IDC

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

How Asia/Pacific enterprises can future-proof cybersecurity strategy amid geopolitical shifts, regulatory fragmentation, and AI-driven risk. source

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. source

(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

As organizations attempt to keep pace with the rapidly evolving landscapes of artificial intelligence and data analytics, many are developing AI centers of excellence (COEs). The goals are to harness the power of AI to create business value, to drive innovation, and to stay ahead of the competition. AI COEs follow the traditional COE model, though obviously focused on the role of AI in business strategies and in corporate culture. They draw the top talent from throughout an organization that can champion how AI can be interwoven into existing processes to create new efficiencies. Consider the example of ECS, a leading provider of cloud, cybersecurity, AI, machine learning, and IT modernization services in Fairfax, Virginia. “Our data and AI center of excellence was established to advance our company’s data and AI culture, practice, products, partnerships, and social eminence,” explains Rick Torzynski, senior data and AI engineer and product architect for Atlas Graph at the company. “With a vision to scope, prioritize, induct, govern, and integrate data and AI opportunities, our COE aims to align our capabilities with the strategic vision and operational needs of our customers.” By creating a dedicated COE for AI and data analytics, Torzynski says the company can leverage the expertise of over 200 data professionals — 50% of whom hold PhD or master’s degrees — to drive innovation and solve complex problems. “Our COE serves as a community of practitioners, providing a platform for knowledge sharing, expertise exchange, and best practice development,” Torzynski says. Investments for the Future As an AI leader, practitioner, and researcher, Adnan Masood has long maintained that an AI COE defines a strategic inflection point in how organizations can future-proof their core competencies. The chief AI architect at UST, Masood says his organization established its COE to address a classic demand: “We needed adaptive leadership and a single source of truth for data-driven strategy, from dynamic risk management to value chain optimization,” Masood explains. “That has been our springboard to scalable solutions and sustained momentum. Since then, I have worked with various client organizations, helping them establish and run their respective COEs.” UST, formerly known as UST Global, is a provider of digital technology and IT transformation services based in Aliso Viejo, California. But AI COEs are hardly limited to technology companies. Among non-tech organizations, Masood says he has watched COEs generate quick wins — such as reducing fraud loss by double digits or serving as a catalyst for deeper initiatives such as advanced customer lifetime value analytics. When an AI COE is designed and staffed effectively, executives can hope to witness strategic cohesion when AI-driven insights support critical-path decisions, aligning people, platforms, and cultural capital, Masood says. “AI COEs help build cross-functional synergy by merging data scientists, domain experts, and finance leaders,” Masood explains. “That cross-pollination strengthens institutional memory while mitigating the strategic ambiguity that so often stymies new tech deployments.” Potential Benefits from an AI COE The benefits of an AI COE can be many and go far beyond technology advancements, Torzynski says. At ECS, they include: Talent development: “Our COE provides opportunities for employees to grow professionally, even in areas outside of their primary job responsibilities. Regular town hall meetings encourage employees to explore various COEs, fostering a culture of continuous learning and development,” Torzynski says. Knowledge sharing: “Our COE serves as a community of practitioners, holding regular meetings and events to share knowledge, expertise, and best practices. This collaborative environment promotes innovation and drives business value.” Strategic partnerships: “Our COE manages and develops strategic, technical partnerships, enabling us to stay at the forefront of AI and data analytics trends.” Certification and training: “Our COE provides flexible and rigorous training, supporting project delivery and ensuring that our teams are equipped with the necessary skills to succeed.” Proposals and solutions: “Our COE supports proposals by providing technical solution strategies, enabling us to deliver innovative solutions to our customers.” Culture of innovation and creativity: “Our COE has been instrumental in fostering a culture of innovation and creativity, empowering employees to pursue their passions and drive business value.” What Organizations Can Expect from an AI COE The expectation of any COE is that it will help drive innovation and improve efficiency first and foremost, says the CTO of AI of a large data storage vendor. It is also important that a COE enhances the decision-making process and facilitates a healthy collaboration between business units and external partners. At the storage vendor, “The COE is ultimately designed to move AI from theoretical exploration to practical implementation, delivering tangible business value by optimizing the many processes, enhancing efficiency, and unlocking data-driven insights,” the CTO of AI explains. It should also be noted that by developing and implementing AI-driven solutions, the COE contributes to the creation of new revenue streams and business opportunities, he says. An ideal COE should serve each business unit with both operational transparency and agile governance, Masood explains. At UST, the company places data engineers side by side with financial analysts to ensure swift translation from concept to execution excellence. “Boards want tangible ROI — like the significant improvement in operational efficiency we saw once advanced ML was integrated into supply chain optimization,” Masood explains. “By embracing this purposeful approach, the COE stands at the center of a broader innovation ecosystem.” Gaining Resilience and Competitive Edge Any organization aiming for market and competitive resilience should consider developing an AI COE, Masood says. “I’ve seen it become a vital change agent that champions iterative prototyping, fosters collaborative innovation, and sustains a high-performance culture,” Masood explains. “Companies with strong AI governance can boost [significant] returns on invested capital. C-suites increasingly view these [gains] as the hallmark of mission alignment, especially when shareholders demand better liquidity analysis and more reliable revenue management.” An AI COE helps accelerate AI adoption by providing a dedicated hub for a more practical application, the storage vendor CTO of AI says. It achieves this by piloting innovative AI solutions tailored to specific industry needs, as evidenced by successful advanced prototypes in

Today’s B2B tech buyers are digitally fluent, AI-assisted, and increasingly independent. They move easily between platforms, researching products and evaluating vendors, without ever needing to speak with a salesperson. For marketing leaders, this changes everything.  When buyers are making critical decisions before sales even enter the conversation, your role expands dramatically. It’s no longer about driving awareness or filling the funnel. You must own the entire buyer journey, from understanding what your buyers want to creating real demand. That’s a tall order, but it’s the new starting line if you want your AI-powered product to stand out in a saturated, fast-moving market. Successfully marketing your AI solution requires orchestrating seamless omnichannel experiences that deliver relevance at every turn. Where to Start: Understanding Digital-First Buyers Most of the B2B tech buying journey happens digitally, but that doesn’t mean you should still be relying on gated PDFs and nurture campaigns. Modern buyers chart their own course, jumping between websites, apps, social platforms, videos, and interactive tools to explore, evaluate, and even purchase solutions. And they’re confident doing it. Seventy-one percent of B2B tech buyers are comfortable using digital channels for large-ticket purchases, and 73% leverage digital tools for complex decisions.  They’re also bringing AI into the process. Digital assistants are increasingly helping buyers compare vendors, configure solutions, and respond to requests for proposals (RFPs). This means AI is reshaping how your buyers make purchasing decisions. Marketing AI is hard. Visit our dedicated resource page for Marketing leaders who need help recalibrating their GTM strategy. The Rise of an Unpredictable Buying Committee To complicate matters further, buying decisions aren’t centralized anymore. What once involved one or two senior decision-makers now requires consensus from a wide, and often unfamiliar, range of stakeholders. A single deal might include a VP of Customer Experience, a cybersecurity lead, an IT procurement manager, and a Head of AI Strategy. Five years ago, many of these roles weren’t even part of the conversation. Today, they have the power to make or break your deal.  What does this mean for marketing leaders? GTM strategies based on traditional buyer personas and outdated messaging will fail to resonate. To reach this modern buying committee, marketing teams need to orchestrate connected, omnichannel experiences that speak to each stakeholder’s priorities and position your AI solution as the one that solves their specific challenge. If you don’t, your competitors will gain influence with the very stakeholders you overlooked. Learn more about shifting buyer behavior from Laurie Buczek’s blog, The Buyer Behavior Shift: Capitalizing on AI’s Potential The New Marketing Playbook is Built on Omnichannel Moments In this environment of self-guided buyers, shifting stakeholder dynamics, and AI-mediated decisions, marketing must prove value earlier and more deliberately than ever. Add to that the challenge of standing out in a saturated market of AI-powered products, plus pressure from a performance-focused C-suite, and the stakes only climb higher. Buyers are accustomed to personalized experiences from the B2C brands they engage with every day, and now they expect the same from B2B. In fact, nearly 70% say their decision on whether to read something is influenced by whether it’s personalized. Omnichannel marketing is the new marketing playbook. Still, personalized content only works if it’s delivered in the right format, on the right channel, at just the right time. And that’s tougher than it sounds. A VP of Customer Experience scrolling LinkedIn has a different set of concerns than a Head of AI Strategy downloading a technical white paper. A short explainer video might catch one stakeholder’s attention, while a peer case study builds credibility with another. The key is creating moments where something clicks for the buyer: “This solves my problem.” But that’s just the spark. To be effective, each moment needs to connect, creating a continuous experience. Ask yourself: What happens after a buyer engages with your content? How are they guided to the next step? Is each interaction building momentum, or starting from scratch? Do our digital and human touchpoints hand off smoothly? True omnichannel excellence isn’t just about being everywhere; it’s about designing deliberate transitions between content, people, and stages of the journey. To lead with that kind of intention, marketing leaders must leave the old playbook behind and navigate this new reality. Here’s how the shift looks in practice. Old Marketing Playbook New Marketing Playbook Marketing owns the top of the funnel, then hands off to sales. Marketing guides the entire buyer journey, including how and when sales steps in. Buyers discover your products through search engines or industry events. Content means connected moments: interactive tools, live demos, short-form video, and real-time prompts. Personalization happens in the sales conversation. Personalization starts with marketing across channels and at scale. Content means white papers and static assets. Content means connected moments: interactive tools, live demos, short-form video, real-time prompts. Trust is built through human interaction. Trust is earned digitally and strengthened through strategic human touchpoints. Omnichannel experiences are at the center of Marketing’s expanded imperative. Watch this webinar to learn more – Marketing’s Imperative in the Dawn of the Experience Era What Successful Marketers Are Doing Differently It’s no surprise that 37% of CMOs say creating a unified, omnichannel customer experience will have the greatest influence on their marketing strategy over the next 12 to 18 months, according to IDC’s 2024 Worldwide CMO Priorities Study. But how are they actually making it happen? They’re designing journeys where every touchpoint, whether self-service, automated, or human, works in harmony.  Consider this scenario: a potential buyer discovers your product comparison guide on a third-party site. That guide links to a chatbot that provides real-time answers to technical questions. The chatbot offers a live Q&A session with a product expert. After the session, the buyer receives a personalized recap that highlights the exact features they asked about. Every interaction builds momentum, and each step feels relevant, timely, and connected. That’s what omnichannel excellence looks like, and it’s what successful marketers are putting into practice. Rather than focusing on how many campaigns