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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 recent years. More niche players like Transcribeglass, Xreal and Gentle Monster are also sprouting up to embrace the opportunity. Additionally, it has long been understood that much of the development of Apple’s Vision Pro and Samsung’s Project Moohan, both virtual reality goggles, has been aimed at laying the R&D groundwork for competing in the future mass-market virtual and augmented reality products. Apparently even Apple understands a first-generation augmented reality device priced at $3,500 is unlikely to have broad market appeal. The increasing focus on smart glasses is clearly demonstrated by Samsung’s recent show casing of its own prototype smart glasses, and the launching of Android XR, an operating system designed out of a collaboration between Google, Samsung and Qualcomm, to work on Smart glasses and Augmented/Virtual Reality goggles.

Can Smart Glasses replace the Mobile Phone?

In many ways, the form factor of a device that can project in front of the eyes is more useful and intuitive than a technological brick you keep in your pocket. This is because the world becomes your screen, you can project tabs and information in every part of your field of vision, should you choose, or simply wear them like a normal pair of clear glasses. This gets around the fact that your phone, though increasingly powerful, has a relatively limited screen size, which can make doing things like watching films and writing out emails annoying. Whereas with a larger field of view, you can watch films as if they were projected on the side of massive buildings or possibly even have 3D interactions within films and games, with the visuals being projected all around you. Both Apple and Meta have shown off hand-tracking technology with the Apple Vision Pro being able to do this through a series of cameras on the headset tracking your hand movements, and the Meta Orion concept glasses getting a similar effect by having the user wear a gesture-tracking wrist strap. This hand-tracking technology allows for the possibility of normal, intuitive interactions with technology through hand gestures. This could be as simple as projecting a virtual keyboard in your field of view and then tracking your hand movements to ascertain what key you are typing, or it could mean interactive gameplay. For example you could play a virtual game of tennis with the glasses tracking and interpreting your wild tennis swings, and seamlessly turning them into a coherent game of tennis against an AI or another player wearing smart glasses half a world away.

The holy grail for smart glasses will be an augmented reality screen that can seamlessly overlay projections onto the real world. Of course this can be done with larger devices like Apple’s Vision Pro and the Meta Quest, but to be able to do this in the slimmed-down format of a normal-ish looking pair of glasses that regular people would consider wearing on a daily basis would be a massive step forward. This, coupled with a significantly improved AI interface, larger battery life, and a slimmed-down sleeker design, could allow these glasses to go from a niche but interesting product type to a product with broad mass-market appeal. If the technology can be improved enough, there is a possibility that in a number of years smart glasses could start taking market share from mobile phones, as they should be able to replicate the majority of the phones features but in a more intuitive form factor.

Conclusion & Forecast

Smart glasses are a rapidly growing market as technological advances are beginning to make the form factor viable. Further advances in technology, greater consumer awareness, and new entrants into the market will likely continue this growth into the long run (IDC is currently forecasting 18.7 million units in 2029 compared to 2.7 million units in our most recent full year of data 2024). A little way off the 1.44 billion mobile phones sold worldwide in 2024, but if there is any product positioned to eventually replace the smartphone, both industry analysts and technology giants are betting it will be smart glasses.

Frederick Stanbrell - Data & Analytics Analyst - IDC

Analyst Profile
Frederick Stanbrell joined IDC in 2022, as an associate research analyst based in London, leading the European Wearables tracker. As head of the European Wearables tracker he collates guidance, tracks market trends and provides insight and forecasts into the region, companies and individual countries. Before joining IDC, he studied an undergraduate degree in Economics from the University of Greenwich, obtaining a first. During this time he was also a prominent member of the University of Greenwich Cricket team.

Across the Asia-Pacific region, enterprises are exploring generative AI (GenAI) with urgency, but scaling remains elusive. IDC research shows that while organizations ran an average of 23 GenAI proof-of-concepts (POCs) between 2023 and 2024, only 3 reached production. Of those, just 62% met expectations. The real challenge? Turning experimentation into enterprise value.

 Why Agentic AI Matters for GenAI Use Cases

GenAI’s potential extends far beyond content creation. But to realize its full value, organizations must move past isolated tools and embrace Agentic AI, intelligent agents that operate with autonomy, context, and integration across systems.

Agentic AI is the missing link between promising GenAI pilots and impactful enterprise transformation. It enables scalable, reusable use cases that drive results in productivity, quality, cost-efficiency, and resilience.

What Makes Agentic AI Different?

Unlike static models, Agentic AI introduces enterprise-grade capabilities, including:

  • Context retention for continuity across interactions
  • Multi-step task execution for complex operations
  • Exception management to handle real-world unpredictability
  • Security compliance for enterprise environments

This marks a shift from isolated AI functions to end-to-end automation, turning GenAI from a productivity tool into a strategic business engine

Super Use Cases: Where Agentic AI Delivers Impact

Not all AI use cases are created equal. The most successful organizations are focusing on “super use cases,” scalable, process-centric applications that integrate AI into decision-making, workflows, and operations. These include:

  • Customer support orchestration
  • Fraud detection and resolution
  • IT and HR automation
  • Context-aware marketing and personalization

These use cases aren’t just feasible with Agentic AI; they thrive because of it.

Building with Reusable Design Patterns

To scale Agentic AI, enterprises must move beyond bespoke solutions. Reusable design patterns enable the rapid and flexible deployment of AI. Key patterns include:

  • Task planning: Break down goals into AI-executable steps
  • Tool orchestration: Connect agents with enterprise platforms
  • Self-reflection: Learn from past actions to improve accuracy
  • Collaboration: Enable multiple agents to work in sync

These patterns act as blueprints, fueling faster time to value across diverse use cases.

Transforming Work, Not Just Tools

While personal GenAI apps, like note-takers and summarizers, are helpful, they’re quickly becoming commoditized. The real edge lies in deeply integrated, business-specific applications. Agentic AI enables a rethinking of work itself:

  • Marketers will optimize for LLMs, not just search engines.
  • CX leaders will deploy agents to unify channels, systems, and data.
  • Ops teams will automate workflows end-to-end.

In 2025 and beyond, Agentic AI won’t just support the work—it will redefine how work gets done.

Accelerating Agentic AI Adoption

As businesses increasingly explore automation, from robotic systems to intelligent assistants and sophisticated agents, agentic AI is poised to reshape daily work across industries. However, many organizations are unprepared to manage the dual challenge of evolving work practices and adopting new technologies.

Leaders need support navigating cross-functional change, especially as new roles like Chief AI Officer (CAIO) emerge. Technical professionals must expand their skill sets to include agentic development platforms, while nontechnical staff will need to learn workflow automation and natural language prompting.

Successful adoption will depend on aligning change management strategies with regional work cultures and technology maturity levels. In 2025 and beyond, agentic AI will not just change tools; it will redefine how work gets done.

Some key considerations:

  • Technical teams need to master agentic development platforms.
  • Non-technical users must learn prompt-based automation.
  • Leaders should align transformation efforts across regions, each with its own pace and culture of adoption.

Success relies on cross-functional collaboration and a clear strategy to integrate agentic AI into daily operations.

Measuring What Matters: A Holistic View of AI’s Business Value

One of the biggest barriers to AI adoption is the difficulty in measuring return on investment. To address this, IDC’s AI Business Value Benefit Framework outlines nine key dimensions, ranging from revenue growth and customer experience to innovation, resilience, and sustainability, that help organizations evaluate both the direct and indirect impacts of AI. By taking a broader view of AI’s value beyond just cost savings, this framework enables businesses to align AI investments with long-term strategic goals and drive meaningful outcomes across operations.

For more on this topic, please refer to the IDC report From AI Return on Investment to Business Value.

Final Word: Your Next Move Starts Here

Agentic AI turns GenAI from an experiment into a strategic differentiator. By focusing on super use cases and embracing reusable patterns, enterprises can move confidently from POC to production and unlock the full promise of AI.

Your next move? Let’s make it count, with Agentic AI at the core.

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Deepika Giri - Associate Vice President - IDC

Analyst Profile
Deepika manages and leads the research programs in big data and analytics (BDA), artificial intelligence (AI), blockchain, and Web3. Deepika is a seasoned data and AI professional and brings extensive knowledge about the impact of data engineering, big data cloud platforms, and data science across critical sectors. She has extensive experience in software delivery as well as sales leadership and management. She also has over 20 years of experience in IT services, including leadership roles, at Capgemini, Infosys, and Accenture, and has strong industry expertise in the telecommunications and retail industries. More so, Deepika has an entrepreneurial spirit and has previously founded her own online retail fashion business.

The automotive industry is interconnected and global. That is not going to change, with or without the presence of tariffs.  Auto manufacturers in North America rely on parts, subassemblies, transmissions, semiconductor chips, as well as software, rare earth metals, and other metals like aluminum and steel from Canada, Mexico, Europe, China, Japan, and elsewhere.  Tariffs will impact vehicles assembled in the US just as they will affect vehicles imported from other countries. It is possible for sourcing strategies and production locations to shift, but this takes years.  As a 25% tariff by the U.S. on imports from Japan and other countries looms in response to a perceived uneven trade playing field, automotive OEMs (Original Equipment Manufacturers) and suppliers are strengthening connections with customers and partners across the supply chain, as well as with the end consumer. 

Automotive ecosystem partners are working together to develop equitable business models and approaches to alleviate tariff cost impact and reduce the risk to consumers of vehicle price increases and availability declines.  These connections and new approaches are particularly important in Japan, where much of the auto supplier base consists of small and medium-sized businesses (SMBs).  We have seen, for example, SMBs and large OEMs accelerate vehicle shipments and manufacturing of parts during the 90-day tariff pause which ended on 9 July.  Manufacturers tell us that the absence of significant price increases in response to tariffs thus far may be a result of working down older, lower cost inventory.  With this strategy, however, comes financial risk and potential cash flow issues (particularly for the small vendors) as tariffs are paid up front in a short period of time.  There is also risk across interconnected regions, such as in Asia Pacific: multiple Japanese auto OEMs (Toyota, Honda, Isuzu, Mitsubishi) have big investments in Thailand particularly for electric vehicles (EVs) and the U.S. is Thailand’s top auto export destination (18%).  

The on-again/off-again tariff situation makes it extremely challenging for OEMs and suppliers in the industry to properly plan for new R&D and production. Companies are struggling to commit to new US production based on this unpredictability–building new facilities or even reopening and improving shuttered ones is not something that happens quickly.  Although it is likely that large auto manufacturers and suppliers that had plans in place for new facilities and/or joint ventures with American companies will accelerate those plans.  For example, battery providers such as LG Energy Solution and Panasonic are working with auto EV OEMs such as GM, Ford, Tesla, and Rivian, as well as semiconductor companies such as Qualcomm.  GM recently announced a $4B investment in three existing U.S. factories. Automotive production output as of June 2025 is mostly flat everywhere globally, with this expected to continue through CY2027 (source: S&P Global Mobility). Notably, however, as of June 2025, Japan’s manufacturing purchasing managers’ index (PMI) rose to 50.4 from 49.4, after 11 months of contraction (below 50). It remains to be seen whether this will spark consistent growth or is a brief increase in response to the 90-day tariff reprieve.

In IDC’s 2025 Supply Chain Survey, automotive manufacturers identified their top three strategies to mitigate supply chain risks as improving supply chain agility, improving supply chain visibility, and prioritizing local ingredient/component supply (near-shoring) over global sourcing. These priorities underscore a broader industry shift toward localized resilience and faster response capabilities, driven not just by tariffs but by chronic disruptions, digital transformation pressures, and a push for ecosystem alignment.

At the same time, the automotive industry continues to face other monumental structural shifts, all of which could be impacted by tariffs: the expansion of software-defined vehicles, the growth of electric vehicles (EVs), and the ongoing digital talent shortages and lack of new workers entering the industry.  An upcoming IDC Perspective will expand on these three challenges and opportunities and the related tariff impact.

The ambiguous tariff environment that global automotive OEMs and their suppliers are currently living through may ultimately turn out to be a benefit by forcing continuous collaboration, data sharing, and knowledge visibility, if this is not currently present.  Automotive is already an ecosystem-driven industry, with participants from the private and public sectors, multiple tiers of the supply chain, and other industries working closely together.  Sharing risk, resources, talent, and data across this ecosystem will enable rapid response to increasing consumer demand for software-rich, electric vehicles, as well as a flexible response to economic and geopolitical disruptions.

Take the next step and discover how IDC’s research can help you with your Supply Chain strategy, implementation, and digital transformation.  Contact IDC via this form.  For research specific to Industry Ecosystems & Business Networks, please go to this page.

Simon Ellis - Program GVP - IDC

Analyst Profile
As Group Vice President, Simon Ellis currently leads the U.S. Manufacturing Insights, U.S. Energy Insights, and Global Supply Chain Strategies practices at IDC, specializing in advising clients on manufacturing/energy strategies, supply chain digital transformation, sustainability, cloud migration, network, and ecosystem design. Mr. Ellis works with end user companies, supply chain organizations and technology providers to develop best practices and strategies leveraging IDC quantitative and qualitative data sets. Within the Supply Chain practices, Mr. Ellis contributes extensively to the Supply Chain Planning and Multi-Enterprise Networks Strategies practice while also overseeing the Supply Chain Execution practices. These supply chain practices specialize in advising clients on supply chain network design, S&OP, global sourcing (Profitable Proximity and Low-Cost Sourcing), warehousing and inventory management, transportation, logistics, and more.

Stephanie Krishnan - Associate Vice President - IDC

Analyst Profile
Stephanie Krishnan leads IDC’s Asia/Pacific research and advisory for supply chain, manufacturing, retail, and adjacent industry domains. As Associate Vice President for IDC Insights, she guides organizations through the rapid transformation toward digitally enabled, AI-driven, and highly interconnected operations. Her work centers on the future of supply chain ecosystems, operational resiliency, sustainability, and the rise of agentic and autonomous decision-making across global networks.

Jeffrey Hojlo - Research Vice President - IDC

Analyst Profile
As Research Vice President, Future of Industry Ecosystems, Innovation Strategies, & Energy Insights at IDC, Jeff Hojlo leads one of IDC's Future Enterprise practices at IDC - the Future of Industry Ecosystems. This practice focuses on three areas that help create and optimize trusted industry ecosystems and next generation value chains in discrete and process manufacturing, construction, healthcare, retail, and other industries: shared data & insight, shared applications, and shared operations & expertise. Mr. Hojlo manages a group focused on the research and analysis of the design, simulation, innovation, Product Lifecycle Management (PLM), and Service Lifecycle Management (SLM) market, including emerging strategies across discrete and process manufacturing industry such as product innovation platforms and the closed loop digital thread of product design, development, digital manufacturing, supply chain, and SLM. He also manages IDC's North American Energy Insights group, with a focus on key topics such as energy transition & sustainability, distributed energy resource management, and digital transformation in the Oil & Gas and Utilities industries.

A New Era of AI-Driven Healthcare in Asia Pacific

Asia/Pacific’s healthcare sector is entering a revolutionary era – driven by a surge in clinical data powered by AI and GenAI, and more recently, Agentic AI. This era will be shaped by the need to balance dual priorities of efficiency and effectiveness across workflows and workforce productivity.

To meet these demands, healthcare provider organizations are now focusing their investments on four immediate priorities.

  • Workflow automation to increase workflow efficiency for enhanced care outcomes
  • Patient-centric care delivery models to ensure care accessibility and convenience
  • GenAI solutions to augment clinician efficiency while creating a hyper-personalized patient experience (PX)
  • Cybersecurity to maintain cyber-resilience as emerging technologies become the imperative for modernized healthcare

AI-Driven Workflow Automation: Scaling Efficiency and Outcomes

As healthcare providers across the Asia-Pacific region pursue greater operational efficiency, improved quality of care, and scalability, AI and automation are becoming a top priority. Repetitive and data-intensive processes are placing a heavy burden on healthcare providers, draining valuable time and resources. By automating these tasks, organizations can relieve this strain, optimize internal resources, and significantly reduce administrative overload.

At the same time, there is growing pressure from rapidly aging populations—particularly in super-aged nations like Japan and South Korea. This, along with the rising prevalence of non-communicable diseases (NCDs), is increasing demand for more efficient healthcare delivery.

To address these shifts, healthcare providers have identified healthcare-specific use cases for automation in the next two years: clinical workflows, operational workflows, and administrative workflows.

Electronic Health Record (EHR) platform, with its tools and functionalities, serves as a robust foundation for automation investments. One-third of healthcare providers have already invested in CDSS (Clinical Decision Support Systems), while more than half plan to invest within the next two years.

IDC data shows that almost half (47%) of healthcare organizations consider health data platforms as the topmost investment potential, owing to the need for large-scale data integration, data leveraging, and real-time analytics for “Intelligent Automation.”*

New Patient-Centric Care Models: From Telemedicine to Hospital-at-Home

Innovations in patient-centric care delivery solutions continue to accelerate. This is also driven by the rising consumerization of care and supported by a maturing health tech ecosystem.

For example, telemedicine is transforming into comprehensive Telehealth platforms. What began as basic virtual consultations has now expanded to include integrated access to electronic medical records, e-prescriptions, lab results, and patient education—all within a single interface. This empowers patients to make informed decisions and take greater ownership of their health.

In another case, Remote Patient Monitoring (RPM) is progressing into full-fledged “Hospital-at-Home” (H@H) models. Over half of regional care providers are investing in H@H technologies. For example, Singapore General Hospital (SGH) and Khoo Teck Puat Hospital (KTPH), under the National University Health System (NUHS), have launched the Mobile Inpatient Care@Home (MIC@Home) program. Spearheaded by the MoH Office for Healthcare Transformation (MOHT), the program supports patients with general medical conditions such as skin infections, urinary tract infections, and congestive heart failure. Following a successful pilot, the initiative has expanded to four more hospitals: Changi General Hospital (CGH), KK Women’s and Children’s Hospital (KKH), Sengkang General Hospital (SKH), and Tan Tock Seng Hospital (TTSH). Similarly, in Australia, 44 hospitals in Victoria are now offering Hospital-in-the-Home (HITH) services. To scale these models effectively, healthcare providers are increasingly reshaping their investments through Digital Front Door (DFD) strategies. By leveraging the broader healthtech ecosystem and adopting innovative, patient-focused delivery models, they aim to create more efficient, scalable, and responsive healthcare systems across the region.

IDC predicts that by 2027, driven by the demand for enhanced care collaboration, expanded clinician and consumer access, and enhanced digital literacy, 80% of patients in APeJ (Asia/Pacific except Japan) will utilize Hybrid Care.*

Augmenting Clinician Efficiency and Hyper-Personalized Patient Experience with GenAI and Agentic AI

GenAI and Agentic AI.are poised to make healthcare more accessible to underserved populations. Recognizing its potential, over half of the region’s healthcare providers plan to invest in GenAI solutions within the next two years.

Healthcare organizations are set to transition from early experimentation to developing comprehensive, enterprise-wide AI strategies. CIOs from both multi-specialty and super-specialty hospitals are already exploring targeted GenAI use cases, not only to optimize resource alignment but also to identify the prerequisites necessary to become truly GenAI-ready.

IDC predicts that, by 2026 healthcare GenAI investments are expected to double in Asia/Pacific excluding Japan (APeJ), driven by the rapid deployment of use cases, more curated clinical data, and increased organizational buy-in.

In the context of GenAI, hospital chains across the region have begun integrating data across their networks to effectively deploy large language models (LLMs). For example, Apollo Hospitals in India has developed a Clinical Intelligence Engine (CIE) powered by LLMs, which leverages extensive clinical datasets from its hospital network to deliver faster, more informed patient responses. In Singapore, Synapxe, the national healthtech agency, has implemented a GenAI tool called “Russel GPT”, designed to generate rapid summaries from patient data to boost clinician efficiency and enhance the overall patient experience. As Agentic AI adoption among care providers emerges, the primary focus is on enhancing productivity far beyond that provided by GenAI. This focus will demand for almost a third of the GenAI investments in Agentic AI in 2026. Encouraged by the potential of these use cases, healthcare providers across the region are specifically seeking partners with strong AI security capabilities, cloud ecosystems integrated with AI services, a commitment to responsible AI practices, and robust data governance frameworks to ensure safe and effective deployment of GenAI solutions.

AI-Powered Cybersecurity: Core to  Healthcare Resilience and Patient Data Safety

The healthcare sector in the Asia-Pacific region remains highly vulnerable, as the frequency and severity of cyberattacks on major hospitals continue to increase. In India, a recent ransomware attack on AIIMS (All India Institute of Medical Sciences) forced operations into manual mode, disrupting critical services. Similarly, in Australia, a cyberattack led to a significant data breach at St. Vincent’s Health. Considering such incidents, healthcare CIOs across the region are not only prioritizing investments in cybersecurity but are also focusing on cyber-resilience. This translates into proactively detecting and responding to threats earlier through AI-driven security solutions that enhance threat intelligence, response, and recovery.

IDC reports that by 2026, growing cybersecurity risks will prompt 40% of healthcare organizations in APeJ to adopt AI-based threat intelligence solutions to ensure care continuity and safeguard patients.*

A targeted attack on an AI system could compromise its output, potentially endangering patients, such as altered radiation dosages in cancer treatment plans. These threats underscore the critical need for robust security measures to safeguard the integrity and accuracy of AI-driven healthcare applications.

To address these risks, hospitals in the region are heavily exploring AI-specific cybersecurity strategies, including advanced encryption methods to secure data transmission, real-time threat detection systems to identify anomalies, and stringent access controls to prevent unauthorized use.

The current landscape of the Asia/Pacific healthcare sector limits organizations’ ability to enhance their IT security capabilities. IDC data indicate that regional healthcare providers prioritize managing internal and external security risks, achieving greater visibility into the threat landscape, and having proactive threat detection, response, and remediation capabilities. Security service providers need to align their capabilities with these priority areas for initial pitching and successful engagement. CISOs and CIOs of regional healthcare providers have indicated to IDC that real-time threat intelligence and predictive analytics for identifying potential security risks are the most valuable functionalities they seek in AI-powered security tools, reflecting a strong focus on proactive and efficient threat detection and response.

Defining the path ahead to secure the future

To truly unlock the potential of GenAI and Agentic AI, healthcare providers must take a thoughtful and strategic path forward. It starts with building a strong foundation by establishing a data governance framework led by a team of clinicians, data scientists, legal experts, and patient safety officers to guide responsible use. One of the most impactful steps is integrating GenAI into EHR workflows, especially for automating documentation, something IDC identifies as a top priority for care providers. Just as important is strengthening the data architecture behind these systems, ensuring they are secure, scalable, and ready to support the future of AI-powered healthcare.

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*SOURCE: IDC FutureScape: Worldwide Healthcare Industry 2025 Predictions — Asia Pacific (Excluding Japan) Implications

Manoj Vallikkat - Senior Research Manager - IDC

Analyst Profile
Manoj Vallikkat currently works as a senior research manager for Healthcare Insights in IDC Asia/Pacific. His research covers digital transformation (DX) across care delivery systems in the region, focusing on areas such as evolving healthtech ecosystem, patient-centric care, and predictive care management. He also covers the life sciences segment, with special interest in artificial intelligence (AI)-based drug discovery and remote clinical trial practices. Manoj has led key consulting engagements across the country markets in the Asia/Pacific region. He has also handled various GMS engagements for tech providers, which include tailored reports, round-tables, and speaking gigs.

As geopolitical disruptions, tariff uncertainties, and economic slowdowns prompt organizations to reevaluate budgets, one area that remains non-negotiable is cybersecurity, risk, and compliance. Across Asia/Pacific, this domain has proven remarkably resilient to budget contractions, emerging as a critical enabler of AI-driven innovation, trust, and long-term business viability.

According to IDC’s Worldwide Security Spending Guide, Asia/Pacific enterprises are expected to invest USD $44.4 billion in cybersecurity in 2025, with spending projected to grow at a CAGR of 10.6%, reaching USD $60.6 billion by 2028. This upward trajectory underscores a critical shift: cybersecurity is no longer viewed as a discretionary cost, but as a strategic imperative that is deeply embedded into digital transformation, regulatory readiness, and AI adoption initiatives across the region.

While Asia/Pacific is home to four of the world’s top ten digital economies, it is also at the epicenter of a dual inflection: the aggressive integration of artificial intelligence (AI) into enterprise workflows and the intensifying complexity of the cybersecurity threat landscape. Enterprises across sectors, from banking and healthcare to manufacturing and public utilities, are experiencing the push and pull of this convergence. The accelerated adoption of GenAI, the rise of autonomous decision-making systems, and increased reliance on sensitive data have reshaped the risk surface.

This confluence of AI acceleration and security pressure is driving a new breed of enterprise questions:

  • How can we ensure our GenAI deployments are compliant, transparent, and ethically aligned?
  • How can AI be used to counter AI-driven threats while ensuring explainability and trust?
  • What does an integrated approach to AI risk governance, security operations, and regulatory compliance look like?

According to IDC’s Asia/Pacific Security Study, 2024, 76.5% of regional enterprises admit they are not confident in their ability to detect and respond to AI-powered attacks. The most pressing threats include AI-driven vulnerability scans, zero-day exploits, ransomware with adaptive extortion tactics, and highly personalized social engineering attacks. These risks are particularly acute in regulated industries such as financial services, telecom, and healthcare.

Despite the urgency, organizations in Asia/Pacific face several barriers in building AI-resilient security postures. These include:

1. Integration and cost complexities

AI holds immense promise for security automation, but its adoption is hindered by poor integration with legacy environments and high costs. IDC predicts that by 2027, only 25% of consumer-facing companies in Asia/Pacific will adopt AI-powered identity and access management (IAM) systems, citing operational complexity and financial constraints as core reasons. This growing trust gap makes consumer authentication and identity protection increasingly vulnerable.

2. Regulatory fragmentation and governance gaps

While countries like Singapore and Australia have advanced AI governance policies, the broader region remains fragmented. China’s regulations prioritize algorithmic transparency and national security. Japan emphasizes Responsible AI under self-regulation. India, meanwhile, is still shaping its framework under the Digital India mission. This patchwork of mandates creates compliance confusion, especially for multinational enterprises. A major shift ahead is the expected rise of AI Bills of Materials (AI BoM). By 2028, IDC expects 70% of data products will be accompanied by BoMs detailing consent trails, model training inputs, and risk assessments i.e. a new layer of accountability for enterprise AI deployments.

3. GenAI growth without guardrails

As organizations race to scale GenAI solutions beyond proof-of-concept, risk governance is often left behind. IDC forecasts that in 2025, one in five APJ enterprises will move to production with GenAI without a comprehensive risk-based trust assessment. This opens the door to data leakage, algorithmic bias, reputational damage, and hefty regulatory penalties. In the absence of structured governance, enterprises risk building innovation on a fragile security foundation.

A blueprint for AI-resilient security

Building a future-ready posture

Cybersecurity in Asia/Pacific is moving from reactive to predictive. It is no longer about responding to known threats but is about anticipating emerging risks in a world where AI shapes both offense and defense. Enterprises must future-proof their security architecture by investing not only in technologies but also in governance, skills, and regulatory alignment.

Organizations that embed trust into the core of their AI strategies will be the ones that lead in both innovation and resilience. AI-powered businesses must ensure that privacy, explainability, and compliance are not afterthoughts, but integral components of the design and delivery process. In this new era, cybersecurity is inseparable from AI transformation and trust is its ultimate currency.

Join the Responsible and Secure AI: The Cornerstone of AI-Driven Growth webinar on 23 July 2025 to stay ahead of evolving AI risks, CSO expectations, and regional regulations. Register today!

Partner with IDC | CSO to elevate your brand presence at Asia’s leading gathering of CISOs and IT security executives. Position your unique capabilities to become security leaders’ trusted vendor of choice in safeguarding their valuable corporate data in the cloud and in exploring the pivotal role of AI and quantum-proof technologies. Happening across 7 Asia/Pacific cities from April to November 2025, join us at the event to showcase your case studies, success stories, and more!

Learn more here

Sakshi Grover - Senior Research Manager - IDC

Analyst Profile
Sakshi Grover is a senior research manager for IDC Asia/Pacific Cybersecurity Services, supporting its research and client engagement activities across Asia/Pacific markets. Additionally, she serves as the lead security analyst for IDC India. Sakshi is responsible for delivering syndicated custom research and consulting engagements on next-generation emerging and disruptive technologies. Her tasks include developing and socializing IDC's point of view within security services, covering both legacy and modern cybersecurity technologies. Her role involves close collaboration with technology vendors and buyers, developing market insights, and providing research, consulting, and advisory services in the fields of security software and services. This includes partnering on research efforts with relevant country analysts in the local IDC offices. Sakshi's views on security have been quoted in numerous publications, such as the Economic Times, Business Standard, Data Quest, CRN, and others.

Remember the good old days when “Shadow IT” was just about rogue Excel spreadsheets and unauthorized Dropbox accounts? But the times they are a-changing! Now we’re dealing with something far more insidious: Shadow AI. And no, it’s not just lurking in the corners of your or anyone’s organization anymore. Now, it’s driving productivity gains while simultaneously creating security nightmares that, hopefully, keep CISOs wide awake at night.

From private drives to GPT instances

Shadow IT has been the bane of enterprise administrators’ existence for decades. We’ve all seen it: marketing teams building up their own CRM systems, sales departments hoarding customer data in personal cloud drives, and finance teams creating elaborate Excel macros that, unnoticed, somehow have become mission-critical applications. But nowadays we have Shadow IT on AI steroids.

Because instead of innocent unauthorized OneDrive instances, we have unauthorized ChatGPT accounts, private Perplexity subscriptions, custom Copilots, and Excel automation scripts integrated with GPT APIs. And they ALL operate completely outside of IT oversight.

As many experts repeat: shadow IT hasn’t disappeared – it has evolved. And artificial intelligence has given it turbocharged engine.

The staggering scale of unauthorized AI adoption

IDC’s Global Employee Survey from April 2025 reveals that 39% of EMEA employees are using free AI tools at work, another 17% use AI tools they privately pay for. Only 23% of employees declare they use AI tools provided by their organization, and it still does not mean they are not using private tools simultaneously. Another survey I’ve come across shows that 52% of workers won’t admit to using AI in their jobs. And the percentage of sensitive corporate data being fed into AI tools has skyrocketed from a not insignificant 10% to over 25% in just one year.

Why are these numbers so high? The answer is frustratingly simple: on a basic level, AI can be ridiculously easy to use. You need a browser, a prompt, and you’re done. No coding, no server configuration, no IT tickets that sit in queues. Just pure, immediate productivity enhancement. Maybe with a bit of compliance catastrophe on the side, but who’s looking?

March or die

However, let’s be brutally honest about why else these numbers are so high. Employees aren’t just using AI tools to work smarter – they’re often using them to survive increasingly unreasonable workplace expectations. In an era where headlines scream about companies replacing entire departments with AI, workers are fighting hard to prove their relevance.

The pressure is palpable and justified. When employees read about firms cutting 30% of their workforce while boasting about AI-driven efficiency gains, the message is clear: march or die. Shadow AI adoption isn’t just about productivity enhancement, more than anything it can be about professional self-preservation.

This creates a weird dynamic where the very people organizations depend on feel compelled to hide the tools that make them valuable. Are they being rebellious or just rational? When your job security depends on meeting targets that seem designed for superhuman capabilities, you’ll probably use whatever tools necessary to achieve them, authorized or not.

Most AI tools don’t require dedicated client applications. They operate seamlessly through web browsers or as mobile apps, making them almost invisible to traditional IT monitoring systems. The vast majority of ChatGPT, Google Gemini or other tools usage at work happens through non-corporate accounts, meaning corporate data or IP is being processed by AI models that organizations have zero visibility into, zero control over, and zero ability to audit.

How pursuit of productivity kills strategic AI adoption

Many organizations, in their relentless pursuit of productivity metrics and efficiency gains, are creating an environment where employees feel compelled to hide their AI usage to meet impossible expectations. This creates a vicious circle where leadership demands productivity improvements while threatening job cuts, employees discover AI tools that help them meet unrealistic expectations, IT blocks access, so employees use unauthorized tools to avoid becoming the next layoff statistic.

The result? Organizations end up with lower overall AI adoption rates than they could achieve, precisely because they created a fear-based environment where survival instinct eats strategy for breakfast. Define irony: companies that publicly celebrate AI’s potential to replace human workers are simultaneously frustrated by their inability to achieve coordinated, strategic AI implementation.

The education paradox or one-time is here to fail you

And here’s where most organizations spectacularly miss the mark. They roll out a single “AI Awareness” training session, check the compliance box, and wonder why employees still go rogue.

Basic communication theory tells us that people need to hear a message seven times before it truly registers. Yet organizations treat AI education like a software update: deploy once, assume adoption. The learning curve for responsible AI usage isn’t a gentle slope. Or maybe gentle but the road will be long and winding. Employees need ongoing, contextual education that evolves with the changing AI landscape. They need to understand not just the “what” and “how,” but the “why” behind AI governance policies. (And you need AI governance, do we even need to say that?) When people understand the reasoning behind restrictions, compliance rates soar. When they don’t, Shadow Everything thrives.

Smart organizations recognize that AI literacy requires sustained and strategically planned education programs. They build comprehensive learning pathways that revisit core concepts with increasing depth over time, ensuring employees develop genuine understanding rather than superficial compliance. This investment isn’t just about risk mitigation – it’s about creating a workforce capable of strategic, responsible AI adoption.

Hope for the transparency solution? BYOAI!

The IEEE Computer Society proposes a solution that might make traditional IT nervous: BYOAI (Bring Your Own AI). This approach emphasizes transparency, risk assessment, and responsibility while allowing employees to work with their preferred AI tools.

The concept acknowledges a fundamental truth that many organizations refuse to face, although they should have learnt already: you can’t stop Shadow AI, or anything else for that matter, adoption through prohibition. Prohibition will only drive it deeper underground, where it becomes even more dangerous. Think about Chicago, Valentine’s Day, circa 1929, So if a ban is not the answer, then what? The easiest, yet most reliable, way to mitigate risk is good old, albeit boring, education…

Embrace reality, manage risk

Shadow AI isn’t going away. The productivity gains are too compelling, the tools are too accessible, and the competitive pressure too intense. Organizations have two choices: build frameworks for managing Shadow AI or watch it manage them.

What will smart companies do?

  • Invest heavily in ongoing employee AI education (not one-shot training)
  • Create transparent AI governance frameworks
  • Design security policies that enable rather than restrict innovation
  • Build trust through collaboration rather than control
  • Measure success by strategic AI adoption, not just productivity metrics

The question isn’t whether Shadow AI is a threat or an opportunity – it’s whether your organization will respond with wisdom or wishful thinking. Choose wisely!

Listen back to Ewa on the following webcast: AI in 2025: Deliver or Wither

To learn more about how International Data Corporation (IDC) can support your technology market data needs, please contact us.

Ewa Zborowska - Research Director, AI, Europe - IDC

Analyst Profile
Ewa Zborowska is an experienced technology professional with 25 years of expertise in the European IT industry. Since 2003, she has been a member of the IDC team, based in Warsaw, researching IT services markets. In 2018, she joined the European team with a specific emphasis on cloud and AI. Ewa is currently the lead analyst for IDC’s European Artificial Intelligence Innovations and Strategies CIS.

Tech leaders are under more pressure than ever – not just to make strategic decisions, but to prove they’re the right ones.

Across industries, organizations are rethinking every tool, every vendor, and every dollar spent. It’s not just about cutting costs. It’s about improving performance, aligning to evolving business needs, and unlocking long-term value. Boards want results. CFOs want savings. And teams want tools that support how they work today – not five years ago.

Renewals are approaching fast. But too often, platforms are re-signed without a meaningful evaluation of whether they still deliver. Legacy systems linger out of inertia. Duplicative solutions quietly drain budget. And leaders are left accountable for platforms that no longer fit.

But some CIOs are changing the narrative.

Instead of reacting to budget pressure, they’re using it as an opportunity to lead. They’re consolidating redundant vendors, replacing lagging platforms, and establishing a consistent way to evaluate what’s truly worth keeping and what’s not. Most importantly, they’re aligning these decisions across IT, procurement, finance, and business teams – so every move is backed by data, not just instinct.

They’re asking questions like:

  • “Which systems are actually earning their keep?”
  • “What could we consolidate to save time and money?”
  • “Where are we overpaying for tools that no longer align?”

They’re replacing reactive renewals with strategic choices and it’s having real impact. When every dollar counts, tech leaders need more than instincts. They need insight.

Introducing the IDC Tech Leadership Transformation Series

To make this path easier, IDC created the Tech Leadership Transformation Series: two complimentary executive guides that work together as a practical, step-by-step playbook.

Part One helps you reassess and reprioritize your tech investments.
Part Two helps you optimize renewals, streamline decisions, and deliver measurable ROI from every platform in your stack.

Whether you download one or both, they’re designed to help tech leaders simplify renewals, cut waste, and align every decision to business impact.

This series is grounded in IDC’s decades-long commitment to helping technology leaders make data-driven decisions with clarity and confidence. Whether you download one or both, they’re designed to help tech leaders simplify renewals, cut waste, and align every decision to business impact.

Part 1: Prioritize to Lead

Start with a strategy. This guide gives you a structured framework to realign your tech investments with current business goals, identify friction points, and zero in on areas where legacy tools no longer serve your future.

Download Now

Part 2: Do More With Every Tech Dollar

Now, execute. This second guide helps you turn strategy into action by providing practical ways to optimize renewals, reduce vendor sprawl, and prove ROI from every sourcing decision.

Download Now

Together, these guides help tech leaders:

  • Evaluate platforms based on real outcomes, not assumptions
  • Cut costs without compromising business needs
  • Align IT, procurement, and finance around shared KPIs
  • Simplify vendor portfolios to increase agility and speed

Be the Tech Leader Who Sees the Whole Picture

Happy middle aged business man ceo wearing suit standing in office using digital tablet. Smiling mature businessman professional executive manager looking away thinking working on tech device.

2025 is shaping up to be a defining year for tech leadership. The margin for error is shrinking. Budgets are tighter. Expectations are higher. And the pressure to deliver ROI is real.

Yet many organizations still approach renewals and replacement decisions without a clear process – relying on legacy contracts, internal politics, or guesswork. The result? Wasted spend. Underperformance. Missed opportunities.

When tech leaders stop defaulting to renewals and start demanding results, everything shifts.

Spend becomes more strategic. Renewals become opportunities to optimize, not obligations to maintain the status quo. Platform decisions start reflecting where the business is going, not where it’s already been.

We’ve seen it firsthand:
• IT leaders consolidating overlapping tools and freeing up budget to invest in innovation
• Cross-functional teams making faster, smarter sourcing calls using shared benchmarks
• CIOs walking into board meetings with defensible ROI narratives and walking out with stronger support

The pressure hasn’t gone away. If anything, it’s growing. But leaders who embrace this approach are meeting that pressure with clarity and control.

The strongest tech leaders aren’t reacting to budget cuts, internal politics, or renewal dates. They’re driving the conversation. And they’re building tech portfolios that are leaner, stronger, and better aligned to the business.

The Best Leaders See and Seize the Bigger Opportunity

Presentation, training and workshop with a senior manager, leader or CEO coaching and teaching staff during a meeting in the boardroom. Boss talking to colleagues about the company vision and mission

The most effective tech leaders don’t just navigate change. They lead it with conviction. They don’t default to what’s familiar. They challenge it. And in a time defined by complexity, compression, and change, they know instinct isn’t enough. Clarity is what sets them apart.

But clarity takes the right tools.

AMAROK, a fast-scaling perimeter security provider, found themselves at a critical crossroads as they faced a high-stakes ERP replacement in the middle of business transformation. Like many IT teams, they were managing rising costs, mounting complexity, and pressure to act fast. We see this pattern across industries: smart teams stuck in slow, manual cycles, burdened by disconnected insights and consultant-heavy processes.

What changed the game for AMAROK was IDC TechMatch.

Instead of five weeks of spreadsheets and siloed interviews, AMAROK used IDC TechMatch to model scenarios in real time, reprioritize requirements as they aligned stakeholders, and take control of the evaluation process with trusted IDC data guiding every step. The result? A confident, defensible ERP decision and a 387% ROI from the evaluation process alone.

More importantly, they walked into leadership conversations equipped with answers. They could explain “why this, not that” with confidence, align their decision with business outcomes, and accelerate executive buy-in without guesswork or delays.

AMAROK’s story is one we’ve seen again and again. When you match strong leadership with the right platform, transformation gets faster, smarter, and easier to justify.

Because that’s what today demands. In the Tech Reset Era, it’s not just about managing renewals or reducing spend. It’s about building a stack that supports the future of your business, not the past.

The Tech Leadership Transformation Series is your starting point. These two executive guides walk you through how to assess, realign, and act with clarity, giving you a practical path to simplify renewals, replace what no longer fits, and do more with every tech dollar. Download both guides. Then see how IDC TechMatch helps you go further and faster, with the visibility and confidence to lead what’s next.

Learn More about IDC TechMatch

In today’s global economy, tariffs significantly shape various markets, including the used smartphone market. When governments impose tariffs on imported goods, they directly affect supply chains, pricing structures, and consumer behavior. IDC will examine tariffs, their impact on the secondary phone market, and what this means for consumers and sellers.

Price Increases and Market Dynamics:  

The most immediate and undeniable effect of tariffs on the used smartphone market is the potential for significant price increases. When tariffs are imposed on new smartphones, the cost of these devices rises. Consequently, consumers turn to the used smartphone market in search of more affordable alternatives. However, this shift results in increased demand for used devices, which in turn drives up prices in that market. Sellers take advantage of this heightened demand by raising their prices. As a result, tariffs intended to protect domestic industries often backfire, making used smartphones less affordable for consumers.

Supply Chain Complications

Tariffs disrupt the complex supply chains that are essential for distributing used smartphones. Most used devices come from networks that include trade-in programs, resellers, and refurbishment centers, all of which rely heavily on new devices for parts and support. As tariffs raise the cost of importing these vital components, bottlenecks occur, causing delays in repairs and refurbishments. This ultimately decreases the availability of quality used smartphones. Furthermore, the complexities introduced by tariffs may lead resellers to limit their inventory or focus solely on local markets. As a result, the variety of available used devices diminishes, frustrating consumers who are looking for specific models or brands.

Consumer Behavior Shifts

As prices continue to rise and availability declines, consumer behavior changes significantly. Buyers become more cautious, often opting to keep their devices longer instead of upgrading frequently. This trend can lengthen the lifespan of smartphones and negatively affect resale values, making it challenging for sellers to maintain their pricing. Additionally, the uncertainty surrounding tariffs leads consumers to delay purchases, hoping for better prices or availability in the future. This behavior results in fluctuating demand cycles, contributing to market volatility.

Environmental Impact

It is essential to recognize that tariff-induced changes can also impact the environment. Electronic waste may decrease when consumers keep their devices longer and turn to the used smartphone market. By extending the lifespan of these devices, we can adopt a more sustainable approach to technology consumption, which helps slow the ongoing cycle of new manufacturing and disposal.

Conclusion

The impact of tariffs on the used smartphone market underscores the complex relationship between government policy and consumer behavior. While tariffs aim to strengthen domestic industries, they often produce contrary results, pushing prices higher, disrupting supply chains, and altering consumer purchasing habits. Remaining informed about tariff policies is essential for buyers and sellers in the used smartphone market. Navigating this evolving landscape demands adaptability, awareness, and a solid perception of used and new markets. Understanding these complex dynamics prepares consumers to make informed decisions and promotes a more sustainable approach when buying a new device. Whether embracing the used market or extending refresh cycles, clear opportunities remain to lessen the adverse effects on current and new potential tariffs moving forward.

Worldwide Quarterly Used Device Tracker

Anthony Scarsella - Data & Analytics Director - IDC

Analyst Profile
Anthony Scarsella is a Research Director with IDC'S Mobile Phone research team. Mr. Scarsella is responsible for researching, synthesizing, and analyzing data on the U.S. and worldwide mobile phone market for IDC's Worldwide Quarterly Mobile Phone Tracker and the Mobile Phone CIS subscription service. Mr. Scarsella incorporates his expertise and experience to establish a quantitative and qualitative view of the mobile phone market. He also leads IDC's research on the secondary smartphone market, including sizing and forecasting, used mobile phones worldwide.

(Editor’s note: This is the second of a two-part series on AI centers of excellence. Part 1 covers the benefits of an AI COE and how to measure its performance.)

Many organizations are racing to adopt artificial intelligence in the hope of creating new business efficiencies, gaining competitive advantages, and boosting the bottom line. But a recent survey finds that most organizations face a series of challenges before they can reap benefits from those investments.

For instance, IDC’s July 2024 Future Enterprise Resiliency and Spending Survey found that 26% of respondents had already introduced several GenAI-enhanced applications or services into production, up from 17% of respondents in a similar IDC study in April. Common challenges slowing down GenAI deployments include securing private information, preventing hallucinations, controlling costs, as well as how best to monitor and manage GenAI applications in production (GenAI Operations: A Guide to People, Process, and Tool Requirements, IDC #US52781824, December 2024).

“Using AI and, more specifically, GenAI has become an all-encompassing strategic initiative for business — but it’s not yet clearly defined,” says Jason Hardy, CTO of AI at Hitachi Vantara.

That fact is inspiring some organizations to develop AI centers of excellence (COEs). The goals are to better understand AI capabilities, to align AI initiatives with broader organizational strategy and ethics, to build internal trust and external credibility, and to put governance and guardrails in place early in the process, explains Richard Buractaon, head of artificial intelligence at Andesite AI, an AI architecture firm based in McLean, Virginia.

“An AI center of excellence helps cut through the noise. Its role is to educate, dispel myths, and ground AI initiatives in reality,” Buractaon says.

Using the COE to Spread AI Knowledge in the Organization

Because widespread interest in AI is still fairly recent, there is a supply-and-demand gap for experienced AI professionals. An AI COE can help bridge this gap by gathering top employees from throughout the organization to work together in a new team, share expertise, and then bring newly gained knowledge and culture back to their original units.

For this reason, it is important that the “right” employees are assigned to an AI COE, which is expected to provide a community of practitioners who can share knowledge, expertise, and best practices in AI and related technologies, says Rick Torzynski, senior data and AI engineer and product architect at ECS, a leading provider of cloud, cybersecurity, AI, machine learning, and IT modernization services in Fairfax, Virginia.

“The COE should generate excitement and interest in AI and related knowledge domains, encouraging employees to learn and explore new technologies,” Torzynski explains. “The COE should also provide training and development opportunities for employees, enabling them to acquire the skills and expertise needed to work with AI and related technologies.”

Experiences and Skills Wanted with Team Members

When HItachi Vantara builds out an AI COE team, it taps a mix of disciplines and backgrounds, Hardy says. “On the tech side, think data scientists, AI engineers, and machine learning gurus — the people who can wrangle the data, build the models, and actually get these AI algorithms up and running.”

But it’s not just technical expertise that is important when it comes to staffing the COE, Hardy says.

“We also need business leaders and execs from the different departments that’ll be using AI — bringing the real-world know-how and making sure our AI projects actually solve business problems. And of course, we can’t forget the IT and cybersecurity crew who are crucial to making sure everything integrates smoothly and stays secure.”

Across the board, everyone on a successful COE team needs to be a good communicator, a team player, a solid problem solver, and someone who’s always up for learning new things, he explains. That’s what really drives innovation and gets AI adopted across the organization.

Job Roles Commonly Found in an AI COE

There are several specific job roles typically assigned to an AI COE, Torzynski says. They include:

  • Data scientists, with a background in data science, machine learning, and statistics
  • Software engineers, with a background in software engineering, computer science, and programming languages
  • Business analysts, with a background in business analysis, operations research, and management science
  • Subject matter experts, with a strong understanding of the AI knowledge domain and its applications
  • Project managers, with a background in project management, agile methodologies, and scrum

Certain technology and business skills should also be included in the makeup of any AI COE, though not every member must possess them all, Torzynski explains.

Essential skills in the team include a strong understanding of the AI knowledge domain and its applications; a solid foundation in programming languages, data structures, and software development methodologies; the ability to analyze complex problems, identify patterns, and make data-driven decisions; the ability to communicate effectively with both technical and non-technical stakeholders; and the ability to work collaboratively with cross-functional teams and stakeholders.

“The COE’s team composition requires talent density in full-stack AI (machine learning, generative AI, deep learning and systems development life-cycle experts), domain fluency, and a flair for entrepreneurial mindset,” says Adnan Masood, chief AI architect at UST, a provider of digital technology and IT transformation services based in Aliso Viejo, California.

“We hire data strategists who appreciate how liquidity risk or M&A synergies intersect with quantitative modeling. We recruit engineers who can pivot to mission requirements at scale. We rely on AI-savvy project managers who spur iterative prototyping and keep strategic bet decisions on track.”

As to personal traits that will serve an AI COE well, Torzynski cites the following: a passion for learning and staying up-to-date with the latest AI trends and technologies; eagerness to work with cutting-edge technology and willingness to experiment and innovate; an ability to communicate effectively with both technical and non-technical stakeholders; adaptability to changing requirements and priorities; and the ability to think creatively and come up with innovative solutions to complex problems.

Qualities and Capabilities Wanted in AI OCE Team Leaders

Ideal leaders for AI COEs should have significant leadership capital and a vision-to-execution mindset, Masood explains. These individuals are often a chief AI officer or data-centric executive who practices radical candor and spurs creativity.

Leadership stamina is non-negotiable, since the COE’s arc extends from short-term projects to broad-stroke transformation blueprints, Masood says. They manage change while forging institutional legitimacy around data-driven decision-making.

Further, ideal leaders for an AI COE are visionary individuals with a strong understanding of both AI technologies and business strategy, Hardy says. They should typically have a proven track record of leading successful AI projects and building cross-functional teams. As such, they need to be influential and collaborative, capable of fostering a culture of innovation and knowledge sharing.

Also: “It goes without saying, but I’ll go ahead and say it anyway to be clear: Leaders should be adaptable and resilient, given the rapidly evolving nature of the AI field, and possess strong ethical considerations regarding AI implementation,” he explains.

By getting the team makeup and leadership right, one of the most significant benefits of an AI COE is that it can help create a company culture of creativity and innovation, Torzynski says.

“By bringing together experts from different departments and providing them with the resources and support they need, COEs can foster a culture of collaboration, experimentation, and risk-taking. This can lead to the development of new and innovative products, services, and processes that can help the company stay ahead of the competition.”

By following these lessons and adapting them to their own organization’s needs, companies can create a successful AI COE that drives innovation and growth, Torzynski says.

David Weldon - Research Adjunct - IDC

Analyst Profile
David Weldon is an adjunct research advisor with IDC's IT Executive programs, focusing on IT business, digital transformation, data management and artificial intelligence. He has extensive experience as a research analyst and as a business and technology journalist. His special concentrations are in the areas of technology, business and finance, education, healthcare, and workforce management. David started his national-level journalism career at IDG's Computerworld, a sister publication to IDC. He began as a features editor working on the Management section, covering topics of interest to chief information officers and other IT executives. He then took over Careers coverage and handled most of the editorial research projects for the publication. Computerworld's Careers section won several journalism awards and was the leading source of insights and advice on careers in information technology.

The technology landscape across Europe, Middle East, and Africa (EMEA) is changing rapidly in 2025, with innovations actively reshaping industries and creating new business opportunities.

The Emerging Tech Radar: Current Market Drivers 

The EMEA region’s technology environment encompasses a diverse range of emerging technologies at various maturity stages. Organizations demonstrate different levels of readiness and capability in adopting these technologies. Across EMEA, IDC observes technology gaps between industries and individual countries, highlighting variations in economic, financial and R&D power, and maturity.

These variations exist because countries and industries across EMEA differ in economic strength, investment levels, regulatory environments, and access to skilled talent, all of which impact their ability to adopt and develop new technologies. And as these technologies evolve, new trends are emerging.

Critical Topics Shaping EMEA’s Tech Conversation in 2025

1.     Quantum Computing’s Regional Applications

The state of quantum computing in EMEA reveals how this technology is moving from theoretical to practical applications across various sectors. Quantum computing in the region is rapidly advancing from theory to real-world use, with pilot quantum computers now integrated into supercomputing centers to tackle complex challenges in fields like drug design, supply chain management, and financial modeling.

2.     Tech Maturity Assessment

Organizations are evaluating adoption versus maturity, making critical assessments of emerging technologies and market readiness to guide implementation decisions. Structured maturity models and cross-functional assessments are essential to benchmark their current capabilities, identify gaps, and align technology adoption with business objectives and market readiness.

3.     Change Forecast: 2025–2030

The projected disruptions from emerging technologies over the next five years will reshape how businesses operate and compete in the EMEA region. From AI integration into virtual worlds, to European quantum computing centers, space initiatives, and next-generation batteries, the next five years are crucial for the region’s global competitiveness.

4.     GenAI as a Technology Catalyst 

GenAI is accelerating the development and adoption of other emerging technologies, creating and opening exciting new pathways to innovation for organizations. Its ability to rapidly generate code, simulate complex scenarios, and automate content creation is streamlining R&D processes and enabling faster prototyping across industries.

5.     Digital Natives Drive Innovation

Digital-first businesses, with their deep integration of technology and agile operating models, are often at the forefront of implementing emerging technologies and developing innovative use cases that set industry standards. Their ability to rapidly experiment, scale solutions, and leverage data-driven insights enables them to act as key partners and leaders in digital transformation initiatives across sectors.

6.     Investment Patterns Reveal Priorities

Current investment plans for 2025 and beyond highlight that emerging technologies are attracting capital and organizational focus across EMEA. Investments are supported by significant public and private funding initiatives, such as venture capital for deep tech and government-backed projects in clean energy, digital infrastructure, and advanced manufacturing, all aimed at boosting competitiveness and technological leadership across sectors.

7.     Beyond AI: Work Transformation by 2030

Five specific emerging technologies beyond AI are positioned to reshape how work happens by the end of the decade, enabling new forms of collaboration, automation, and real-time data exchange. The focus will be on streamlining secure transactions and digital identity management, creating immersive training and remote work environments, automating logistics and manufacturing, and supporting seamless connectivity for smart workplaces and IoT-driven operations.

Driving Adoption Through Measurable Results

Organizations across EMEA are adopting these technologies for specific business outcomes. Successful implementations connect directly to KPI improvements, with clear links between technology adoption and business performance metrics. Adoption barriers include challenges that limit EMEA organizations’ ability to implement emerging technologies effectively.

Technology Integration Benefits

Companies that combine multiple emerging technologies report stronger results than those implementing isolated solutions. The combination approach generates meaningful synergies across business processes.

The technical foundation is crucial. Organizations need the right technology backbone to exploit emerging technologies and generate maximum value.

What This Means for Your Business 

These emerging technological trends and their practical applications will influence your organization’s market position in 2025 and beyond. Understanding the key applications and use cases driving technology demand can help position your organization to capitalize on these developments as they mature.

The question isn’t whether these technologies will transform business — it’s how prepared your organization is to adapt to them. 

Did You Know?

IDC analysts are continuously monitoring and identifying emerging technologies through our Continuous Information Services. This resource empowers organizations to make informed decisions by providing comprehensive analyses, forecasts, and strategic guidance at the global, regional, and country levels.

To learn more about how our experts can assist you, feel free to reach out!

Lapo Fioretti - Senior Research Analyst - IDC

Analyst Profile
Lapo Fioretti is a Senior Research analyst in IDC Digital Business Research Group, leading the European Emerging Technologies Strategies research. In his role, he advises ICT players on how European organizations leverage new technologies to create business value and achieve growth and analyzes the development and impact of emerging trends on the markets. Fioretti also co-leads the IDC Worldwide MacroTech Research program, focused on the intertwined connection between the Economical and Digital worlds - analyzing the impact key MacroEconomic factors have on the digital landscape and viceversa, how technologies are impacting economies around the world.