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The global smart vacuum cleaner market didn’t just grow in 2025, it reorganized. Shipments reached 17.42 million units in the first three quarters of the year, up 18.7% YoY, with Q3 alone up 22.9%. Chinese brands; Roborock, Ecovacs, Dreame, Xiaomi, Narwal dominated the top five reflecting a structural shift powered by faster product cycles, ruthless price segmentation, and deep ecosystem plays. Consumers signaled what they value: AI-driven navigation, obstacle recognition, self-emptying docks, and seamless integration with voice and home platforms. Vendors that delivered those at mid-tier and entry-level prices won share while those that didn’t are now playing catch-up.

iRobot: A Pioneer That Missed the Turn

The news is unambiguous: iRobot filed for Chapter 11 and agreed to be acquired by its primary Chinese manufacturer, Shenzhen Picea Robotics, with the plan to take the company private and continue operations under Picea’s ownership. It’s an ending few expected a decade ago.

What caused the fall from grace? iRobot’s decline boils down to three things:

  • Tech posture: iRobot resisted LiDAR navigation for too long, opting for vSLAM (camera-based visual mapping) that proved less consistent in real-world homes and lighting conditions.
  • Price architecture: iRobot clung to premium pricing while rivals shipped better-specced mid/low SKUs globally.
  • Balance sheet & policy shocks: Debt refinancing and new U.S. tariffs raised costs just as competition intensified and the Amazon acquisition collapsed.

Market Implications

Innovation cadence beats incumbency – The winners executed rapid, visible upgrades such as the inclusion of LiDAR, AI, auto‑empty bases, low‑profile designs, and more. The pace of innovation sometimes led to multiple product releases a year while the laggards optimized margins and brand heritage. The market rewarded the former.

Ecosystems matter more than SKUs. Tight integration with Mi Home, Alexa, Google Assistant, regional retail, and services is now as important as suction ratings because it drives repeat purchase and lock‑in.

2026 Outlook: Five Predictions to Watch

AI Navigation Goes From ‘Specs’ to ‘Outcomes’. Expect vendors to market room‑level autonomy such as predictive pathing, dynamic no‑go zones, seasonal routines, rather than sensor acronyms. The winning KPIs will be coverage completeness, cleaning time, and failure‑free runs per week. Chinese leaders already test and iterate on these claims aggressively.

Platform Moats Deepen. Roborock/Ecovacs/Dreame will push first‑party apps and hubs into broader home control (air purifiers, mops, window cleaners). Xiaomi will double down on Mi Home stickiness. Other brands that can’t anchor a platform will lean on Amazon, Apple, and Google integrations and retail partnerships.

Design Improvements: Thin Is In — Smart vacuums are evolving to tackle real-world challenges with slimmer profiles that reach under low-clearance furniture, enhanced ability to clear taller thresholds, and AI-powered object recognition for hazards like cords, socks, and pet waste. These innovations are increasingly being brought to more affordable models, making advanced navigation and hands-off cleaning accessible to a wider audience.

Regionalization of Portfolios. MEA and parts of Europe will continue to outgrow North America, driven by tuned SKUs (tile/stone floor focus, water tank size, voltage standards) and offline retail investment. Brands that localize service and spares win loyalty.

Bottom Line (for 2026)

Expect Chinese brands to extend their lead, especially in Europe/MEA, on the back of holistic ecosystems and relentless iteration. Watch iRobot under Picea: it may emerge as a good-enough brand with improved navigation and cost structure, but it must earn back trust and relevance quickly. The consumer win continues: more capability at lower prices.

Jitesh Ubrani - Research Manager - IDC

Analyst Profile
Jitesh is a Research Manager for the Worldwide Mobile Device Trackers, including Wearables, Augmented Reality (AR), Virtual Reality (VR), Tablets, and Phones. The team focuses on the market sizing, forecasting, and analyzing trends to provide insight into the competitive landscape of the worldwide mobile industry. Prior to joining IDC in 2012, Jitesh was part of the Market Analysis and Intelligence team at Bell Mobility, one of Canada's largest telecom service providers, where his role focused on understanding smartphone adoption and usage as well as consumer purchasing behavior. Mr. Ubrani holds a bachelor of commerce degree with a major in Economics from Ryerson University and is currently based in Toronto, Canada.

After 38 years with IDC, I have decided that it’s the right time to step into the next chapter of my career. Beginning in January, I will transition out of my current role into supporting the company as a Special Advisor, where I will continue to champion IDC and the critical role we play in guiding the technology community forward. 

When I joined IDC as an associate research analyst, I could not have predicted the opportunities and experiences that would follow. I was drawn to IDC because of its unique vantage point on the technology industry, and I stayed because of two things: the constantly changing nature of technology and its impact on the world, and the opportunity to learn from some of the smartest people in the industry—across IDC, our customers, and the broader market. 

Throughout my career, I’ve had the privilege of working with exceptional colleagues and leaders who shaped IDC’s global research and data offerings. Together, we created, honed, and strengthened IDC’s position as the trusted source for technology intelligence used by organizations around the world. 

What’s next 

During my time here, IDC has evolved through multiple technology cycles—from client/server, to mobility and cloud, and now AI. With strong leadership, a talented global team, and a clear vision for what trusted tech intelligence looks like in the AI era, IDC is stronger than ever. 

To my colleagues: thank you for your dedication, your partnership, and the professionalism that defines IDC. 

And to our customers and partners: thank you for trusting us with your most important decisions and challenging us to continuously raise the bar. 

IDC’s future is bright and I am excited to support it.  
 

Crawford 

Crawford Del Prete - President - IDC

Analyst Profile
Crawford Del Prete was appointed President of IDC in February 2019. Prior to his current role, he served as IDC's Chief Operating Officer. Through his leadership, IDC has established a leading position as the world's most prominent and trusted technology market intelligence provider. Crawford joined IDC in 1989 as a research analyst. Throughout his IDC career, he has grown multiple IDC businesses to industry leadership positions. He was instrumental in creating IDC's high visibility research and data tracking products which are used daily in the IT industry for strategic planning. Crawford is a leading authority on the IT industry and has completed extensive research on the structure and evolution of the information technology industry. He advises technology and business leaders on how to adapt and change in a time when technology is changing the world. He is frequently quoted in publications such as The Wall Street Journal, The Financial Times, The New York Times and other leading media sources. He is a regular guest on Bloomberg Technology TV, offering insight and perspective on daily technology events. He was awarded The Patrick J. McGovern Award for Management Excellence in 2014. In 1995, he was awarded IDC's James Peacock Award for research excellence, IDC's highest research honor. He holds a B.A. from Michigan State University and in 2012, he was named a Distinguished Alumni of the University. Follow Crawford on Twitter @craw.

As the IDC Government Insights team developed this year’s IDC FutureScape: Worldwide Smart Cities and Communities 2026 Predictions, one trend became clear: cities of all sizes are rapidly adopting LLM-driven AI tools. As cities confront tighter budgets, rising public needs, and the accelerating pace of AI adoption, two predictions stand out: One prediction on Agentic AI and workflow orchestration, and the other on unlocking the value of government data through fine-tuned large language models (LLMs).

Together, these prediction signal a shift from technology-as-a-tool to technology-as-a-teammate (or as “a personal intern”)— where intelligent systems collaborate with humans to simplify complexity, bridge data silos, and elevate service delivery. For mayors, CIOs, and innovation officers, this is more than automation, it’s a reimagining of how government works.

Agentic AI Connects the Dots Across City Systems

By 2027, 65% of cities will deploy AI agents across systems and data to orchestrate end-to-end workflows and reduce workloads while addressing risks of misuse and overreach and “process debt.”

Local governments have long wrestled with what IDC has termed “process debt” — the accumulated workflow inefficiency of fragmented systems, redundant data entry, and manual workarounds. Agentic AI changes that equation. Unlike traditional AI models built for narrow tasks, AI agents can understand goals, coordinate across systems, and execute full workflows — from processing applications to reconciling budgets to automating permit approvals.

But this evolution demands groundwork. Before AI agents can drive real impact, state and local governments must map workflows, clean data, and redesign processes that currently constrain efficiency. As we often discuss, automating broken processes “rarely delivers better outcomes.” Instead, success depends on combining automation with human oversight, workforce readiness, and transparent governance.

Human + Machine Collaboration

Agentic AI will shift how public sector teams work — not by replacing people in the near-term, but by augmenting their capacity. Entry-level clerical roles may evolve, but new opportunities will emerge for “AI process managers,” ethics officers, and cross-agency data specialists. IDC emphasizes that HR must be a strategic partner in this transformation, guiding reskilling and maintaining morale during rapid change.

The payoff? Smarter workflows, faster decisions, and lower service delivery costs. When AI agents manage the repetitive, city staff can focus on what humans do best — strategic decisions, innovation and empathetic human interactions.

Unlocking the Hidden Value of Government Data

By 2026, 50% of state and local governments will invest in fine-tuning LLMs on data the models have never seen, unlocking value from decades of protected records and siloed systems.

Every city sits on a goldmine of data — from zoning and traffic to health, housing, and economic development. Yet much of it is trapped in systems that don’t talk to each other; not only that, this data is private and has not been used to train the LLMs that are serving up GenAI results.

The next wave of Smart City innovation will come from fine-tuning LLMs on this untapped data. Cities will begin training models on internal records — with strict governance — to capture local context and institutional knowledge. The result: AI systems that “speak government”, understand regulatory nuances, and generate insights and recommendations grounded in real municipal operations. This provides faster insights for planning decisions and actions that support mission outcomes.

From Locked Archives to Living Intelligence

Imagine an AI system trained on decades of urban planning documents, council minutes, and building permits. It could summarize past precedents for new zoning requests, detect policy inconsistencies, or surface patterns in infrastructure maintenance failures. Or consider a model fine-tuned on social services data — capable of predicting which households may need early intervention to prevent homelessness.

These capabilities hinge on one foundation: responsible data governance. IDC advises governments to invest in “AI-ready data” — standardizing formats, labeling metadata, and implementing data governance technologies to ensure security and trust. As models become more specialized, leaders must also modernize infrastructure, upgrading government clouds and integrating intelligent computing power to support large-scale inferencing.

Bringing It Together: The Convergence of Agentic AI and Data Intelligence

The two predictions are two sides of the same coin. Agentic AI depends on data liquidity; data intelligence depends on intelligent orchestration. Together, they form the digital nervous system of the future city.

As IDC’s broader FutureScape 2026 report underscores, the Smart City of the near future is not just connected — it’s context-aware. AI agents will move seamlessly across departments, drawing on fine-tuned LLMs to provide decisions informed by a city’s own history and conditions.

The FutureScape highlights key trends:

  • Agentic AI is the next leap in digital government, transforming automation into orchestration across workflows.
  • Fine-tuned government LLMs will unlock decades of hidden data, fueling more contextual and accurate decision-making.
  • Responsible governance is the foundation — without ethical frameworks, AI progress can erode rather than build trust.
  • The future is collaborative: Humans define intent and context; AI executes and optimizes — together delivering public value faster and smarter.

Guidance for City Leaders

Smart City success depends not just on adopting AI, but on designing for agility, responsibility, and inclusion. Based on Predictions 1 and 6, here are three critical actions:

  1. Modernize the Data Core
    Build secure, interoperable data platforms that connect siloed systems. Invest in metadata management, data lineage, and ethical AI governance frameworks that prepare your data for fine-tuning and automation.
  2. Pilot Agentic Workflows in High-Impact Areas
    Start small but strategic — automate processes where the value is measurable (e.g., licensing, fleet maintenance, or procurement). Use sandboxed environments to test AI agents safely before scaling.
  3. Center People in the Process
    Partner with HR to redefine job roles and develop AI literacy. Transparent communication and change management are essential to maintain public trust and employee confidence.
  4. Design for Accountability and Transparency
    Incorporate audit trails, explainable AI, and citizen feedback loops. The legitimacy of AI-driven decisions will determine long-term success more than the sophistication of the technology.

The FutureScape 2026 predictions make one thing clear —when agentic AI and data governance converge, cities can be better proactive orchestrators of well-being, equity, and sustainability.

Cities like Boston, Singapore, and Barcelona are already using AI-powered urban planning platforms to integrate policy, climate, and citizen feedback — showing how government-specific data can supercharge innovation responsibly. These early movers demonstrate what’s possible when leaders treat AI not as a black box but as a civic partner.

As Smart City leaders plan their 2026 strategies, now is the time to evaluate your readiness for agentic AI and data-driven transformation.

Explore the full IDC FutureScape: Worldwide Smart Cities and Communities 2026 Predictions report, join the IDC Directions 2026 event series, and dive deeper into the IDC PlanScape: An Agentic AI Guide for Smart Cities to chart your next steps.

If your city is already advancing innovative, AI-enabled initiatives, consider submitting your project for the IDC 2026 Smart Cities and Communities North America Awards, now open for nominations.

Ruthbea Yesner - Program VP - IDC

Analyst Profile
Ruthbea Yesner is the Vice President of Government Insights at IDC. In this practice, Ms. Yesner manages the US Federal Government, Education, and the Worldwide Smart Cities and Communities Global practices. Ms. Yesner's research discusses the strategies and execution of relevant technologies and best practice areas, such as governance, innovation, partnerships and business models, essential for government and education transformation. Ms. Yesner's research includes analytics, artificial intelligence, Open data and data exchanges, digital twins, artificial intelligence, the Internet of Things, cloud computing, and mobile solutions in the areas of economic development and civic engagement, urban planning and administration, smart campus, transportation, and energy and infrastructure. Ms. Yesner contributes to consulting engagements to support K-12 and higher education institutions, state and local governments and IT vendors' overall Smart City market strategies.

AI will continue to shape the enterprise communications landscape in 2026, with organisations seeking practical value while navigating cost, governance, and deployment constraints. Interest in AI is high, but companies still face gaps around affordability, readiness, and real-world use cases. As a result, the market will progress through grounded, incremental steps, supported by stronger data foundations, evolving pricing models, and greater collaboration across ecosystems and service partners.

1. AI Adoption Will Remain Pragmatic and Focused on Clear ROI

AI will continue to gain momentum, but organisations will prioritise capabilities that deliver immediate, measurable value, such as summarisation, transcription, call insights, and automated follow-ups.

While interest in agentic AI grows, mainstream adoption will be limited by cost and narrow use-case readiness. Vendors will increasingly focus on making agentic capabilities more affordable, modular, and easier to deploy.

2. Data Foundations Will Become the Enabler for Context and Automation

As organisations look into value extraction, data quality and connectivity become essential. AI will need access to contextual, structured, and cross-functional data to deliver accurate outcomes and automate workflows.

To meet these needs, vendors will open their ecosystems, deepen integrations with CRM, ERP, and workflow tools, and begin supporting agent-to-agent orchestration (A2A/MCP) across front-, mid-, and back-office processes.

3. Pricing Models Will Evolve to Reflect AI Consumption Patterns

As AI features become more widely used, traditional subscription pricing will feel less aligned with the way organisations actually consume AI. Vendors will gradually introduce usage-based or metered models, allowing customers to scale AI adoption at their own pace.

To ensure reliability, AI will increasingly blend generative and deterministic approaches, supported by stronger AI observability to maintain accuracy and trust.

4. Verticalisation and Professional Services Will Help Close the Adoption Gap

AI adoption challenges vary significantly by industry. In 2026, more vendors will develop vertical-specific UC&C solutions that reflect distinct workflows in sectors such as healthcare, retail, financial services, and manufacturing.

Because the gap between vendor innovation and customer adoption persists, vendors will collaborate more closely with professional services providers who can translate innovation into practical transformation through guided deployment and workflow redesign.

5. Europe Prioritises Hybrid Deployment and Democratized AI for SMBs

In Europe, concerns around data sovereignty and transparency will continue to influence technology decisions, prompting sustained interest in private cloud and selective retention of on-premises components. Most organisations will move toward hybrid models that offer both innovation and control.

At the same time, European vendors will intensify their focus on SMBs, which represent the bulk of the region’s economy. 2026 will see continued efforts to democratise AI, offering simpler, lighter-weight solutions—such as AI receptionists—as well as modular capabilities that make AI adoption accessible to smaller businesses via partner-led delivery.

Conclusion

In 2026, enterprise communications will move forward through practical AI adoption, deeper data integration, flexible pricing, verticalised innovation, and hybrid deployment models. Markets like Europe will emphasise sovereignty and SMB accessibility, but globally, success will depend on vendors balancing innovation with pragmatism—offering AI that is trustworthy, affordable, and genuinely transformative for how people and organisations communicate and work.

For more information, drop your question in here.

For more predictions, watch IDC’s EMEA FutureScape predictions webcast here.

Oru Mohiuddin - Research Director - IDC

Analyst Profile
Oru Mohiuddin is a Research Director in the European Enterprise Communications and Collaboration team. Based in London, she is responsible for IDC’s coverage of Unified Communications and Collaboration in the region. Her work focuses on tracking the markets for premise-based and cloud solutions and new developments and trends, particularly in the light of changing work patterns impacting the traditional mode of enterprise communication. Prior to joining IDC, Oru worked for Euromonitor International, where she focused on Future of Work and technology in the SMB context. She also worked in New York and Bangladesh and speaks English and Bengali. Oru was awarded Chevening Scholarship by the British Foreign and Commonwealth Office to pursue her MSc in International Development from the University of Birmingham. In addition, Oru has a BA from Marymount Manhattan College in New York.

Graham Fruin - Senior Research Analyst, European Enterprise Communications and Collaboration - IDC

Analyst Profile
Graham Fruin is a senior research analyst in IDC's European Enterprise Communications and Collaboration team. Based in the U.K., his primary focus is on the voice and data connectivity markets. His work has a particular emphasis on the migration from legacy voice solutions to IP-based platforms and the way they are used in conjunction with unified communications. In addition, he analyzes the evolution of the internet access market, which includes the rapid proliferation of Fiber to the Premises (FttP) across Europe.

In 2026, the consumer technology landscape will not be defined by any single breakthrough, but by the convergence of many. Artificial intelligence, once a novelty, is becoming a companion. Agentic AI—systems that act on our behalf—will quietly weave itself into the fabric of daily life. From digital therapists to AI fashion designers, the consumer experience is evolving in ways that feel both exhilarating and uncertain.

How AI Is Reshaping the Everyday Consumer Experience

The Rise of Gaming as the New Social Platform

Let’s start with the familiar: social media and entertainment. For younger generations, gaming has surpassed traditional social platforms as the preferred means of connection. It’s not just about play; it’s about presence. Virtual worlds are becoming the new public squares, and the lines between creator and audience continue to blur. As AI-generated content continues to grow exponentially, the feed will soon feel less like a window into our friends’ lives and more like a reflection of the collective imagination: curated, algorithmically enhanced, and infinitely scalable.

AI Generated Content and the Search for Trust

But this abundance brings a new kind of scarcity: trust. When anyone can generate professional-quality content at the tap of a prompt, the question shifts from “Can I create this?” to “Can I believe this?” Consumers will increasingly gravitate toward authenticity and brands, creators, and platforms that prove what’s real. Paradoxically, the same technologies that blur the lines between truth and fiction may also help rebuild trust, as AI-driven verification and blockchain-based provenance tools become integral to the digital experience.

Emotional AI Companions and the Changing Definition of Care

Meanwhile, the definition of care is changing. Many consumers are already turning to AI companions for support and self-reflection, redefining what therapy and connection look like. This trend says as much about access and affordability as it does about comfort with machines. For some, these AI listeners will offer judgment-free emotional support; for others, they may highlight just how transactional our relationships with technology have become. The opportunity is enormous, but so is the ethical weight: How do we ensure empathy doesn’t become an illusion?

Home Cybersecurity Becomes a Daily Essential

Security, too, is being redefined. The home network, once a patchwork of passwords and devices, is fast becoming a managed ecosystem. Cybersecurity is emerging as a household utility—not an optional service, but a baseline expectation. The idea of paying a monthly fee to protect your family’s digital life will feel as natural as paying for electricity. Yet the same networks that safeguard us will also collect more behavioral data than ever before, creating a delicate balance between safety and surveillance.

AI in Fashion and the Future of Personal Identity

Elsewhere, new rituals of consumption are taking shape. In fashion, AI is learning our tastes faster than we can articulate them. Intelligent design systems are already shaping collections, anticipating preferences, and personalizing garments in real time. It’s a model that can cut waste and returns, but it also raises questions about identity and expression. When algorithms dress us, do they amplify individuality or narrow it to what the data thinks we want?

Augmented Reality and the Return of Local Connection

And then there’s augmented reality: the layer of digital context now emerging atop our physical world. Increasingly, consumers will engage with hyper-local AR experiences that blend art, culture, and commerce. The technology is no longer about novelty; it’s about connection. Imagine walking through your neighborhood and seeing local art, history, or community stories overlaid on the landscape. AR has the potential to restore a sense of place in an increasingly placeless digital age.

Deepening Human Machine Relationships

Threading through all of this is a new kind of intimacy between humans and machines. Emotional bonds with AI systems are deepening as interactions become more personal, responsive, and persistent. That reality alone should give us pause. For decades, technology has mediated our relationships with one another; now it is becoming one of those relationships. Governments and platforms alike are beginning to explore ethical and legal frameworks to protect people from exploitative or deceptive AI companionship. This is a necessary step as emotional computing becomes mainstream.

These shifts aren’t uniformly positive or negative. They reflect a world moving from transaction to immersion, from ownership to orchestration, from control to collaboration. The question for leaders across industries is not whether AI will shape consumer behavior—it already has—but whether we will shape it responsibly. The future consumer will demand more than convenience; they will demand confidence in privacy, authenticity, and purpose.

How Technology Providers Can Lead With Trust and Transparency

The crosscurrents of innovation, trust, and identity are strong. Navigating them requires clear strategy and steady ethics. Because in this next era, technology won’t just serve us—it will know us, represent us, and, increasingly, reflect who we are.

For technology vendors, the challenge is to lead with empathy and accountability. Consumers will reward brands that prioritize transparency, data stewardship, and meaningful engagement over novelty. For B2C and B2B2C innovators alike, the future belongs to those who design AI experiences that empower rather than manipulate, that personalize without intruding, and that build trust as deliberately as they build code. In a world of intelligent systems, trust will be the ultimate differentiator.

To explore these insights in greater depth, check out the IDC FutureScape: Worldwide Consumer 2026 Predictions. It offers a comprehensive view of how AI, trust, and emerging technologies are transforming the consumer landscape—and what technology vendors can do today to prepare for tomorrow.

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.

The linear supply chain, which was optimized solely for cost, speed, and sequential handoffs, is over. In this model, if one link breaks, the entire chain comes to a halt, as there is no built-in redundancy or networked capability to navigate around the problem. As we look toward 2030, the key characteristic of successful operations is no longer just efficiency; it is intelligence at scale. This shift to an “ecosystem” or “network” model is critical for 2026 and beyond.

The last few years have served as a brutal stress test for legacy models, exposing structural fault lines that “optimization” can no longer hide. In late 2024 and throughout 2025, we witnessed a convergence of volatility that linear chains simply could not absorb.

Three specific industry failure modes have emerged from this period, signaling why a new direction is inevitable:

  • The Tier-N Blindspot (The Visibility Gap): A major automotive manufacturer recently halted production when a climate event impacted a Tier 3 sub-component provider. Lacking multi-tier visibility, the planning team remained unaware of the risk until Tier 1 shipments ceased.
  • The “Digital Tower of Babel” (The Interoperability Gap): During recent port congestions, manual handoffs between disparate systems prevented logistics networks from adapting, causing cascading delays. Agile firms pivoted instantly using open platforms while traditional operators remained trapped by disconnected data.
  • The Expanded Attack Surface (The Security Gap): Rapidly increasing IT and OT connectivity without robust security has turned supply chain networks into prime targets for ransomware, cyber-physical attacks on IoT equipment, and AI-enabled attack vectors. Enterprises are deploying distributed, AI-driven systems to proactively neutralize risks from external partners to internal operations.

These are not isolated incidents; they are the growing pains of a sector in transition. They underscore why the next five years will not be defined by better silos, but by the dissolution of silos altogether.

These insights reflect IDC’s 2026 FutureScape: Worldwide Supply Chain and Industry Ecosystems research, which outlines the forces reshaping global operations and the capabilities leaders must prioritize. Explore the full predictions in the global report.

Emerging from this volatility are three distinct trends that will define the path to 2026 and beyond.

1. Multi-Enterprise Orchestration: Visibility That Extends Beyond Boundaries

Disruptions now emerge across extended supplier tiers, logistics partners, and regional networks. Traditional visibility approaches anchored in ERP data and Tier 1 insights are no longer sufficient.

Supply chains must evolve into multi-enterprise networks that enable:

  • Real-time visibility beyond Tier 1 suppliers
  • Shared alerts and contextual intelligence among all partners
  • Coordinated response actions across nodes

This shift moves visibility from a standalone tool to an integrated capability woven through planning, execution, and risk management.

As a result, IDC predicts(1):

By 2028, 50% of enterprise-scale supply chains will use business networks to enable n-tier visibility, serving as a key mechanism to reduce the impact of disruption and improve response speed by 25%.

Organizations that build this foundation gain faster detection, more accurate impact assessment, and greater confidence under volatility.

2. Supplier and Partner Ecosystems: Interoperability as a Performance Multiplier

The ability to work seamlessly across partner ecosystems will define future competitiveness. Interoperability, once a technology challenge, is now a strategic one.

Next-generation supply chains require platforms that:

  • Integrate supplier, logistics, and customer systems with minimal friction.
  • Support shared workflows, not just shared data.
  • Enable AI agents to operate across organizational boundaries.
  • Maintain consistent process logic, metrics, and governance across nodes.

As more partners connect to shared platforms, these networks become orchestrated ecosystems rather than loose collections of bilateral relationships.

As a result, IDC predicts:

By 2029, 45% of G2000 companies will have adopted agentic AI–driven channel management and orchestration, driving a 20% revenue uplift and a 30% improvement in partner and customer satisfaction scores.

This interoperability amplifies agility: when market conditions shift, changes cascade across partners in hours, not months.

3. Data Foundations and Distributed AI-Driven Security: Trust at Ecosystem Scale

As supply chains become more interconnected, the surface area for cyber and data risk expands dramatically. At the same time, AI’s effectiveness depends on high-quality, secure, and interoperable data.

A modern supply chain must invest in:

  • Federated data models enabling domain-level control with shared standards.
  • Governance frameworks, ensuring consistent semantics, lineage, and quality.
  • Distributed AI-driven security that continuously assesses ecosystem risk.
  • Zero-trust principles applied across suppliers, platforms, and data flows.

Trust is no longer about internal compliance. It is about ensuring safe, reliable data movement across the entire network, because partner data is now operational data.

As a result, IDC predicts:

To secure supply chains, by 2030, 60% of large enterprises will deploy distributed AI-driven cybersecurity, enabling proactive third-party risk management as AI adoption intensifies cyber risks.

These foundations ensure AI-driven decisions are grounded in secure, high-integrity data flowing consistently across partners. Trust now means ensuring safe, reliable data movement across the network—partner data is operational data.

Future Imperatives for Operations and Supply Chain Leaders

The predictions point to one conclusion: supply chains must operate as intelligent, interconnected ecosystems. To lead in this environment, COOs and CSCOs should focus on five strategic imperatives anchored in the three core themes.

1. Transform N-Tier Visibility into Operating Infrastructure

Treat your supply chain as a system of systems. Visibility must shift from periodic reporting to a live intelligence layer that detects disruptions at their source, whether in a sub-tier supplier or a regional hub. Establish shared workflows and coordinated decision-making models to reduce blind spots and shorten recovery times.

2. Architect for Interoperability to Accelerate Execution

Shift from one-off integrations to platform-based ecosystems where suppliers, carriers, and manufacturers connect with minimal friction. When systems “speak” fluently, coordination becomes orchestration, leading to fewer handoffs, lower latency, and faster alignment under stress. Select platforms that enable partners to plug in without extensive customization.

3. Treat Data Readiness as the Precursor to AI Scale

AI agents cannot scale without clean, governed, and interoperable data. Conduct a cross-functional audit of data availability and structure. Ensure that core datasets, including supplier, logistics, and product data, are aligned and secure. Data readiness is now AI readiness; without it, advanced capabilities like automated forecasting and risk sensing will fail.

4. Embed Distributed Security as a Resilience Pillar

As connectivity grows, security becomes the foundation that protects visibility and orchestration. Integrate third-party cyber assessments into supplier scorecards and deploy continuous monitoring tools. Adopt zero-trust principles across systems and data flows to detect anomalies early and maintain continuity even when threats emerge elsewhere in the network.

5. Leverage Ecosystem Intelligence for Value Beyond Productivity

Use interoperable platforms to enable new service models, dynamic capacity sharing, and sustainability-led optimization. Expand the definition of value to include resilience, customer trust, and ecosystem performance, turning the network itself into a competitive advantage.

The Leadership Mandate

Supply chains are becoming ecosystems. AI will accelerate this shift, but its value depends on network strength: visibility, interoperability, and data integrity.

Leaders must champion modernization that aligns partners, platforms, and data—core to strategic growth and operational continuity.

Investing in multi-enterprise orchestration, ecosystem interoperability, and AI-ready data foundations enables organizations to build responsive, resilient, future-ready supply chains.

Join Stephanie Krishnan for an upcoming webinar on 24 February 2026, 1:30 PM SGT on what agentic AI readiness means in Asia Pacific and how organizations can move from proof of concept to production responsibly. Register now!

Stephanie Krishnan - Associate Vice President - IDC

Analyst Profile
Stephanie Krishnan leads IDC’s Asia/Pacific research and advisory for supply chain, manufacturing, retail, and adjacent industry domains. As Associate Vice President for IDC Insights, she guides organizations through the rapid transformation toward digitally enabled, AI-driven, and highly interconnected operations. Her work centers on the future of supply chain ecosystems, operational resiliency, sustainability, and the rise of agentic and autonomous decision-making across global networks.

Announced 5th December, these increases are set to take place from the 1st July 2026 and will impact most Enterprise subscribing customers at their next major agreement renewal. Using justification for increases based on additional features, functionality and AI elements, these increases affect customers of all types in all territories and currencies. These list price increases differ from the recently announced changes to automatic entitled volume license discounts and whilst pricing is still negotiable, list price increases will ultimately influence end customer pricing.

Microsoft 365 SuiteCurrent List PriceJuly 1st 2026 List PriceIncrease %
Microsoft 365 E3$36.00$39.008%
Microsoft 365 E5$57.00$60.005%
Microsoft 365 F1$2.25$3.0033%
Microsoft 365 F3$8.00$10.0025%
Office 365 E3$23.00$26.0013%
Business Basic$6.00$7.0017%
Business Standard$12.50$14.0012%
Pricing in other currencies and territories are expected to increase by similar deltas

Of specific note are the exceptionally large increases to Frontline Worker SKU’s, typically deployed by customers with shared computer environments, providing more cost-effective options for these users than Full User licenses, with the savings delta now significantly impacted by this price increase. Customers utilizing these products should carefully plan and consider their current position and forward strategy.

Whilst these price increases impact all customers, government customers specifically will see these increases in some cases split across a two-year period. The timing of actual impact may be dependent on agreement renewal timing.

What this means

For most customers subscribing to these products governed by a current Enterprise Agreement (EA) or Enterprise Agreement Subscription (EAS), Microsoft may look to assert increases on renewals after July 1st 2026 . Until renewal customers with these agreement types will typically have agreed pricing which will be unimpacted.

Whilst some of these increases look to be close to inflationary increases (E5), many enterprise customers already have negotiated discounts, and renewing customers would almost always see cost increases at renewal through the reduction of discounts and/or the ramping of discounts during the agreement term. This is important as these new list price increases may be levied in addition to discount reductions, therefore customers should expect larger increases than perhaps previously anticipated.

IDC Sourcing Advisory Services had already observed more restrictive discounting for Frontline Worker products and customers with these can expect to see large compound increases at renewal. We note that the F5 addon product is not currently in scope of these increases, however as this is an add-on product, customers will still be impacted overall.

With the removal of entitled discounts, the path is now clear for Microsoft to assert more aggressive unit cost increases, through both these list price increases and discount reductions

Also, customers with Unified Support face a double impact through these price increases, as their Unified Enterprise Base cost is calculated on a percentage of categorized product spend, and any product cost increases will result in Unified Support increases. These cost increases may not necessarily be co-termed to the wider renewal or when the price increases impact the customer, indeed these may come at a later date. Customers should look at the impact across agreements not only to budget but also to provide leverage for future negotiations.

What can Enterprise Customers do

Notwithstanding typical Microsoft renewal actions and strategies, customers should immediately consider the following;

  • Act early & Plan now – Customers should begin assessments now, and in some cases may look to shift contractual timelines, so as to mitigate some of these cost increases in the near term
  • Pricing is Negotiable – Whilst list prices might increase, Microsoft continues to incentivize customers and pricing is always negotiable.
  • Leverage – Customers can seek to leverage many aspects of direct and indirect Microsoft investments and strategic product adoptions in order to drive optimal pricing. Collating current investments and identifying future requirements, even seemingly unrelated ones such as Azure, will help to build an overall investment growth profile and negotiation leverage
  • Strategy – As always customers should develop a renewal strategy, aligned to their technology strategies, to drive optimal product selection, rationalization, adoption and negotiations. However, customers should now take a specific view on the potential impact of these increases and how this strategy may be influenced by, or equally influence, future Microsoft commercials
  • Frontline Workers – Where customers subscribe, or plan to subscribe, to Frontline Worker SKUs, careful impact assessment and value analysis might be undertaken with a view to identifying risks and opportunities for mitigation.
  • Early Renewal – Customers may consider renewing their current agreement early, prior to 1st July 2026, to maximize price protection, however should carefully balance this on the understanding that early renewal pricing may likely increase overall costs in the immediate term.
  • Extensions – Those customers with contractual Extension options with fixed pricing might plan to utilize these in order to extend price protection durations.
  • Alternatives – Where customers are egregiously impacted by the changes, or where the full functionality of the suites is not being leveraged, customers may choose to realign their requirements and potentially look to competitive solutions. These options may provide direct cost mitigation and/or give competitive leverage when commencing renewal discussions.
  • Benchmarking – With global variance in discounts and incentive funding customers should benchmark their Microsoft investments and renewals against their peers and the market to ensure they are cost optimal and provide independent justification for decisions and change.

Summary

In summary these price increases, in tandem with potential reductions in discounts, present some clear commercial cost challenges for many Microsoft customers, in some cases significant ones. For Enterprise customers pricing remains negotiable and those customers that act early and assess the impact of these changes may identify opportunities for successful mitigation and cost optimization.

IDC’s Sourcing Advisory Services (SAS) provides you with the industry’s most-recognized price benchmarks, analyst advice and IT optimization insight. With globally recognized expertise in Microsoft commercials, IDC supports customers around the world helping them to achieve commercial clarity and cost optimization.  For more in depth information, advice and support to find out how IDC can support you with the above please contact us.

Neil Stewart - Vice President-Software Contracting Advisory (Major Vendors) - IDC

Analyst Profile
Neil Stewart, IDCs Senior Research Director for the Sourcing Advisory Service, provides expert coverage and insight into the Software Procurement and Commercial Market for Global Customers. Focusing on Major Software Vendors, Mr Stewart provides research, data and competitive intelligence helping customers to optimise their Software Investments, providing research and commercial insight on optimal pricing, contract vehicles and terms, available concessions, and proven negotiation strategies. Where Vendors might be transitioning to new product offerings, or where customer requirements are yet to be fully developed, he also provides more consultative assistance and strategic insight helping organisations both right-size software services and product requirements, but also understand their ongoing investments, entitlements and contractual responsibilities.

The IT industry stands on the brink of one of its most transformative eras, triggering major consolidations in many long-standing sectors and the emergence of major new markets. IDC’s latest research shows that the infusion of autonomy, adaptability, and decision-making into products and services with agentic AI is redefining the very foundation of technology design, delivery, and value creation. It goes beyond an evolution in AI adoption. It’s a structural shift that will determine which technology providers lead in the emerging agent economy and which struggle to adapt.

Agent Use Will Surge 10x by 2027

IDC predicts that by 2027, G2000 agent use will increase tenfold, with token and API call loads rising a thousandfold [FutureScape 2026; Category: Worldwide IT Industry; Prediction 2]. For technology providers, this is a customer adoption story and a capacity challenge. Every software provider, cloud platform, and hardware vendor will need to optimize compute delivery, token use/delivery efficiency, and orchestration tools to manage unprecedented scale. Vendors that help enterprises measure, govern, and contain the costs of agentic automation will become essential partners in the next phase of digital transformation.

Slide describing Agentic Surge. By 2027, G2000 agent use will jump 10 times and token/call loads 1000 times, making agent vetting, orchestration, and optimization essential It responsibilities. 40% of US enterprises and 27% of Chinese enterprises have already put AI agents in prodution. In-app AI agents and greater use on no code/low agentic orchestration platforms will make it easier than ever to deploy new agents. Limited vetting of agent options, lack of orchestration/guardrails for agent fleets, and limited insights into long-term costs become major risks.

The Coming Surge: Agents, Actions, and Industry Transformation

IDC developed our new  Agent Economics Adoption and Delivery Model to provide a foundation for tracking the worldwide pace and scale of agent adoption by type of agent and to extend that tracking by geography and  sector over the next 5 years.

In our first release, IDC projects that the number of actively deployed AI agents will exceed 1 billion worldwide by 2029 which is 40 times more than in 2025. They will include in-application and standalone agents built and operated by cloud, software and services providers, but they will also include a growing number of custom-configured (no code/low code) and bespoke agents optimized to address the unique needs of individual enterprises.

Image of a slide showing the number of active agents per day in 2029 to exceed 1 BILLION.

More significantly, these agents will execute over 217 billion actions per day and consume 3.7 TeraTokens/Calls (3,700,000,000,000) daily to support this still rapidly expanding inferencing load. The token delivery cost worldwide for supporting all these agent actions will surpass $68 billion annually, but the cost to complete an ever more complex and sophisticated individual action will be 87% lower.

A chart showing how many agent actions will happen per day by 2029 to approach 217 billion.

For the IT industry, this represents both a windfall and a reckoning. The software, infrastructure, and service providers who can enable this scale efficiently will dominate the market. Those who can’t because they are still tied to monolithic licensing models, siloed architectures, or restricted data practices will see margins shrink as automation commoditizes legacy value propositions.

Applications Become Agentic Platforms

A critical early driver of agent adoption and token load growth comes from IDC’s Worldwide AI-Enabled Enterprise Applications and Agents 2026 Predictions which forecasts that by 2027, agentic automation will enhance capabilities in over 40% of enterprise applications [FutureScape 2026; Category: AI-Enabled Enterprise Applications and Agents; Prediction 1]. This signals a new design imperative for the IT industry: applications are becoming actors, not just interfaces.

The winners will be those who transform products into platforms built on collaborative ecosystems of agents that anticipate user intent, orchestrate processes autonomously, and continuously optimize operations through feedback. This agentic pivot will blur the boundaries between software, infrastructure, and services delivery, pushing the entire IT industry toward modular, interoperable architectures.

Data Readiness Becomes a Competitive Advantage

Data is the lifeblood of the agent economy. IDC warns that by 2027, companies that fail to establish high-quality, AI-ready data foundations will suffer a 15% productivity loss as generative and agentic systems falter [FutureScape 2026; Category: Worldwide Agentic Artificial Intelligence; Prediction 1]. For technology providers, this means opportunity. The leaders will be those that offer the tools to unify data governance, enhance observability, and create federated architectures, fueling agents with trusted, real-time intelligence.

A New Tech Industry Transformation

For the IT industry, the coming years will bring a massive reallocation of value. Demand for hardware optimized for AI inference and training will surge. Cloud providers will see unprecedented pressure on network and compute resources. Software vendors will need to evolve beyond seat-based licensing toward models that measure value by actions, outcomes, and intelligence generated. Managed service providers, in turn, will need to automate their own operations with agentic platforms that deliver speed, transparency, and scale.

IDC’s research underscores that as agents proliferate; the IT industry must take on a new role: the orchestrator of autonomy. Providers will no longer just deliver technology; they will deliver the frameworks that govern intelligent digital resources by balancing efficiency, ethics, and economic sustainability.

Building the Foundation of the Agentic Economy

For technology providers, the time to act is now:

  • Engineer for scale and sustainability. Design architectures and platforms that can handle the exponential growth in agent numbers, actions, and token/call demand.
  • Reimagine pricing and delivery models. Move beyond seats and licenses to outcome-based models that reflect continuous, autonomous operation.
  • Champion data integrity. Invest in AI governance, observability, and interoperability to ensure agents operate with accuracy and accountability.
  • Embrace modularity and interoperability. Partner across ecosystems to create open, agentic frameworks that integrate seamlessly with others.
  • Lead with responsibility. As agents gain autonomy, establish ethical guardrails and compliance mechanisms that earn enterprise and public trust.
See the full Agent Economics report to see the detailed data, research methodology and IDC’s expert guidance.

The IT industry has faced transformation before, from mainframes to client/server, to cloud, and now to AI-infusion. The question for every provider is no longer if this transformation will happen, but whether they will steer it.

For IT leaders and technology providers, the path forward is clear.

Design for orchestration. Deliver for scale. Govern for trust.

Success in agent economics  will belong to those who can turn autonomy into advantage and help the world’s enterprises navigate this next great inflection point with confidence.

Rick Villars - Group VP, Worldwide Research - IDC

Analyst Profile
Rick is IDC's chief analyst guiding research on the future of the IT Industry. He coordinates all IDC research related to the impact of Cloud and the shift to digital business models across infrastructure, platforms, software, and services. He helps enterprises develop effective strategies for using their diverse portfolio of cloud investments and applications. He supplies early guidance on implications of critical innovations such as the shift to cloud-based control platforms for deploying/managing infrastructure, data, and code delivery as well as the emergence of AI as a critical IT workload and part of all IT products/services.

Many executive teams are asking the same question: How can we shorten the distance between a business event and a decision that matters? IDC’s FutureScape: Worldwide Data and Analytics 2026 Predictions points to a clear direction:

By 2029, 60% of enterprise data platforms will unify transactional and analytical workloads, powering agentic AI and enabling real-time, continuous intelligence.

In this post, I explain what converged workloads mean in practice, why adoption is accelerating, how vendors are packaging the approach, and what to prioritize as you plan the next phase of your database strategy.

What “converged” really means

Converged workloads bring transactions and analytics together so insight and action can occur simultaneously on the same data. Instead of exporting from operational systems to a separate analytics stack, with the copies, cost, and delay that entails, a converged approach runs both in one governed environment. The outcome is straightforward: decisions based on live data, not yesterday’s batch.

This shift turns databases from systems of record into systems of intelligence, where every transaction can be analyzed and acted on immediately. It forms the foundation for continuous intelligence in areas such as fraud prevention, asset health, and customer personalization.

Why it is accelerating now

Three forces are turning convergence from concept into practice. Cloud elasticity allows IT teams to right-size mixed workloads as demand changes, avoiding unnecessary cost and overprovisioning. Streaming and in-memory processing make it possible to ingest and analyze data as it arrives, significantly reducing latency. IDC research shows that 96% of enterprises are using or planning to use streaming for AI and analytics.

Bringing AI closer to the data further reduces pipeline friction. Seventy-five percent of organizations use or plan to use integrated vector databases to store and query embeddings for AI. Adoption of agentic patterns is also accelerating, with 53% of enterprises already running AI agents in production and another 28% planning deployments within six months.

A look at one approach in the market

Vendors are packaging convergence in different ways. Oracle’s approach connects Oracle AI Database 26AI, which serves as the operational system of record with in-database AI and vector search for real-time decisioning on multi-model data, and Oracle Autonomous AI Lakehouse, which provides the enterprise analytics and governance layer. The two work together to unify operational and analytical data. The lakehouse extends discovery and governance across environments, integrates with third-party catalogs, supports open engines and formats, and runs AI (including vector search) directly on lake tables. Real-time pipelines keep information synchronized across sources.

Across the market, the pattern is consistent: unify workloads, minimize data copies, keep analytics and AI close to where data resides, and apply governance end to end.

Other leading providers are taking similar paths, adding operational capabilities to lakes, analytical depth to transactional systems, and stronger governance across both.

What leaders should expect

Simplification and speed. Early wins come from fewer data copies and fewer ETL hops, which shorten time to insight and reduce integration work. Embedded automation, including self-tuning, anomaly detection, and workload management, shifts focus from maintenance to innovation.

Performance without trade-offs. Modern converged platforms are designed to analyze live operational data while preserving transactional responsiveness. In practice, that means fewer compromises between “run the business” and “analyze the business.”

Governance up front. As AI becomes operational, unified auditing, lineage, and policy enforcement are non-negotiable. Converged designs help by applying consistent controls in one place rather than stitching them together across multiple stacks.

A market tilting to cloud. Database spending continues to concentrate in cloud services. Public-cloud DBMS revenue is projected to grow at 18.3% CAGR through 2029, reflecting the shift to flexible, scalable architectures that support mixed workloads.

How to get started

  • Start with a few high-value, time-sensitive use cases. Fraud detection, predictive maintenance, and key customer interactions are strong candidates. Allow legacy systems to coexist while you validate latency, reliability, and governance controls.
  • Build governance and observability in from day one. Prioritize clear lineage, unified access policies, and end-to-end monitoring across both operational and analytical environments.
  • Choose AI-ready data platforms. Integrated retrieval and in-database AI reduce pipeline complexity and keep inference close to the data for faster insights.
  • Plan for Agentic AI. Establish real-time connections between converged data stores and agent frameworks, with clear policies for access, lineage, rollback, and audit.

Takeaways

Converged workloads are transforming databases from systems of record into real-time systems of intelligence. This shift is driven by cloud elasticity, streaming and in-memory processing, and AI that operates close to the data, with agentic AI emerging as the main demand signal. In the near term, expect simpler architectures and greater automation, but make governance and observability first-class priorities from the start. Begin with a few high-value, time-sensitive use cases, validate performance and controls, and expand as operating patterns stabilize.

For the full analysis, frameworks, market context, and buyer guidance behind this summary, read the IDC report Converged Workloads: Enabling Real-Time Enterprise Intelligence and AI Innovation.

You can also explore other key predictions shaping the future of data and analytics in IDC FutureScape: Worldwide Data and Analytics 2026 Predictions.

Devin Pratt - Research Director, Data Management - IDC

Analyst Profile LinkedIn
As Research Director of Data Management within IDC’s AI, Automation, Data & Analytics practice, Devin analyzes market trends and vendor strategies shaping the Data Plane, including database management software and tools. He advises technology vendors and enterprises on product strategy, cloud and AI adoption, and the shift toward Agentic AI, delivering custom research, business value studies, and speaking engagements. His work focuses on providing clear, research-driven insights that support informed decisions and accelerate progress toward an AI-powered future.

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.