approval any faster? Does it reduce burden or improve care quality and experiences? Rarely. Even traditional automation tools, like RPA, while helpful for repetitive administrative tasks, were never built to handle unstructured data or the dynamic, exception-heavy nature of prior authorization workflows. These tools, in essence, served more as digital band-aids over deep systemic wounds, not solutions. Market Signals Tell Us to Move On According to IDC survey data, 52.5% of U.S. healthcare providers are now adopting composable IT architectures to drive electronic prior auth (ePA), moving toward modular, plug-and-play systems designed for agility and continuous evolution. Meanwhile, only 6.6% remain dependent on rigid, custom-built platforms. The message is clear: the market is shifting toward flexibility, interoperability, and intelligent orchestration. I won’t go so far as to say the tide is turning, but signals are getting louder. Across the board, I’m seeing more healthcare leaders on both the technology buyer and supplier sides acknowledging that traditional automation has reached its limits. The complexity of an area like prior auth demands something more adaptive, scalable, and intelligent. Enter agentic AI, not just as another layer of automation, but a new class of automation. Where agents shine is that they can bridge the gap between automation with intelligence, autonomy, and context awareness, working not just faster, but smarter. As opposed to traditional rule-based systems or narrowly trained models, agentic AI can adapt, interpret, and learn on the fly. This is a significant leap from simply executing pre-coded functions. What sets agents further apart is their ability to perform zero-shot reasoning, as well as their capability to handle new inputs or scenarios that haven’t been trained on by leveraging generalized knowledge across domains. This adaptability reassures healthcare leaders that agentic AI can function even in the face of edge cases, real-time policy updates, and unstructured clinical complexity, making it particularly well-suited for prior auth, where variability is the norm, not the exception. Rather than following static rulesets or requiring periodic retraining, agentic AI can: Interpret unstructured data by leveraging NLP and LLMs to extract relevant information from free-text sources such as physician notes, discharge summaries, radiology reports, and lab results. This enables the system to understand the clinical rationale behind a treatment or diagnostic order, allowing for more accurate and context-aware authorization decisions without requiring structured, template-based documentation. Adapt dynamically to evolving payer rules, rather than relying on static rule engines or periodic manual updates. Agentic AI can ingest real-time payer policy feeds, API-accessible rule libraries, or even scrape payer portals (when necessary) to automatically apply

The smart glasses market has been growing rapidly in the last couple of years, mostly led by the second generation of Meta Ray-Ban glasses that have come onto the market and taken consumers by storm. Interestingly, the first generation of Meta’s smart glasses, the Ray-Ban Stories released in 2021, fizzled not really capturing the consumers’ interest and underwhelming in terms of sales. In comparison, the second generation did more than 900k sales in just the fourth quarter of 2024, and holds over 65% global market share. We have also seen devices from major brands like Google with Google Glass and Bose with Bose Frames, which have failed to take hold and have both been discontinued. So why the current growth? The Tech Behind the Specs Well, part of the answer is clearly improving technology, this means the glasses can be relatively light whilst cramming in more features. Meta Ray-Bans second-generation Wayfarers weigh around 50 grams, which is only 5 grams heavier than the 45-gram non-smart Ray-Ban Wayfarers. At the same time, the glasses manage to pack in a 12-megapixel ultrawide camera, open-ear speakers on each arm of the glasses, five microphones, 32 GB of internal storage, the ability to connect via Bluetooth and Wi-Fi, and batteries capable of powering the glasses for up to 4 hours of use. Not too bad for an additional 5 grams. This impressive bundle of features rolled up in such a small and unobtrusive form factor means that consumers are now viewing  smart glasses as a legitimate technology product with significant real world use cases. The last couple of years have also seen rapid advances in Artificial Intelligence, which, when integrated into smart glasses, gives them far more functionality than just a pair of bulky glasses that have a camera and speakers bolted to them. These new AI features let users access information and interact with the world in real time, for example using the built-in cameras first person viewpoint to seamlessly identify landmarks by asking “What’s that building in front of me?”. Or allowing the user to spontaneously ask questions, just as they would with a search engine on a smartphone, but without the hassle of getting the phone out of their pocket and typing out the question, then trolling through results. This elevates the glasses from a nice-to-have gimmick into a useful tool for everyday life. There is clearly still a long way to go though; the AI features are still relatively primitive, and it’s doubtful that most people will be eager to randomly start asking AI questions out loud in public, given people’s desire for privacy. There are also interesting technological developments in the use of smart glasses as discreet hearing aids, with many people suffering from partial hearing loss being reticent to wear traditional hearing aids, due to the attached stigma and the implied acceptance of one’s age. This is a sizable and growing market with The World Health Organisation (WHO) estimating roughly 20% of the worlds population has some degree of hearing loss, this translates to over 1.5 Billion people. EssilorLuxottica has recently released it’s Nuance Audio smart glasses that have built-in microphones that pick up sounds the glasses are pointed at and then amplify them through built in speakers in the arms of the glasses. The idea being a partially deaf individual wearing the glasses in a somewhat noisy environment, like that found in a popular bar on a Friday night, can more easily hear a person they are trying to hold a conversation with. Transcribeglass has taken a slightly different approach to the same problem. Their smart glasses also use microphones to pick up conversations in the glasses field of view, but then the conversations are transcribed in writing onto the glass in front of the wearers eye. Allowing the hearing impaired individual to read conversations like subtitles in a film. Transcribe’s glasses can also be used to translate foreign languages in real time, giving them an even broader market appeal. Both companies approaches are interesting and highlight a huge opportunity in the market for a discreet solution to help individuals with hearing loss and foreign language translation, which could create a significant tail wing for smart glasses sales. Going Hands Free We have also seen additional use cases being added, like video streaming—especially with Meta making their glasses easily compatible with their social media platforms, allowing things like live-streaming Instagram Reels from the glasses. Smart glasses have the advantage that they can record first-person videos whilst allowing the person recording to remain in the moment. This was one of the key talking points from Apple when they launched their Vision Pro, but the bulky screen in front of people’s faces, coupled with the slightly off-putting projection of their eyes, means that “in the moment” is a relative term. Thinner, more normal-looking smart glasses from the likes of Meta allow the wearer to be as in the moment as any other glasses wearer. This will allow people to experience key events like birthday parties or watching New Year’s Eve celebrations and then still have the videos to last a lifetime, or more likely, post on their social media. There are obviously still problems with this technology, like restricted memory storage capacity and the quality of the videos recorded, but these have been rapidly improving in recent years and will likely continue to do so. Meta Dominates, But Rivals Are Emerging Right now, the smart glasses segment is a small one that is dominated by Meta, with the next biggest competitor being Chinese technology company Huawei, Meta owning 66% and Huawei 6% in 2024. But seeing the success of the smart glasses market, other competitors are circling. As mentioned previously, EssilorLuxottica, the owner of the brand Ray-Ban and the company with a virtual monopoly on the standard glasses market, has recently launched the Nuance Audio smart glasses. Amazon is also a significant player with its line of Echo Frame smart glasses doing hundreds of thousands of sales in