AI-enabled devices are set to transform the consumer technology landscape, with a goal of permeating nearly every corner of the market by 2028. This sweeping forecast points to a future where artificial intelligence is not a niche feature but a fundamental characteristic of the devices people use daily. The integration of GenAI capabilities across smartphones, laptops, wearables, home appliances, and connected gadgets is expected to redefine how users interact with technology, enabling smarter defaults, proactive assistance, and increasingly personalized experiences. As manufacturers embed AI deeply into hardware and software, the resulting product ecosystems will blur the line between devices and services, driving a more seamless, interconnected user experience that relies on GenAI to optimize performance, energy efficiency, and user satisfaction. In this shifting landscape, developers, device makers, and platform providers are aligning strategic priorities around scalable AI architectures, reliable model performance, and durable value for end users in both consumer and business contexts. The convergence of GenAI with hardware is not merely about adding a clever feature; it represents a strategic transformation of product architecture, consumer expectations, and the economics of hardware-software co-design. Over the next few years, this transformation is expected to accelerate, pushing AI-enabled functionality from optional enhancements into standard, often invisible, capabilities embedded in devices at the point of sale. The overarching narrative is one of increasingly intelligent consumer devices that autonomously contribute to efficiency, convenience, and decision-making across everyday activities, while reshaping how companies plan, develop, and market new products. The implications extend beyond individual devices to entire product lines and service platforms, necessitating new standards for interoperability, data governance, and security in AI-enhanced ecosystems. In short, the era of AI-powered devices is approaching as a foundational shift rather than a discrete upgrade, with far-reaching consequences for technology developers, consumers, and the broader economy.
Market Outlook for GenAI-Enabled Devices
The forecast for GenAI-enabled devices envisions a fundamental shift in how consumer electronics are designed, manufactured, and deployed. Analysts anticipate that AI features will become a standard expectation rather than a differentiator, reshaping competitive dynamics across device categories. This broad-based AI integration is expected to influence hardware specifications, software development priorities, and the timing of product launches, as makers race to deliver AI-accelerated capabilities that are both powerful and energy-efficient. The trend suggests a move toward devices that anticipate user needs, adapt to contexts, and deliver ongoing value with minimal user input. In practice, this means more devices capable of learning from user behavior, optimizing performance in real time, and offering proactive recommendations that enhance productivity, entertainment, and daily routines. The market is also likely to see deeper collaboration among chip vendors, AI model providers, and device manufacturers to standardize AI accelerators and interfaces, enabling more seamless deployment of GenAI across diverse hardware ecosystems. Such interoperability is crucial for achieving the promised scale and reliability of GenAI in mass-market devices. As AI becomes embedded by default, consumers will experience fewer friction points when enabling AI-powered features, accelerating adoption across demographics and regions. For developers and manufacturers, the implication is clear: success will hinge on delivering robust, secure, and privacy-respecting AI experiences that can operate efficiently on the constrained resources of typical consumer devices. In this environment, product roadmaps will increasingly emphasize long-term AI performance, update cadence, and sustainable value creation rather than short-term feature bets. The ultimate objective is to create a durable, AI-enabled device infrastructure that supports a broad spectrum of applications—from smart assistants and health monitoring to intelligent cameras and energy-management systems—while maintaining affordability and user trust. The competitive landscape will be defined by who can deliver scalable AI capabilities with dependable quality of service, reliable safety controls, and transparent value to users.
Spending Trajectory for GenAI in 2025 and Beyond
Worldwide spending on generative AI is projected to reach 644 billion dollars in 2025, representing a substantial 76 percent increase from the previous year. This rapid growth signal underscores the central role GenAI is expected to play in both enterprise and consumer technology strategies. The surge in spending reflects a broad-based push across multiple axes of the AI value chain, including hardware integration, software platforms, cloud services, and professional services designed to enable AI-enabled operations at scale. The allocation of resources across these domains signals a strategic commitment to expand AI capabilities rapidly while maintaining a focus on reliability and security. Among the key drivers of this expansion is the increasing incorporation of AI features into core hardware components, such as servers, smartphones, and personal computers. Analysts emphasize that a substantial portion of GenAI investment is directed toward hardware, with a projected 80 percent of GenAI expenditure focused on physical devices and associated infrastructure. This hardware-centric emphasis aligns with the practical realities of deploying large-scale AI models, where data processing, storage, and local inference capabilities are critical to achieving low latency and responsive user experiences. The result is a spending pattern that treats hardware as a foundational platform upon which AI software and services can operate effectively. The forecast also highlights ongoing commitments from foundational model providers who continue to allocate billions of dollars annually to expand model size, improve performance, and enhance reliability. These investments are seen as essential to sustaining progress in GenAI, even as expectations around capabilities fluctuate. In this context, the ecosystem exhibits a paradox: despite mixed sentiment about current results and PoC outcomes, the behind-the-scenes investment in model architectures remains robust because leaders recognize the long-term strategic value of scalable, high-quality GenAI. The pace of spending is expected to remain robust through 2025 and into 2026, reflecting a multi-year horizon in which hardware, software, and services converge to deliver durable AI-enabled value. For organizations planning IT budgets, the forecast suggests that capital expenditures on devices and related AI-enabled infrastructure will be a significant and ongoing line item. Enterprises are anticipated to continue integrating GenAI into data centers, edge environments, and end-user devices, reinforcing a shift toward more pervasive AI-driven operations across industries. The forecast also anticipates cross-market effects, with GenAI influencing procurement strategies beyond pure technology budgeting, such as product development cycles, marketing automation, customer experience platforms, and supply chain optimization tools. As a result, GenAI spending is expected to become a barometer for broader digital transformation initiatives, with companies using AI investments to drive efficiency, innovation, and competitive differentiation in a rapidly evolving market.
The Paradox of Growth: Expectations vs Investment
Industry analysts observe a paradox in the GenAI landscape: expectations for capabilities appear to be moderating due to a high rate of unsuccessful early proof-of-concept efforts and dissatisfaction with initial GenAI outcomes. At the same time, providers of foundational models are directing substantial, ongoing investments to grow model scale, improve performance, and increase reliability. This tension creates a dynamic where optimism about long-term potential remains high, even as near-term results temper enthusiasm among some stakeholders. The implication is that the AI ecosystem will continue to experience elevated investment activity, with billion-dollar commitments flowing into model development, infrastructure, and deployment platforms, as providers seek to strengthen the core capabilities that underpin GenAI applications. Analysts anticipate that this paradox will persist through 2025 and 2026, influencing how organizations plan, test, and implement GenAI initiatives. In such an environment, ambitious internal projects initiated in 2024 are likely to be scrutinized in 2025 as CIOs reassess risk, return on investment, and the practicality of large-scale AI experiments. The outcome is expected to tilt in favor of more predictable, commercially viable solutions rather than bespoke, internal PoC efforts that may lack scalable impact. CIOs are predicted to pivot toward commercially available, off-the-shelf GenAI capabilities embedded within established software platforms, rather than pursuing extensive, independent development work. This shift underscores a preference for proven, repeatable value delivery over the allure of novel experiments that may not translate into measurable business outcomes. The broader market implication is a move toward standardized AI features that can be integrated into existing technology stacks with less risk and greater interoperability. For technology leaders, the challenge is to balance ongoing investments in foundational AI capabilities with the practical demand for reliable, time-bound business value. The industry’s trajectory suggests that while the promise of GenAI remains compelling, execution will favor controlled deployments, vendor partnerships, and scalable solutions that minimize disruption while maximizing measurable returns. As 2025 approaches, companies will likely emphasize risk management, governance, and vendor ecosystems that can deliver consistent AI performance across diverse use cases, from customer service automation and content generation to predictive maintenance and operational optimization.
CIOs, PoC Reduction, and the Move to Commercial Solutions
Within the corporate technology landscape, chief information officers face a decisive recalibration of strategy as they reassess high-velocity AI experiments with real-world business value. The tactical reality is that many ambitious internal GenAI projects from 2024 will be subjected to stricter scrutiny in 2025, given the need to demonstrate predictable outcomes, scalable implementation, and measurable return on investment. CIOs are increasingly prioritizing commercial off-the-shelf GenAI capabilities embedded in widely-used software platforms rather than continuing expansive in-house PoC programs. The rationale is straightforward: established software solutions offer more predictable deployment timelines, robust security models, and clearer pathways to value realization. In addition, relying on off-the-shelf GenAI enhancements reduces the burden of building, maintaining, and updating bespoke AI systems, which can be resource-intensive and difficult to govern at scale. This shift toward commercial solutions does not imply a retreat from innovation; rather, it reflects a pragmatic approach to balancing experimentation with risk management and governance. Organizations seek to leverage mature AI features that come with tested workflows, standardized interfaces, and integrated compliance controls, enabling faster time-to-value and easier cross-functional adoption. The broader implication for the market is a stronger demand signal for AI-enabled capabilities that are ready for enterprise-scale use, integrated with existing ERP, CRM, and analytics ecosystems, and backed by vendor support and service-level agreements. As CIOs orient their portfolios toward dependable GenAI features, vendors and platform providers will be pressured to deliver more robust, interoperable, and transparent AI components that can be trusted in critical business scenarios. This environment is likely to accelerate the commoditization of GenAI features, with a premium placed on reliability, governance, and integration capabilities rather than bespoke, experiment-driven deployments.
Transformation Across Core Markets and Submarkets
GenAI spending is expected to drive a broad transformation across core IT markets and their numerous submarkets in 2025. Analysts anticipate that AI technologies will become increasingly integral to business operations and consumer products, reshaping budgets, procurement decisions, and technology roadmaps across industries. The integration of GenAI into hardware—such as servers, smartphones, and PCs—will be a central pillar of this transformation, reinforcing the trend toward hardware-enabled AI acceleration. The scale and dispersion of spending across diverse submarkets—ranging from edge computing and data center infrastructure to software platforms and services—highlight the pervasiveness of GenAI as a strategic priority. As AI capabilities are embedded across a wider array of devices and platforms, organizations will increasingly view GenAI as a core driver of digital transformation, enabling capabilities such as automated content creation, intelligent automation, data-driven decision-making, and enhanced customer experiences. The forecast emphasizes that AI technologies will become integral to both enterprise IT and consumer product ecosystems, driving new business models, monetization strategies, and competitive differentiation. Consequently, vendors across hardware, software, cloud services, and professional services are expected to align their portfolios to support GenAI-enabled workflows, analytics, and experiences. The result is a cascading effect: AI investments influence procurement strategies, R&D priorities, and go-to-market plans, ultimately shaping the technology landscape over the next several years. The interconnectedness of hardware acceleration, AI models, and software applications will require a coordinated approach to product development, integration, and governance, with stakeholders across the organization collaborating to ensure that AI delivers measurable business value, mitigates risk, and fosters innovation.
Hardware as the Primary Driver of GenAI Spending
A central theme in the GenAI spending outlook is the predominant role of hardware in enabling AI capabilities. Forecasts indicate that 80 percent of GenAI-related expenditures will be allocated to hardware components, including servers, smartphones, and personal computers. This emphasis on hardware underscores the reality that scalable, responsive GenAI experiences require substantial on-device or edge-enabled processing power, storage capacity, and energy-efficient architectures. The shift toward hardware-centric spending reflects the practical needs of deploying large-scale GenAI models, particularly in environments where latency, privacy, and data sovereignty are critical considerations. As devices and data centers are equipped with AI accelerators, specialized chips, and optimized memory hierarchies, the performance and cost-effectiveness of GenAI applications improve significantly. The emphasis on hardware also aligns with the broader trend of edge computing, where processing is pushed closer to the user or data source to reduce round-trip times and enhance privacy protections. In this context, hardware investments are not merely about raw power; they are about enabling AI workloads to run efficiently at scale across diverse environments, including remote locations, endurance-critical devices, and consumer electronics that operate in real-time. The 80 percent figure highlights the importance of device-level and edge infrastructure as the foundation upon which GenAI features can be reliably delivered to end users. It also signals to manufacturers and investors that spending on silicon, memory, interconnects, power efficiency, and thermal management will be a perpetual priority as AI models grow larger and more capable. Moreover, the hardware-led spending pattern suggests a synchronized approach to product development, where AI software capabilities are designed to exploit hardware accelerators from the outset, enabling more efficient inference, faster training cycles for on-device learning, and better adaptability to evolving model architectures. This dynamic reinforces the need for ongoing partnerships across the supply chain—between chipmakers, device manufacturers, cloud providers, and software platforms—to ensure that GenAI workloads can be deployed reliably, securely, and at scale across a wide range of devices and use cases.
Gartner’s Methodology and Data Backbone
The spending forecasts for GenAI are grounded in rigorous analysis of sales data spanning more than 1,000 vendors across the full spectrum of GenAI products and services. This methodological approach integrates supplier performance metrics, market penetration trends, and product lifecycle considerations to build a comprehensive view of where GenAI investments are most concentrated and how they are evolving over time. By aggregating data across multiple market segments—hardware, software, platforms, and professional services—Gartner aims to capture the complex dynamics that drive GenAI adoption in both consumer and enterprise contexts. The methodology emphasizes cross-verified vendor data, market segmentation, and scenario planning to account for variations across industries, regions, and organizational sizes. Analysts apply a combination of quantitative modeling and qualitative assessment to interpret how shifts in technology capability, governance frameworks, and customer demand translate into measurable spending changes. The result is a forecast that reflects the interconnectedness of GenAI developments with broader IT spending trends, digital transformation initiatives, and the evolving role of AI in product design and operations. The data backbone supports a nuanced narrative about how organizations plan to adopt AI—balancing experimentation with risk management, prioritizing scalable deployments, and aligning investments with articulated strategic objectives. While the 2025 projection is ambitious, it aligns with the observed trajectory of AI-enabled hardware adoption, model improvement, and the increasing integration of GenAI features into mainstream software ecosystems. The forecast thereby provides a framework for executives and planners to navigate the complexities of AI investments, understanding how hardware, software, and services will jointly shape the market in the coming years.
Consumer Adoption and the 2028 Horizon
The market trajectory for AI-enabled devices hinges on a combination of rapid technological advancement and consumer engagement. Analysts note that while the total market trajectory is compelling, consumer enthusiasm for new AI features may not match the pace of device-level innovations. Instead, manufacturers are embedding AI as a standard feature in consumer devices, which means that consumers may not consciously seek out AI capabilities but will encounter them as a built-in expectation when purchasing new products. This dynamic could lead to a situation where AI-enabled devices become ubiquitous not through overt demand signals from consumers, but through the normalization of AI as a baseline attribute of modern hardware. By 2028, the expectation is that AI features will be pervasive across the consumer device market, shaping purchase decisions in ways that emphasize overall device value, reliability, and seamless integration with AI-powered services. For consumers, this shift implies a period of adjustment as AI becomes part of everyday device interactions, from smart assistants that anticipate needs to cameras and sensors that optimize performance in real time. For manufacturers, the challenge lies in delivering AI that feels intuitive and non-intrusive, while maintaining privacy safeguards and clear guidance on how data is used. The market momentum is driven by interoperability, with vendors seeking to create cohesive AI-enabled ecosystems that work across devices and services. As AI becomes a standard platform feature, consumer choice may increasingly hinge on the broader value proposition of an entire AI-enabled journey rather than on isolated AI capabilities alone. In summary, the 2028 horizon envisions a world where AI-enhanced devices are deeply integrated into daily life, shaping consumer expectations, product design, and the economics of the technology market in a way that makes AI a routine aspect of modern digital experiences.
Implications for Businesses and Market Strategy
The accelerated GenAI spending and the hardware-centric deployment strategy carry important implications for business leaders and market strategists. Enterprises must navigate a landscape in which AI capabilities become embedded in everyday technology assets, requiring governance, risk management, and compliance considerations that accompany rapid deployment. The shift toward commercial off-the-shelf GenAI features embedded in widely used software platforms offers a clearer path to value, but it also creates competitive pressure to ensure that AI implementations are well-integrated with existing systems, data pipelines, and security controls. Vendors across the ecosystem will need to prioritize interoperability, standardized interfaces, and support for enterprise-scale rollout to meet demand for reliable, scalable AI solutions. Organizations should prepare for a wave of AI-enabled capabilities that can be adopted with lower risk and faster time-to-value, while still maintaining robust governance frameworks to address privacy, data sovereignty, and ethical considerations. The broader market transformation suggests that IT budgets will increasingly reflect AI-driven priorities, with allocations for hardware acceleration, edge devices, cloud-based AI services, and the software platforms that tie these components together. Companies that can align AI investments with clear business outcomes—such as improved operational efficiency, enhanced customer engagement, and more accurate decision-making—are more likely to realize measurable value from GenAI initiatives. As GenAI becomes deeply integrated into both consumer devices and enterprise software, the importance of a coordinated strategy across product development, procurement, and risk management grows. Leaders should emphasize a balanced approach that accelerates experimentation where it yields demonstrable ROI while preserving governance and reliability standards. The coming years will also test organizations’ ability to manage data quality, model governance, and lifecycle management at scale, ensuring that AI investments translate into sustained competitive advantage rather than isolated pilot successes. From a consumer perspective, the broad adoption of GenAI-enabled devices promises more personalized experiences and greater convenience but will also raise expectations for privacy, security, and transparent usage of AI features in everyday life. As the market matures toward 2028 and beyond, businesses will converge on the need for scalable, secure, and user-centric GenAI strategies that can adapt to evolving technologies, regulatory environments, and consumer preferences.
Conclusion
The trajectory for GenAI is shaping a future where AI-enabled devices are foundational to both consumer experiences and enterprise operations. The expected 2025 spending surge, underpinned by a heavy emphasis on hardware, reflects a strategic focus on building the practical infrastructure necessary for scalable AI. While expectations for GenAI capabilities may experience fluctuations due to early PoC challenges, substantial ongoing investments by foundational model providers indicate a long-term commitment to expanding the size, performance, and reliability of AI models. The shift among CIOs—from ambitious internal experiments toward commercial off-the-shelf GenAI features embedded in established software platforms—signals a preference for predictable value delivery, reduced risk, and faster time to impact. By 2028, AI-enabled devices are anticipated to be nearly ubiquitous in the consumer market, with manufacturers embedding AI as a standard feature and consumers increasingly experiencing AI-driven benefits as a baseline expectation rather than a novelty. The market is poised for transformative changes across core IT markets and submarkets, with hardware serving as the primary engine of GenAI adoption. Gartner’s methodology, drawing on data from thousands of vendors, provides a robust framework for understanding these dynamics and the likely paths of growth, risk, and opportunity. Taken together, these developments point to a future in which GenAI is deeply integrated into devices, services, and business processes, driving efficiency, innovation, and new value propositions across sectors while challenging organizations to manage governance, interoperability, and user trust in an AI-enabled world. The convergence of AI with hardware and software ecosystems will continue to redefine what is possible, setting the stage for sustained investment, rapid evolution, and broad-based impact on both industry standards and consumer expectations. In this evolving landscape, success will hinge on delivering reliable AI capabilities at scale, integrating them thoughtfully with existing architectures, and communicating clear, tangible value to users and stakeholders alike. The coming years will reveal how quickly AI-enabled devices move from ambitious visions to everyday realities that reshape the way people live and work.
