IDC

We’re a few weeks into what I’m calling the AI PC Rally of 2024, where nearly back-to-back industry events pull back the curtain to reveal more AI capabilities and use cases to make the latest generation of PCs and phones more compelling. So far, Google I/O and Microsoft Build have passed. In a few more weeks’ time, Computex and Apple’s WWDC beckon. We’ll revisit those when time comes (and a deeper report for clients is on the way from my teammates Tom Mainelli and Linn Huang), but a number of things have caught our device team’s eye so far. Microsoft Build was one of the events that we were most eager to see given all of the interest around Copilot and its potential to pivot the industry. Specific to devices, the big question was not just the new features that Microsoft would unveil, but also whether their existing AI models would migrate from the cloud to the device to take advantage of NPUs unlocked by suppliers like Qualcomm. To that end, Microsoft delivered 40 local AI models via its Windows Copilot Runtime layer, not to mention Microsoft’s own 3.3 billion parameter SLM called Phi-Silica to run locally on AI PCs. Not surprisingly, creative use cases were one major focus, as seen in the impressive Cocreator drawing app. Other use cases included live captions, Auto Super Resolution upscaling, as well as Copilot even helping users to play games like Minecraft. These won’t dramatically change our outlook for 20% of PCs this year to be AI-enabled, but the demos were a plus nonetheless. What was more provocative was the Recall for Windows 11 feature, which perhaps has the most potential for changing user behavior by providing users with a scrollbar to easily search their PC activity including web browsing, meeting notes, and productivity files for recalling later. But it needs an allocation of at least 25GB of storage for roughly three months of screenshots, which raised privacy concerns. Of course, privacy is one of the main reasons for running an AI model locally on the device’s NPU rather than in the cloud (and that is on top of the encryption, automatic deletion, and options to exclude applications or disable the feature altogether), but fears of exposed paper trails triggered strong responses nonetheless, especially when involving screenshots. It is worth pointing out that Rewind AI, a similar third-party app with an inclination toward Apple users, captures a user’s screen too, and yet hasn’t drawn as much scrutiny, perhaps because of its lower profile. Another important thing to watch is how developers take advantage of the hardware and software tools at their disposal, which is one of Microsoft’s objectives for its Build conference after all. Given the rocky history of Windows on ARM, it was certainly refreshing to see native apps like Photoshop, Spotify, and Amazon Prime available. More importantly, third party browsers like Google Chrome, Firefox, and Brave are now native, playing a critical role as more applications become browser-based. Legacy applications – which can be important in enterprises in particular – can still be run through the Prism emulator, which Microsoft claims to be as efficient as Apple’s Rosetta 2. Qualcomm even told gaming developers two months ago that many games will run through emulation at full speed, although some big titles like Roblox, Valorant, League of Legends, PUBG, and Fortnite, don’t run due to anti-cheat drivers at the kernel-level. And of course, there was a big hardware reveal that the Windows ecosystem has been eagerly awaiting in light of all of the attention that Apple’s MacBooks have been gaining in recent years. An ecosystem of OEMs will be rolling out what Microsoft deems Copilot+ PCs. It’s an odd name, but the systems look promising with an NPU capable of at least 40 TOPS as well as 16 GB RAM and 256 GB of storage, with offerings starting at $999 and shipping on June 17th. Performance benchmarks naturally are of interest, but power efficiency via the NPU is also one of the pitches, with a range of Snapdragon X Elite proof points such as 20% better battery than the latest 15″ MacBook Air and double battery life of an Intel-based Surface Laptop 5. Qualcomm’s many OEM design wins included: Dell offered designs spanning its Latitude, Inspiron, and XPS, which is notable given that Dell tends to lean toward Intel for its commercial-heavy customer base. Lenovo’s Yoga Slim 7x and ThinkPad T14s Gen 6, the former of which also uses a proprietary “Lenovo AI Core” chip, which is likely the LA3 that it has used on Legion gaming laptops for power efficiency. HP and Acer both offered products that featured their own AI branding, which was obvious not only in its product naming suffixes, but also with their own logos on the products themselves. Acer’s logo is even featured on the touchpad and lights up when Copilot is activated. ASUS launched a product in its Vivobook line, which is targeted at creators but on a more budget-friendly level than its ProArt line with with discrete GPUs. Samsung naturally leveraged its broader ecosystem by including its Knox secure enclave to share data with Galaxy phones and bundled a free 50″ TV in some geographies. Microsoft’s own Surface Pro line for 2024 included both detachable tablet and clamshell offerings, with the former offering not only an OLED option but also a wireless Flex keyboard. Not to be outdone, Intel talked up its upcoming Lunar Lake platform, whose NPU also does 45 TOPS and thus also can power Copilot+ PCs. We are sure to hear more about Lunar Lake at Computex next month along with archrival AMD’s Strix Point. See many of you in Taipei! Discover how IDC’s AI Use Case Discovery Tool can elevate your AI strategy—learn more here. source

IDC’s Quick Take The recent IT outage caused by silent updates pushed out by CrowdStrike to its Falcon agent exposes an issue that is at the heart of how the IT industry operates. It highlights the contrasting trust and attestation mechanisms taken by operating system vendors like Microsoft, Apple, and Red Hat in allowing its ecosystem of independent software vendors (ISVs) direct access to certain parts of the operating system stack and especially software that can potentially severely negatively impact the system kernel. While this issue impacted Windows devices– network and human centric – managed by CrowdStrike, none of the iOS, MacOS, or even Linux devices were affected. That is very telling and should compel vendors like Microsoft and Apple to take a long hard look at what “openness” means in the wake of regulations like EU’s Digital Markets Act (DMA). It should also compel the largely Windows-dependent customer base to redefine their long-term cyber recovery strategy. It should include making a shift to more modern operating system environments. Event Highlights On July 19, 2024, at 04:09 UTC, a sensor configuration update was released by CrowdStrike for Windows systems as part of the Falcon platform’s protection mechanisms. This update contained a logic error that led to a “blue screen of death” (BSOD), affecting certain systems. A remediation was implemented by 05:27 UTC on the same day. According to CrowdStrike, the impact of this event was specific to customers using Falcon sensor for Windows version 7.11 or higher. It needs to be pointed out that to make their endpoint protection products effective, vendors like CrowdStrike require access to the system files. Any configuration issues with these files can lead to unpredictable behavior at best and leave the system in an unrecoverable state at worst. The resulting outage caused disruptions to airlines, businesses and emergency services and could be the largest IT outage in history. In time, we will know whether the scale and impact of the outage will reach the level of the “NotPetya” cyberattack in 2017. At the time of writing, two days later, airlines – the biggest group of affected enterprises – were still reeling from the outage. It is important to note that this incident was not caused by a cyberattack but rather routine update to configuration files, often referred to as “Channel Files.” In the context of the Falcon sensor, Channel Files are integral to the behavioral protection mechanisms that safeguard systems against cyber threats. These files are dynamically updated multiple times daily. The Falcon sensor’s architecture, designed to incorporate these updates seamlessly, has been a foundational component. In Windows environments, Channel Files are typically located within the directory path C:WindowsSystem32driversCrowdStrike, identifiable by their “C-” prefix. Each file is uniquely numbered, serving as an identifier that aids in the management and deployment of updates. For instance, Channel File 291, denoted by the filename “C-00000291-“, plays a crucial role in how Falcon assesses the execution of named pipes—a standard method for interprocess communication within Windows systems. The significance of Channel File 291 came to the forefront during an update aimed at neutralizing the threat posed by malicious named pipes associated with prevalent Command and Control (C2) frameworks. The update introduced a logic error, leading to a system crash. IDC’s Point of View For historical context, this is not the first time something like this has happened. For example, in 2010, McAfee had an issue with a “DAT” file. The issue with McAfee’s DAT file version 5958 caused a reboot loop and loss of network access on Windows XP SP3 systems due to a false positive that misidentifies the Windows binary “svchost.exe” as the virus “W32/Wecorl.a”. In 2017, Webroot released an update that misidentified Windows system files as malware and Facebook as a phishing site. This update quarantined essential files, leading to instability in numerous computers. In 2021, a mass internet outage was caused by a bad software update by Fastly, there have been many others. This situation – which is not unique to CrowdStrike – exposes four key issues that are fundamental to the IT industry and its complex ecosystem of ISVs. First, it exposes the fact that by giving its ecosystem ISVs direct access to the system kernel, the operating system vendor is essentially removing itself from the trust value chain. Thus, the trust value chain now only includes the ISV and its customers. Second, the process of silent updates in which the customers implicitly rely on the QA process employed by the ISV leaves them inadequately prepared for drastic and timely intervention in the case of mass outages that leave the system in an unrecoverable state. Third, this situation is a wake-up call for the industry on what a system of checks and balances means and what kind of accountability operating system vendors, ISVs and customers must play to avoid this kind of a situation from repeating itself. And finally, fourth, this situation indirectly exposes the fragile human-centric Windows stack that unlike modern network-centric Unix and Linux operating systems cannot robustly manage exception errors instead defaulting to a manually recoverable state. The first point exposes contrasting approaches taken by leading operating system vendors. On one side there are vendors like Apple that take a very prescriptive and closed approach to endpoint protection making it almost impossible for any ecosystem ISV/provider like CrowdStrike to push out configuration changes that can potentially catastrophically impact on the operating system (e.g., iOS or macOS) kernels. Apple has been a fierce advocate of a “walled garden” approach implementing stringent attestation mechanisms to ensure that no one – and we mean no one – gets to modify the system kernel without express approval from Apple. This has made Apple run afoul of the European Commission, and its hawkish regulatory approach to open up operating systems under the premise of fair competition. On the other hand, Microsoft takes – or more importantly was forced to take – a more open approach enabling at least a dozen ISVs in offering modern endpoint protection

Artificial intelligence is consuming the attention of IT and business leaders. So much so that many organizations are racing to hire or promote individuals to the role of chief AI officer. Call it the rise of the CAIO.  The rapid and widespread interest in this strategic job role has helped inspire the CAIO Summit, which will be held this October in Washington, D.C. This is the second such summit – the first was held earlier this spring. It is being hosted by the CDO Club, which also hosts the CDO Summit.  I had the opportunity recently to speak with the man behind the CAIO Summit, David Mathison. We discussed the ideal background for an individual targeted for the CAIO role, the technology and business skills they should bring to the table, and the personal traits that add to a candidate’s likelihood of success.  The Ideal Background for a CAIO Candidate  A successful CAIO should possess a combination of technical expertise, strategic vision, leadership skills, ethical awareness, and the ability to collaborate effectively across disciplines and teams, Mathison says. This multidimensional skill set enables them to drive AI innovation and create value for the organization.  A CEO typically expects the CAIO to be a strategic leader. They should be able to drive innovation, deliver results, uphold ethical standards, foster collaboration, and effectively communicate the value of AI to all stakeholders, Mathison explains. These expectations will vary depending on the organization’s industry, size, strategic priorities, and the specific objectives outlined for the CAIO role.  Although artificial intelligence has been around for decades, interest in it has skyrocketed in the last few years — largely due to the popularity of generative AI tools such as ChatGPT. Since many organizations have little prior experience with AI from a strategic perspective, CEOs look to the CAIO to develop a long-term vision and road map for AI adoption and innovation.  The CAIO should anticipate future AI trends, opportunities and challenges and ensure that AI strategies align with the organization’s long-term goals and objectives, Mathison explains.  Top Experiences, Skills, and Traits Needed in the CAIO Role  Mathison says the following are AI-related skills and qualifications that a CAIO should have in order to be successful:  Deep understanding of AI technologies: A CAIO should have a strong technical background and a deep understanding of various AI technologies, including machine learning, natural language processing, retrieval-automated generation (RAG), and robotics.  Data science and analytics proficiency: This is essential to effectively leverage data-driven insights and develop AI models. Statistical analysis, data visualization, and predictive modeling skills are required.  Risk management and compliance knowledge: They should understand risk management principles and regulatory compliance requirements related to AI and ensure that AI initiatives adhere to legal and ethical standards.  Strategic vision and leadership: A CAIO should possess strong strategic planning and leadership skills to develop and execute a comprehensive AI strategy aligned with the organization’s goals. This involves setting priorities, making informed decisions, and inspiring teams to achieve objectives.  Knowledge of ethical and responsible AI practices: Given the ethical implications of AI, a CAIO should be well-versed in ethical considerations and trustworthy, responsible AI practices. This includes addressing issues related to fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.  AI growth and learning mindset: Given the rapid pace of AI innovation, a CAIO should have a growth mindset and a commitment to continuous learning around what is a rapidly evolving technology. They should stay updated on the latest developments in AI technologies, trends, and best practices.  AI project management skills: Proficiency in project management is important for a CAIO to effectively plan, execute, and monitor AI projects. This includes defining AI project scopes, allocating resources, managing timelines, and mitigating risks.  There are some skills and traits that a CAIO should possess that are not unique to this leadership role. They include business acumen, ability to communicate and engage stakeholders, mastery of change management, and collaboration and interdisciplinary thinking. Assuming that a CAIO has all of the above skills and traits, attitude is equally important for success.    Mathison recommends that a CAIO be patient and persistent. Driving AI adoption and transformational change takes time, patience, and persistence. CAIOs must stay focused on the long-term vision and objectives and celebrate incremental successes along the way, he says.   Finally, a successful CAIO must gain a thorough understanding of the organization’s industry, business model, goals, and challenges. They should align AI initiatives with the organization’s strategic objectives and identify opportunities for AI to create value and drive innovation.  Discover how IDC’s AI Use Case Discovery Tool can elevate your AI strategy—learn more here. source

For organizations globally, experience matters, be it the employees, customers, suppliers, partners, and the business itself.  In the digital world, an exceptional experience will create technology stickiness, while also potentially reshaping the relationship between constituents. With great experience, an individual, regardless of the role within an organization, will look at it positively and is usually not opposed to continuing on with it. But if the experience is negative, another opportunity that is more positive will quickly overtake the negative experience, shifting away from a potential business decision to another that is more appealing. This results in lost revenue, profitability, and/or future growth for additional products and services.   Individuals cement relationships based on the experience they have with others.  Sometimes relationships are forged immediately and other times, they take time. Regardless, relationships have been at the heart of businesses for years. Robin Sharma, an author and speaker says it well, “the business of business is relationships; the business of life is human connection.” Human Experience in the Digital World The IDC FutureScape: Worldwide Intelligent ERP 2024 Predictions, finds by mid-2024 30% of global organizations will take advantage of humanlike interfaces in their enterprise applications to gain more insights quickly, improving decision velocity. At the heart of this prediction is the use of Generative AI (GenAI) and its ability to elevate the user experience to a new digital level.  If an individual no longer must search through data, spreadsheets, reports, and across enterprise applications to find information and can interact with technology differently, it changes the experience.  A digital world experience means instead of searching through many data sets, an individual can interact with the technology in a conversational form, by inputting text, or interacting with a chatbot or digital assistance. This information can be surfaced in a matter of moments creating a likely positive experience.  This aspect requires the large language models (LLMs) that generate a human like recommendation based on the sets of data the system has been trained on. With a multitude of data that can be tied to the systems overall, the individual will discover a wealth of information that delivers more insights, opportunities to weigh different points and elements against each other, and thoughtful considerations for better decisions. These information interactions mimic exchanges of helpful information between individuals, enabling faster and more decisive actions. Individual and organizational productivity gains are initially massive and then enable both to start to see the power of technology enabling a better experience while also improving overall performance.  And it creates a positive experience and an opportunity to recreate this methodology for the next interaction. From a technology point of view it creates more stickiness of the applications and data. Experience Creates Differentiating Value and Relationships Using a mobile phone or tablet, one has a different experience with the technology than they do with their enterprise software. An individual can talk or type what it needs with the mobile devices and they provide answers.  While not always correct and in some cases needing more clarification, it is quicker than using enterprise software applications.  The mobile devices represent technology platforms with a multitude of cloud software enabled by a common design, even though the software is all different.  The value is clear in the use of the software on the devices and its incorporation into ones life.  But with current enterprise software, mostly on-premise and legacy, the experience is less than stellar and doesn’t even begin to mirror the mobile experience. Automation of simple tasks is typically lacking, importing/exporting of data and sharing it is done in a plethora of applications, and integrating the workflows and data to enable a decision point can take time. In the digital world of on-demand experience and answers, most enterprise software falls short.  Unfortunately finding the information is dependent upon the individual organization and its technology stack, as well as preference for particular technologies.  The value derived is not only a bad experience but also one fraught with creating a better solution than the currently used technology solution.  This only leads to devaluing the current technology and moving the value to the employee that can navigate to the right solution the fastest.  In this way the experience is a competition of legacy technology savviness to meet performance requirements.  With so many organizations still in the modernization stage with their enterprise software, the struggle towards a better experience and a better value for their employees overall continues to be unattainable.  Innovation Creates and Reshapes the Experience In the absence of personal relationships in the digital world, experience is a replacement to help foster a better relationship with an organization, employee, customer, supplier and partner.  The better the experience, the more replication of the process and usage of the enterprise software.  In a nutshell the experience is positive and the usage will continue to grow.  Shaping the experience in the enterprise software world is GenAI which improves the business by streamlining operations through the automation of business tasks.  Typically workflows are long and cumbersome and don’t work from one line of business application to another nor are tasks the same for mirror types of processes such as ordering and buying.  This itself makes it hard for the organization to interact with each part of the business and come together to solve a business problem.  GenAI can help streamline long tasks by learning the workflows and data that impact it, and reshaping it to a shorter workstream. Once this is done some of the previous processes go away or become autonomous because they have been consumed into the workflow already.  So, for instance, if I need to call customer service I might wait in a queue for awhile – to the point where they need to call me back and it could be a few days. But if I use the enterprise software that I ordered through, I can ask the question and a bot or agent will come up with several recommendations for me.  In this way, the

The Asia/Pacific healthcare sector is at a pivotal moment, as the industry is transitioning from just ‘care anywhere’ to ‘AI everywhere in care management’ phase. This essential transition is supported and driven by the focus on robust clinical data sets and evolving connected care ecosystem. To efficiently manage the care delivery process, care providers are prioritizing enhancing clinical, operational and administrative workflow productivity. As a result, GenAI has emerged as a transformative force, with a huge potential to revolutionize the future of healthcare workflow processes and care delivery system. Dedicated Budget for Generative AI, Initial Investments Focused on PoCs IDC’s recent survey shows that in Asia/Pacific over half of healthcare and life science organizations are planning to have a dedicated budget for GenAI. IDC data also shows that almost 40% of healthcare providers and nearly a third of life sciences firms in the region are currently focused on proofs of concept (POCs) of GenAI models as part of testing tools and solutions. This trend is mainly owed to the nascent stage of tech adoption and to the testing of the scalability and partnership ecosystem by healthcare organizations. GenAI adoption is set to create a positive impact on clinician experience, patient engagement, and workflow efficiency management for healthcare provider organizations. In life sciences, the initial impact will be mostly on drug discovery and design, enhanced patient engagement, streamlined clinical trials, and patient safety. Data is at the core of AI, and this is driving healthcare organizations to increase their focus on EHRs and investments on cloud adoption to make their digital infrastructure and data platforms more robust and secure. Evolving Partner Ecosystem Most healthcare and life sciences organizations are of the opinion that GenAI models that leverage their own business data will give them a significant advantage over competitors. Those with mature and secure clinical data foundations, such as multi-cloud and hybrid cloud architectures are better positioned to take full advantage of GenAI at a faster pace. At the same time, the focus on data security has increased as the healthcare sector faces intense threats through cyberattacks. As a result, the GenAI ecosystem involves IT SPs, security and platform providers, and hyper-scalers in solution deployments. Time to Align GenAI Use Cases for Healthcare Organizations As the demand for GenAI intensifies, healthcare and life science organizations in the region are shifting their focus towards healthcare-specific GenAI use cases. To accommodate this demand, IDC recently released a study documenting GenAI use cases in the healthcare provider segment GenAI Use Case Taxonomy, 2024: The Healthcare Industry, and life sciences sector GenAI Use Case Taxonomy, 2024: The Life Sciences Industry, addressing business impact, metrics, and data modality for each use case. Current GenAI case study deployments in the Asia/Pacific region predominantly address clinical documentation and summarization, content creation for clinician-to-patient communication, personalization of patients/consumer experiences, patient engagement, and drug discovery and design. These use cases are set to accelerate personalization, care experience, and workflow productivity. Challenges on the Journey to GenAI Adoption The journey to GenAI adoption is not without risks. Regulatory risks and higher infrastructure costs are the limiting factors for GenAI adoption among healthcare and life sciences organizations in the region. Organizations will carefully consider tech partnerships and align use cases to implement GenAI solutions effectively and safely. IDC data shows that healthcare CIOs expect a software provider to have robust data security, seamless and intuitive AI models to work with, and GenAI models that support a broad range of content, both structured and unstructured Road Ahead Moving ahead, creating a robust clinical data foundation, aligning GenAI use cases between organizational priorities with that of the tech providers’ offerings, and exploring flexible pricing options, along with trust and transparency in the solution engagement would ensure a smooth transition of GenAI use case deployments from PoCs into production. GenAI adoption in healthcare and life sciences, though at its nascent stage, is set to have a significant impact on enhancing clinician efficiency, improving workflow productivity, and hyper-personalization of patient experience. Currently, there is increased priority towards POCs as part of GenAI model deployments but this is set to transition to full-fledged deployments supported by matured clinical data sets, regulatory support, enhanced skill sets, clinician buy-in, and alignment of GenAI use cases with organizational priorities. For additional reference, please access IDC Health Insight’s report GenAI in Healthcare and Life Sciences: Current Trends and Future Potential in Asia/Pacific. Learn what matters most to your customers with IDC’s AI Use Case Discovery Tool—find out more. source

When something breaks in your home, your office, or in a venue you frequent what is your expectation? Is it that you will just deal with having a broken product, offline printer, or down elevator? Most of us would expect a service technician will show up to the rescue to return the given product or asset to operations so we can get back to productivity. In IDC’s recent Product Innovation and Aftermarket Services Survey, service leaders noted a priority to improve service (quality and speed) to customers. But too often, aftermarket service organizations have focused on just ensuring a warm body arrives on a customer site within the service level agreement (SLA) with little importance put on actually achieving resolution or enhancing the customer experience. As customers explore options for the services they receive, aftermarket service providers will need to get better at delivering more than just the minimum to enable the field service team to become experts on engaging a customer in a special and personalized way. Field service and the aftermarket are too often driven by meeting a SLA. This minimum requirement of meeting a service window of 4-8 hours after a failure has been reported, or processing a warranty claim within 30 days, or ensuring an asset is available 80% of the time has long been the norm. Meeting minimal requirements is quite profitable for the service organization, but can be short-sighted as competitors enter the market and begin to offer service, support, and enhanced experiences of the same or better quality. To address this pending disruption of competitive factions and heightened customer expectations, field service organizations will need to prioritize value and not just meeting an SLA. This will raise the cost to serve in the short term but in turn result in having the right to request more share of customer wallet as value delivered improves for the customer or operator. This shift to value and enhanced/personalized experiences will ultimately require better quality data, contextualized customer insights, and freed up time to focus on delivering value. Artificial intelligence (AI) provides an opportunity to close the gap between data and insights on the front line. IDC defines AI as the ability of computers to learn without being programmed, applied to large sets of data for business advantage. But how should field service organizations reconcile the hype around AI to usher in the era of intelligence at the point of service? Field service organizations should prioritize the following as they explore the potential of AI in the coming weeks, months, and years: Understand the pulse of your employees and customers. Voice of customer and voice of employee activities often are established for the primary benefit of the organization (i.e., increase sales/margins, increase retention rates). In this new era of AI, field service organizations will need to listen to the needs and concerns of customers and employees. As AI becomes more pervasive across industries, field service organizations must tackle the elephant in the room around AI – privacy, and job displacement. Too much of the discussion around AI in the B2B world has been the fear that it will replace jobs or result in IP theft. This view of potential negatives neglects to amplify all of the potential positive outcomes of what AI can offer, Educating customers and service employees about the value of AI and how these technological capabilities can improve the service experience, customer outcomes, and employee productivity is crucial to adoption and comfort. Without understanding customer and service employees’ fears about AI, organizations will struggle to maximize the opportunities that will come with this innovative technological advancement.   Shift the KPI that measure success in the field. The promise of AI in field service revolves around improved operational efficiency, predictive/prescriptive service outcomes, and improved productivity of the team. However, there is a bit of a gap between the current metrics that are being measured and what should be measured in the AI era. If AI is to improve the speed of service, technicians should be measured on the value they are providing to the customer and not on how many more jobs they can complete. The improved speed of issue resolution as a result of AI providing better answers to the reason for failure should allow the humans on the service team to focus on the customer. This shift in what role a field service technician can play in customer outcomes is profound, no longer is the technician solely in place to turn a wrench but to prioritize customer engagement. Therefore, the KPI that matter aren’t work orders closed in a given day but experiential and value based. These new metrics may be more difficult to measure but will tell a better story of customer impact, future revenue opportunities, and lifetime value. Highlight the positive and address the (potential) negatives. Right now, there are too many field service technicians that can efficiently get on site in front of a customer or asset but fail to resolve the issue on a first visit. Issue resolution is becoming more and more complex as assets are smarter, supply chain networks struggle with resiliency, and the field force ages out. The ability to have the right part, right skills, right insights, at the right time is becoming a fairy tale for too many service organizations. On the front line is the field service engineer who has to advise a customer or operator in need that service cannot be completed resulting in assets, products, and equipment remaining down. Service leaders must communicate to the field service team both the in office planning/dispatching teams and the engineers in the field the ability for AI to drive insights and efficiency while reducing non-value added task work. The skepticism of technology from service teams has preceded the AI era, but the AI conversation brings with it the fear of machines taking over to the detriment of the humans. However, fear comes from a lack of communication, visibility, and buy-in around strategy and

On June 10, 2024, Apple entered a new era of intelligence. At their annual Worldwide Developers Conference (#WWDC24), Apple unveiled how artificial intelligence will be embedded into their ecosystem and announced several enhanced features for iOS, iPadOS, and MacOS. These new smarter features will empower users through the use of AI. This year’s WWDC marks a pivotal moment for Apple, being one of the most significant events in recent years. Since the introduction of the iPhone, the iPad, and the Apple watch, Apple has been the undisputable leader in terms of the user experience, and dominated sales of smartphones, tablets and smartwatches in value terms. Over the last 17 years, Apple has not faced any other disruptive technology as potentially detrimental for its business and its future as AI, if not tackled in the right way. WWDC 2024 offered Apple and its CEO, Tim Cook, an opportunity to demonstrate how the company will lead in making AI a transformative, advanced, and intelligent experience for their users. With smartphone, tablet and PC sales slowing down due to high adoption rates and offering just incremental improvements from the previous version of devices, Apple needs to reignite consumers excitement to encourage more frequent upgrades. We all remember the long queues outside Apple stores when a new iPhone was launched! The excitement generated by past product launches is critical to Apple’s business and brand. AI Will Enable Apple To Offer Unique and Intelligent Features, Experiences and Services to Their Customers. However, the key question remains: will these announcements be enough to secure Apple’s leading position? Many competitors (from phones to PCs vendors) have already revealed their AI strategies and devices. How can Apple stand out and how can a partnership with OpenAI help? AI-Enabled Devices Will Be the Fastest Growing Segment in for Smartphones and PCs. IDC forecasts that AI Smartphones to reach 170 million units in 2024, and AI PCs to account for nearly 60% of all PC shipments by 2027. Historically, the App Store has been at the heart of the value proposition of Apple’s iPhone and the iPad. The slogan, “there’s an App for that” became iconic. However, AI is set to “kill” the apps and a new era is about to begin for Apple and the industry. Previously, the more apps the smartphone (and the iPad) was able to run, and the more powerful the apps, the better the smartphone (or iPad). Today, the smartphone is not ‘smart’ anymore. With AI, the fewer apps needed and the more a phone can use data contextually to assist the user, the better the phone will be. This will revolutionize the user experience, requiring an operating system able to learn from all devices and services in Apple’s ecosystem. Apple’s full control of its ecosystem, hardware and software, and seamless integration gives it a significant advantage over competitors. This is particularly important when the experience will be defined by how well the phone “knows” the user, which can only be achieved by this seamless integration, while making sure the personal information and data remains private. Although Apple is not the first to offer AI-enabled features, it is in its DNA to offer the perfect experience. AI will not just offer the best experience, but the most powerful, disruptive, personalized and private, and that’s exactly what Apple showed today with Apple Intelligence. While AI-generated emojis will grab the headlines, Apple showcased how AI will empower users in several ways, particularly around productivity, education and entertainment. These are the areas where Apple already excels with its range of services and applications. As User Interaction Shifts From Opening an App To Asking the Phone, Siri Will Be the Glue. This killer feature will attract consumers to AI enabled devices. Siri will offer a unique way to interact with all Apple devices using our voice in a more personalized and conversational manner. It will become a true digital assistant, contextually understanding various aspects of users’ lives and providing human-like responses by accessing data across all devices to provide the right response to the users’ needs. This conversational digital assistant fully integrated with all devices will be a game changer, and I believe it will offer a compelling reason for many to upgrade their iPhones in the years to come. Apple’s strength lies in the breath of its portfolio and its ecosystem. For consumers, it does not matter what an AI-enabled iPhone is, or how many TOPs it can run. What matters is how well a phone can assist users in managing their lives. By reducing the need to navigate multiple apps, Apple’s AI-enabled devices will offer a smoother, more intuitive and smarter experience. This marks the beginning of a new era for Apple and for their users. For more insights and views, watch this conversation with Tom Mainelli at the Apple Park. Learn what matters most to your customers with IDC’s AI Use Case Discovery Tool—find out more. source

Digital business is just standard business in 2024. Companies striving to participate in the digital economy are looking to invest in technology that fits the needs of their company size, employee personas and functions, industry verticals, and current level of technology maturity. Vendors hoping to sell and implement digital technologies need to consider all these factors when meeting with a potential customer. Segmenting and grouping customers by all these factors can help technology vendors provide the best messaging and service. At IDC, we do research that considers the impacts of all these factors, and that gives us insight behind the tech buying curtain into the needs and wants of buyers at all stages of their digital journeys. The Small and Medium Business Market In the recent IDC Webcast, “Behind the Tech Buying Curtain: What Vendors Need to Know,” Katie Evans, IDC Senior Research Director for Worldwide Small and Medium Business Markets, said there are three key topics keeping SMB tech buyers up at night: AI/automation, macroeconomic woes, and heightened security concerns largely fueled by AI, remote work, and the moving to the Cloud. The forward-looking investment priorities for SMBs are all automation and AI related—process automation, connectivity automation, AI (non-GenAI), and GenAI, according to IDC’s Worldwide Small and Medium Business Survey. Rising energy prices highlight SMB’s top macroeconomic woes in that survey, while implementing technology securely is the largest technology challenge. Macro Factors Impacting Investment IDC’s Digital Economy Strategies research, led by Research Analyst Elisabeth Clemmons, focuses in part on the macro landscape impacting businesses, even those focused in specific countries or localities. Impactful events and trends include skills shortages, inflation, supply-chain constraints, energy crises, tensions between countries and war, and elections around the world. These events impact technology buying patterns, causing companies to be more cautious with their investments. AI is the technology most likely to persevere and be prioritized in the face of these macro impacts, with AI spending, the AI provider supply chain, and the economic stimulus among AI adopters is projected by IDC’s Macroeconomic Center of Excellence to be 3.5% of global GDP by 2030. The heightened impact of and investment in AI is contributing to two more macro trends impacting businesses: digital regulation and potential raw materials shortages. Data and AI is a top regulatory target with governments taking diverse approaches, while the sheer amount of data in the world is expected to more than triple by 2028—necessitating over 50 times the current annual production levels of neodymium and other critical raw materials. C-Suite Leaders Increase Focus on AI Despite any concerns and challenges that AI my pose, CEOs are scaling AI initiatives, dedicating budgets to AI, and ensuring AI projects receive greater visibility, according to Nupur Singh-Adley, Research Manager leading IDC’s C-Suite Tech Agenda research. As C-suite leaders focus on building digital businesses buoyed by AI technology, they are also mindful of managing risk, including heightened cybersecurity threats and regulations. This is leading to prioritized spending on security, risk, and compliance technologies and greater scrutiny of cybersecurity and risk management at the board level. Along with considering risk, CEOs are also prioritizing responsible AI while building digital businesses that are focused on trust, critical evaluation of tech vendors, and sustainability. Digital Capability Perception vs. Reality While companies of all sizes are facing challenges both internally and externally and they digitize, it’s important for technology vendors to consider that many are also overestimating their digital capabilities. In IDC’s Digital Business and AI Transformation Strategies research, we conduct an annual Digital Business Scorecard that measures the digital capabilities of businesses in four areas: Digital Business Models, Data, Operational Processes, and Organization. Overall, when asked to assess themselves, 41% of those surveyed in IDC’s Digital Executive Sentiment Survey believed they were a “mostly digital business.” However, when applying our Digital Business Scorecard methodology, which correlates investments and strategies to business outcomes, only 11% of that same survey sample were categorized as “Digital Business Leaders.” Digital Business Leaders are taking holistic digital strategies and have implemented world-class technology. They view data as a top AI priority while also expanding its use in core operational processes. They have a digital technology architecture that is in lockstep with IT strategy and are focused on improving recruiting and employee engagement. These are the standards that many of those companies that think they are digital aren’t quite achieving—often finding themselves bogged down by inferior technology, not focusing on capitalizing on the value of their data, not looking to automate and standardize processes, and not taking the view of technology as a competitive advantage. The Important Role of Technology Vendors in the Digital Economy Technology vendors are key to digital business and AI transformation strategies—and many companies need vendor expertise and guidance to help them become digital business leaders. Vendor messaging to these companies can be challenging—but it helps to know how company size, macroeconomic impacts, C-suite leadership, and digital capability and perception impacts buying conversations. It’s helpful for vendors to segment their customers, and specialize their strategy and messaging to those individual segments. That’s beneficial for all parties and will help companies succeed in the digital economy. Learn what matters most to your customers with IDC’s AI Use Case Discovery Tool—find out more. source