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In IDC’s FutureScape: Worldwide Agentic Artificial Intelligence 2026 Predictions, two forecasts capture this divergence clearly.

One warns that by 2030, up to 20% of G1000 organizations will face lawsuits, fines, and CIO dismissals due to high-profile disruptions tied to poor AI agent governance. In contrast, the other prediction anticipates that by 2031, 60% of G2000 CEOs will use agentic AI to inform strategic decisions, leveraging autonomous systems to simulate outcomes and guide boardroom planning.

These predictions describe opposite potential outcomes of the same adoption curve: one driven by unchecked automation, the other by disciplined governance and transparent design.

The governance gap: Where failures occur

The early wave of GenAI deployments surfaced a pattern where speed sometimes outpaced safeguards. Under board and competitive pressure, CIOs deployed GenAI applications before implementing comprehensive processes to mitigate the potential for inaccurate or poor results.

The stakes are potentially higher when it comes to agentic AI implementations, particularly if they are deployed into mission-critical workflows — from logistics optimization to financial approvals — before governance frameworks are in place. The potential includes:

  • Uncontrolled decision cascades. When agents are authorized to take action, considerations should be made for how those actions may propagate through interconnected systems. Lack of control and visibility could lead to unintended consequences.  
  • Opaque behavior. When teams lack the explainability tooling to trace why an agent took a specific action, leaders may be left unable to defend outcomes to regulators or customers.
  • Fragmented escalation protocols. When human oversight is nominal and when governance is split across data, IT, and legal functions with no unified escalation path, problems may go undetected.

The consequences of these scenarios are immediate and potentially dramatic, including service outages, privacy violations, shareholder lawsuits, and loss of executive confidence.

It’s not about technology failure, but organizational unpreparedness.

From control to confidence

By contrast, organizations that treat governance as infrastructure and not insurance are finding that control and confidence grow together.

One of the predictions envisions a near future where CEOs use agentic AI not for operational efficiency, but for strategic insight. These systems may model mergers, simulate supply chain disruptions, and forecast policy impacts faster than human teams can aggregate the data.

To make that shift, enterprises will need to embed three design principles at the core of their AI programs:

  1. Traceability by design. Every autonomous decision should carry a data lineage record and confidence score, allowing oversight without throttling performance.
  2. Integrated governance. AI ethics, risk, and compliance functions should be unified and integrated, and applied across the development and operations lifecycle.
  3. Accountability loops. Decision thresholds or hard-coded events trigger human interventions before outcomes cross defined boundaries.

When these design principles are followed, governance doesn’t slow innovation or adoption. Instead, it builds confidence. When leaders trust the system, they can push AI further, including into strategic applications such as board-level scenario modeling, capital planning, and long-horizon strategy.

Bridging the divide

IDC’s research shows that the organizations succeeding with agentic AI share a common mindset: they see governance and growth as inseparable. The message from the 2026 FutureScape is clear: the problem isn’t that AI agents act autonomously, it’s that too few enterprises are ready for them to do so.

The next era of competitive advantage will belong to organizations that can govern autonomy, not constrain it.

Explore more FutureScape agentic AI predictions: Watch the on-demand webinar.

Nancy Gohring - Senior Research Director, AI - IDC

Analyst Profile
Nancy Gohring is a senior research director, co-leading IDC's GenAI and Agentic AI Strategies program. Nancy covers big picture trends related to enterprise adoption of AI, including GenAI and agentic AI. Key research themes include business, organizational, and technology architecture transformation, in the context of AI and GenAI. As part of the Worldwide AI, Automation, Data & Analytics Research practice, Nancy supports a range of clients across the technology stack including hyperscalers, developer tool providers, enterprise application vendors, professional services organizations, automation frameworks providers, and infrastructure suppliers.

As a CIO and CTO, my responsibility is less about answering “can we do something”, and more about “should we do something”, even when not asked.  Agentic AI orchestration exemplifies this, and tech leaders must weigh in, even if not being asked. One of the biggest misconceptions to avoid is to think Agentic AI is a new tool when really, it’s a completely new way of achieving business objectives, it’s a new Architectural Archetype.

Although nascent, the Agentic AI architectural archetype can’t be ignored. Following traditional best practices today for technology developments could lead to costly over-engineered platforms, and potential obsolescence even before you finish deploying them!  Not understanding the criticality of architecture versus tooling could lead to deploying attractive point solutions (even AI ones), that ultimately lead to future complexity proliferation, silos, costs, and rigidity.  Agentic AI orchestration isn’t improving or automating technology solutions, it is offering a complete re-think on the underlying solution design from the ground up providing unprecedented dynamic enterprise outcome driven agility.

The base building blocks for agentic orchestration are AI Agents, self-directed independent mini-systems (workers) that sense, decide, learn and act to achieve goals. Unlike microservices and APIs, agents have contextual understanding and reasoning with minimal human input. Federated Agentic AI Orchestration is a coordination fabric including orchestration, capabilities and governance where multiple specialized autonomous AI agents collaborate across distributed systems, utilizing tools coordinated by a master orchestration layer that governs policies and allows for sub-orchestration. This enables agents to operate autonomously, learn continuously, and be swapped or upgraded modularly, while maintaining interoperability through standardized protocols like MCP (Model Context Protocol) to achieve organizational objectives. It’s the difference between planning your trip with a paper map versus asking your car to figure out how to get to a destination and the car automatically adapts based on real-time traffic and road-closures.It’s the leap from static workflows to dynamic orchestration, from rigid integration to capability onboarding, from deterministic execution to bounded emergence.

Reference Architecture Considerations

To achieve this target state, there are several emerging, and at this time, often immature components of the reference architecture required including: 

  • Orchestration through standardized inter-agent protocols (e.g., MCP; others will emerge), memory management, routing, evaluation, recovery.  
  • Capabilities through tools, tool registries, and capability contracts allow GenAI based agents that are predictive (non-deterministic) by nature, to utilize deterministic capabilities through tools linked to mature hardened deterministic enterprise systems helping to reduce errors (e.g. hallucinations) that could occur within a pure GenAI based workflow.
  • Governance through identity management with enforced least privilege, observability, enforceable policies, human-in-the-loop (HITL), lineage with fall-backs to deterministic paths all play a vital role in not only providing confidence in what is achieved, but also in how it is being achieved. 

Four Mindset Shifts for CIOs and IT Leaders to consider

To unlock agentic potential, CIOs must embrace four mindset shifts:

  1. From “deploy a workflow” to “design a market”:  Your orchestration is based on a marketplace where multiple agents, tools, data, and models can be considered to achieve an objective.  This is a shift from building rigid solutions to building reusable flexible capabilities for orchestration in multiple solutions.
  2. From deterministic workflows to policy-bounded emergence:  Expect non-determinism. Engineer bounded variability with approvals on sensitive actions, human-in-the-loop thresholds, deterministic fallbacks for regulated steps. Think of it as a policy cage that offers autonomy with guardrails and contingencies.
  3. From integration backlog to capability onboarding:  Stop wiring systems point-to-point. Start onboarding capabilities with contracts (inputs, outputs, pre/post-conditions, risks, costs) published to a registry
  4. From vendor lock-in to composition strategy:  Assume a rotating cast of agents/tools. Prioritize interchangeability.  Monitor and compare agent and tool efficiency and effectiveness with intent to swap out for better agents and tools through continuous improvement.  It’s not about best practices it’s about next practices.  

The decisions we make today as technology leaders will either enable enterprise agility or entrench systemic fragility. Agentic AI Orchestration demands that we shift thinking in terms of pre-determined workflows and integrations, and start designing for emergence, modularity, and policy-bound autonomy. The allure of deploying isolated AI-powered solutions is strong, but seductive simplicity often leads to architectural entropy. Federated orchestration offers a path forward: one where agents collaborate across domains, utilizing reliable tools, governed by shared protocols and enforceable policies, enabling continuous learning and safe autonomy.

The question isn’t whether disruption is coming, it’s whether your enterprise will be ready when it does. Because in the age of bounded emergence, agility isn’t built… it’s orchestrated.

Rex Lee - CITO - Canadian Tire Corporation

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Rex is the Chief Information & Technology Officer (CITO) at Canadian Tire Corporation (CTC), one of Canada’s most iconic and trusted companies with multiple retail banners spanning general merchandise, sporting goods, apparel, and businesses in automotive, financial services, real estate, and petroleum all with a brand purpose of “Making Life in Canada Better”. His mandate includes strategy, architecture, governance, development, operations, and cybersecurity across all retail locations, digital properties, corporate operations, and global facilities.

In December 2024, one year ago, Microsoft CEO Satya Nadella declared on the BG2 podcast that “SaaS is dead.” The comment set off a shockwave across the technology industry and many felt provoked. After all, software-as-a-service (SaaS) has defined enterprise computing for nearly two decades, representing a massive share (over 10% according IDC’s Black Book) of IT spending in 2024 and forming the backbone of digital transformation strategies worldwide.

Yet, when we cast a cold IDC analytical eye beyond the provocative statement, a crucial truth emerges: SaaS, as we know it, is being disrupted, not by decline but by evolution.

The Status Quo: SaaS at Its Peak

Today, most of the world’s leading software vendors are, in some form, SaaS companies. Among the ten most valuable software players, including Microsoft, Salesforce, Oracle, SAP, and Shopify, SaaS delivery models dominate. Enterprises have grown dependent on the SaaS ecosystem, licensing countless applications to manage HR, payroll, CRM, expenses, and vertical industry workflows.

However, the sheer sprawl of SaaS adoption has created complexity for business users. Employees navigate dozens of interfaces daily, shifting context between multiple systems that rarely communicate smoothly. Despite efforts to simplify workflows through integrations and APIs, SaaS remains a patchwork of interfaces and data silos, forcing users to adapt to the software rather than the other way around.

The Complexity Problem and the AI Opportunity

This complexity is the Achilles’ heel of the SaaS model. Each SaaS application demands its own learning curve and user interface, often used sporadically and inefficiently. In this environment, AI offers a compelling remedy.

Instead of navigating multiple dashboards, users could interact with agent-driven, conversational interfaces that perform tasks across systems. Imagine instructing an AI agent to “approve last week’s expense reports” or “generate next quarter’s sales forecast” and having the agent orchestrate workflows across HR, finance, and CRM systems behind the scenes.

This agentic, “flow-of-work” user experience could replace much of today’s direct interaction with SaaS applications. The result? AI as the new interface layer, which is one that abstracts away complexity, automates repetitive processes, and redefines how enterprises consume software.

The Disruption: From Seats to Outcomes

Such a shift has profound implications for how SaaS is bought and sold. The traditional per-user, per-month licensing model becomes increasingly obsolete as digital labor replaces manual interaction. IDC predicts that by 2028, pure seat-based pricing will be obsolete, with 70% of software vendors refactoring their pricing strategies around new value metrics, such as consumption, outcomes, or organizational capability (please see IDC FutureScape: Worldwide Agentic Artificial Intelligence 2026 Predictions, IDC #US53860925, October 2025).

This agentic IT disruption will impact IDC’s existing forecasts for the various levels in the IT stack differently as shown below. Also, the impact will change over time, as for examples SaaS Applications and IT Services will feel a negative impact in the short term, while recovering if we look five years out to 2030.

For infrastructure hardware, IDC sees a different impact with a short term boost, followed by headwinds as inference costs drop exponentially.

Source: Charting the Agentic Future: 10 Vision Statements for 2030 (IDC #US53909225, November 2025)

Inside the enterprises, this evolution changes the economics of enterprise software. Companies optimizing AI agent development to reduce licensing costs will need to revisit their roadmaps as vendors adjust to these emerging pricing paradigms. Meanwhile, process owners may gain more flexibility, designing application-neutral operational efficiencies that transcend the limitations of current SaaS systems.

Business and IT Implications

The rise of AI agents doesn’t just alter pricing, it transforms how technology functions within organizations.

From a business perspective, enterprises may initially lose the tactical benefit of reduced software costs but gain strategic control over innovation and process optimization. Process teams will design workflows around end-to-end outcomes rather than application silos, supported by a new breed of “headless” software modules accessible via APIs and marketplaces.

From an IT standpoint, this means a fundamental re-architecture of the enterprise tech stack. Where today’s stack is built around SaaS interfaces, tomorrow’s will revolve around AI agents that interact with modular backend services. Data lakes and live data connections become critical enablers, while vendor relationships evolve from UI-centric engagement to agentic enablement partnerships.

Guidance for Technology Buyers

For IT and procurement leaders, this transformation demands foresight and experimentation. Buyers should assume that software vendors will increasingly position their offerings to accommodate or counteract the impact of digital labor.

Before adopting agentic systems, IDC advises enterprises to:

  • Build proofs of concept (POCs) and define clear ROI metrics around cycle time, productivity, and revenue improvements.
  • Evaluate end-to-end process efficiency, not just individual task automation.
  • Explore packaged AI agents offered by existing SaaS vendors, integrating them as part of broader operational redesigns.

In other words, the transition to AI-driven enterprise software should be intentional, data-backed, and aligned with measurable business outcomes.

The Road to 2030: SaaS Reimagined

By the end of this decade, the enterprise technology landscape will look radically different. The AI agent will become a new enterprise SKU, purchased via marketplaces and powered by modular backend capabilities rather than monolithic SaaS platforms. User interfaces will still be critical to productivity but so will orchestration of more-or-less autonomous workflows.

SaaS is not dead, but it is metamorphosing. The software industry is entering a new chapter defined by AI, automation, and outcome-based economics. For vendors, it’s a challenge to reinvent their business models. For buyers, it’s an invitation to rethink how software delivers value.

Either way, the next generation of enterprise technology will be less about screens and more about agents.

Got a question? Drop it in here.

You may be interested in listening to IDC EMEA’s predictions for 2026 and beyond.

Bo Lykkegaard - Associate VP for Software Research Europe - IDC

Analyst Profile
Bo Lykkegaard is associate vice president for the enterprise-software-related expertise centers in Europe. His team focuses on the $172 billion European software market, specifically on business applications, customer experience, business analytics, and artificial intelligence. Specific research areas include market analysis, competitive analysis, end-user case studies and surveys, thought leadership, and custom market models.

Agentic AI, generative models, and AI-driven automation workflows are reshaping how organizations operate. Yet behind the excitement lies an emerging financial reality: AI is expensive, unpredictable, dramatically different than traditional IT projects (think ERP and Warehouse management), and growing faster than most budgets can track. AI Agents, designed to act autonomously, make decisions that carry unchecked cost implications in real time.

IDC’s FutureScape 2026: CIO and CTO Agenda warns that by 2027, G1000 organizations will face up to a 30 percent rise in underestimated AI infrastructure costs. The reason isn’t simply overspending—it’s under-forecasting and completely missing the expenses unique to AI-specific projects. AI-enabled applications are often resource-intensive, coupled with opaque consumption models, and have outpaced the traditional IT budgeting playbook. AI Agents may be deployed by the thousands inside G2000 companies, which will exponentially compound this issue.

The implication is clear: IT leaders must expand and educate their teams, including expanding governance and scope of their FinOps teams.

Enterprises are now pivoting from AI pilots and experimentation. Yet as AI moves from pilot to production, an uncomfortable truth is emerging: AI is expensive. Not because of reckless spending, but because the economics of AI are unlike anything technology leaders have managed before.

Most CIOs and CTOs underestimate the financial complexity of scaling AI. Models that double in size can consume ten times the compute. Exponential should be your watchword. Inference workloads run continuously, consuming GPU cycles long after training ends, which creates a higher ongoing cost compared to traditional IT projects. Data pipelines, compliance monitoring, and storage replication can silently add significant operational overhead. What once looked like a contained line item now behaves like a living organism — growing, adapting, and draining resources unpredictably.

IDC’s FutureScape 2026 calls this emerging reality the “AI infrastructure reckoning.” Organizations are realizing that traditional cost management models are insufficient for a world where workloads self-scale and budgets can balloon overnight. For technology leaders, this shift marks a turning point: financial governance has become as strategic as technological innovation.

When innovation outpaces accountability

In the early days of cloud, enterprises learned the hard way that on-demand infrastructure could just as easily become ungoverned infrastructure. FinOps emerged as the antidote — a way to bring finance, IT, and business together around a shared language of consumption, optimization, and value.

AI now demands a second evolution and expansion of that discipline. The new cloud+ mantra of FinOps, which includes ITAM, SaaS, and on-premise software costs, should now incorporate AI. The volatility of AI workloads — from bursty training cycles to unpredictable inferencing spikes — means that static budgeting and quarterly forecasts can’t keep up. Every new experiment, every dataset added, every prompt creates a ripple in compute, storage, and energy consumption – often in exponential amounts.

The irony is that even as AI drives operational efficiency, its own operating costs are becoming one of the biggest drags on IT budgets. IDC’s research shows that, without tighter alignment between line of business, finance, and platform engineering, enterprises risk turning AI from an innovation catalyst into a financial liability.

FinOps becomes a strategic instrument

The organizations successfully navigating this challenge are ones that effectively share a common trait: they’ve reimagined FinOps as a strategic team, not an after-the-fact accounting exercise. They treat AI economics as a living ecosystem — measurable, visible, and continuously optimized.

FinOps for AI is not only about controlling costs; it’s about translating complexity into clarity. It requires real-time observability across the AI lifecycle and business returns: how much each model costs to train, what data pipelines contribute to that cost, how inference demand scales across geographies, and where optimization delivers the greatest business impact.

This is not a simple extension of cloud cost management.  AI workloads cut across infrastructure, application development, data governance, and business operations. Many AI workloads will run in a hybrid environment, meaning cost impacts for on-premises as well as cloud and SaaS are expected. Managing this multicloud and hybrid landscape demands a unified operating model that connects technical telemetry with financial insight. The new FinOps leader will need fluency in both IT engineering and economics — a rare but rapidly growing skill set that will define next-generation IT leadership.

The expanding mandate of the CIO and IT leaders

For CIOs and IT leaders, the expansion of FinOps scope is not optional — it’s existential. Enterprises tell IDC that the most common reporting structure of FinOps teams is to the office of the CIO. AI has moved technology spending from predictable consumption to probabilistic behavior. That means financial visibility must become continuous, not periodic.

In the coming year, IDC expects more technology leaders to integrate FinOps directly into their AI governance framework. They will create cross-functional teams that include finance, data science, and platform engineering, working together to balance performance and value in real time. These teams will use predictive analytics to forecast budget impact before workloads scale. They will experiment with new pricing models, such as universal tokens and business value delivery, which align with business outcomes rather than raw consumption.

The cultural change may be even more profound than the technical one. Engineers must begin to see financial efficiency as a measure of innovation, not a constraint on it. Vendors need to provide cost estimates within the CI/CD DevOps pipeline to optimize costs before it goes into production. Finance teams, in turn, must become comfortable with the iterative, experimental nature of AI development. The CIO’s role is to unify these objectives— to make financial discipline part of the innovation fabric.

From guardrail to growth engine

When done right, FinOps becomes more than a mechanism for control; it becomes a catalyst for growth. Companies often see significant savings in the first year after implementing FinOps. As they mature and expand FinOps practices, additional value of the cloud is realized. More importantly, they gain agility — the ability to reallocate budgets quickly toward the projects that deliver measurable value.

This agility matters because AI economics are rapidly changing. The market for compute, energy, and AI services is shifting almost monthly. Vendor lock-in, data sovereignty, and emerging regulatory compliance costs add new layers of financial risk. Without adaptive financial governance, enterprises can find themselves constrained just as competitors accelerate.

In this sense, FinOps is evolving into a form of strategic navigation — the compass that lets organizations steer through cost turbulence while maintaining innovation velocity. It aligns with IDC’s broader FutureScape theme of “Charting the Agentic Future:” navigating unseen crosscurrents, adjusting course with evidence, and turning disruption into momentum.

The future of FinOps: Intelligent, integrated, invisible

By 2027, the most advanced enterprises will mature and expand FinOps team’s scope. It will be embedded into every project phase and even driven by AI itself to catch anomalies faster. Intelligent monitoring tools will autonomously optimize resource allocation and recommend the most cost-effective placement of new workloads. Predictive analytics will forecast budget drift before it occurs. Compliance, sustainability, and financial reporting will converge into a single pane of visibility, accessible to both engineers and line of business executives.

In that future, the CIO becomes not just a steward of technology but a chief investment officer as well, guiding the organization through a complex AI landscape where every model run, every query, and every agent carries both potential and cost.

Conclusion: Intelligence needs insight

The coming years will test whether enterprises can match the speed of AI with equal precision in financial governance. The winners will not be those who spend the most on AI, but those who understand its economics best while holding teams accountable for business returns.

FinOps, reimagined for the age of agentic and AI, is how IT leaders deliver business value. It’s how they ensure that the AI driving enterprise does not outpace the human insight and governance guiding it.

In the agentic future of the enterprise, innovation and accountability are no longer opposing forces. They are the twin engines of growth — and FinOps is the system that keeps them in balance.

Jevin Jensen - Research Vice President, Infrastructure and Operations - IDC

Analyst Profile LinkedIn
Jevin Jensen is Research Vice President, Intelligent CloudOps Market service at IDC where he covers infrastructure as code/GitOps infrastructure Automation, cloud cost transparency, DevOps, hybrid/public/multi cloud management platforms, and edge management.

As we approach 2026, enterprise networking in Europe, the Middle East, and Africa (EMEA) is at a pivotal moment. The findings from IDC’s 2025 EMEA Enterprise Networking and Life-Cycle Services Survey reveal a landscape shaped by rapid technological change, evolving security threats, and a complex macroeconomic environment. Here’s what organizations, partners, and technology suppliers need to know about the strategies and priorities shaping the future of enterprise networking in the region.

Investment Priorities: Security, AI, and Wi-Fi 7

Security remains the undisputed top priority for EMEA enterprises. With the rise of sophisticated cyberattacks and new regulatory frameworks such as NIS2 and DORA, organizations are doubling down on network security investments. This focus is not just about compliance, it’s about ensuring business continuity and resilience in an unpredictable world.

A notable shift in 2025 is the surge in networking investments to support AI workloads. For the first time, “networking for AI” has become the second-highest investment priority, reflecting the growing adoption of AI-driven applications and the need for robust, high-throughput, and low-latency infrastructure. While many organizations are still defining their AI use cases, there is broad consensus that network architectures must evolve to support these new demands, particularly in datacenters and cloud environments.

Wi-Fi 7 is also gaining momentum, with many enterprises planning to leapfrog Wi-Fi 6/6E and move directly to the latest standard. The promise of higher speeds, improved device density, and enhanced security is driving aggressive deployment targets, especially in Western Europe and the Middle East & Africa.

The Macro Environment: Growth Amid Uncertainty

Despite ongoing geopolitical tensions, inflationary pressures, and energy cost volatility, the outlook for networking investments in EMEA is positive. IDC’s survey shows that nearly 60% of enterprises expect to increase their networking budgets in 2025, with the strongest growth among large organizations and in verticals such as manufacturing and business services. However, budget scrutiny remains high, and organizations are seeking to optimize costs while modernizing their infrastructure.

The Role of Automation and Services

Automation and AI-driven operations are increasingly seen as essential for managing network complexity and addressing skills shortages. Yet, the survey reveals that most organizations are still early in their automation journey, balancing manual processes with emerging automation tools. The appetite for “self-driving” networks is growing, but cultural and technical barriers persist.

This is where life-cycle services and managed services come into play. Enterprises are relying on integration, deployment, and support services to bridge skills gaps and accelerate technology adoption. The use of cloud-managed platforms is expanding, valued for their ability to improve visibility, user experience, and security.

SD-WAN, SASE, and the Convergence of Networking and Security

SD-WAN adoption continues to rise, but many organizations are re-evaluating their technology vendors, seeking better AI capabilities, security features, and cost optimization. The convergence of networking and security is accelerating, with Secure Access Service Edge (SASE) models gaining traction, especially where networking and security teams are closely integrated.

Looking Ahead

The EMEA enterprise networking market is forecast to grow steadily through 2029, driven by AI, cloud, and the ongoing refresh of campus and datacenter infrastructure. Success in this environment will require agility, a focus on security and compliance, and a willingness to embrace new technologies and service models.

IDC’s EMEA Networking and Life-Cycle Services research program continues to track these trends, providing actionable insights for enterprises, partners, and technology suppliers navigating this dynamic landscape.

If you have any questions, drop them in this form.

Len Padilla - Senior Research Director, European Networking and Life-Cycle Services - IDC

Analyst Profile
Len Padilla is a senior research director for IDC's European Networking and Life-Cycle Services program, focusing on the enterprise and telecom segments. Before joining IDC in 2022 he spent 21 years on the service provider side of networking at NTT, from operations to engineering to portfolio marketing. He built and operated multidatacenter networks across Europe and a global content delivery network, and he was early to cloud computing with a 14-site public cloud infrastructure that spanned 10 countries. Most recently he was part of a portfolio marketing team that oversaw the integration of 28 companies, service lines, and brands.

As AI systems grow more capable, their ability to interact with and automate the physical world depends on real-time, granular data — like knowing the exact location and condition of every item in a warehouse, or tool on a factory floor. Ambient Internet of Things (IoT) could unlock that capability.

This shift toward real-world sensing demands a new class of IoT devices — affordable, scalable, and battery-free. Traditional IoT devices are too costly and complex for pervasive automation. That’s where Ambient IoT enters the picture — not just as a new device class, but as a foundational layer for sensing the physical world.

Ambient IoT can sense the physical world

Ambient IoT is a key part of 5G-Advanced, the next phase of 5G evolution. Release 19 of the 3GPP standards, expected to be finalized by the end of 2025, formally introduces Ambient IoT as a new device class, enabling ultra-low-power, battery-less communication.

Whereas many mobile standards are pushing for higher bandwidths and more advanced capabilities, Ambient IoT is aiming for the simplest mobile-connected devices possible. The vision for Ambient IoT is for extremely cheap, printed tags to be used on virtually everything. Ambient IoT tags use so little energy that they don’t need batteries, drawing power from ambient sources. Similar to radio frequency identification (RFID), a small printed tag can be attached to an item, enabling it to receive power from ambient radio waves and transmit its location and conditions, such as temperature or humidity.

How low can the costs go?

The economics of Ambient IoT are what make it truly transformative. If tags could be produced cheaply enough, Ambient IoT tags could be placed on every item moving through a supply chain, every product in retail, and every asset, person, safety device, and tool in a factory. Huawei Wireless believes volume prices of the tags could be as low as $0.50 each in 2027, and down to $0.10 each a couple of years later, making widespread tagging feasible.

What makes Ambient IoT revolutionary compared to RFID?

While Ambient IoT shares RFID’s ultra-cheap and battery-less features, the standard aims for superior capabilities:

  • Real-time visibility: Continuous data streams versus point-in-time scans
  • Extended range: More than 100 meters range versus less than 10 meters
  • Higher accuracy: Over 99.99% inventory accuracy versus 90%
  • Positioning: 5-7 meters, with expectations to improve over time

Small devices, huge impact

These features are compelling for tracking individual items, but at a systemic level — tracking billions of items — Ambient IoT takes on larger meaning. It can provide real-time visibility for location, conditions and motion of everything in a facility, and eventually across the wider mobile network. That data can power digital twins, enabling real-time monitoring, management, optimization, and eventually automation of complex systems.

Addressable market

The market for Ambient IoT is potentially enormous. Tags may start on valuable objects like pallets and forklifts, then move to item-level tagging. There are hundreds of billions of packages, tools, and assets that might be tagged, and eventually trillions of consumer goods.

Key use cases

Ambient IoT will bring major benefits across diverse use cases:

  • Supply chains: Real-time management and automation to ensure inventory is in the right place at the right time.
  • Warehouses and retailers: Continuous, high-accuracy inventory.
  • Food tracing and pharmaceuticals: Cold chain monitoring and item traceability for safety and compliance.
  • Hospitals: Tracking patients, assets, and staff to boost utilization and reduce delays.

When will Ambient IoT be ready?

The  industry expects Ambient IoT to be quick out of the gate. The standards will be published within weeks, and much of the ecosystem is ready. Additionally, tags are available, there are mobile indoor base stations that can support it, and already Chinese operators have shown that connectivity management platforms can support it.

Telcos should explore Ambient IoT

While 5G-A brings many advanced capabilities, mobile operators shouldn’t overlook Ambient IoT. Telcos can support it with little new cost. They will need to develop new pricing models for supporting low-cost tags, but the aggregate benefits for enterprises are enormous. Operators should explore the potential of this new technology.

Conclusion

Ambient IoT isn’t just a technical innovation — it’s a strategic enabler for the next wave of automation and intelligence. As 5G-Advanced evolves, Ambient IoT could become the invisible infrastructure powering real-time visibility, operational efficiency, and AI-driven decision-making across industries. The time to move is now.

If you have a question about anything, please fill in this form. 

John Gole - Director, European IoT and Mobility - IDC

Analyst Profile
John Gole leads IDC's European Internet of Things (IoT) research as well as researching the mobile industry growth opportunities. He provides research and consulting on these topics to suppliers and enterprise technology users. Prior to this role, John managed IDC’s telecommunications, IoT, and mobility businesses for Central and Eastern Europe, the Middle East, and Africa. John is a frequent speaker at industry events. He also mentors start-ups on Prague’s start-up scene.

For years, B2B marketing revolved around the funnel. Awareness led to consideration, then to decision. It was a clean, linear model that helped teams structure campaigns and measure success. But that model was built for a predictable buyer, and today’s buyers don’t follow the same rules.

Modern B2B buyers are using AI tools to guide their own discovery, compare vendors, and evaluate fit long before they ever engage with sales. They expect interactions to be immediate, relevant, and personalized. They’re in control of how, when, and where they move.

AI doesn’t just influence how buyers behave. It has become the connective tissue of the entire journey, reshaping how decisions are made and how marketers respond.

From linear funnels to living journeys

AI has replaced the static funnel with something dynamic: a constantly adapting journey that reflects intent in real time. IDC predicts that 62% of traditional demand generation will be AI-led by 2028, transforming engagement into an orchestrated system that continuously learns and evolves.

The sequence is no longer awareness to conversion. Buyers move between exploration, validation, and decision at their own pace, sometimes looping back, sometimes skipping ahead. The buyer journey has become a network of decisions powered by data and context. That shift in buyer behavior demands a new kind of marketing system—one that can interpret, predict, and act in real time. This is where AI becomes the orchestrator.

AI is not just changing how buyers behave—it’s changing what marketing must deliver. Download IDC’s Six Scenarios for Marketing Leaders to see how organizations are adapting their strategies, reshaping demand engines, and turning AI into a growth advantage.

AI as the new orchestrator

AI reads the signals that marketers used to miss. It detects intent in real time by tracking actions like page visits, content engagement, chat interactions, and sentiment changes. It connects these dots instantly and determines what should happen next.

This orchestration isn’t about replacing the marketer. It’s about giving teams the intelligence and agility to meet buyers where they are.

  • AI-triggered journeys adapt automatically based on engagement and readiness.
  • Dynamic segmentation updates audiences as intent changes.
  • Predictive models identify in-market buyers early and route them to sales faster.

Orchestration is only effective when every interaction feels personal. As AI takes control of timing and delivery, marketers must ensure it also enhances relevance.

The personalization mandate

Buyers expect relevance across every touchpoint. IDC research shows that 69% of buyers engage only with content that feels personalized. This expectation extends beyond targeted emails or landing pages. It includes every conversation, chatbot, webinar, and digital ad.

AI enables that scale of personalization by unifying data across systems and continuously learning from buyer behavior. It helps marketers build cohesive experiences where every message feels timely and specific to the individual’s needs.

Personalization creates opportunity, but it also raises expectations around responsibility. As experiences become more automated, buyers want assurance that AI operates transparently and ethically.

Trust as the foundation

With automation advancing, digital trust has become the new measure of loyalty. Buyers want to understand when and how AI is being used, what data informs personalization, and how it is managed ethically.

The brands that communicate openly about their AI use will earn confidence and stand out. Trust is the foundation. Building on it requires marketers to evolve their role—from storytellers to orchestrators of growth.

The new role of marketing

In this AI-driven era, marketing’s role has expanded from awareness generation to full journey orchestration. The modern marketing organization connects product, sales, and customer experience through a single source of buyer intelligence.

Every signal, every conversation, and every piece of content becomes part of a coordinated system designed to move buyers forward with clarity and confidence.

The traditional funnel structured marketing. AI now defines how growth happens.

Ready to see where your strategy stands?

Download IDC’s Six Scenarios for Marketing Leaders to explore how AI is reshaping every stage of marketing—from visibility and trust to orchestration and execution.

Christina Cardoza - Content Marketing Manager - IDC

LinkedIn
Christina Cardoza is a Content Marketing Manager at IDC, where she specializes in brand content and social media strategy. With a background in journalism and editorial leadership, she has a proven ability to transform complex technology topics into clear, actionable insights.

In 2026, the connected devices landscape stands at an inflection point. What was once a conversation about incremental innovation in laptops, tablets, and smartphones is evolving into a larger story of intelligence, autonomy, and sustainability. Across organizations large and small, devices are no longer passive tools; they are becoming active participants in productivity, security, and decision-making. This transformation brings new challenges — shifting supply chains, emerging sustainability standards, and rising demand for trust and transparency.

The next few years will redefine how enterprises think about device strategy. Major shifts are already underway in how, where, and why devices are built and deployed — reshaping not only technology choices but also broader approaches to resilience, sustainability, and security.

Intelligent, sustainable, and secure devices

The age of intelligent endpoints has arrived. Devices are gaining the ability to learn, adapt, and act on behalf of users. Increasingly, AI will live not just in the cloud but on the device itself — enabling real-time performance optimization, contextual responsiveness, and stronger security. This evolution reduces reliance on centralized processing and cuts latency for mission-critical tasks. Imagine a PC that understands your work patterns, adjusts to your environment, and autonomously mitigates potential threats without human intervention.

For IT teams, this shift signals a move toward distributed intelligence and new management paradigms. Devices will act as secure nodes within a broader ecosystem, each with a defined role in data processing and protection. The challenge will be managing that complexity while maintaining consistency and compliance.

At the same time, the environmental footprint of technology has become a defining factor in device strategy. Sustainability is no longer a differentiator; it’s an expectation. As organizations work toward carbon neutrality, the devices they purchase — and how those devices are produced, used, and retired — directly influence their ESG performance.

Forward-thinking vendors are integrating sustainability into design, building devices that are repairable, recyclable, and energy-efficient. Enterprises, in turn, are making procurement decisions based not only on technical specifications but also on lifecycle carbon metrics.

Resilience, regionalization, and responsibility

Geopolitical and trade disruptions are accelerating the reconfiguration of global supply chains. Device manufacturers are diversifying production footprints and sourcing across regions to reduce risk and increase flexibility. This isn’t just about avoiding disruption — it’s about enabling faster delivery, shorter lead times, and more predictable procurement cycles.

For enterprise buyers, regionalized manufacturing can mean greater stability and a wider range of device configurations. Yet it also demands new approaches to vendor relationships, procurement planning, and lifecycle management. Agility will be as important as cost efficiency.

As devices grow more intelligent, they are also becoming more self-aware. Future systems will dynamically adapt to user behavior, time of day, or location — tuning performance for productivity or enforcing stricter security protocols when risk is detected. This is the dawn of the context-aware enterprise, where devices themselves become front-line defenders against threats.

But with autonomy comes responsibility. The proliferation of on-device AI introduces new challenges around governance, data integrity, and trust. Organizations will need oversight frameworks to ensure models remain accurate, unbiased, and secure over time.

For IT departments, this transformation represents both opportunity and obligation. The opportunity lies in achieving greater resilience, performance, and employee productivity through intelligent, secure devices. The obligation lies in managing those devices responsibly — with clear governance, compliance, and lifecycle policies.

For technology vendors, success will hinge on transparency and adaptability: building devices that are trustworthy, sustainable, and aligned to enterprise AI strategies. For technology buyers, the next few years will require a holistic view of procurement that considers total lifecycle value, not just acquisition cost.

No single innovation will define the future of connected devices; rather, success will depend on how organizations balance intelligence, sustainability, and responsibility.

Those who act now — by modernizing infrastructure, prioritizing trust, and embracing sustainable design — will be best positioned to lead in the next era of the connected enterprise.

The commercial technology landscape is evolving rapidly. IDC FutureScape: Worldwide Connected Devices 2026 Predictions explores how AI-driven intelligence, resilient supply chains, sustainable manufacturing, advanced security, and edge innovation are reshaping the global device ecosystem. The report connects these shifts to broader market and workforce trends, helping leaders across industries turn transformation into opportunity and chart their next move with confidence.

To explore the full set of predictions shaping the agentic-AI era, visit the IDC FutureScape 2026 Resource Center.

Tom Mainelli - Group Vice President - IDC

Analyst Profile
Tom Mainelli heads the Device & Consumer Research Group, overseeing a wide array of hardware and technology categories that cater to both home and enterprise markets. His team's research spans PCs, tablets, smartphones, wearables, smart home devices, thin clients, displays, and virtual/augmented reality headsets. He also co-manages IDC's supply-side research team, which monitors display and ODM production across various categories. IDC's consumer research, anchored by the Consumer Market Model, employs regular surveys and proprietary models to forecast numerous consumer-focused activities and spending across hardware, software, and services. As Group Vice President, Tom collaborates closely with company representatives, industry contacts, and other IDC analysts to provide comprehensive insights and analysis on a diverse range of commercial and consumer topics. A frequent speaker at public events, he travels extensively, enjoying every opportunity to engage with colleagues and clients worldwide.

This year’s IDC FutureScape centers on a global shift taking place across the technology industry: the agentic pivot. Over the next few years, agentic AI is expected to enter every layer of the enterprise, transforming how businesses and industries operate.

IDC predicts that agentic automation will enhance the capabilities of more than 40% of enterprise applications by 2027, laying the foundation for next-generation AI operating models and reshaping one-third of business processes and workflows.

The tech industry has always been effective at helping businesses adopt AI. Today, 42% of enterprises already have AI agents in production, and another 40% plan to follow in the next year. But easier deployment does not guarantee effective use.

“Their ability to further use and leverage agents is limited by basically the need to vet all the growing range of options that they’re getting, to preset guardrails for orchestration and workload and data security of these growing fleets of agents, and a lot of concerns about the long-term costs of operating agents,” Rick Villars, Group Vice President, Worldwide Research at IDC, said in a recent webinar about the tech industry’s agentic future.

As IT leaders move further into the agentic future, the focus must shift from adoption to purposeful application, using agents to transform operations, partnerships, and business models.

According toIDC analysts Rick Villars, Jevin Jensen, and Lars Goranson share tech industry predictions around the agentic pivot and explore what IT leaders can do to prepare today.

The tech industry’s own agentic transformation

Innovation is no longer just about new platforms or products; it’s about how technology interacts with itself.

For enterprise customers, this means the way they engage with IT environments will change dramatically. Every device, application, and platform will soon include an agentic layer capable of self-management and adaptive interaction.

The most strategic IT leaders will take a deliberate approach, understanding where autonomy adds value, where human oversight remains essential, and how this transition affects governance, cost, and security.

“Whether it’s hardware, software packages, services contracts, this is going to be one of the most fundamental things that you need to prepare for in the next several years,” said Villars.

One visible area is in IT Ops, where automation is evolving into autonomy.

Rethinking IT operations: From automation to autonomy

Most enterprises have achieved a high degree of automation for Day 0 and Day 1 operations such as provisioning, configuration, and deployment. But the real complexity begins at Day 2, when systems are live, serving customers, and generating revenue.

This is where agentic AI changes the game.

“Unlike traditional IT automation and rules-based type systems, agentic AI can continuously learn from events, adjust strategies in real time, and escalate these issues for human judgment and decision-making. This means that future AI agents will take on more and more operations of these day-two tasks,” said Jevin Jensen, Research Vice President, Infrastructure and Operations at IDC.

By 2030, IDC expects AI agents to handle hundreds of operational processes simultaneously, significantly reducing human involvement in repetitive work. The result is greater efficiency, resilience, and scalability. Organizations that adopt this hybrid model will be able to manage complex digital ecosystems without losing control.

Watch the webinar: The Tech Industry’s Agentic Pivot

To get there, IT leaders must set clear guardrails, define escalation paths, and ensure every agent’s decisions can be audited and explained.

As internal operations become more autonomous, the same logic is reshaping the services ecosystem. The way technology is delivered and paid for is changing just as quickly.

Services become products and outcomes become the measure

The agentic pivot is also transforming how IT services are designed and delivered. For decades, service engagements were built from scratch and customized for each client. That model is rapidly evolving.

“Most IT services were delivered as projects, custom-built engagements for each client. And that model still matters, but the economics are changing now. And with AI and automation accelerating both development and delivery, enterprises want faster, more predictable results, and providers are looking for ways to scale expertise without rebuilding it each time. And that’s giving rise to what IDC refers to as service as a product,” said Lars Goranson, Vice President, Research, Worldwide Services at IDC.

For enterprise buyers, this shift changes how partners are evaluated. The key questions become:

  • What reusable IP or frameworks are you bringing to the engagement?
  • Have you validated them internally as “customer zero”?
  • How will success be measured and shared?

By 2029, IDC predicts that 30% of global IT services will be delivered as modular, platform-based products, and 30% of contracts will tie payment to business outcomes rather than inputs.

In this new landscape, transparency builds trust, and trust becomes a differentiator. Providers that share their experiences and lessons learned will stand out as credible, accountable partners.

As automation scales across platforms and services, a new challenge emerges: managing the growing number of agents operating across the enterprise.

Managing the agent surge

As adoption accelerates, organizations face a new challenge: agent sprawl. IDC forecasts a tenfold increase in the number of agents within large enterprises and a thousandfold increase in the actions and data calls they perform.

That scale introduces both complexity and cost. Each agent consumes compute, interacts with data, and performs actions that must be tracked, governed, and optimized.

Enterprises that act now will have the advantage. That means:

  • Creating a central registry of agents and their roles.
  • Applying FinOps for AI principles to monitor usage, token consumption, and ROI.
  • Establishing orchestration frameworks so agents collaborate rather than compete across systems.

Without this discipline, organizations risk repeating the inefficiencies of early virtualization and multi-cloud sprawl. The winners will be those that can scale autonomy while maintaining oversight.

As agents multiply, governance and trust become essential.

Governance and trust: The foundation of the agentic enterprise

As agents gain independence, governance becomes a leadership priority. The more decisions AI systems make, the more critical it becomes to ensure they are made safely, transparently, and within defined boundaries.

Agentic AI also introduces new considerations for data sovereignty and collaboration. IDC expects data clean rooms—secure environments where organizations can analyze shared data without exposing it—to become foundational to multi-enterprise AI strategies.

“Organizations that underinvest in time, money, and training of AI governance, including transparent frameworks, these guardrails, auditability, and fail-safe escalation mechanisms, will be more vulnerable to these unexpected outages,” said Jensen.

Strong governance does not end inside the enterprise. It extends to every partner in the ecosystem and is redefining what CIOs should expect from technology providers.

The new CIO–provider dynamic

In this new era, IT leaders will need more than vendors. They will need partners who can lead by example.

IDC recommends that CIOs expect three things from every technology partner:

  1. Transparency about how they are using agentic AI internally and what they have learned.
  2. Operational guardrails for cost, data, and security across multi-agent systems.
  3. Human alignment, with a clear commitment to using AI to amplify human capability.

Partnerships built on these principles will reduce risk and accelerate innovation, helping organizations learn faster and execute with confidence.

Navigating the agentic future

Agentic AI is redefining how software is built, how services are delivered, and how humans and machines collaborate.

For IT leaders, success will require both boldness and balance:

  • Boldness to reimagine how work gets done.
  • Balance to govern what is automated, protect what is human, and demand accountability from partners.

Those who approach the agentic pivot with transparency, trust, and financial discipline will turn disruption into direction and set the pace for the next era of enterprise technology leadership.

Learn more about the trends shaping the tech industry’s agentic pivot in the IDC FutureScape 2026: The Agentic Pivot in the Tech Industry webinar.

Christina Cardoza - Content Marketing Manager - IDC

LinkedIn
Christina Cardoza is a Content Marketing Manager at IDC, where she specializes in brand content and social media strategy. With a background in journalism and editorial leadership, she has a proven ability to transform complex technology topics into clear, actionable insights.

Artificial intelligence (AI) is flooding the world with information, raising expectations for instant answers, and reshaping how decisions are made. But faster answers don’t always mean better ones.

At IDC, we’ve been listening closely to what our customers are telling us in this moment. You want speed, but not at the expense of trust. You want tools that make it easier to access IDC intelligence, but without losing the rigor, depth, and human judgment behind it. You want insights you can act on, not just more materials to sift through.

Information is everywhere — but intelligence you can trust is not. Your choices have been a tradeoff between speed and credibility. Our goal is to change that.

The next chapter of trusted intelligence

For more than 60 years, IDC has set the standard for how the world measures, forecasts, and understands technology markets. Our intelligence spans more than 11.5 billion proprietary data points and over 115,000 pieces of published research across more than 500 technology domains and 110 countries. That depth of expertise is why leading global businesses turn to IDC to help them make confident decisions that move their organizations forward.

Now, we’re building on that foundation to unleash the true power of trusted technology intelligence in the AI era.

Our three-part AI ecosystem

Our vision is clear: combine the speed of AI with the depth and credibility of IDC intelligence.

And we’re approaching it with one principle at the center: you, our customers.

It’s a promise that demands action, not just intent. For IDC, it has meant a year-long effort to simplify how we work, rethink the products we create, and formulate a new approach for how we deliver. And it’s what makes our approach to AI innovation unique.

Rather than simply bolting generative AI tools onto existing platforms, we are going beyond the “AI as UX” approach and re-engineering our foundation to meet you where you work, on your terms, without compromising IDC’s time-tested rigor or quality.

Our new intelligence ecosystem includes three connected components:

1. AI Intelligence Platform: 
IDC’s new AI intelligence platform will make our proprietary data, research, and analyst insight conversational, contextual, and workflow-ready so you can move from question to decision faster and with more confidence than ever before.  

2. Direct Access via APIs: 
You’ve told us you want easier, more integrated access to IDC data and research. We’re making that possible through enhanced APIs and AI usage rights, so you can securely and responsibly integrate our intelligence directly into your organization’s advanced analytics, AI tools, and related workflows – expanding access to more users across your business.

3. Strategic Partnerships: 
We’re also expanding how and where you can access IDC intelligence by partnering with leading enterprise platforms to embed IDC insights directly into the applications and tools where business decisions happen every day. 

Together, these elements form an open ecosystem that makes IDC intelligence faster to access, easier to integrate, and simpler to apply. It shifts the long-standing industry dynamic from requiring you to come to us for insights, to bringing essential intelligence to you, exactly when and where you need it.

AI-fueled, human-driven

IDC’s approach to innovation is deliberate: AI-fueled but human-driven.

Our analysts see what AI can’t yet: the signals through the noise, the implications behind the data, and the “why” that turns information into strategy. Every AI capability we build is designed to amplify that human advantage and backed by years of unbiased, evidence-backed expertise from analysts who deeply understand the business of technology. The result? Answers that not only fuel faster decisions, but better ones.

We’re also using AI to strengthen how our own teams work by standardizing research processes, automating routine tasks, and freeing our experts to focus where it matters most: with you, turning intelligence into action.

This is how we’re empowering better outcomes: accelerating insight and broadening access without compromising quality, and helping every customer stay ahead of change with intelligence they can trust.

What’s ahead

We’re grateful for the feedback from our customers, the curiosity of the broader tech community, and the ingenuity of our IDC teams, which have helped bring this vision to life.

In the months ahead, we’ll share more about our expanded API capabilities and new partnerships, and the upcoming launch of IDC’s AI intelligence platform.

Watch this space for more in Q1 of 2026 — the next chapter of trusted tech intelligence is just beginning.

Genevieve Juillard - Chief Executive Officer - IDC

LinkedIn
Genevieve Juillard is the Chief Executive Officer of IDC, responsible for leading the company’s global strategy, operations, and growth. A proven operator with nearly two decades of experience, she is known for building high-performing teams and driving transformational change across complex, data-driven businesses.