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Software development is at an inflection point. As agentic AI reshapes how teams build, deploy, and manage applications, the boundaries between developers, tools, and systems are dissolving.

The 2026 IDC FutureScape: Worldwide Developer and DevOps Predictions explores this evolution across four major shifts: from developers guiding AI-augmented tools, to intelligent agents reshaping DevOps, to organizations mastering multi-agent orchestration, and finally to the rise of structured agent development itself.

These predictions trace a dual shift: developers are simultaneously learning to work with intelligent agents and learning to build them. Both paths demand new skills, new development paradigms, and new models for scaling and governing AI across the enterprise.

The path of transformation: Developers as orchestrators

By 2030, 70% of developers will partner with autonomous AI agents, shifting human developers toward planning, design, and orchestration and transforming the developer tools ecosystem.

Autonomous AI agents will redefine what it means to build software. These systems will act as intelligent extensions of the development process, generating code, identifying bugs, refactoring systems, and proposing architectural improvements. This shift allows developers to move from repetitive work to higher-value problem-solving.

The human role becomes one of oversight: assigning tasks, validating outputs, and refining results. Architecture and code reviews remain essential, with human teams ensuring that AI-generated contributions meet performance, design, and security standards. At the same time, AI enhances productivity by flagging vulnerabilities, enforcing consistency, and surfacing optimizations that might otherwise go unnoticed.

As AI integration deepens, developers will take on greater responsibility for designing, guiding, and governing agent behavior. Their focus will shift toward planning, orchestration, and oversight to ensure that automation supports organizational goals while remaining ethical, explainable, and secure.

From linear pipelines to adaptive systems

By 2030, 70% of organizations will embed AI agents in DevOps and DevSecOps pipelines to execute development and security workflows, as platform engineering enables standardization at scale.

Software delivery is evolving from automated pipelines to intelligent ecosystems. AI agents will be embedded across development and security workflows, automatically handling code testing, deployment, and compliance checks. These agents will work around the clock, accelerating delivery while reducing the chance of human error.

Platform engineering will provide the foundation for this model. Consistent standards, APIs, and observability across teams will ensure that agents can operate securely and reliably at scale. This transformation allows organizations to balance innovation with governance as automation reaches new levels of efficiency.

The shift to agentic delivery represents a significant inflection point for DevOps. It’s not just about doing things faster but about creating a pipeline that can continuously learn, adapt, and improve. Organizations that prepare for this change will see shorter release cycles, stronger security, and a level of agility that defines the next generation of software delivery.

The governance imperative

By 2029, the risks and complexity of multi-agent orchestration will drive organizations to intensify strategy, boost COE resources, and increase spending on AI governance and monitoring tools by 35%.

As organizations move from using a handful of independent agents to managing vast networks of interconnected ones, the challenge becomes one of control and accountability. This scale and complexity introduce new risks: agents operating outside policy boundaries, misaligned decision-making, and cascading failures that can ripple across entire platforms.

Organizations that succeed will treat governance as a continuous discipline embedded in every layer of operations. Investing in robust oversight, centers of excellence, and monitoring systems will not only mitigate risk but also unlock faster innovation. With the proper governance structure, multi-agent systems become an engine for resilience.

For technology leaders, the message is clear: as AI-driven automation scales, so must your governance. The companies that get this balance right will be the ones that innovate confidently, able to harness the full potential of agentic systems, while others are still managing unexpected complexity.

Building agents, not just using them

By 2028, facing a 10x increase in agents deployed, 60% of the G2000 will adopt an agent development life cycle for effective scale-up of agentic AI across the enterprise.

As AI agents multiply across the enterprise, organizations will need a structured way to manage their creation, training, and governance. Traditional development methods aren’t built for the complexity of agentic systems that learn, reason, and evolve. The Agent Development Life Cycle (ADLC) will become the backbone of how companies scale AI safely and effectively.

ADLC introduces a new paradigm for development. It integrates large language models with reasoning engines, memory systems, and continuous feedback loops to ensure agents can adapt intelligently over time. This advancement means development must evolve from static product releases to dynamic, ongoing systems of improvement. The ADLC provides the structure and guardrails to keep pace with AI’s rapid learning cycles while maintaining transparency and trust.

For business leaders, this is more than an IT initiative. It’s a strategic capability that redefines how value is created and maintained. Companies that achieve ADLC maturity early will be able to deploy agentic AI faster, respond to market shifts in real time, and continuously improve business outcomes. Those who delay will find themselves limited by outdated processes, unable to manage AI complexity at scale.

The new developer paradigm takes shape

As developers build with AI agents, they’re also building AI agents. These aren’t separate tracks but interconnected practices that inform and reinforce each other. The new paradigm is characterized by developers who are simultaneously users, creators, and governors of intelligent systems. Organizations that recognize this evolution will move faster and more confidently, developing the skills and structures needed to operate at both levels. Mastery of this dual capability will define what it means to develop software in the agentic era.

These predictions come from IDC’s FutureScape: Worldwide Developer and DevOps 2026 Predictions. For the complete research on how agentic AI is reshaping software development, delivery, and governance, explore the full report.

To understand how these developer shifts connect to the broader agentic enterprise transformation, visit IDC’s FutureScape 2026 Predictions and join our webinar series for actionable insights on navigating the agentic era across your organization.

Jim Mercer - Program Vice President, Software Development, DevOps & DevSecOps - IDC

Analyst Profile
Jim Mercer is a Program Vice President managing multiple programs spanning application lifecycle management (ALM), modern application development and trends, emerging generative AI software development, DevOps, DevSecOps, open source, PaaS for developers, and cloud application platforms. His focus areas are DevOps and DevSecOps Solutions research practices. In this role, he is responsible for researching, writing, and advising clients on the fast-evolving DevOps and DevSecOps markets.

As enterprises accelerate their use of AI, the importance of secure data sharing has never been greater. In IDC’s recent FutureScape 2026 predictions, it was predicted that by 2028, 60% of enterprises will collaborate on data through private exchanges or data clean rooms.

With Amazon Web Services (AWS) announcing new privacy-enhancing synthetic data generation within AWS Clean Rooms, we are already starting to see that prediction take shape.

We sat down with Lynne Schneider, Research Director for Data Collaboration and Monetization, and Location & Geospatial Intelligence at IDC, to unpack this prediction, explore the impact of AWS’s announcement, and offer guidance for enterprises preparing for the next era of AI-driven data collaboration.

You predicted that by 2028, 60% of enterprises will collaborate on data through private exchanges or clean rooms. What’s driving that shift?

Over the next several years, we anticipate that the majority of global enterprises will be collaborating through some form of private data exchange or data clean room. The reason is simple: the only sustainable advantage in an AI world is data, and novel data combinations.

What frightens people is the idea that their private data might leak or reach people they never intended to share it with. That’s why data collaboration technologies, including private exchanges and clean rooms, will rise from “nice to have” to must-have.

Amazon recently announced privacy-enhancing synthetic dataset generation within AWS Clean Rooms. How does this validate the direction you predicted?

This announcement sits at the nexus of two IDC predictions: growth in data collaboration and growth in synthetic data.

People turn to synthetic data for two reasons:

  1. To expand small datasets when training models.
  2. To add privacy protection by creating an equivalent privacy-safe dataset.

AWS’s announcement is focused on that second reason — privacy.

Before secure data collaboration was technologically feasible, people relied on contractual promises to keep shared data private. Now the technology itself enforces privacy. Synthetic data was one way organizations tried to protect sensitive elements (like social security numbers or addresses) to reduce the risk of re-identification.

What AWS has introduced is essentially a second layer of privacy protection. You bring your proprietary data into the clean room, activate the AWS service, and it generates a synthetic dataset. AWS also provides instruments to measure how well that synthetic data meets your privacy requirements before you use it.

How does combining clean rooms with synthetic data expand what enterprises can safely do with AI, especially as we head into the agentic AI era?

It’s really an up leveling when you combine the two.

Clean rooms already support federated training and let both humans and AI agents access and combine data securely. Synthetic data adds another privacy option on top of that. Together, they allow organizations to explore more advanced AI use cases — including generative and agentic AI — without exposing raw sensitive data.

From a trust, governance, and privacy standpoint, what does it mean that enterprises can now generate synthetic datasets inside the clean room rather than relying on external tools?

When people build synthetic data today, we often see “synthetic audiences” — personal data that’s transformed for advertising or marketing applications. We’re also seeing emerging use cases in life sciences and healthcare, where the data is extremely sensitive and sometimes scarce. Synthetic data helps expand those datasets for modeling and experimentation.

The challenge is that synthetic data can go wrong in two ways:

  • It may stray too far from the original data and become meaningless, or
  • It may stay too close, raising re-identification risks.

Combining synthetic data generation with a clean room solves both issues. The clean room governs access and also controls what analyses can be performed. It provides an extra seal of privacy.

What should enterprises start doing now to prepare for this shift, both in terms of data strategy and AI readiness?

Enterprises should start by identifying what kinds of data they need to make their AI, analytics, or decision intelligence more effective.

For example:
If you’re forecasting demand for a product and weather impacts that demand, you may need to combine:

  • A general LLM
  • Your enterprise’s historical demand data
  • External weather data (public or partner-provided)
  • Logistics partner data about fleet availability

Each party may hold sensitive information they don’t want to expose. Clean rooms allow you to combine all those pieces securely.

Is there anything else enterprises should know about the direction this market is heading?

We have some great examples of how enterprises are benefiting from data collaboration in a recent IDC report: From Adoption to Advantage: Experiences of Data Cleanroom Innovators.

There was an initial period when “data clean rooms” were a popular buzzword — the same way “AI” is today. Many organizations wanted to say they were doing it. But once you get past the check-the-box phase, you need to prove the value.

This research highlights 11 different use cases, challenges, outcomes, and guidance on how companies are realizing value through data collaboration technologies.

See more predictions shaping the agentic AI era. Explore the full IDC FutureScape insights.

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.

IDC and Amazon are teaming up to make high-quality business insights faster, easier, and more accessible. IDC announced a new strategic partnership that brings its proprietary technology intelligence directly into Amazon Quick Research, an AI-powered research agent inside Amazon Quick Suite.

Trusted intelligence now built into AWS workflows

Amazon designed Quick Research to help business professionals generate, synthesize, and analyze complex information across multiple data sources. By integrating IDC’s premium research and more than 11.5 billion data points, the tool now delivers a new level of depth, accuracy, and credibility, all within the user’s existing AWS environment.

For many organizations, this solves a growing challenge: business users are overwhelmed by fragmented data sources and a surge of unverified AI-generated content. Embedding IDC’s validated intelligence directly into an AI-driven agent helps close that trust gap at a moment when clarity and speed are more critical than ever.

Through the integration, customers will gain:

  • Faster, more precise insights that blend next-generation AI with IDC-validated research
  • Seamless access to IDC content inside daily AWS workflows
  • Higher productivity and confidence in AI-generated recommendations and analyses

A milestone in IDC’s AI-fueled ecosystem strategy

The partnership also marks a milestone in IDC’s plans to deliver trusted intelligence directly into the tools and environments customers use every day. This integration is also part of IDC’s broader shift toward an AI-fueled, human-driven model for delivering trusted technology intelligence. IDC recently shared its vision for evolving from static research delivery into a connected intelligence ecosystem powered by APIs, partnerships, and agentic AI. The collaboration with Amazon Quick Research is one of the first visible steps in bringing that strategy to life, meeting customers where they work and embedding IDC insights into the flow of everyday decision-making.

By pairing IDC’s analyst-validated intelligence with Amazon Quick Research, we’re accelerating our AI-fueled, human-driven approach and giving customers faster, more precise insights to support confident decision-making,” said Meredith Whalen, chief product, research and delivery officer at IDC

And this is only the beginning. IDC will continue expanding its intelligence ecosystem in 2026 with additional integrations, enhanced APIs, and new ways for customers to tap into analyst-validated insights across the platforms where they already work.

Access to IDC insights is available to Amazon Quick Research users with select IDC subscriptions.

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.

Many enterprises are eager to deploy AI-driven capabilities, yet their ambitions are constrained by accumulated technical debt — outdated systems, fragile integrations, and limited data interoperability. IDC research shows that unmanaged tech debt can consume 20–40% of development time, diverting resources away from innovation and modernization.

For CIOs, the problem isn’t only technical, it’s strategic. Systems that were once fit for purpose now inhibit agility, scalability, and trust in data-driven decision-making. Vendors have an opportunity to become partners in reducing this friction by linking modernization roadmaps directly to the organization’s AI goals and measurable business outcomes.

An aging learning and development platform

Consider a global manufacturer whose workforce skilling system was built a decade ago on a rigid, on-premises learning management platform. The system stores static course libraries and tracks completions but cannot personalize training or integrate real-time performance data. As the company explores AI-enabled, adaptive training that generates custom learning paths based on employee behavior, role, and skills gaps, the legacy system becomes a liability:

  • Technical debt: Custom code and outdated integrations make migration costly and complex.
  • Operational drag: Manual updates and data entry consume IT hours that could support AI adoption.
  • Business risk: Workforce skills lag behind new digital processes, slowing innovation and productivity.

Without modernization, the organization cannot take advantage of new agentic or AI-driven learning systems capable of dynamically tailoring training to role, performance, or predicted need.

How vendors can accelerate modernization and build shared value

Vendors can play a critical role in helping technology leaders move from technical debt management to technical health improvement.

The most successful vendor strategies go beyond replacing legacy systems; they co-develop modernization plans that align with enterprise strategy and deliver mutual, long-term benefit.

  1. Quantify and visualize technical health.
    Provide assessment frameworks and tools to measure the client’s “technical health” across systems — highlighting how legacy systems inhibit AI adoption. This gives CIOs a defensible, data-driven case for investment.
  2. Link modernization to AI outcomes.
    Position upgrades not as infrastructure refreshes but as enablers of AI-readiness — improved data access, reduced integration friction, and scalable infrastructure that supports machine learning and automation.
  3. Co-own the transformation roadmap.
    Collaborate on a phased modernization plan that addresses immediate technical debt while embedding continuous improvement and governance models. This partnership ensures measurable progress toward an AI-enabled enterprise.
  4. Embed learning modernization in the platform.
    Vendors offering AI-driven learning solutions can integrate adaptive skilling, microlearning, and real-time performance analytics directly into their technology, helping organizations cultivate the AI literacy and workforce agility needed for sustained transformation.

The strategic payoff

For the enterprise, addressing technical debt becomes a launchpad for AI advantage. For the vendor, guiding this transition cements long-term strategic partnership and stickier platform adoption. By aligning modernization efforts with business impact for faster upskilling, improved productivity, and data-driven workforce performance vendors move from being solution providers to co-architects of enterprise resilience and AI maturity.

Daniel Saroff - GVP, Consulting and Research Services - IDC

Analyst Profile
Daniel Saroff is Group Vice President of Consulting and Research at IDC, where he is a senior practitioner in the end-user consulting practice. This practice provides support to boards, business leaders, and technology executives in their efforts to architect, benchmark, and optimize their organization's information technology. IDC's end-user consulting practice utilizes our extensive international IT data library, robust research base, and tailored consulting solutions to deliver unique business value through IT acceleration, performance management, cost optimization, and contextualized benchmarking capabilities.

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

LinkedIn
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

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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.