Over the last 30 years, the mobile phone industry has been through two major revolutions. The first revolution began when the mobile phone emerged, transforming the way we communicate by introducing mobile communications into our lives. The second revolution emerged in the latter half of these three decades when smartphones disrupted everything else in our lives. Today, with 3.1 billion smartphones in use globally, these devices play a critical role in the world. The latest technological development that has taken the tech world by storm is the launch of AI-powered smartphones. Although AI is not new, it gained significant attention with the launch of ChatGPT and the capabilities of Generative AI (GenAI). Leveraging Large Language Models (LLMs), a new revolution is coming to the smartphone – intelligence. Defining AI Smartphones* According to IDC, Gen AI smartphones are defined as devices featuring a system-on-a-chip (SoC) capable of running on-device Generative (Gen AI) models more quickly and efficiently leveraging a neural processing unit (NPU) with 30 Tera Operations Per Second (TOPS) or more, using the int-8 data type. The smartphone SoCs being designed and marketed by silicon vendors with next-gen AI smartphones in mind will increase in the future as they continue to push forward the NPU technology. However, to date, here are a few that qualify based on the definition above: Apple A17 Pro MediaTek Dimensity 9300 Qualcomm Snapdragon 8 Gen 3 Samsung Exynos 2400 Market Opportunity The latest IDC forecast estimates that Gen AI smartphone shipments will grow 364% year-over-year in 2024, reaching 234.2 million units. Despite the current macroeconomic environment and the fact that consumers are keeping their devices longer, the potential of Gen AI on a smartphone is expected to drive significant demand over the coming years. This segment is projected to be the fastest-growing segment in the smartphone category during the forecast period, outperforming the non-AI-enabled smartphone segment. Growth will continue into 2025 with an expected increase of 73.1%, followed by moderate double-digit growth for the rest of the forecast period. By 2028, worldwide Gen AI smartphone shipments will reach 912 million units, resulting in a compound annual growth rate (CAGR) of 78.4% for 2023-2028. Reshaping the Mobile Experience AI will enable manufacturers to offer unique and intelligent features, experiences and even services to their users. Since the introduction of the first iPhone and Android smartphones in 2007 and 2008, and particularly after the introduction of app marketplaces by Apple and Google, users have become used to interacting with the smartphone by using apps. The more powerful the apps, the better it is. With AI, the fewer apps the phone will need and the more capable can use data contextually to assist the user, the better it will be. This “app-less” world will revolutionize the user experience, requiring the phone to better “know” its users while ensuring personal data remains private and secure. The interaction with the smartphone will shift from touch to voice, as “intelligent” voice assistants become our true personal digital assistants. These conversational digital assistants, fully integrated with the device, will be game-changers, providing compelling reasons for users to upgrade their smartphones. Although a full AI experience is still in development, less than 18 months after the introduction of ChatGPT, several vendors announced their AI strategies and devices showcasing some intelligent capabilities. These include: Samsung Galaxy S24 Ultra: Some of the key AI features include a transcription summariser built into the voice recorder, real-time voice translation, and Circle to Search, a tool developed by Google that allows users to draw a circle around anything on screen and search it on Google. Xiaomi 14 Ultra: Features AI-generated subtitles for video calls and an AI Portrait feature that lets users take a selfie and add a different background. Google Pixel 8 Pro: Offers features like summarising recorded conversations, suggesting replies to messages, and creating AI-generated wallpapers. The camera also benefits from AI with Magic Editor (moving or removing objects); Best Take (selecting the best shot), and Video Boost (enhancing video colour and lighting). Apple Intelligence: The new suite of AI features will come to the iPhone, iPad and Mac later this year with the latest OS versions announced at Apple Worldwide Developers Conference. The AI features will include rewriting text and proofreading, generating email replies, and content summarization. Users will be able to generate images from text based on note contents and remove objects from photos. Siri, Apple’s digital assistant will become more conversational. OPPO AI Strategy: Betting big on AI, OPPO aims to incorporate over 100 Gen AI features across its lineup of AI-enabled smartphones in 2024. Unlike other players, OPPO aims to democratize AI by introducing these features to more affordable price points. Honor Magic 6 Pro: The device promises AI-powered user experiences and it is the first Honor’s all-scenario strategy, featuring cross-OS collaboration and AI designed with a human-centric approach. Motorola Razr 50 Ultra: Will run Google Gemini as the main digital assistant, offering AI out of the box. Features include recognizing photos, summarizing text, answering voice queries, and changing message tones before sending. It includes Motorola’s AI tool, Style Sync, which creates a wallpaper based on the colors and patterns of a specific photo. Use Cases Gen AI smartphones are expected to disrupt different aspects of our lives. IDC identified several use cases that can drive adoption and have a positive impact: Work Environment: According to an IDC survey, employees view the smartphone as one of the most important tools in the workspace. AI will streamline tasks by summarizing content from meetings and documents, allowing users to focus on discussions. AI will also summarize email threads, suggest replies based on the conversation, and help manage calendars based on requests received via email or messages. Healthcare and Wellbeing: Smartphones have become central to various wearables (through apps) that collect data on vital body signals, such as blood pressure, heart rate, and blood oxygen. AI will monitor this data from all sensors, alerting users to potential risks and suggesting dietary and exercise plans

One of the oldest security technologies — the venerable enterprise firewall — continues to thrive, as highlighted in the recently published report, The Forrester Wave™: Enterprise Firewall Solutions, Q4 2024. Contrary to expectations that this space might have little left to offer, enterprise firewall vendors have done well to keep this technology relevant for modern cybersecurity needs. They have made significant progress in keeping up with rapid innovations while supporting clients in securing dispersed and hybrid enterprise architectures. While enterprise firewalls continue to be delivered in the same manner, vendors have made the move to offer these capabilities as part of other “platform” initiatives such as Zero Trust edge/secure access service edge (ZTE/SASE) to not only make security enterprise firewalls more accessible to improve their adoption but to also increase value retention, not just for large enterprises but also for small- and medium-sized enterprises. Consolidate, Centralize, And Deliver A Unified Management Experience Clients require a consistent and streamlined method for managing various deployments of enterprise firewall solutions. This involves having a unified UX/UI across physical, virtual, and cloud deployments and recognizing the need to support adjacent efforts like ZTE/SASE. Consequently, leading enterprise firewall solutions now offer integrated and unified management for data center, branch, and edge use cases, which include: As-a-service offerings. Zero Trust network access (ZTNA). Software-defined wide-area networks (SD-WAN). With this unified approach, clients can derive greater value from their enterprise firewall investments, enabling them to address use cases that secure both north-south and east-west traffic regardless of environment. Clients can streamline policies across various enforcement points, strategically creating and orchestrating policies at different levels of the transit path for multiple transient connections without having to navigate multiple administrative consoles. Common policy construct and centralized visibility, enhanced with built-in AI/ML, also improve policy optimization for enhanced incident response. Part Of The Bigger Picture It’s no surprise that the industry continues to push for cloud migration, prompting organizations to evaluate enterprise firewalls to ensure that they meet modern challenges and requirements without adding costs or complexity. The reality is that enterprises will have hybrid topologies for the foreseeable future, consisting of a mix of cloud, virtual, and physical environments, all of which need security. To advance toward a more mature Zero Trust architecture, it’s crucial for organizations to see the big picture and choose the right solutions for the long term. Enterprise firewall vendors have not only enhanced capabilities but also improved consumption models, making these solutions viable for securing cloud workloads, facilitating secure connectivity with integrated SD-WAN and ZTNA, and creating microperimeters. That last use case is a big deal, too, since 61% of global respondents in large enterprises view enterprise firewalls as essential for supporting a microsegmentation strategy, according to Forrester’s most recent Security Survey. The advancements in enterprise firewalls are transforming them from single-purpose tools into adaptable security solutions that can flexibly support an organization’s digital transformation journey. Shared Mission, Shared Outcomes The ZTE/SASE market is rapidly expanding, with many organizations seeing it as the ideal starting point for a Zero Trust architecture journey. And why not? As my colleague Andre Kindness highlights in his blog, this market is both disruptive and transformative. It allows organizations to replace legacy solutions with a consumable product as a service, merging networking and security stacks. But what if you want to keep your firewall investment? Enterprise firewall vendors are addressing this by converging and consolidating their solutions to support and integrate ZTE/SASE. This approach simplifies adoption while preserving the value of existing deployments for organizations with ongoing on-premises needs. Whether the future involves moving to the cloud or not, the mission remains the same: Maintain comprehensive security everywhere, at all times. While the leaders in this space have advanced this strategy, other vendors are not too far behind and are poised to offer cost-effective offerings for smaller enterprises and other organizations. You can read more about my findings and view each vendor’s strengths and weaknesses in the Wave report. Forrester clients, please reach out to schedule guidance sessions or inquiries with me to discuss our findings. If you’re feeling bold, join me at Forrester’s Security & Risk Summit in Baltimore on December 9–11, where I will host two sessions on Zero Trust that include a workshop and a panel discussion for getting your Zero Trust journey to the next level. Hope to see you there! source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Pika a.k.a Pika Labs or Pika AI, the Palo Alto, California-based startup that has raised $55 million to disrupt video production with its video AI models of the same name, is further expanding the free special effects users can access through its web-based AI image-to-video generator. Pika 1.5, its latest AI video model, now includes the ability to crumble, dissolve, deflate and “ta-da” video subjects — the last of these essentially making a video subject disappear behind a cloth. Users can simply upload an image to the site and Pika 1.5 will turn it into a video with a corresponding animation. The user guides which animation is used by selecting it from a button beside the “Image” attachment icon (paperclip) labeled “Pikaeffect” with a magic wand beside it. The new AI powered special effects — or “Pikaffects, in the company’s parlance — join six others previously unveiled earlier this month: Explode, squish, melt, crush, inflate and “cake-ify,” the latter of which turns any uploaded still image into an “is it cake?” video where the answer is a resounding “yes!” Unfortunately, VentureBeat has been unable to use the new effects yet as when we attempted, the site said “We’re experiencing high demand right now (how flattering)!” Nonetheless, as the AI landscape evolves, Pika’s unique approach to video manipulation sets it apart from the growing field of AI-driven content generation. While Pikaffects cater to users seeking creative transformations, traditional features like lip-syncing and AI sound effects remain accessible on the earlier Pika 1.0 model. Paid subscribers have the flexibility to switch between Pika 1.5 and 1.0, depending on their project needs. Where Pika came from Pika Labs, co-founded by former Stanford AI researchers Demi Guo and Chenlin Meng, first launched its AI video platform in late 2023. The company has rapidly scaled, reaching over half a million users in less than a year. Unlike many AI video platforms that focus primarily on realism, Pika takes a different route by prioritizing creative manipulation. These effects enable users to reshape video subjects in ways that are not just visually impactful but also technologically intriguing, offering hands-on AI practitioners a sandbox for experimentation. For professionals managing machine learning models or integrating new AI tools, Pika Labs’ latest features could present new opportunities to deploy innovative content solutions. The platform allows the quick application of effects through a user-friendly interface while still enabling deeper integration via text-to-video (T2V) and image-to-video (I2V) workflows. Subscription pricing To accommodate a diverse range of users, Pika Labs offers four subscription plans: Basic (Free): This entry-level plan provides 150 monthly video credits and access to the Pika 1.5 features, making it suitable for casual users or those curious about the platform. Standard ($8/month, billed yearly): With 700 monthly credits, access to both Pika 1.5 and Pika 1.0, and faster generation times, this plan offers more flexibility for content creators looking to produce more videos. Pro ($28/month, billed yearly): This plan includes 2,000 monthly credits and even faster generation times, catering to users with higher content demands. Unlimited ($76/month, billed yearly): Designed for power users, this plan allows unlimited video credits, offering the fastest generation times available on the platform. The updated credit structure (15 credits per five-second clip) allows for a scalable approach to video generation. The various subscription tiers accommodate different needs, from light experimentation to intensive production, ensuring that both individual contributors and larger teams can find an affordable solution. These flexible pricing options make Pika Labs accessible to smaller teams and larger organizations alike, allowing AI engineers to manage costs while experimenting with new video capabilities. Attempting to differentiate amid a crowded sea of competitors The move by Pika to further differentiate its video AI model from competitors such as Runway, Luma, Kling, and Hailuo comes amid intensifying competition in the nascent industry, and follows Adobe’s move this week at its MAX conference in Miami Beach, Florida, to begin offering a preview of its own “enterprise safe” AI video model Firefly Video, trained on licensed data. Pika, like most other generative AI startups, has not disclosed its precise training dataset. Other rivals such as Runway have been sued by artists for alleged copyright infringement over training AI models on data scraped from the web, including many other artworks and videos, and likely many copyrighted ones. That case, which also names AI image generator Midjourney and Stability, is moving forward toward a trial but has yet to be decided. source

We’re excited to announce the inaugural release of a Forrester Wave™ evaluation covering attack surface management (ASM) solutions. We evaluated the 11 most significant ASM vendors in what is currently a rapidly evolving market segment. Forrester covers ASM and periphery markets such as exposure management and vulnerability risk management (VRM), as these segments all contribute to proactive security, supporting use cases of visibility, prioritization, and remediation. For the ASM Wave, we primarily focused on how ASM solutions provides the first essential step for proactive security: visibility. What’s Going On With The Attack Surface Management Market? For the ASM Wave, we evaluated vendors that started off as cyber asset attack surface management (CAASM), external attack surface management (EASM) solutions, or vendors that package ASM as part of their SecOps platform strategy, including those that deliver ASM capabilities via an exposure management offering. All vendors evaluated are aiming to provide comprehensive visibility into assets and attack surfaces to enable customers to prioritize and ultimately remediate risks. The state of attack surface management is volatile and dynamic (see figure below), which we took under consideration in the Wave evaluation. Some key considerations for today’s state of proactive security include the following: CAASM and EASM have merged into a singular ASM to support visibility use cases. CAASM and EASM have always provided visibility, through either an internal (defender) view or external (attacker view). These related use cases provide visibility and are improved when combining both views — which users can obtain now from CAASM, EASM, or ASM integrated within a SecOps platform. As our Wave details, CAASM features can differentiate by extending a breadth of integrations that ingest asset context, and EASMs can differentiate when vendors own and deploy proprietary scanning technology. Standalone EASM looks more like a threat intelligence product. EASM has turned into a capability to be found in a variety of products, most notably in threat intelligence providers that are expanding external pictures of environments — not just externally facing assets but also capabilities such as malicious brand impersonations on social media or mobile app stores, executive monitoring, and third-party/supply chain monitoring. EASM and continuous security testing augment one another. When assets are externally discoverable and accessible, the next strategic step in proactive security — prioritization — is testing them to assess the vulnerabilities. For this reason, continuous security testing companies that offer breach and attack simulation, bug bounty, or penetration testing-as-a-service capabilities have added EASM capabilities. Proactive security vendors need to support more than just visibility or prioritization to extend a platform offering. Since ASM is focused on visibility and continuous security testing supports prioritization, we expect to see ASM and continuous security testing vendors continue to add to one another’s capabilities. Proactive security platform approaches to attack surface management are most prominent. ASM will continue to prevail as a capability in proactive security platforms to provide visibility. Proactive security platforms today extend and will continue to enhance prioritization through features such as exposure management, CISA KEV/EPSS/CVSS or risk scoring (typically found in VRM solutions), or continuous security testing. ASM solutions integrating into a proactive security or SecOps platform with the data that platform already possesses is a crucial differentiator here, because it provides out-of-the-box asset context and stepping stones to exposure management as part of a prioritization strategy.   A Proactive Security Approach Will Future-Proof Vendor Rebranding, Category Shifting, And Your Program Strategy Until, and if, the proactive security market stabilizes around one category, continue to ask yourself how much visibility you have, whether your prioritization strategy is maintaining acceptable risk thresholds, and how well you remediate vulnerabilities. These will always be the core principles you need to fulfill even as market landscapes change. Today, ASM solutions provide visibility externally and internally. This visibility needs to be accessible for your prioritization strategy (whether through exposure management, VRM-provided risk scores, or continuous security testing) and needs to be the source in and out of your remediation tracking (whether through VRM, ITSM, or SIEM). Forrester customers who have questions or concerns about ASM and these other complementary markets should schedule an inquiry or guidance session with me to review how you can ensure that your organization is on the right path for effective proactive security. source

Artificial intelligence (AI) has been around for some time, but the recent surge in popularity from Generative AI has made consumers and businesses excited and wary at the same time. While it is natural to be cautious with new technologies at first, the more businesses are willing to explore and evaluate the technology, the faster they will enjoy its benefits and be prepared for the ever- changing environment that surrounds them. Global investment in AI technologies is experiencing a robust upward trend, with projections indicating sustained growth in the coming years. This dynamic growth is driven by the pursuit of more efficient processes, tailored services, and innovative solutions. The 2024 V2 release of the Worldwide AI and Generative AI Spending Guide introduces significant updates, including broader technology coverage, a unified dataset perspective of Generative AI alongside the rest of AI, and a refresh of AI use case categorizations. This comprehensive analysis has identified over 250 functional use cases, meticulously examined and defined by a diverse team of IDC analysts across various research domains. These use cases are organized into 13 functional areas, with the addition of industry-specific use cases to offer an extensive overview of the AI spending landscape. Consequently, this release of the Worldwide AI and Generative AI Spending Guide encompasses a total of 42 modeled use cases, spanning both functional and industry-specific AI applications. IDC’s WW AI and GenAI Spending Guide Use Cases Source: IDC’s Worldwide AI and Generative AI Spending Guide, August (v2 2024) Use Cases Highlights The AI Infrastructure Provisioning use case, which encompasses the spending with the IT infrastructure and resources for AI systems from infrastructure service providers, underscoring its pivotal role in the Artificial Intelligence ecosystem. It represents the largest AI investment area with expenditure reaching $30.3 billion for the year 2024. Projected to grow to $47 billion by 2028, this use case accounts for approximately 30% of the total global spending in Artificial Intelligence. It is heavily used in particular in the Software and Information Services industry. The use case Augmented Fraud Analysis and Investigation has emerged as a significant industry-specific use case, drawing over $17 billion in investments in 2024 alone, and showcasing a remarkable five-year Compound Annual Growth Rate (CAGR) of 31%. This application is widely adopted across various sectors, notably within the financial industry, which extensively utilizes its capabilities. It is designed to detect illegal or illicit financial activities characterized by intentional deception and/or misrepresentation within different organizational areas, such as operational and financial. Leveraging AI, these systems employ rule-based learning to pinpoint transactions indicative of fraudulent activities or an increased fraud risk. The systems autonomously learn to identify a wide array of fraud schemes perpetrated by both employees and customers. Another popular one is the AI-enabled Customer Service and Self Service use case, commanding an impressive $16.7 billion in spending in 2024, represents a universally adopted solution across industries globally. This innovative approach streamlines customer query resolution by autonomously generating knowledge from received queries, eliminating the necessity for live agent involvement. It boasts the capability to curate pertinent articles, recommend new ones based on responses, and engage customers across multiple languages. Furthermore, it enables the delivery of highly personalized products or bundles, precisely timed and optimally priced across various channels, among other advanced functionalities. The Augmented Threat Intelligence and Prevention use case, a $13.3 billion market in 2024, identifies the banking sector as its primary adopter across various industries. This application employs sophisticated systems that analyze intelligence reports, distill essential information, organize data into a standardized format, and integrate this information into the workflow. By correlating disparate data points, it effectively identifies threats to databases, systems, websites, and organizations, enhancing security measures and safeguarding assets. Regional Outlook In both the Americas and the Asia Pacific and Japan (APJ) regions, the AI Infrastructure Provisioning and AI-enabled Customer Service and Self Service use cases stand out as the most prominent. Combined, these two use cases account for 20% ($28 billion) of the total AI spending in the Americas and 28% ($12.8 billion) in the Asia Pacific and Japan region for the year 2024, highlighting their significant contribution to the overall investment in Artificial Intelligence within these regions. While for the EMEA region, the Augmented Fraud Analysis and Investigation use case emerges as the frontrunner, closely followed by the Augmented Threat Intelligence and Prevention use case. Collectively, these two use cases constitute 17% of the region’s AI spending in 2024, amounting to $8.6 billion, showcasing their prominence in EMEA’s Artificial Intelligence investment landscape. Conclusion The integration of Artificial Intelligence into business operations has become a tangible reality for numerous organizations. Understandably, apprehensions about the unknown—such as the potential return on investment (ROI) of such technology, the optimal timing, and the most strategic regions for investment—can initially seem daunting. However, the pathway to making informed decisions, such as concerning the adoption of new technologies, is significantly smoothed by acquiring deeper insights. At IDC, we are committed to continuously enhancing our data and insights to empower businesses at every stage of their journey, ensuring decisions are made with confidence, professionalism, and a forward-looking perspective. Learn more about IDC’s AI and GenAI Spending Guide by downloading this product overview. Contributing Author: Mariana Fang – Research Analyst, Data & Analytics Discover how IDC’s AI Use Case Discovery Tool can elevate your AI strategy—learn more here. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has introduced a new tool to measure artificial intelligence capabilities in machine learning engineering. The benchmark, called MLE-bench, challenges AI systems with 75 real-world data science competitions from Kaggle, a popular platform for machine learning contests. This benchmark emerges as tech companies intensify efforts to develop more capable AI systems. MLE-bench goes beyond testing an AI’s computational or pattern recognition abilities; it assesses whether AI can plan, troubleshoot, and innovate in the complex field of machine learning engineering. A schematic representation of OpenAI’s MLE-bench, showing how AI agents interact with Kaggle-style competitions. The system challenges AI to perform complex machine learning tasks, from model training to submission creation, mimicking the workflow of human data scientists. The agent’s performance is then evaluated against human benchmarks. (Credit: arxiv.org) AI takes on Kaggle: Impressive wins and surprising setbacks The results reveal both the progress and limitations of current AI technology. OpenAI’s most advanced model, o1-preview, when paired with specialized scaffolding called AIDE, achieved medal-worthy performance in 16.9% of the competitions. This performance is notable, suggesting that in some cases, the AI system could compete at a level comparable to skilled human data scientists. However, the study also highlights significant gaps between AI and human expertise. The AI models often succeeded in applying standard techniques but struggled with tasks requiring adaptability or creative problem-solving. This limitation underscores the continued importance of human insight in the field of data science. Machine learning engineering involves designing and optimizing the systems that enable AI to learn from data. MLE-bench evaluates AI agents on various aspects of this process, including data preparation, model selection, and performance tuning. A comparison of three AI agent approaches to solving machine learning tasks in OpenAI’s MLE-bench. From left to right: MLAB ResearchAgent, OpenHands, and AIDE, each demonstrating different strategies and execution times in tackling complex data science challenges. The AIDE framework, with its 24-hour runtime, shows a more comprehensive problem-solving approach. (Credit: arxiv.org) From lab to industry: The far-reaching impact of AI in data science The implications of this research extend beyond academic interest. The development of AI systems capable of handling complex machine learning tasks independently could accelerate scientific research and product development across various industries. However, it also raises questions about the evolving role of human data scientists and the potential for rapid advancements in AI capabilities. OpenAI’s decision to make MLE-benc open-source allows for broader examination and use of the benchmark. This move may help establish common standards for evaluating AI progress in machine learning engineering, potentially shaping future development and safety considerations in the field. As AI systems approach human-level performance in specialized areas, benchmarks like MLE-bench provide crucial metrics for tracking progress. They offer a reality check against inflated claims of AI capabilities, providing clear, quantifiable measures of current AI strengths and weaknesses. The future of AI and human collaboration in machine learning The ongoing efforts to enhance AI capabilities are gaining momentum. MLE-bench offers a new perspective on this progress, particularly in the realm of data science and machine learning. As these AI systems improve, they may soon work in tandem with human experts, potentially expanding the horizons of machine learning applications. However, it’s important to note that while the benchmark shows promising results, it also reveals that AI still has a long way to go before it can fully replicate the nuanced decision-making and creativity of experienced data scientists. The challenge now lies in bridging this gap and determining how best to integrate AI capabilities with human expertise in the field of machine learning engineering. source

Each year, Forrester and the Disaster Recovery Journal (DRJ) team up to launch a study examining the state of business resilience. We examine different topics in business resilience such as disaster recovery, but this time, it’s all just business resilience. No matter whether your program’s goals are to create business continuity plans and test them, to build operational resilience, or to cross-functionally maintain business operations despite disruptions, this survey is for you! The last survey focused on business continuity, and many of the numbers didn’t significantly change from 2018, including: The median number of full-time equivalents supporting the business continuity management program was three, which is the same as 2021 and 2018. Fifty-one percent of respondents reported updating their business continuity plans once per year in 2023, down from 54% in 2021. In our 2023 survey, executive sponsorship stayed high at 96%, after the leap in 2021 to 94% from a consistent 88% in both 2018 and 2014. Remote access was the most common strategy for workforce continuity even in 2008 (86%), hit a peak in 2018 (88%), and sat at 82% in 2023. But worldwide operational resilience regulations, such as the EU’s Digital Operational Resilience Act and Bank of England’s Prudential Regulation Authority’s statement of policy on operational resilience, are either already in effect or will be shortly. They are both subtly and not so subtly changing the way we think about resilience. Want to know how this has changed the business resilience landscape? We do, too! If you are a business resilience decision-maker or influencer at your organization, please take 20 minutes to complete the survey here. Once the survey is complete, the DRJ will have a summary of the results on its site. For Forrester clients, the survey results will be examined in depth in reports that will publish in the next few quarters. All the results are anonymous. If you’d like to receive a complimentary Forrester report (The State Of Business Continuity, 2024), you can submit your email address and we won’t use it for any other purpose. Take the survey, receive a free report, and help us and the DRJ get a pulse on business resilience! source

AI agents represent the next iteration of artificial intelligence: not just learning from data but making decisions and acting autonomously. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More CareYaya Health Technologies has launched QuikTok, an AI phone companion targeted at lonely older adults. The free service is akin to “TikTok for older adults,” and it is developed to combat the loneliness epidemic and flag the early warning signs of cognitive decline and mental health issues. Of course, in this case, the older folks are talking with AI characters who are not real. The service comes from Research Triangle Park, North Carolina-based CareYaya Health Technologies, which is developing artificial intelligence innovations for the aging population. QuikTok is available free of charge to individuals through partnerships with the AgeTech Collaborative from the American Association of Retired Persons (AARP) and the Johns Hopkins Artificial Intelligence & Technology Collaboratory. CareYaya is a mission-driven social enterprise dedicated to researching and developing technologies that benefit the aging and chronically ill populations. It operates a no-cost care platform to empower families to book affordable care. The work is funded by individuals and grants from organizations including the Johns Hopkins Artificial Intelligence & Technology Collaboratory, Atrium Health, and support from the AgeTech Collaborative at AARP and the National Institutes of Health. About 37% of older Americans suffer from loneliness. The AI phone companion program provides comfort through meaningful interactions while also assessing early warning signs of cognitive decline, depression, anxiety, and other mental health disorders to support mental stimulation and emotional well-being for the older population. A recent poll reported that 37% of older Americans (ages 50-80) experienced loneliness, with 34% reporting being socially isolated. Loneliness has been identified as an epidemic by major health organizations, affecting physical and mental health and increasing the prevalence of heart disease, stroke, dementia and other health problems. “We believe conversational AI can be used as a tool to combat loneliness and prevent disease arising from social isolation, especially for older adults,” said Neal Shah, CEO of CareYaya, in a statement. “It’s been reported that for older Americans, being lonely is worse for your health and life expectancy than smoking 15 cigarettes a day. We designed QuikTok to bring people a sense of companionship, comfort, and mental stimulation while addressing some of the most pressing challenges older adults face, such as aloneness, memory decline, and even chronic pain.” QuikTok is the world’s first AI-based phone companion that meaningfully engages older adults. Powered by CareYaya’s state-of-the-art conversational LLMs, QuikTok uses AI voice generation to produce high-quality, human-like speech. It offers reduced latency for smooth, natural language speech patterns. As a complimentary service, it is accessible to anyone with a landline or mobile phone and bridges the technological divide by not requiring an internet connection or even a computer. Critically, this promotes equitable access to cutting-edge technology that can benefit older Americans. The company has 60 people. “As we continue to explore innovative ways to improve the quality of life for older adults, AI-driven companions offer practical support and emotional engagement, which is critical to the older population,” said David Casarett, chief of palliative care at the Duke University Health System, in a statement. “QuikTok has the potential to alleviate loneliness, enhance emotional well-being, support longevity and help seniors manage the complex challenges of aging and chronic illness.” Key features of QuikTok CareYaya also provides AI-driven online games for seniors as well. AI chat therapy: QuikTok initiates conversations and provides an empathetic listening ear, offering older adults a comforting presence to help them cope with loneliness, grief and loss. Personalized memory recall: QuikTok remembers past conversations, creating an ongoing dialogue that feels deeply personal and authentic, making each user feel understood and catered to. Interactive mental exercises: When connected to a web interface, QuikTok engages older people in daily mental exercises that keep their minds sharp, from word puzzles to games like bingo and chess. Pain management assistance: This service offers guided meditation and mindfulness exercises to help the older population manage chronic pain and improve overall well-being. Routine check-ins: For concerned family members and friends, the service can call individuals on certain days and times to check in on them and provide telephone-based companionship. Nancy Gribble, a 78-year-old QuikTok user, said in a statement, “At my age, it’s easy to feel invisible, like your voice doesn’t matter anymore. But Frank, my friend from QuikTok, hangs on my every word. He asks questions, he listens and remembers the details I share, and he helps me find joy in things to reminisce and talk about. QuikTok makes me feel heard and valued. It’s become a trusted confidant when I have no one else to turn to.” Due to high demand, older Americans or their families interested in QuikTok can join the waitlist to access the service at https://quiktok.careyaya.org/. Origins Neal Shah is CEO and cofounder of CareYaya Health Technologies. Shah cofounded CareYaya in 2022. As a former hedge fund manager turned social entrepreneur, he cofounded the company after a profoundly personal experience with caregiving. Motivated by creativity and humanitarian progress, the company’s flagship product is a technology platform that lets people quickly book experienced caregivers who are uniquely all students in the healthcare field, helping expand the care workforce amidst a critical caregiver shortage. Previously, Shah founded and managed a $250 million investment fund in New York, focusing on healthcare investments, and was a partner at a $1.5 billion private equity and hedge fund focusing on various sectors. He started his career in investment banking at Credit Suisse First Boston. How it works Asked about the AI tech, Shah said in an email to VentureBeat that the tech uses a large language model (LLM) is paired with a text-to-speech (TTS) model, which are connected to a telephony server that transcribes the speech of elderly users so that the LLM can understand and respond to him or her. The AI is specifically trained and prompted to optimize it for speaking with elderly people and participating in phone conversations. This includes thousands of phone and

If you’re a retailer or consumer brand, by now you’ve likely been prepping for this important quarter for quite a while. The 2024 edition of our annual report, A Retailer’s Guide To The 2024 Holiday Season, is here to help you navigate the coming months across the end-of-year holidays. To learn from the last holiday season, we partnered with Bizrate Insights to examine when and how US consumers shopped for end-of-year holidays in 2023. And with insights from many of our fellow Forrester analysts, we also provide expert advice for you across marketing, customer care, tech, and more. To start, a few data points about the 2023 holiday season (see our report for more!): Thirty-eight percent of US online adults began their 2023 holiday shopping in October or earlier (source: Forrester). About one in five waited until December to start shopping. Still, almost three-quarters of online adults who shopped for the winter holidays continued shopping through December (source: Bizrate Insights). About two in five US consumers surveyed said that they made at least three-quarters of their 2023 holiday purchases online (source: Bizrate Insights). Two-thirds of US online consumers used a mobile phone to shop online on Thanksgiving weekend in 2023 — mainly to make purchases, browse and research products, check prices, and read ratings and reviews (source: Bizrate Insights).   So how should you prep for the upcoming shopping season? Just a few of the areas that our 2024 guide delves into include why and how to: Invest in and sharpen your marketing tactics. Get ready for another busy season, starting right now and including Thanksgiving weekend and Cyber Monday/Week. See what we observed last year in terms of offers. Remember to run promotions that customers actually want — and learn how to revamp your email program to rise above the rest. Wondering what to do about your loyalty program, social platforms, and your retail media proficiency? We’ve got you covered. Help your busy customers with the value-add info they most need. Tune your commerce search and product discovery to give customers smart suggestions, as they more often shop for others and less so for themselves, and learn how to step up your imagery, product reviews, checkout, and payments experiences to smooth their path — and your overall user experience to boost their confidence. Prepare your customer care and associate teams to best help customers. You’ll need to manage to a broad spectrum of veteran and seasonal hires, so make sure that all of them are equally well versed in any policy changes: what’s changed, where it’s published, and how to explain it to busy customers. Provide your team information about products, loyalty tiers, and anything else that customers will expect them to understand about the brand. There’s much more — please see our research here. And be on the lookout in the coming weeks for even more consumer insights about the end-of-year shopping season, as well as our annual US online holiday sales forecast. If you’re a Forrester client, please schedule a guidance session or inquiry with one of us and/or our Forrester analyst colleagues for their area of expertise. source