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As organizations navigate an era defined by economic uncertainty, geopolitical shifts, and relentless technological change, one truth stands out: the future belongs to those who can harness AI, automation, and sovereignty to build resilience, drive innovation, and unlock growth. The theme of this year’s IDC EMEA FutureScape predictions  webcast – The Agentic Business Future – is about technology; but also readiness, governance, and a human-centered approach that transforms ambition into measurable business value. 

IDC defines the Agentic Business Future as a fundamental transformation of enterprise operations, customer experiences, and business models driven by the widespread adoption of AI agents and agentic workflows. Agentic workflows are processes involving AI agents capable of planning, using tools, perceiving their environment, and remembering past interactions to improve performance. These agents autonomously execute complex tasks, make decisions, and interact in human-like ways, enhancing operational efficiency and decision-making. Unlike traditional AI, agentic AI can collaborate, trigger actions, and adapt dynamically, underpinning new operating models and real-time orchestration across the enterprise. 

Our IDC European CIO Xchange Survey (2025) reveals that 85% of EMEA CIOs are already exploring agentic use cases or piloting intelligent agents within isolated departments. The challenge now is to move beyond pilots. Scaling AI means modernizing architectures, consolidating fragmented systems, and enabling data to flow freely – securely and responsibly – across hybrid and sovereign clouds. 

Why This Future Matters

IDC’s latest CEO survey reveals what’s keeping leaders awake at night: digital business execution gaps, economic pressures, and geopolitical risks. For EMEA CEOs, driving digital initiatives and leveraging AI for competitive advantage are top priorities. In fact, 50% believe AI will reinvent their business model within the next 3–5 years. Yet ambition alone isn’t enough. Without foundational readiness and robust governance, organizations risk falling short of ROI targets: by 2026, we expect that half of AI-fueled digital use cases will fail to meet ROI expectations. 

Five Imperatives for the Agentic Future

  1. Reimagining Digital Value 
    Economic headwinds and global instability are forcing organizations to rethink business models and redefine digital business value. Competitive advantage will increasingly hinge on how effectively companies integrate AI into their core strategies – not as a bolt-on, but as a driver of transformation. 
  2. Architecting for Scale 
    Scaling AI requires more than enthusiasm; it demands modernization and hybrid architectures that overcome tech debt and fragmentation. IDC research shows EMEA IT spending grew 13% in 2025, signaling strong investment momentum. But without a clear architectural vision, these investments risk becoming sunk costs. 
  3. Building Trust Everywhere 
    Trust is non-negotiable. Governance frameworks, responsible AI, and sovereignty are critical for compliance and risk management. Recent geopolitical shifts have accelerated this trend, with 61% of EMEA organizations favoring sovereign clouds for AI workloads. This isn’t just about regulation — it’s about ensuring resilience in a volatile world. 
  4. Leading Through Change 
    Technology alone doesn’t deliver transformation; leadership culture and organizational agility do. The AI pivot requires leaders who can embed innovation into the DNA of their organizations, fostering adaptability and collaboration across every layer. 
  5. Empowering the Workforce 
    The future of work isn’t just automation — it’s augmentation. Three out of four EMEA employees expect their jobs to be impacted by AI by 2026, underscoring the urgency of talent development. Organizations that invest in upskilling and human-machine collaboration will be best positioned for sustainable success. 

Find out more

The Agentic Business Future is not a distant vision; it’s unfolding now. But success requires moving beyond hype to measurable outcomes. The organizations that thrive will be those that combine technological ambition with governance, trust, and human-centric leadership.  

Join IDC’s senior regional analyst team for FutureScape 2026 webcast on December 3, where we will unpack these imperatives — backed by data, case studies, and practical guidance to help digital leaders design for resilience, agility, and growth in an increasingly complex environment. Register now here.  

If you have a question, you can submit that in our form here.  

Below are a couple of related eBooks:  

The manufacturing industry is no stranger to artificial intelligence. Process manufacturing sectors such as chemical, pulp & paper, oil & gas, food & beverage have embedded AI routines into their systems for decades to automate workflow and product processes. But this represents only one dimension of AI adoption in manufacturing, Across the broader industry, significant opportunities remain, especially in three key areas:

  • enabling the adoption of cloud platforms and applications,
  • making sense of the massive amounts of data from connected assets and products,
  • and augmenting the workforce 

These opportunities collectively define the next phase of AI maturity in manufacturing—one where generative, agentic, and predictive AI will enable hybrid cloud connectivity, unified data intelligence, and more capable, empowered workforces.

Our 2026 Manufacturing Industry FutureScape explores how these opportunities are reshaping the sector while accounting for existing challenges and investments. Manufacturers are navigating the need to deploy cloud platforms and applications consistently across production sites, extend digital twins of products and assets across the value chain, and upskill a workforce that faces both resource and digital skills constraints. These dynamics are redefining competitiveness across the industry and shaping the key trends driving our predictions for the next five years.

AI adoption remains cautious—but accelerating.

There is slow adoption of GenAI and Agentic AI across manufacturing overall, perhaps because, as one manufacturer said, “it is [perceived as] taking away the fun part of being an engineer—problem solving.” Yet it now sits at the top of the CIO, Chief AI Officer, and VP of Manufacturing’s agenda, driven by the realization that these capabilities will only make engineering, R&D, production, and operations better. Our survey data shows that process manufacturing organizations are more mature than discrete manufacturing industries, both of which are far ahead of the energy sector. Early GenAI and Agentic AI use cases focus on design augmentation, procurement optimization, guided customer service, and enterprise quality assurance.

Data is both a challenge and a catalyst.

Manufacturers are deluged by data, and as data fabrics and foundations mature, AI adoption will accelerate—driving automation, process optimization, and workforce efficiency. It also helps that manufacturers are finally embracing cloud-based infrastructure and applications, which will make multimodal and ecosystem-driven innovation more attainable. Despite this momentum, cloud in the industrial space will likely remain a hybrid approach over the long term, varying by industry, due to regulatory and IP management concerns.

Sustainability and the energy transition take center stage.

The energy transition is underway for manufacturing and energy organizations, with a focus on (a) greenfield and brownfield facility design and sustainability, and (b) supply chain efficiency and optimization. Organizations are exploring how to integrate BIM (building information modeling) and MOM data with point-cloud scans and models of both new and legacy facilities to improve energy efficiency in buildings—which notoriously account for 30–40% of global CO₂ emissions. Scope 3 emissions regulations (which hold manufacturers accountable for environmental impacts beyond their direct operations), combined with growing customer demand, are driving investment in supply chain visibility, analytics, and optimization.

Software-defined automation becomes mission critical.

Software-defined automation for products, assets, and facilities is becoming mission critical, and low-code development tools play an increasingly important role in improving data and process flow, analytics, and collaboration. Organizations need a rapid, streamlined way to update and create new software that enhances data movement, analytics, ecosystem collaboration, and overall operations. This approach must also integrate with traditional full-code software development tools used for products and assets to ensure enterprise-wide quality and consistency.

Industry ecosystems drive innovation and resilience.

Industry ecosystems continue to drive innovation and business performance, particularly in meeting regulatory compliance and advancing sustainability and circularity. Manufacturing and energy organizations have recognized that they operate more effectively through extended ecosystems that include diverse external skills, resources, and expertise across marketing and sales, R&D and engineering, production, supply chain, and customer or field service. IDC’s four years of global survey data on industry ecosystems show that organizations taking this approach are better able to share data internally and externally, accelerate innovation, and meet customer, consumer, and citizen needs.

2026 manufacturing industry predictions

Building on these trends, IDC’s 2026 Manufacturing Industry FutureScape identifies 10 key predictions that highlight how AI will reshape operations, supply chains, and workforce strategies across the manufacturing ecosystem over the next five years.

  1. Software-defined factory: Driven by the potential of autonomous operations, by 2029 30% of factories will configure and manage control systems centrally utilizing open, virtualized, software-defined automation platforms.
  2. Autonomous production scheduling: By 2026, over 40% of manufacturers with a production scheduling system in place will upgrade it with AI-driven capabilities to start enabling autonomous processes.
  3. Agentic IT/OT connectivity: By 2027, 40% of all operational data will be integrated across applications and platforms autonomously due to increased standardization and the use of AI agents purpose-built for specific data.
  4. Cross functional circular field service: To close the loop between service and design, by the end of 2026 45% of G2000 OEM and aftermarket firms will use AI to connect field and engineering data, improving product and service quality.
  5. Predictive industrial data security: To counter data model poisoning risks, 75% of large manufacturers will use AI-enabled OT cyber defense by 2029, autonomously flagging low-level threats and cutting detection times by 60%.
  6. Human/robotic skills transfer: By 2028, firms that fail to design closed human-robot skill loops will face 20% higher downtime and retraining costs, and reduced efficiency compared with peers that implement bidirectional training.
  7. Agentic product/process simulation: By 2028, 65% of G1000 manufacturers will use AI Agents in conjunction with design and simulation tools to continuously validate design changes and configurations/ variants against product requirements.
  8. Connected worker reskilling platforms: By 2027, over 50% of manufacturers will utilize AI-enabled knowledge management tools to re-/upskill their workforce and foster collaboration across industry ecosystems.
  9. Hybrid AI industrial focus: By 2030, 60% of manufacturers will leverage AI Agents to build data models and manage hybrid-cloud workloads, ensuring knowledge sharing and collaboration that lowers cost of quality by 2%.
  10. Industrial model management: By 2027, 60% of manufacturers will leverage hyperscaler ecosystems to build, deploy, & scale new AI solutions, unlocking the value of their data and accelerating transformation.

The full 2026 Manufacturing FutureScape report is now available on IDC.com.

The road ahead for manufacturers

Looking at everything as a whole, we begin to see how AI is becoming foundational to manufacturing strategy. But realizing this potential requires deliberate action, cultural change, and sustained investment.

Manufacturers today face the challenge of managing two overlapping transformations: the migration to cloud and the adoption of AI. Cultural and structural barriers remain—reluctance to share data across teams and ecosystems, uncertainty about AI’s impact on jobs, and uneven governance models all slow progress. Yet, as with past technology shifts, those who evolve will lead.

Success in this next phase requires a pragmatic, use case–driven approach. Organizations should begin experimenting with AI while establishing centers of excellence, building strong data governance frameworks, and investing in training and enablement. For IT and business leaders alike, industry-specific foundation models will be key to enabling effective and trusted use of generative, agentic, and predictive AI that can address increasingly complex industrial challenges.

The potential upside is immense. AI offers the ability to accelerate automation, strengthen data flow, and augment workforces that face ongoing skills shortages. Leading manufacturers are already treating AI as a core element of digital transformation—integrating it with cloud platforms, big data analytics, AR/VR, and emerging technologies such as blockchain.

IDC’s 2025 AI MaturityScape Benchmark confirms this trajectory: the most advanced organizations view AI not as an isolated tool, but as an enabler of enterprise-wide transformation. The lesson is clear—AI maturity grows hand in hand with digital maturity.

It is only a matter of time before AI becomes deeply embedded across the manufacturing sector. The question is no longer if—but how fast manufacturers can scale adoption to unlock new value, improve resilience, and redefine what’s possible in the next industrial era.

Jeffrey Hojlo - Research Vice President - IDC

Analyst Profile
As Research Vice President, Future of Industry Ecosystems, Innovation Strategies, & Energy Insights at IDC, Jeff Hojlo leads one of IDC's Future Enterprise practices at IDC - the Future of Industry Ecosystems. This practice focuses on three areas that help create and optimize trusted industry ecosystems and next generation value chains in discrete and process manufacturing, construction, healthcare, retail, and other industries: shared data & insight, shared applications, and shared operations & expertise. Mr. Hojlo manages a group focused on the research and analysis of the design, simulation, innovation, Product Lifecycle Management (PLM), and Service Lifecycle Management (SLM) market, including emerging strategies across discrete and process manufacturing industry such as product innovation platforms and the closed loop digital thread of product design, development, digital manufacturing, supply chain, and SLM. He also manages IDC's North American Energy Insights group, with a focus on key topics such as energy transition & sustainability, distributed energy resource management, and digital transformation in the Oil & Gas and Utilities industries.

B2B engagement is at a turning point, and buyer expectations are accelerating faster than most organizations can adapt. Static campaigns, siloed systems, and disconnected handoffs no longer match the ways customers want to interact with brands.

This urgency is reflected in the market. By 2027, IDC projects companies will spend $150 billion on AI-related infrastructure, platforms, and services to compete on delivering highly personalized customer experiences.

For CMOs, this level of investment underscores a simple reality: engagement has become a decisive factor in growth, loyalty, and competitive differentiation.

To succeed in the new B2B landscape, leaders must understand:

  • How buyer expectations have shifted
  • The principles that guide effective marketing teams
  • The new AI-driven rules of engagement
  • What it looks like to put those rules into practice.

Transform insight into measurable business outcomes. Explore the Omnichannel Experience Playbook to see how leading organizations are aligning teams and technology for impact.

Three shifts in buyer expectations

Today’s B2B buyers are digitally fluent, AI-assisted, and more self-directed than ever. They research, compare vendors, and evaluate solutions on their own terms, often without speaking to sales until late in the process. Marketing must now own the entire journey, not just the top of the funnel.

Three shifts define this new reality.

  1. Customers expect personalization powered by real-time data to make every interaction relevant. Nearly 70% of buyers say personalization influences whether they engage with content.
  2. Buyers are relying on AI-driven tools and insights to shape decisions, with 77% of B2B buyers depending more on AI tools than traditional search engines.
  3. Trust, transparency, and agility determine brand credibility. Brands risk losing over 79% of customer loyalty by 2026 if they fail to implement digital trust practices for AI transparency and ethical data handling.

These shifts have redefined the baseline for engagement. Omnichannel orchestration isn’t just about connecting campaigns, it’s about creating journeys that grow with the buyer, foster trust, and make every interaction feel relevant.

Core principles to guide marketing teams

To meet these expectations, CMOs are embracing three guiding principles:

  • Buyer intelligence requires building a full picture of the customer journey by integrating intent signals across content, events, demos, pricing activity, and social interactions. With deep insight into the buyer experience, teams can deliver omnichannel engagement that adapts in real time instead of relying on static campaigns.
  • Intentional transitions ensure consistency across touchpoints. Consider a buyer who attends a webinar: in a standard scenario, they receive generic emails that ignore the details of the session. A strong, omnichannel approach references the session topic, offers related resources, and provides sales with context for the next conversation.
  • Cross-functional alignment creates a unified relationship with the buyer. Marketing and sales must act on the same intelligence. Integrated platforms, shared dashboards, and common definitions of success allow teams to orchestrate experiences that feel coherent across every channel.

By following these principles, marketing leaders can create the foundation for adaptive, buyer-centric engagement that moves the needle from discovery to loyalty.

The new AI-driven rules of engagement

AI isn’t just enabling marketing efficiency, it’s reshaping how buyers experience brands. New rules now define how engagement must be delivered.

  • Dynamic personalization

Static nurture paths no longer resonate with digital-first buyers who expect experiences to adapt instantly to their behavior. Instead of generic follow-ups, leading strategies trigger personalized interactions that build on a potential customer’s last action.

IDC research shows that 80% of buyers say personalized communications make it feel like a brand cares about them, a reminder that personalization isn’t just a tactic, it’s an avenue to build trust. In an omnichannel world, this means tailoring not only what content is delivered, but where and how it appears across touchpoints.

  • Conversational engagement

Modern buyers don’t want to wait for gated PDFs or delayed responses: they expect dialogue in real time. By 2026, 65% of individuals will engage with brands through GenAI, signaling a shift from static content toward dynamic, two-way engagement.

The challenge is scaling these conversations authentically. Successful organizations embed conversational AI into their omnichannel strategy, ensuring digital dialogues seamlessly transition to well-informed live sales interactions when the time is right.

  • Proactive trust and transparency

As AI becomes more integral to both the sales and buying process, buyers need clarity from brands about how AI is used. They want to know when they’re interacting with a computer or a person and how their data is being handled.

IDC research shows only about a quarter of marketing executives currently prioritize enabling trust-based marketing practices. This is a gap, and an opportunity. Brands that lead with transparency and governance will differentiate themselves in a market where credibility pays off in loyalty.

Together, these rules highlight a new reality: AI is no longer behind the scenes. In an omnichannel, buyer-led world, it is the front line of customer engagement, shaping perceptions, guiding decisions, and determining loyalty.

Practical examples of modern engagement

Principles and rules matter only when they are translated into action. These examples show how these elements work together in real-life scenarios to create connected, intelligent, omnichannel experiences that resonate with today’s buyers.

  • Turning missed clicks into seamless journeys

A buyer abandons a demo request form. Because transitions are thoughtfully designed, the system triggers a tailored follow-up referencing the product they explored and suggests next steps. Instead of losing the lead, the personalized interaction builds momentum and draws the buyer back in.

  • Powering conversations with insight

A buyer exploring pricing models engages with a GenAI chatbot. The bot responds with context drawn from centralized buyer intelligence (previous site activity, firmographic data, or content downloads) and then routes the conversation to a live expert when needed. This blend ensures the AI-driven dialogue is relevant, accurate, and supports the sales team with the knowledge they need to close the deal.

  • Building trust across the organization

A buyer interacting with AI-generated recommendations accesses the brand’s AI and data protection policies. Because the entire organization operates under consistent governance standards, transparency is embedded in every practice, reinforcing credibility and ensuring every department is in alignment.

These examples show the path to buyer-centric engagement isn’t about isolated touchpoints. It’s about executing the plays that create connected moments – where personalization, communication, and trust work together across the omnichannel journey.

The CMO’s mandate for engagement

For marketing leaders, the imperative is to orchestrate engagement by aligning the principles and rules, grounding every journey in intelligence, designing AI interactions with intention, and aligning teams and buyers around shared trust.

The rules of engagement have shifted, and buyers are moving quickly. Those who adapt now will define what success looks like in B2B marketing, building buyer journeys that not only meet expectations but set new ones.

Discover IDC’s Omnichannel Experience Playbook and learn how to align your marketing organization with the new rules of engagement and future-proof your GTM strategy. You’ll find game-changing plays and an accompanying AI Supplemental Guide to operationalize intelligence, build trust, and build winning customer experiences.

As an industry analyst, I’ve had a front-row seat to the dramatic changes sweeping through the IT services sector. Recently, I reviewed the changes at many service providers, and it struck me just how quickly agentic AI and automation are reshaping the way service providers operate and deliver value.

It’s clear to me that the old ways of relying on billable hours and armies of consultants are fading fast. Clients today want more than just advice or manpower; they’re looking for outcomes, speed, and transparency.

Data from IDC’s APJ Services Sourcing Survey 2025 really drives this home: many organizations are pulling services in-house, not just to save costs, but because they believe AI can solve problems that previously required external expertise.

There’s a growing sense that innovation is lacking from traditional providers, and that’s a wake-up call for the industry.

I’ve watched as leading service providers have started to restructure, reducing headcount and doubling down on AI and automation. It’s not just about efficiency, it’s about survival. The pricing models are shifting too, with clients demanding more value and less dependence on time and materials. This is a fundamental change in how services are bought and sold.

What excites me most is the rise of agentic AI across the service delivery chain. We’re seeing AI agents embedded everywhere—from onboarding and compliance to diagnostics and insights. The case studies being highlighted are compelling: AI-augmented consultants and outcome-based services aren’t just buzzwords—they’re delivering real results, like double-digit sales growth and significant cost reductions.

But this transformation isn’t just about technology. It’s about a new value equation, where continuous delivery, co-creation, and data-driven insights matter more than ever. Clients want stewardship, not just service. They want partners who can help them navigate complexity and deliver tangible business outcomes.

Looking ahead, I believe the journey to agentic AI maturity will be a defining story for our industry.

By 2035, we could see a world where a significant portion of services are delivered autonomously by AI agents, freeing up human talent for higher-value and more nuanced work.

For providers, the challenge is clear: invest in shared platforms, rethink financial and talent models, and build trust through robust risk management.

As I reflect on these changes, I’m convinced that those who embrace agentic AI and orchestrated value will be the ones who thrive. The future of IT services is not just about agentic technology—it’s about reimagining how we deliver outcomes, build relationships, and create lasting impact for clients.

Get Ahead of the Curve: Attend IDC FutureScape Technology and AI Predictions in Singapore

If you’re a leader in services, now is the time to rethink your strategies—before AI forces the change for you. Join IDC and industry peers to gain exclusive insights from our 2026 technology and AI predictions for Asia/Pacific and catch my presentation on how AI is reshaping the services landscape to prepare organizations for the agentic era.

Linus Lai - Group Vice President, Research - IDC

Analyst Profile
Linus Lai is a distinguished member at IDC Asia/Pacific, in which he spearheads research in digital business, trust, infrastructure, and services. With over 25 years of industry experience, Linus is based in Sydney and serves as the chief analyst for Australia and New Zealand (ANZ). He is a founding member of IDC's Emerging Technology Advisory Council and a respected senior member of the region's CIO100, CSO, and Future Enterprise awards. In his role, Linus provides strategic insights for digital leaders and the technology sector, focusing on sourcing strategies and emerging technology across Asia/Pacific. His expertise has earned him numerous accolades for his contributions to country, regional, and quality research. Previously, as the head of research in Southeast Asia, Linus was instrumental in expanding IDC's presence and influence in the region. His thought leadership is frequently sought after through regular features in various publications and media outlets. He is also a prominent speaker at industry forums, keynote events, and strategy workshops. Before joining IDC, Linus worked with a leading outsourcing service provider with a digital banking focus. He holds a Master of Science degree from the University of Lincoln, United Kingdom.

As organizations advance deeper into the age of agentic AI, productivity gains are no longer enough. The next era of enterprise value will be defined by innovation, the ability to reimagine business models, uncover new revenue streams, and achieve outcomes once out of reach.

IDC’s FutureScape 2026 research signals this shift. After years of experimentation, AI has become a structural force of transformation. IDC believes that the enterprises that lead in the coming decade will use agentic AI to go beyond automation and embrace it for invention.

Watch the on-demand webinar: Building an AI-Fueled Business Strategy

The new forces shaping enterprise growth

The landscape for 2026 and beyond is being redefined by several powerful currents. Trusted, high-quality data has become the foundation of every scalable AI initiative. Without it, decision-making falters and innovation is constrained. At the same time, organizations face mounting pressure to modernize legacy systems that hold back agility and increase costs.

Workforce transformation is just as important. AI is now woven into daily work, demanding role redesign, new skills development, and change management focused on helping people thrive alongside intelligent systems.

Meanwhile, trust, ethics, and transparency have become central to every AI strategy. Regulatory scrutiny is rising, and customers expect greater empathy and accountability from intelligent systems. Security and compliance remain top priorities as digital threats evolve. Together, these factors define how enterprises plan, invest, and compete in the years ahead.

The innovation imperative

While much of the focus on AI use cases to-date has been on cost savings and incremental productivity enhancements, IDC predicts that by 2026, 70% of G2000 CEOs will focus AI ROI on growth, driving C-suites to reinvent business models without expanding headcount

Agentic AI expands human capacity for exploration and experimentation, allowing organizations to model complex scenarios, synthesize data across ecosystems, and identify new opportunities in real time.

IDC predicts that 60% of new economic value generated by digital businesses by 2030 will come from companies investing in and scaling AI capabilities today.

This acceleration signals that innovation, not efficiency, will become the defining competitive advantage.

The new currency of growth

In the agentic era, innovation becomes a measurable advantage. IDC predicts that enterprises using AI-driven development will release products and services up to 400% faster than their peers.

This acceleration reshapes how organizations capture opportunity, from R&D and financial modeling to supply-chain optimization, compressing timelines and amplifying creativity.

Innovation across industries

In manufacturing and operations, agentic systems are transforming how organizations design, produce, and deliver.

By 2028, 40% of global supply-chain organizations will deploy integrated execution platforms that orchestrate humans, robots, and vehicles across multi-node networks, cutting logistics costs by 10%.

Manufacturers are using AI agents to simulate production scenarios, manage sustainability goals, and anticipate disruptions, creating more resilient, adaptive enterprises capable of continuous innovation.

The future of work is evolving in parallel. By 2029, G1000 organizations that measure human–AI collaboration will achieve operating margins up to 15% higher than those focused solely on automation. Leading enterprises will see employees as innovators working with AI, blending curiosity and computational power to accelerate product development, experience design and process reinvention.

Innovation anchored in trust

Trust is becoming an increasingly important element of the AI-fueled business. CEOs consistently point to trust as being critical focus area for business success. As AI systems gain autonomy, governance, transparency, and human oversight become essential to scaling safely. Organizations that embed accountability into design will innovate faster by reducing friction, improving regulatory alignment, and increasing stakeholder confidence. Trust frameworks provide the foundation for responsible experimentation and accelerated adoption.

The human engine behind innovation

Discussions about AI almost always include its impact on the workforce and the future of work. Agentic AI has a real potential to amplify human ingenuity. IDC predicts that by 2026, 40% of all G2000 job roles will involve collaboration with AI agents.

When planned thoughtfully this evolution allows people to focus on creativity, strategy, and problem-solving. Innovation becomes a partnership between human and machine, with humans providing context and imagination and AI agents delivering speed and scale. Together, they form an innovation engine capable of transforming industries and driving sustainable growth.

Designing for enterprise-wide orchestration

The future belongs to organizations that treat agentic AI not as a collection of siloed projects but as the operating logic of the enterprise. After years of experimentation, the focus is shifting to orchestration, where AI agents interact, share data, and make autonomous decisions within trusted governance frameworks.

IDC predicts that by 2027, 40% of organizations will invest in AI-infused data architectures to avoid poor decisions, lost opportunities, and reduced competitiveness in a data-driven economy. When data is clean, connected, and governed, it fuels agility and innovation across every function. Organizations that make this transition toward enterprise-wide orchestration will create an adaptive business model capable of reinvention and growth.

Helping you chart the path to AI-fueled growth

Join IDC’s FutureScape 2026 webinars to explore the next frontier in AI-fueled business strategies. Learn how agentic AI is redefining innovation, the CIO agenda, and the enterprise of the future.

Gain insights that will help you move confidently from aspiration to execution.

Explore the FutureScape 2026 webinar series.

Tony Olvet - GVP, Worldwide C-Suite & Digital Business Research - IDC

Analyst Profile
Tony Olvet is Group Vice President, Worldwide C-suite and Digital Business Research at IDC. His team's global research focuses on the connection between business transformation and digital investments across enterprises. Tony's analysis and insights help vendors, IT professionals, and business executives make fact-based decisions on technology strategy and digital business. Tony has worked with clients across a variety of organizations including global IT manufacturers, enterprise software vendors, telecom service providers, financial institutions and public sector organizations. He has been quoted in major business and industry media including CIO Magazine, The Globe and Mail, CBC and The Financial Post.

Artificial Intelligence has officially reached the “you can do anything with it” stage of technological hype. Judging just by AI vendor narratives, and by some of the media, it’s the golden key to productivity, efficiency, optimization and innovation. And if you add agents? They will think, write, negotiate, design, respond, validate automate, and probably brew your coffee and pick your tie, if you wear one. All you need to do is… well, everything else. And clients are trying, with 59% of organizations in Europe declaring they are using Agentic AI. 

Because while vendors keep promising moving mountains with a few lines of AI code, clients are still trying to figure out how to move their own data out of legacy systems and into AI. 

The Vendor: Here’s your AI, you can do anything with it! 

Two years into generative AI world, and still, you walk into any tech conference, and you can hear the same verse of a song we all know: “With (our) AI, you can/must/need/should/want to transform your business!”. 
And it does sound wonderful until you realize that behind the shiny demos and glossy ppt decks, there’s a subtle assumption that it is the client, who will have to make it happen. 

The vendor provides the vision; the client does the homework. 
And let’s not be fooled, it is not just a little homework. We’re talking about a full school year project, that, if you’ve ever done it, you’re dreading; from data preparation, to process standardization, to employee training, to infrastructure modernization. Let’s not forget we also need to build convincing business cases for the board. The same board that may also add to the noise with inflated expectations, like parents tricked by the school with a promise of great grades and a ticket to the best university, if only your child works hard enough. 

The Client: We’d love to, if only we had time… 

When you talk to most enterprises, you’ll probably hear a slightly different tone than that of vendors. Clients are not rejecting AI; they are overwhelmed and exhausted by it: 80% of clients in Europe say they manage to move beyond the PoC stage, but only half of those projects bring measurable outcomes. They’re juggling existing priorities often squeezed by budgets (even if AI spending seems to be relatively immune to budget cost, as we see in IDC research). And they are asked to lead AI transformation projects as if they had a spare team of data scientists and engineers hiding somewhere. 

And most painfully, vendors are often seen as teachers not that willing to help. As one manufacturing executive put it: “They come excited, but when asked for business cases or scenarios we can use, there’s silence”. 

That silence speaks volumes. Clients know that AI isn’t plug-and-play, it is much more complex and time-consuming.  You need to clean, prep, build, deploy, test and hope it scales. They understand that before you even dream of “autonomous decision making,” (though the sheer promise of autonomy can also scare some clients!) you need data consolidation, process standardization or cross-department alignment. Without that backbone connecting all stakeholders, no model will do magic. And oh, yes, change management, anyone? This needs to be planned and taken care of, too. Plus, once you have done all the work, you realize, this work continues: 49% of companies today focus also on making existing AI projects work better.  

But that’s rarely what the vendor pitch slides say. 

Only we are not at school anymore… 

Let’s agree, for many clients this feels like school all over again. Vendors show up as enthusiastic teachers, armed with the latest learning materials (“Look, a new LLM!” or “How about a fresh set of agents?”), and clients are expected to do the homework: research, structure, and present working examples at the next review. 

The difference? In school, a good teacher stays after class to help you understand the assignment. In AI world, too many vendors drop the textbook on a client’s desk, or a presentation into their mailbox, and head to the next classroom, I mean, to the next client… 

What is really needed is a different educational approach. Good teachers, like good business partners, guide, not grade. They adapt to the student’s (or client’s) level, pace, and context. They explain the “why” before demanding the “how”. That’s what’s so painfully missing in much of today’s vendor-client dynamic: tutorship instead of lectures. 

The missing link between a vision presented and the value expected  

The truth is, you cannot “do AI” without doing groundwork, in that sense, homework is needed. You are as good at math as the number of math problems you solved. And sure enough, that groundwork is not glamorous. 
And this much needed AI readiness isn’t about another platform or toolset; it’s about structure, sense, and support. The real hard work happens behind the scenes: making data talk to each other, mapping or streamlining processes, and, most importantly, preparing people to trust and use new systems. It’s less about technology as such, and much more about maturity: operational, organizational, and yes, managerial. 

To achieve that, what companies need most are partners who understand their business language as well as their data models. As one CIO put it: “We need partners, but we must drive the strategy, not follow vendors”. 

And that’s precisely where the conversation must change. We, or the market, desperately need to shift from unrealistic ambition – AI for all! to very realistic accountability – AI that fits. 

Going forward less pitching, more partnering, please… 

For vendors, this gap should be more than a talking point – it’s a wake-up call. The distance between what’s promised and what’s practical keeps growing, and clients are losing patience and hope. We’ve all heard the same speeches about collaboration, co-innovation and customer-centricity and yet too often, the market still gets more promise than substance. 

Clients aren’t lazy students skipping their homework. They’re professionals keeping production lines running, supply chains stable, and customers satisfied and all while being told they’re not “AI-ready” enough.  

If vendors want to stay relevant, they need to move from selling solutions to solving problems. That means stepping off the stage, where they are fighting with competitors and into the trenches to fight for clients, learning the nuances of each industry and individual organization’s needs. It also means realizing that success isn’t measured in the number of features added, dashboards set up or models deployed, but in business outcomes that actually matter. 

This may sound like a cliché but still true: at the end of the day, clients don’t buy AI (or any other technology for that matter); they buy results.  

We all need less hype and more help 

The promise of (well executed) AI is real. But so is the fatigue with AI at this point. It’s time to close the gap between “you can do anything” and ” but you need to do everything yourself”. 

If vendors truly want clients to embrace AI, they must act less like teachers assigning homework – think Bismarck-style education, and more like tutors walking beside their students – think Socrates before he drank a hemlock infusion. It’s this moment, when we realize, unlike at school, there’s no final exam in business, only continuous learning. 

And I would bet those who help their clients learn with them, not for them, will be the ones still standing when the hype cycle fades. 

You will hear more about AI and agents in IDC EMEA’s FutureScape Predictions webcast this December. You can register here,  

You may also be interested in a webcast Ewa is presenting on AI Sovereignty. You can register for that one here.  

For an overall look at AI in EMEA, you can download this eBook.  

To learn more about how International Data Corporation (IDC) can support your technology market data needs, please contact us.

Ewa Zborowska - Research Director, AI, Europe - IDC

Analyst Profile
Ewa Zborowska is an experienced technology professional with 25 years of expertise in the European IT industry. Since 2003, she has been a member of the IDC team, based in Warsaw, researching IT services markets. In 2018, she joined the European team with a specific emphasis on cloud and AI. Ewa is currently the lead analyst for IDC’s European Artificial Intelligence Innovations and Strategies CIS.

Economic volatility, sovereignty and security concerns, and the rapid advance of AI are creating powerful crosscurrents that few enterprises or providers can ignore. IT leaders on both the enterprise and services sides are under pressure to deliver innovation at speed while maintaining control, transparency, and trust.

IDC’s FutureScape: Worldwide Services 2026 research identifies this moment as the agentic pivot: a stage where AI is shifting from experimentation to orchestration. It marks a turning point that is redefining how technology is designed, delivered, and consumed. For IT decision makers, the focus moves from operational efficiency to business impact. For service providers, it demands a redesign of delivery around intelligence, automation, and measurable outcomes.

Early indicators are already visible. Enterprises that have begun integrating automation and orchestration into their IT services stacks are achieving faster innovation cycles and more predictable cost control. These are the first signs of a broader transformation that will redefine how IT services are built, priced, and delivered.

Prediction: Services will become products

By 2029, 30% of global IT services will be delivered as modular, platform-enabled products, driven by demand for speed, transparency, and agentic AI-enabled orchestration.

The traditional project model of IT services—long defined by customization, labor intensity, and phased delivery—is giving way to platform-based, increasingly AI-augmented offerings that can be deployed and scaled more quickly. For enterprises, this opens access to innovation with greater speed and clearer accountability for outcomes. For providers, it requires moving beyond resource-based contracts to productized portfolios that blend human expertise, automation, and data insight.

IDC research indicates that platform enablement is rapidly moving up enterprise sourcing agendas—an early sign that productized delivery is becoming a mainstream expectation.

As IT services evolve into productized, platform-driven offerings, how those services are valued is also transforming. The emphasis on agility and automation naturally shifts attention from inputs and effort to measurable business outcomes. That leads directly to the next major development in services transformation.

Prediction: Outcome-based economics will take hold

By 2029, driven by agentic AI, 30% of all contractual engagements with service providers will be outcome-based.

As automation expands, value is shifting from effort to impact. Many enterprises are beginning to explore payment models tied to performance—reliability, agility, or experience quality—rather than hours or inputs.

For service providers, this trend requires delivery architectures that make outcomes measurable and repeatable. IDC research shows that agentic automation is already reshaping operations and business-process services, enabling tasks to run autonomously while people focus on governance and innovation. These efficiencies pave the way for broader adoption of outcome-based models, where innovation and performance become quantifiable components of value.

As AI-driven orchestration takes hold, IT operations themselves are evolving toward more intelligent, self-managing systems, setting the stage for the next prediction.

Prediction: Intelligent orchestration will redefine operations

By 2028, 60% of organizations will adopt hybrid-cloud orchestration frameworks for multi-agent systems across data, cloud, and applications.

The emerging operating model for enterprises and their service partners is increasingly autonomous by design. Agentic orchestration brings together AI, automation, and human oversight to manage performance, compliance, and cost in real time. Some organizations are already experimenting with digital command centers that provide unified visibility across hybrid environments.

For CIOs, this means IT architectures will need to evolve toward self-optimization within defined policy boundaries. For services vendors, it signals a move toward intelligent ecosystems, where continuous insight replaces manual triage and analytics guide real-time decision-making.

But the agentic future isn’t only about automation and intelligence — it’s also about trust, governance, and human alignment.

Trust, sovereignty, and skills as enablers

Trust remains a central concern in the agentic economy. By 2027, most global enterprises are expected to reassess partners based on their ability to guarantee sovereign operations, data protection, and transparent AI governance. Providers that embed compliance and security into their automation frameworks will be better positioned to serve regulated industries.

At the same time, workforce priorities are shifting toward orchestration, oversight, and innovation, as AI increasingly manages parts of execution. Success will depend on how effectively organizations align human judgment and machine precision, whether within enterprise IT teams or across service-delivery networks.

These shifts are redefining the relationship between enterprise IT and the services industry.

A new alignment between IT buyers and providers

The dynamics of the agentic era are narrowing the distance between enterprise IT and service providers. CIOs now expect partners who can support and deliver against their transformation agendas, while providers are increasingly embedding their own IP and automation into every engagement.

This emerging alignment is built on trust, IP-led orchestration, and shared accountability for outcomes.
The result is a shared mandate:

  • Orchestrate talent and technology to deliver continuous innovation.
  • Modernize delivery and sourcing around platforms and measurable outcomes.
  • Operationalize trust through data governance and sovereign architectures.

Your next move

IDC’s FutureScape: Worldwide Services 2026 provides a data-driven roadmap for navigating this convergence. Begin with the Worldwide FutureScape Overview to understand the macro forces shaping the AI-driven economy, then explore the Worldwide Services FutureScape for deeper guidance on productization, automation, and orchestration strategies.

In the agentic era, the lines between enterprise IT and IT services continue to blur. Those who understand both sides of the equation are best positioned to shape what comes next.

Explore IDC’s 2026 FutureScape predictions.

Lars Goransson - Vice President, Research, Worldwide Services - IDC

Analyst Profile LinkedIn
Lars Goransson is Vice President of Research, Worldwide Services at IDC. He leads IDC’s global research and advisory for IT and business services, focusing on how technology suppliers and buyers can navigate market shifts, innovation, and business transformation. Lars’s research explores the evolving dynamics of the worldwide services landscape, providing clients with trusted tech intelligence and evidence-based insight to make confident decisions in a fast-changing digital economy. His work illuminates the path forward for organizations seeking to anticipate demand, validate investments, and seize new opportunities.

Artificial intelligence is already reshaping how work gets done, but not in the way most people imagine. The popular narrative of AI as a “co-worker” oversells its role and misunderstands its limits. AI systems are not peers; they are instruments: programmable, bounded, and entirely dependent on human judgment. Their impact will come not from collaboration, but from how effectively organizations learn to design, deploy, and govern them.

In IDC’s FutureScape Future of Work 2026 research, we see this transformation unfolding unevenly but decisively.

Agentic AI tools are advancing faster than most enterprise structures can adapt. The technology is ready to execute, but organizations are still learning how to make it productive, ethical, and sustainable.

The success of AI at work will depend less on technical power and more on the human systems built around it, including the processes, accountability, and oversight that turn automation into advantage.

Tools for all: From developers to line-of-business teams

The notion that AI is joining the workforce as a co-worker misreads its function. AI systems are tools used by both deeply technical developers and employees across business functions. IDC forecasts that by 2026, 40% of G2000 job roles will involve direct interaction with AI systems.

For developers, this means designing, securing, and maintaining the architectures that make AI dependable and compliant. Their work defines the system’s boundaries. For business users, AI will become part of daily operations; refining analysis, monitoring performance, and automating repeatable steps. The distinction is not about hierarchy but about fluency: the ability to use AI effectively without mistaking its capabilities for comprehension.

AI agents can recognize patterns within data and past interactions, but they lack awareness of intent, nuance, or institutional goals. They do not understand the broader organizational context in which decisions are made or the values that guide them. Their reliability depends on the quality of input and the competence of the person providing it.

The practical challenge ahead is building fluency across these roles. Developers must ensure systems perform as intended, while managers and employees must learn to apply them responsibly. The future of work will depend on dual skill sets: technical mastery and the human capacity for context, critical thinking, and ethical judgment.

As these technologies become embedded in daily operations, the question is no longer who uses AI, but how its presence changes the shape and distribution of work itself.

Redefining work and work roles

Agentic AI is reshaping the workforce through both elimination and creation. Some roles are being reduced or retired as AI systems take on repetitive functions that can be performed more efficiently and at lower cost. At the same time, new roles are emerging to oversee AI operations, manage governance and compliance, and translate technical performance into business outcomes.

IDC predicts that by 2027, half of all AI-enabled enterprise applications will require new oversight positions dedicated to governance, risk, and accountability.

These jobs will not replace those lost on a one-to-one basis. Instead, they redefine where expertise and decision-making authority reside. As tasks once distributed across many functions consolidate into automated systems, organizations must confront how to rebalance reporting structures, reassign responsibilities, and revise expectations.

This transition requires more than reskilling; it demands structural redesign. Organizations must let go of long-held roles that no longer add distinct value while supporting employees in taking on new responsibilities within evolving positions. Those that handle the shift effectively will go beyond adding job titles or automation layers. They will create clearer accountability and stronger alignment between human oversight and machine efficiency.

These shifts in responsibility and reporting require an organizing mechanism. One that ensures both governance and innovation evolve together.

Building structure: Centers of Excellence as engines of transition and trust

As organizations adapt to these changes, structure becomes the mechanism that determines whether transformation succeeds or stalls. The emergence of AI and Agentic Centers of Excellence (CoEs) marks a deliberate move from experimentation toward an integrated model for governance, innovation, and value creation.

First, CoEs help organizations make the transition from traditional automation to new ways of working. Rather than viewing AI as a replacement for existing tools, CoEs define how it should extend enterprise capability. They guide the redesign of processes, clarify which roles will evolve or disappear, and establish the standards that ensure AI is deployed responsibly and with purpose.

Second, CoEs serve as hubs for systemic, cross-functional governance. They bring together data, risk, technology, and human resources leaders to define how AI is used, monitored, and improved across the organization. This alignment prevents fragmented implementation and enforces accountability for outcomes, ensuring that AI decisions are traceable, ethical, and compliant.

Finally, CoEs sustain focus on innovation and long-term value. Internally, they drive a culture of continuous improvement through shared learning and benchmarking. Externally, they ensure AI investments translate into measurable client outcomes; improved service quality, faster delivery, and more adaptive customer engagement.

IDC’s Future of Work 2026 research shows that organizations with mature AI or Agentic CoEs are 20% more capable of competing on innovation, speed, and service excellence. These centers are not symbolic. They are the connective tissue linking technology capability to human expertise, operational discipline, and customer trust.

Redesigning work for a new conversation

The integration of agentic AI is not simply a technical evolution. It is an organizational negotiation.

C-suite leaders often approach AI with a “more, faster” mindset; a drive for scale, speed, and measurable productivity. Employees across functions experience the same transformation from a different vantage point: one that involves redefining responsibilities, acquiring new skills, and navigating the uncertainty of work that no longer looks or feels the same.

Bridging these perspectives will determine whether agentic AI delivers on its potential. The future of work depends on an ongoing dialogue between those who set direction and those who carry it out. It requires leadership that sees AI not only as an efficient tool but as a catalyst for redesigning how work is structured, supported, and rewarded.

The successful adoption of agentic AI will depend on a deliberate partnership between leadership and the workforce; a recognition that innovation and adaptation must advance together.

 The future of work will not be defined by speed alone, but by how organizations align ambition with understanding, progress with purpose, and productivity with shared accountability.

Access the full IDC Future of Work 2026 report to explore all predictions, and visit our FutureScape content hub for additional insights and resources.

Amy Loomis, Ph.D. - GVP, Research - IDC

Analyst Profile
Amy Loomis is Group Vice President for IDC's worldwide Workplace Solutions. Amy leads a team of analysts focused on the evolving nature of human resources, skills development, collaboration, and leadership across the employee lifecycle. Her research into the Future of Work explores the influence of hardware and software technologies such as artificial intelligence, data analytics, augmented and virtual reality and automation on the changing the nature of work. Her research also explores how technology and business strategy influence workers' skills and behaviors, organizational culture and how the workplace itself is enabling the future enterprise.

B2B buyers have rewritten the rules of engagement. They research independently, shift effortlessly between digital and in-person touchpoints, and expect every interaction to feel connected. The customer journey is no longer linear. It’s fluid, self-directed, and omnichannel.

Seventy four percent of buyers now prefer digital-first engagement, according to IDC’s2024 B2B Tech Buyer Behavior Survey. That preference means marketing leaders must coordinate across more channels than ever before, from brand websites and social media to in-person events and sales meetings.

The challenge? Many strategies look good on paper but break down in execution. Campaigns run in parallel instead of in sync. Content lands out of step with intent. Team handoffs create gaps buyers can’t ignore.

The result: fractured engagement, wasted resources, and pipeline momentum that stalls before deals have a chance to progress.

Omnichannel excellence isn’t about doing more. It’s about orchestrating journeys that connect signals, smooth transitions, and move buyers forward with purpose. 

Find the right play for your business needs with IDC’s Omnichannel Experience Playbook, your guide to updating your omnichannel strategy.

The most common omnichannel mistakes and how to fix them

Recognizing the pitfalls is the first step. Knowing how to counter them is what turns a disconnected approach into a system that drives real growth.

Mistake 1: Treating channels as silos instead of orchestrated journeys.

Too many organizations still manage channels in isolation. The website belongs to digital. Events sit with operations. Sales owns in-person conversations. Each team runs its own playbook. This leaves buyers to experience the brand in fragments.

Smart marketing leaders know that buyers disengage when the journey feels disjointed. In fact, more than a third of CMOs say creating a unified omnichannel journey will shape their strategy in the next 12-18 months, according to IDC’s 2024 Worldwide CMO Priorities Study.

The fix is orchestration: designing intentional transitions, linking intelligence with context, and enabling sales to activate in real time. When teams rally around the buyer—not their silos—the journey flows. And everyone wins.

Mistake 2: Over-automating without personalization.

Automation has scaled marketing, but when applied without personalization it can backfire. Too many flows and triggers run on autopilot, delivering impersonal and repetitive outreach that feels robotic.

And buyers notice. Personalized interactions consistently generic touchpoints by 30%, according to IDC’s FutureScape: Worldwide Chief Marketing Officer 2025 Predictions.

The solution isn’t to ditch automation. It’s to evolve it.

AI-powered personalization and adaptive content turn automation into a precision tool, ensuring every message is timely, relevant, and buyer-specific.

Download the AI Supplemental Guide to see how AI can elevate your strategy.

Mistake 3: Keeping marketing intelligence separate from sales.

Marketing often collects rich intent data, but sales either can’t see it, or lacks the context to act on it. Sales Development Representatives chase leads without intel. Meanwhile, buyers continue down self-directed paths.

The disconnect is expensive. Nearly 80% of buyers say they plan to use more digital sources for complex buying decisions and rely less on salespeople, according to the 2024 B2B Tech Buyer Behavior Survey. Which means every sales interaction matters even more.  

The fix is signal-based activation: turning every meaningful action, like demo requests or  pricing page visits, into a clear and contextual sales play. With insights from marketing, sales can move faster, build trust, and drive real progress.

Mistake 4: Sticking to static content sequences.

Many nurture programs still rely on rigid sequences: download an asset, get added to a drip. Attend an event, receive a standard follow-up. But buyers don’t move in neat, linear paths anymore.

Without integration and real-time intelligence, signals are missed. Someone ready to buy gets slowed down. Someone just starting out gets overwhelmed. Both disengage.

Why? Systems often don’t talk to each other.

When data flows freely and AI interprets signals in real time, content evolves with the buyer. The result? Dynamic flows that respond to behavior, reduce drop-off, and keep engagement moving forward.

From friction to flow

Each of these mistakes creates friction. Together, they derail growth.

  • Disjointed channels frustrate prospects
  • Over-automation without personalization erodes trust
  • Sales teams miss the signals that matter
  • Static content fails to keep up with today’s buyers

Fixing them takes more than ad hoc patches. It takes a disciplined approach—built on buyer intelligence, intentional transitions, and AI-driven alignment.

  • Orchestrate the journey to create flow
  • Personalize at scale to build relevance
  • Align with AI to unify teams and accelerate growth

Buyers expect more. With orchestration and AI in your toolbox, you can deliver the seamless omnichannel excellence they demand and turn every interaction into a moment that matters.

Download IDC’s Omnichannel Experience Playbook and AI Supplemental Guide to get started.