Futurum Intelligence provides critical insights into digital transformation, focusing on adoption, innovation, and disruption. Backed by a team of industry experts, we deliver research through personalized analyst-client interaction and client portals with visualization dashboards and qualitative and quantitative data reports.
Our research is organized into practice areas aligned with key digital transformation topics, addressing critical business questions. Each area includes analyst coverage and planned deliverables for the year. Through collaboration and strong industry relationships, we identify emerging trends early, helping clients make informed business decisions.
The Futurum AI Platforms research agenda for 2026 focuses on the critical shift from experimental AI to the deployment of industrial-scale, resilient, and autonomous systems. This transition, which is moving the market’s focus from general-purpose chatbots to agentic applications, represents a significant inflection point for enterprises, vendors, and investors. For enterprises, navigating the confluence of infrastructure constraints, diverse global regulations, and model optimization is essential to achieving competitive advantage and operational safety. Vendors must align their offerings to address these complex enterprise demands, while investors must understand the technological and regulatory hurdles to identify the next wave of market leaders. Our coverage will focus on these seven areas, all aimed at understanding the path from isolated pilots to the realization of a unified autonomous enterprise, whether that comes in 2026 for the enterprise or beyond.
While Agentic AI will be a dominant theme, 2026 is less about universal scale and more about the foundational struggle for reliability. We are tracking the shift from rigid, human-led interfaces to agents that can navigate multi-step workflows for use cases such as customer service and complex data analysis. This transition may trigger a broad industry shift from per-seat licensing to agent-usage-based pricing, potentially consolidating parts of the software market.
However, significant hurdles remain:
Legacy systems often lack the real-time connectivity and identity management necessary for these agents to act autonomously without regular failure.
Enterprises must decide whether to adopt siloed agent platforms or a unified agentic mesh that abstracts complexity across their entire technology stack.
Even if an enterprise never trains a single foundational model, inference-time compute (the ‘thinking’ phase of AI, where value is realized) will become a significant factor in technology budgets in 2026. Still, there are ways to mitigate potential runaway costs. As the value of AI shifts from static knowledge ingrained during the training phase to compute applied at the moment of query, this shift directly impacts the model consumer, who, rather than the model trainer, incurs the recurring operational costs of running these systems in production. Because inference is a metered, utility-like bill that scales with every interaction, it represents a recurring operational expense that can quickly spiral if not managed. To avoid spiraling costs when a proof‑of‑concept hits production, enterprises are moving beyond simple API calls. They are increasingly opting for dedicated cloud- and on-premises inference services that provide more predictable throughput and better cost management for large-scale autonomous operations. Plus, the strategic use of inference-time scaling enables non-model builders to make smaller, cheaper models perform at elite levels without the need for expensive, proprietary fine-tuning. This is a critical lever for organizations to gain a competitive edge while maintaining a disciplined bottom line. Inference-time scaling enables organizations to buy exactly as much intelligence as they need at any given moment, ensuring their AI spend remains aligned with the actual business value of the output. But it’s not necessarily easy to achieve, so expect more tools to help enterprises do so in 2026.
The scarcity of power and cooling will become the primary constraint on AI expansion in 2026, leading to delays in data center deployment:
The physical pressures mentioned above are contributing to the fragmentation of models, moving the industry away from one-size-fits-all architectures. While the largest models continue to grab headlines for their broad capabilities, enterprises are increasingly deploying specialized Small Language Models (SLMs) at the edge for latency-critical tasks such as local voice assistants, IoT device control, and privacy-sensitive data processing. This fragmentation is driven by a move toward domain-specific intelligence where models are trained on narrow, high-quality enterprise data rather than the entire web. The enterprise IT is evolving to manage this multi-modal complexity, ensuring that data – including images, video, and audio – is processed by the appropriately sized architecture.
Digital sovereignty, once a niche geopolitical idea, is now a global business imperative for companies aiming to safeguard their proprietary intellectual property.
The expectation is that enterprise sovereign AI will insulate businesses from external, often volatile policy changes, minimize third-party risk, and meet global data sovereignty mandates.
The market landscape in 2026 will likely be defined by a thinning of the herd, as the AI sector moves from speculative growth to a high-stakes capital reckoning. We are entering an initial consolidation phase as sub-scale model labs and mid-tier startups struggle with rising compute costs and the challenge of turning research into profitable products. This consolidation will be largely driven by an M&A wave, as sub-scale model labs and mid-tier startups find themselves hitting a money wall – the point where the immense capital required for next-generation compute and infrastructure outstrips their ability to generate immediate profit. To bridge this gap, large cloud providers and incumbents are moving to absorb these independent innovators to rapidly integrate specialized engineering talent and proprietary data into their own platforms. Simultaneously, 2026 is emerging as the potential breakout year for AI IPOs.
Three primary candidates stand out as bellwethers for the industry’s long-term sustainability, and likely IPO candidates, should the market support it:
Finally, all these developments are taking place under the shadow of global AI regulation, which has transitioned from voluntary frameworks to aggressive enforcement:
This creates a highest-common-denominator compliance challenge, forcing global enterprises to implement automated data lineage, risk assessments, and transparency measures to maintain market access across all jurisdictions.
Futurum covers a broad spectrum of cybersecurity-related technologies, including application, cloud, data, endpoint, network security, identity and access management (IAM), and integrated risk management and Security Operations Center (SOC) markets. With this in mind, our vantage point spans key use cases, including threat hunting and intelligence, incident response, attack detection, infusion of AI, and cyber-recovery. Key themes of our coverage include modernizing security operations, security infrastructure, security management, and related areas.
In 2026, the cybersecurity landscape shifts from the initial rush of AI adoption to a more complex phase of “industrialization” and autonomy. While short-term threat vectors remain familiar, the long-term architecture of security is undergoing a meaningful transformation. Our coverage for 2026 focuses on the rise of Agentic AI, the explosion of Non-Human Identities (NHI), and the “Data Fusion” required to protect unstructured data. We will also track the critical strategic pivots organizations must make to address Shadow AI, API complexity, and the looming requirements of quantum readiness and cybersecurity risk quantification.
The AI narrative evolves from simple acceleration to autonomy. We are entering the era of Agentic AI, where autonomous agents (e.g., Agent365) execute complex workflows without human intervention. This drives an explosion in Non-Human Identities (NHI), creating a massive, under-protected attack surface.
The attack surface is no longer just “sprawling”; it is deepening. The rapid adoption of unmanaged AI tools has birthed a Shadow AI situation, necessitating a new era of CASB (Cloud Access Security Broker) and SSPM (SaaS Security Posture Management) capabilities.
The platform vs. point-solution debate continues, but is nuanced by the extreme complexity at the edge. Modern applications are becoming increasingly complex at the edge – a tangled web of APIs, edge dependencies, and content delivery networks, now increasingly adding AI elements as well.
Data protection matures into Data Fusion—the convergence of DLP, DSPM, and traditional backup into a unified data resilience strategy.
Beyond immediate threats, strategic drivers are reshaping the C-level agenda.
Enterprise data is the lifeblood of business. No endeavor, whether a simple order-to-cash process or a complex, agentic AI solution, can survive without timely access to accurate, high-quality, secure, and governed data. As we move into 2026, the market is pivoting from experimentation to engineering, demanding a stack that is not just “AI-ready” but explicitly architected to accelerate AI.
This shift is reshaping the four pillars of Futurum’s market coverage:
Futurum monitors these evolving dynamics across the entire lifecycle, analyzing everything from the raw physical storage of digital assets to the polished, agent-delivered insights that drive decision-making.
Futurum’s Enterprise Technology Buyers practice provides comprehensive coverage of how enterprise technology decisions are made across the organization, from the CIO and IT leadership to the growing universe of business executives who now directly influence technology strategy, budgets, and outcomes. As digital capabilities become embedded in every function, enterprise technology buying has evolved from a centralized IT process into a distributed, multi-stakeholder model spanning marketing, data, security, revenue, operations, and customer experience.
While the CIO remains a central orchestrator of architecture, governance, and enterprise platforms, buying authority increasingly resides across roles such as the CMO, CDO, CISO, and CRO. AI, cloud platforms, automation, and data systems are no longer implemented solely as infrastructure investments but as business capability engines tied directly to growth, efficiency, risk management, and customer engagement. Futurum’s research reflects this reality by examining technology demand through the lens of business outcomes, buyer intent, and real-world purchasing dynamics, not solely IT strategy.
A core focus of the practice is understanding how enterprises operationalize advanced technologies such as AI, analytics, and agentic systems at scale. As organizations move from experimentation to execution, buyers across multiple functions must align on governance models, data foundations, security frameworks, and integration strategies. Our research provides insight into how enterprise buyers evaluate emerging capabilities, where market offerings fall short of expectations, and how organizations balance innovation velocity with trust, compliance, and cost discipline.
The Enterprise Technology Buyers practice also delivers deep analysis of technology ecosystems and vendor landscapes, helping organizations understand how platforms, hyperscalers, best-of-breed vendors, and service partners intersect with evolving buyer needs. By connecting buyer demand signals with market supply realities, Futurum equips technology providers and enterprise leaders alike with a clear view of where investment is accelerating, where friction persists, and where future opportunity lies.
This practice is grounded in continuous engagement with senior enterprise decision-makers and powered by Futurum Intelligence, combining quantitative survey research with qualitative insight to track shifting priorities, spending patterns, and buying behavior throughout the year.
In 2026, CIOs remain foundational to enterprise technology success, but their role increasingly centers on orchestration rather than ownership:
The CMO has emerged as one of the most influential technology buyers outside IT:
Chief Digital, Data, AI, and Revenue leaders increasingly shape enterprise technology direction:
Environment Enterprise buyers are rethinking where workloads run and why:
AI adoption in 2026 is defined by execution, not experimentation:
Enterprise Applications are the lifeblood and framework for accomplishing work in the modern organization. We examine 12 categories of applications used in the enterprise, including Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Workplace Collaboration, Human Resources, Supply Chain & Logistics, Analytics & Business Intelligence (BI), Project & Portfolio Management (PPM), Industry/Vertical-Specific Applications, and Communication Services, and delve into how they shape the broader enterprise information architecture. We also focus on the underlying technologies and systems that power these applications, including artificial intelligence and automation, and assess how trends in employee engagement and experience impact the market.
In just over a year, agentic AI has evolved from a nascent technology with limited use cases and capabilities to a core technology embedded in a wide range of enterprise applications and platforms. And while agentic AI delivers results in relatively simple scenarios or tasks, the substantial ROI promised by vendors is unlikely to materialize until agentic technology can be applied across more complex workflows that incorporate near-real-time data, multi-step reasoning, and self-optimization capabilities. It is these more complex processes that consume significant time, effort, and resources to address and often have the most significant impact on customer, employee, and partner metrics, including experience, effort, and satisfaction, which directly affect a business’s overall health and success:
As the types and complexities of AI workflows and use cases continue to expand, vendors are still struggling to effectively monetize AI. While the traditional, seat-license-based approach appeared to be on the way out in 2025, the challenges of generating a solid ROI from AI led to increased vendor flexibility, with some offering a choice of pricing models, ranging from seat-based to consumption-based to outcome-based. As technology improves and the diversity of use cases continues to expand, customers may adopt a variety of pricing approaches tailored to specific usage patterns and risk tolerances. Vendors that can provide this flexibility will be best positioned to attract new business across a broader range of scenarios. Notably, vendors that are able to successfully automate agentic workflows and tasks that are highly repeatable and scalable will likely shift to an outcome-based pricing model.
Vendors will continue to support the integration of data from disparate apps and systems into the front end of customers’ choosing, while also highlighting the benefits of a unified platform approach:
This will also lead to a growing convergence of disparate functional areas, such as contact center operations, customer service and support, marketing, sales, and fulfillment, into a more unified customer experience that delivers the right messaging, actions, and process flows across the entire customer journey, fed by a unified and real-time data-driven strategy.
As the era of agentic AI continues to evolve and mature, SaaS players have realized the value of not only managing their own data, AI agents, and workflows but also serving as an enterprise-wide orchestration layer capable of monitoring and managing third-party workflows and AI agents. These market participants are realizing that control over enterprise data and workflows – both AI- and human-augmented – drives platform utilization, revenue, and, perhaps most importantly, stickiness, which increases the likelihood of contract renewals and value expansion.
In 2026, expect to see both major SaaS platform vendors, as well as third-party integration vendors, consultants, and application-management platforms, enter the market and fight to control this important function. In fact, it is also likely that a new class of vendors will emerge as agnostic (or mostly agnostic) arbiters that serve as a master control plane for managing agents, humans, and workflows, regardless of the company or organization that built or provided the agents. These vendors may become increasingly important as agentic workflows span disparate systems, organizations, and jurisdictions.
While agentic AI may become the front door for basic horizontal functionality, the predictions of the demise of SaaS applications are premature, due to the large backlog of software implementations already on the books, the complexity of domain-specific workflows and processes, and the desire of many organizations to extract value from their existing technology investments.
As humans and AI increasingly work together in the enterprise application space, there will be a growing need for contextual assistance and training to ensure customers derive the maximum value from their software investments. The increasing use of AI and agentic AI technology portends the deployment of relevant, easily digestible, and context-based assistance and training features on top of or within enterprise applications.
The new and emerging generation of workers, who have little or no patience for reading documentation, combined with the rapid pace of innovation, has rendered obsolete most traditional learning and training resources. Organizations will fail to quickly realize value from their software investments if their workers and customers are unable to adopt and utilize these new learning tools and capabilities within the flow of normal work.
Across the technology landscape, vendors are increasingly leaning into their indirect go-to-market strategies and fostering more ecosystem partnerships. There are several drivers for this changing mindset. Economically, vendors are scrutinizing their cost structures more than ever as we have moved away from near-zero interest environments. Companies can no longer justify ‘rampant hiring’ in their sales & marketing divisions as a GTM tactic. Meanwhile, from a technology standpoint, customer IT environments are becoming more complex, spanning multiple clouds and architectures, while the application landscape is becoming increasingly customized. In short, no technology company can meet a customer’s entire IT needs through its own portfolio; partnering is the only way to close the gaps.
Futurum will explore the discipline of partnering in the technology space amidst these trends and future disruptors such as AI. We will explore how GTM is evolving horizontally (e.g., partnering with other technology stacks) and vertically (e.g., embracing an ecosystem of partners engaged in product deployment and service offerings).
Cloud and software marketplaces will move beyond being transactional hubs and play host to a new generation of AI agents that buy, sell, negotiate, and personalize on behalf of both vendors and customers.
The emerging “agentic commerce” model will enable autonomous procurement, dynamic bundling, and personalized offers, making the buying experience both frictionless and highly adaptive.
ISVs and partners will need to design offerings and operational processes with these digital agents in mind, ensuring compatibility with API-driven processes for negotiation, fulfillment, and support.
Marketplace platforms will become critical arenas for experimentation, allowing companies to quickly test new product configurations, pricing models, and partnership combinations using real-time agent-driven feedback.
Those able to build robust agent-to-agent commerce capabilities will access new revenue streams and unlock GTM opportunities not possible with manual, human-mediated sales alone.
Data is the lifeblood of a modern business, and the underlying storage technology plays a vital role in delivering it. The storage industry is continually adding more data and application-aware capabilities and services to what was historically a box of storage devices with a network attached. The requirements for cloud applications, and now for AI applications, have blurred the line between storage and data, with more software-defined capabilities bringing innovations as fast as new hardware platforms and capabilities.
The model of outright storage purchase no longer dominates; more storage-as-a-service models are being embraced. In many cases, the adoption of STaaS was driven by customers’ inability to predict capacity requirements. These same customers often lack the FinOps rigor to ensure business value from the as-a-Service model. Customers are also recognizing that these newer models do not have the forced decision point of array retirement, which traditionally triggered re-evaluation of suppliers. Vendors must demonstrate that the STaaS model is not a “golden handcuff” and that their TCO is lower than that of an outright purchase. The demands of AI systems are also driving requirements for hybrid- and multi-cloud data mobility from STaaS products.
Unfortunately, ransomware has not died out; it has evolved to enable attackers to earn large sums of money. The ability to guarantee rapid and complete recovery from persistent ransomware tools is vital to customers. While data protection software vendors typically handle recovery orchestration, ransomware often targets backup copies before encrypting primary data. It falls to storage hardware vendors to provide truly immutable storage or offline media to guarantee recoverability. Customers who have been impacted by ransomware are usually very aware of the long restore time for offline media and the challenges of recovering data that may have been compromised gradually over time, requiring offline media from multiple points in time. Auditable recovery capabilities will become mandatory as business insurance, due diligence, and regulatory compliance drive technology adoption.
Mature enterprise cloud adoption is characterised by the use of multiple clouds, both public and private, to achieve business outcomes. The resulting fragmentation of corporate data across multiple clouds impedes the creation of further value from the data estate:
Disruptive technologies often seem to appear from nowhere when they are released, yet in any hardware design, the lead time from concept to production is usually measured in years. CXL has been on the brink of transforming hardware system design for a few years. Recent market moves suggest 2026 will be a big year for CXL products. Computational storage has failed to deliver on its promise as a general-purpose solution in the data center, but may find its place in edge devices, where hardware solutions are often customized to specific applications and deployed at large, distributed scale.
Futurum covers a broad range of AI-enabled consumer and commercial devices. This includes PCs and peripherals, tablets, mobile handsets, XR, hearables and wearables, as well as IoT, IIoT, automotive, and robotics segments. The expansion of AI training and inference from a cloud-centric model to a more hybrid edge-to-cloud model is driving a rapid transformation across the devices segment of the tech stack. With next-gen AI-capable PCs, mobile handsets, and wearables now capable of handling increasingly large AI models, AI and agentic workloads are beginning to expand from thermally expensive cloud-based silicon to the more thermally efficient silicon powering AI-capable consumer and commercial devices.
The next 6 to 12 months will see a significant acceleration in AI capabilities in devices, and will see the start of an expansion towards physical AI, which includes robotics. This transition will disrupt not only core device segments but the entire technology ecosystem around them as silicon vendors, cloud service providers (CSPs), independent software vendors (ISVs), and their partners adapt to new use cases, form factors, and hybrid, interwoven AI services models.
Additionally, the Intelligent Devices practice will also work with adjacent practices to 1) clearly map how AI orchestration will work across platforms and form factors up and down the entire technology stack, 2) validate the roadmaps of the most critical vendors in the accelerating robotics segment, and 3) quantify potential impacts of memory and storage supply chain constraints on key AI device segments.
Enterprise networking is the transport system for advanced technologies on-premises and in the cloud. The landscape of data center networking has shifted in the past few years to focus less on cloud computing and direct Internet access, and now is primarily focused on providing high-speed interconnections for AI workloads. Bandwidth is increasing rapidly as AI models evolve and require more and more resources to execute in a reasonable amount of time. Innovation in the market must also embrace sustainability to ensure that development doesn’t outpace the ability of modern data center infrastructure to provide power and cooling.
Networking architecture has historically been optimized to serve applications or direct users out of the network toward the Internet. This North-South traffic flow has been disrupted by the needs of AI clusters. Traditional Clos architecture with leaf/ spine connectivity cannot keep up with GPUs that frequently exchange large data sets.
800G Ethernet optics and switches with high port counts are increasing the amount of power that each device draws from the available power budget for the rack. Additionally, the mass of cabling needed to interconnect the various networks for the rack is impeding air flow and creating issues for traditional air cooling of these already-hot devices.
In 2026, observability shifts from providing operational visibility into systems toward narrowing the trust gap introduced by non-deterministic AI and autonomous agents. As agents plan, decide, and execute work across the SDLC, observability becomes the mechanism that makes behavior understandable, governable, and safe at scale. This moves observability upstream from post-incident analysis into execution, control, and management, where trust is established through evidence, not assumption. The following key issues define how observability platforms must evolve to support agent-driven systems without sacrificing accountability, control, or enterprise confidence.
AI-driven execution is continuous and non-deterministic, making post-execution explanation insufficient for governance or control. Observability must exist at the moment work is performed, embedded directly into AI and agent execution across the SDLC rather than added as a downstream analysis layer.
Non-deterministic AI challenges assumptions that systems can be trusted through predictability or replay alone. Observability becomes the control surface that makes probabilistic execution explainable, bounded, and governable in production systems.
Agentic systems shift operational control away from managing system health toward managing behavior at scale. Observability becomes the management surface through which agent state, coordination, and impact are directed in real time, forming a core capability of an emerging agent OS.
Semiconductors have created a technology super-cycle powering the AI revolution. The industry is on pace to approach $1 trillion in revenue in 2026, marking a third consecutive year of elevated growth driven by AI training, inference, and new classes of intelligent systems. The semiconductor industry now spans a deeply interdependent global supply chain where constraints at any layer shape overall performance and economics. Beyond traditional data center compute, emerging technologies are expanding the market through new computing form factors that depend on breakthrough semiconductor innovation and frontier AI models, including intelligent robotics, domain-specific XPUs, and early hybrid classical-quantum platforms. Together, these forces are shifting the industry from a focus on standalone chips toward tightly integrated, system-level platforms that define the next phase of AI-driven growth.
As AI shifts from chatbots to agents capable of multi-step reasoning, the storage of inference context has become a first order design challenge. Long-context windows generate massive Key-Value (KV) caches that quickly exhaust expensive HBM on GPUs. In 2026, leading AI clusters will utilize a tightly integrated hierarchy of HBM, SSD, and HDD to optimize the cost and power profile of every token generated, creating a tiered storage strategy where the speed of context retrieval defines the practical utility of frontier models.
XPUs, robotics, and quantum innovation increasingly drive progress in 2026:
In 2026, governments are no longer passive observers of the semiconductor industry; they are active participants shaping its long-term direction. Rising geopolitical tensions and recent supply-chain shocks have reframed semiconductors as core national security infrastructure, on par with energy and defense.
As a result, policy is moving beyond broad trade restrictions toward direct intervention across the value chain. Governments are actively redesigning domestic chip industries to reduce exposure to globalized risks. This intervention shows up in partnership formation, trade policy, and infrastructure investment. The net effect is that governments will accelerate semiconductor innovation while expanding the total addressable market.
The Software Lifecycle Engineering market is moving decisively from AI experimentation to AI accountability across the SDLC. In 2026, enterprises will be required to demonstrate AI-driven business value, operational impact, and measurable risk reduction in development, not just incremental developer productivity gains. Vendors that cannot connect AI investment to durable outcomes will face growing scrutiny from customers, buyers, and boards.
At the same time, the industry is racing to industrialize AI systems capable of meeting enterprise expectations. Vendors are assembling a new agent software stack for AI, agents, workflows, management, and infrastructure, but most stacks remain incomplete. Prompts, LLM modes, and agent builders alone do not produce production-ready systems. The hard work now
lies in designing AI-native lifecycle platforms that embed agent identity, control planes, behavioral governance, security guardrails, testing, operational management, observability, and end-to-end lifecycle control. Decisions made here will either enable enterprise-scale agent adoption or quietly constrain it.
These are not abstract platform choices. They are commitments that shape how vendors earn trust, scale deployments, and remain relevant as buyers consolidate around fewer, AI-native lifecycle platforms.
The next 6–12 months will determine whether software development fully transitions from code-centric execution to intent directed systems built around AI and agents:
Vendors that fail to make this transition explicit risk locking customers into architectures that cannot scale agent-centered systems, capping the impact of AI, and undermining long-term value.
Vendors across application development, testing, security, operations, and platform engineering face a structural choice:
In 2026, AI-centered development will consolidate into a defined software stack that underpins both software engineering and AI applications. This stack establishes how intent is captured, how work is delegated to agents, and how execution is governed across the SDLC, infrastructure, DevOps, platform engineering, and operations.
We will see the emergence of agent‑native infrastructure, the functional equivalent of what Kubernetes brought to the microservices era, built around agent control planes, orchestration, and scalable agent operations. At its core, an agent control plane manages identity, permissions, memory, lifecycle, policy, and observability. Orchestration layers coordinate specialized agents across planning, building, testing, security, deployment, and operations, enabling parallel, asynchronous, interdependent, and long-running execution with human oversight. As this AI stack emerges and matures, existing CI/CD, workflow automation, and policy enforcement layers that cannot operate at agent speed or scale will be bypassed or absorbed.
The next 6–12 months will set the foundation for how agent environments are controlled, observed, and trusted in production. Vendors are racing to define control planes that provide agent identity, scoped authority, behavioral constraints, and real time observability integrated directly into development and operational platforms. Those that succeed will make agent based software viable at enterprise scale, while those that treat governance, security, and observability as add-ons risk being locked out of production deployments.
Agent control models adopted now will determine whether agents are viewed as manageable systems or as ungovernable risks.
The next 12 months will determine where agent-based ecosystems fragment or converge. While existing open standards such as MCP and A2A continue to mature, new open standards and open-source efforts will emerge to address gaps and interoperability challenges in agent harnesses, control planes, policy enforcement, memory management, security, behavior, and more. Vendors must make key decisions about where they lead, where they contribute, and where they align with existing standards.
Misjudging this balance risks either ecosystem isolation or loss of strategic control over core platform value.
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