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Category: Markets and Trends

Market-level analysis, forecasts, buyer behavior, and ecosystem dynamics cutting across products.

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 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.

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.

In the fast-moving world of tech distribution, having the right insights at the right time is critical. That’s why the GTDC Summit APJ 2025 is the premier gathering for leaders in the Asia-Pacific channel. Held in Singapore November 17-18, it’s where strategy meets intelligence, and where IDC and CONTEXT are showing up to bring both.

The Event That’s Redefining the Channel

GTDC Summit APJ brings together global vendor and distributor leaders for two days of high-impact conversations, networking, and insight, including plenary sessions, ESG-focused discussions, and strategic advisory councils. This isn’t just an event, it’s a signal of where the industry is going next.

From in-depth discussions on ESG priorities to closed-door sessions with the leadership, the agenda is packed with moments that matter. And it all happens in the heart of Singapore’s cultural district.

See why this summit matters and who you’ll meet there.

IDC + CONTEXT + GTDC – Distribution Intelligence in Action

Earlier this year, IDC, the Global Technology Distribution Council (GTDC), and CONTEXT formed a landmark global alliance to deliver something the tech industry has never had before: a single, standardized view of sell-through performance across Asia Pacific, North America, Europe, and the Middle East.

CONTEXT, as a market intelligence leader in EMEA, brings deep expertise in distributor panel management and data normalization. GTDC contributes daily invoiced sales data from its network of leading distributors. And IDC connects it all with rigorous forecasting, market modeling, and analyst-driven insight.

Together, this partnership offers vendors and distributors a 360-degree global perspective, accurate, consistent, and actionable.

At the GTDC APJ Summit, this initiative comes to life. You’ll have the chance to meet directly with IDC, CONTEXT and GTDC leadership to explore how this intelligence model applies to your business in Asia Pacific and beyond, and to schedule a 1:1 session to see the platform in action.

  • Discover how IDC’s analytics platform integrates with GTDC member data
  • Explore how CONTEXT’s distributor panel expertise enhances regional visibility
  • Understand what the sell-through signals reveal about emerging APJ trends
  • Learn how to use this new insight layer to fine-tune tactical and strategic decisions

It’s the kind of clarity this market has been missing, and at this Summit, you’ll see exactly how it can work for you.

See how this global data alliance is transforming distributor intelligence.

Meet Us There

IDC and CONTEXT will be on-site, ready to share insights, preview new market data, and connect 1:1 on how we can help you navigate your next move in APJ and globally. Whether you’re a vendor, distributor, or ecosystem leader, this is your opportunity to:

  • Tap into global sell-through insights shaped by trusted analysis
  • Benchmark your business across regions and categories
  • Align with IDC and CONTEXT experts on how to act on what’s next

Come see us at GTDC Summit APJ 2025
November 17–18, Conrad Singapore Marina Bay
>> Register for the Summit

The distribution landscape is evolving fast. The insights are finally catching up. Visit Singapore this November and discover how IDC and GTDC are shaping the next chapter of channel intelligence, and how you can apply these insights to sharpen your 2025 distribution strategy.

Because seeing the future clearly starts with the right data and the right partner.