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Digital accessibility is shifting from a compliance requirement to a key driver of inclusion, productivity, and innovation in the modern workplace. This blog explores where European organizations stand today and outlines a practical, technology-driven approach to turning accessibility into a competitive advantage. 

What Is Digital Accessibility in the Workplace? 

Digital accessibility refers to the design, development, and delivery of digital technologies, products, and services in a way that ensures they can be perceived, understood, navigated, and interacted with by all people, consistent with the principles established by the W3C Web Accessibility Initiative. This applies regardless of ability or disability and includes individuals with physical, sensory, cognitive, and neurodivergent conditions, such as impairments related to vision, hearing, motor function, speech, and information processing, as well as situational or temporary limitations. 

It encompasses not only compliance with accessibility standards and guidelines, but also the proactive inclusion of diverse user needs throughout the entire lifecycle of digital experiences, enabling equitable, independent, and dignified access for everyone. 

In simple terms, in today’s AI-enabled workplace, accessibility is no longer just a legal box to tick. It is a design choice that determines who gets to fully participate, innovate, and grow. 

The State of Digital Accessibility in European Organizations 

European legislation on workforce accessibility is comprehensive but uneven. Countries such as Italy, France, Germany, and Poland enforce employment quotas for people with disabilities, while the UK and Denmark rely on antidiscrimination and reasonable-accommodation laws. To harmonize these differences, the European Commission introduced the Disability Employment Package and the Strategy for the Rights of Persons with Disabilities 2021 to 2030, outlining shared approaches to inclusive recruitment, workspace adaptation, flexible working, and assistive technology adoption. 

Despite this robust framework, execution lags. IDC research shows that diversity and inclusion, including accommodation for people with disabilities, ranks near the bottom of EMEA organizational priorities at just 27 percent, well behind talent retention and reskilling. Around 30 percent of employees say their organization has not adopted any digital accessibility solution at all. Interestingly, employees are more optimistic than their employers. One in two believe AI is already improving digital accessibility and will help close the digital divide. 

The real issue is not missing legislation. It is the gap between what companies say they will do and what they actually deliver. 

How to Build a Digital Accessibility Strategy 

So how can organizations close that gap? IDC’s The Four Tech Pillars to Create a Digital-Accessible Work Environment lays out a closed-loop, five-step journey that keeps accessibility moving instead of getting stuck in a one-off project. 

  1. Start by listening. Assess what employees actually need through surveys, one-on-ones, and functional and contextual evaluations.  
  1. Review your technology stack. Evaluate hardware, software, productivity suites, and assistive tools for compatibility and gaps.  
  1. Embed accessibility early. Integrate accessibility into design and procurement, turning standards such as WCAG and EN 301 549 into mandatory checkpoints.  
  1. Establish governance. Define clear roles, responsibilities, escalation paths, and cross-functional ownership.  
  1. Measure impact. Use data to track ROI and support CSRD reporting on productivity, inclusion, and compliance.  

This should be seen as a continuous loop rather than a checklist, one that evolves alongside people, technology, and regulation. 

The Four Technology Pillars of Digital Accessibility 

Assistive technologies alone are not enough. IDC identifies four interdependent technology pillars, all supported by a foundation of best practices. 

  • AI-enabled assistive technologies: AI-driven screen readers, image and audio descriptions, captioning, and voice input integrated into mainstream collaboration platforms, matching the right technology to the right user.  
  • Accessible-by-design AI-driven platforms: HR, productivity, and collaboration tools built from the outset to meet EU accessibility standards, shifting remediation earlier and reducing long-term costs.  
  • AI-empowered configuration and orchestration layer: A governance backbone that sets standards, automates testing, and scales accessibility across workflows, teams, and vendors.  
  • AI, data, and analytics: Privacy-preserving analytics on usage, barriers, and outcomes to demonstrate ROI, support ESG and DEI reporting, and anticipate future needs.  

Underlying these pillars, best practices, including executive sponsorship, shared accountability, training, and continuous feedback loops, ensure that strategy translates into everyday operations. Without them, even the most advanced technology stack risks remaining underutilized. 

Why Digital Accessibility Is a Competitive Advantage 

Digital accessibility is no longer just about compliance. It plays a critical role in attracting diverse talent, enabling innovation, and responding to increasing ESG scrutiny. European regulation provides the framework, but culture, technology, and governance determine the outcome. 

By combining a closed-loop approach with the four technology pillars and strong best practices, organizations can move beyond risk mitigation and position accessibility as a true competitive advantage. 

Want the full picture? For the European legislative landscape and where organizations stand today, see IDC’s Digital Accessibility for the Workforce: European Legislation and Organizations’ Responses (IDC #EUR154346826, March 2026). For a practical technology playbook, refer to The Four Tech Pillars to Create a Digital-Accessible Work Environment (IDC #EUR154347126, April 2026). 

And if you would like to explore what these trends mean specifically for your business, our experts are always happy to continue the conversation. Simply reach out via the contact form. 

Erica Spinoni

Erica Spinoni - Senior Research Analyst, WW AI-Enabled Future of Work & EMEA Practice Lead

Analyst Profile
As member of the global FoW team, Erica also leads the group’s EMEA-focused research, exploring how new workplace models and technologies such as AI and automation are transforming employee experience and productivity across Europe, the Middle East, and Africa. She…

Three forces reshaping European cybersecurity buying decisions in 2026 

Last week, Joel Stradling, IDC’s Senior Research Director for Global Security and Trust, and David Clemente, IDC Research Director for European Security Services, presented the latest IDC intelligence on three topics that are driving security buying decisions across Europe right now: AI agents, security platform consolidation, and digital sovereignty. 

The session was built around IDC survey data and direct input from European CISOs. Here is a summary of the key findings. The full recording is available on demand. 

What your buyers are prioritising around AI agents 

IDC projects 1.2 billion AI agents in operation by 2029, performing 217 billion daily actions across four categories: custom-configured, in-application, standalone, and bespoke agents. For security vendors, the relevant signal is what this proliferation is doing to your buyers’ security priorities and investment patterns. 

On average, 16.7% of planned AI investment worldwide is currently allocated to AI agent security and governance. That is a meaningful budget line, and it reflects a genuine gap that security buyers are trying to close: most European organizations cannot fully account for the non-human agents already operating in their environments.  

Joel framed this in the session with a question that resonates with CISOs: how many agents are running in your ecosystem right now without being part of any governance programme? The answer, frequently, is that they do not know. 

IDC’s forthcoming MaturityScape Benchmark on AI-fuelled organizations gives a precise picture of where European enterprises stand on AI maturity. The details of that data, including the significant divergence between organizations IDC categorizes as survivors versus those that are thriving, are covered in the recording and available through IDC’s research programme. 

Where the platformization opportunity actually sits 

Joel was direct about the state of the market: security is, to a degree, broken. Vendor sprawl and complexity are the most consistently cited concerns in IDC’s CISO conversations, and the move toward vendor consolidation is real and accelerating. In IDC’s December 2025 survey, 84% of respondents agreed or strongly agreed that their organization is prioritizing security platformization. 

The gap worth understanding, however, is the one between customer intent and vendor positioning. IDC’s CISO Hub data from March 2026 shows that while close to two-thirds of senior security decision-makers say they already have a platform, there is no shared definition of what that means or where it is anchored. The result is fragmented islands of platformization rather than genuine consolidation. 

For vendors, the implication is that the buying conversation is not uniform. Joel walked through how platformization incentives differ by stakeholder: a CISO is looking at visibility, proactive defence posture, and operational efficiency; a CIO is focused on speed, agility, and reduced contract complexity; a CEO is weighing business growth, risk reduction, and regulatory accountability. Understanding which stakeholder is in the room changes the pitch considerably. 

The barriers to consolidation are equally important market intelligence. Tool sprawl, technical debt, skills gaps, upfront costs, and the desire to avoid vendor lock-in are all factors slowing decisions that, on paper, buyers have already made. The session explored each of these in detail. 

What the sovereignty conversation looks like from the buyer side 

David Clemente covered how European security buyers are currently thinking about sovereignty, and it is a more complex and more urgent conversation than it was 18 months ago. 

IDC’s 2025 Worldwide Digital Sovereignty Survey asked European cloud buyers about their main motivations for choosing a sovereign cloud. The top three responses were: concerns about extraterritorial data requests, compliance with national legislation and European directives such as NIS2, DORA, the Cyber Resilience Act, and the AI Act, and the baseline desire to improve data privacy and security. 

David framed the trade-offs buyers are navigating using a cost-versus-control matrix that maps different procurement categories from commodity hardware through cloud services, AI, and sovereign or defense-grade infrastructure. The useful insight for vendors is that these positions are not stable. The past 12 to 18 months have introduced a level of volatility that has caused many European buyers to revisit assumptions they had previously considered settled, including assumptions about suppliers headquartered in the United States. 

During this time, a number of developments have shifted that calculus, and buyers who were not questioning their vendors about sovereignty a year ago are now asking them directly. For US-headquartered vendors in particular, the session covered what buyers are asking and what kinds of responses are building or eroding confidence. 

The core message from David was straightforward: vendors who have genuinely thought through their sovereignty positioning, who can offer tangible provisions and answer specific regulatory and political questions, are in a different position from those who have not. That distinction is becoming visible to European buyers quickly. 

Watch the full session on demand 

The webinar covered substantially more than this summary captures, including the full benchmark data on AI maturity, the complete breakdown of platformization incentives by buyer persona, and a detailed Q&A with both analysts. 

►  Watch the recording here  

IDC Security Summits Europe 2026 are taking place across the continent throughout the year, connecting vendors with 700+ senior security decision-makers in Madrid, Lisbon, London, Paris, Milan, Cologne, Stockholm, and Barcelona. The IDC European CISO Xchange takes place in Sitges, Spain, 8–10 November. Contact IDC for sponsorship and participation details. 

Joel Stradling

Joel Stradling - Senior Research Director, European Security

Analyst Profile
As senior research director for IDC's European Security practice, Joel Stradling leads the content and analyst team for tracking the European security segment. His main focus areas include Zero Trust Network Architecture, Managed Security Services, and Cyber Risk and Resiliency.…
David Clemente

David Clemente - Research Director, European Security

Analyst Profile
Dave Clemente is a Research Director in IDC's European Security practice, with a focus on security services (including managed services and professional services). He is a research professional with more than fifteen years of experience in cyber security, including in…

AI is reshaping how partner ecosystems create value. As automation absorbs traditional delivery work, partner-led growth is shifting toward influence, orchestration, and trusted advisory roles. Here’s what ecosystem leaders need to know for 2026. 

Artificial intelligence (AI) has moved from experiment to operating reality across the technology ecosystem. Models are scaling fast, agents are being embedded into platforms, and automation is absorbing activities that partners have historically delivered as billable services. For ecosystem leaders, this has created an uncomfortable question: where does partner value sit in an AI-driven world – and how does growth happen next? 

The short answer is that partners are not being disintermediated. But their role is being fundamentally rewritten. 

Across IDC’s 2026 partnering ecosystem research, partner advisory board discussions, and recent market analysis, a consistent pattern is emerging. AI is not eliminating partners – it is reshaping where, when, and how value is created across the ecosystem. This shift is happening faster than many organizations’ partner models, metrics, and engagement structures can keep up with. 

How AI Is Shifting Partner Value from Delivery to Decision Influence 

One of the clearest signals we see is that AI is absorbing many traditional partner value activities: configuration, basic implementation, tier-one support, and even elements of solution design. Agentic systems and AI-driven service layers are reducing friction and cost in these areas – but they are also compressing margins and undermining longstanding partner revenue assumptions. 

At the same time, customer expectations are changing just as quickly. Buyers are no longer navigating linear vendor-to-partner journeys, or making decisions through a single buying role. Instead, buying journeys are fragmented, non-linear, and shaped by multiple stakeholders across IT, business, finance, and operations. 

Influence is spread across a blend of direct and indirect touchpoints – platforms, marketplaces, peer communities, embedded AI recommendations, and trusted advisory relationships that cut across suppliers and traditional channel boundaries. 

In this environment, execution alone is no longer a sufficient source of partner differentiation. 

What customers increasingly need is help deciding where and how to apply AI, how to control risk and cost, how to curate models and agents, and how to operationalize them inside real business processes. That shifts partner value up the stack – from doing, to guiding; from delivering, to shaping outcomes. 

Why AI Is Raising the Bar for Partner Value in the Ecosystem 

Another consistent theme from partner advisory board discussions is that while AI lowers barriers to entry in some areas, successful AI adoption at customers is anything but “plug and play.” 

AI must be: 

  • Created and adapted for specific business contexts  
  • Curated across multiple models, tools, and platforms  
  • Governed for security, cost, compliance, and performance  
  • Controlled over time as usage, scale, and risk increase 

These are not one-off tasks. They require ongoing judgment, trust, and ecosystem coordination. In practice, this makes the partner role – particularly the trusted advisor role – more critical, not less, as the pace of change accelerates. 

The challenge is that many partner programs and metrics are still optimized for yesterday’s value creation logic: certifications, headcount leverage, implementation scale, or resale volume. As AI reshapes economics, these signals tell an increasingly incomplete story. 

AI-Driven Partner Transformation: What’s Changing in 2026 

All of this points to 2026 as a transition year. Not a collapse of partner models, but a period of accelerated partner transformation. 

We see clear divergence emerging: 

  • Partners who double down on execution alone face margin pressure  
  • Partners who invest in advisory, IP, ecosystem orchestration, and AI control layers are gaining influence  
  • Vendors and platforms that continue to treat all partners the same risk misallocating investment 

The opportunity now is to rethink ecosystem strategy through the lens of buyer influence, value creation, and AI-driven change, rather than legacy program structures. 

Ecosystem Strategy in 2026: Key Trends and Insights 

In our upcoming webinar, Ecosystem Strategy in 2026: Turning AI Disruption into Partner-Led Growth, IDC will bring together: 

  • 2026 partnering ecosystem trends  
  • Real-world partner advisory board insights  
  • Buyer journey and influence models  
  • Practical guidance on where focused ecosystem investment can drive sustainable growth 

This session is designed for leaders across cloud platforms, enterprise SaaS, GSIs, distributors, infrastructure vendors, and AI-driven ecosystems who are shaping partner and growth strategy for the years ahead. 

If you are navigating how AI is reshaping your ecosystem – and where partners fit next – this is a conversation you will not want to miss. 

Register now and join us on May 20.  

If you have any questions about anything in this blog, please drop them in here.  

Stuart Wilson

Stuart Wilson - Senior Research Director, EMEA Partnering Ecosystems

Analyst Profile
Stuart Wilson is senior research director for IDC’s Europe, Middle East & Africa (EMEA) Partnering Ecosystems program. With over two decades of global experience, Stuart focuses on the rise of complex, connected ecosystems and how platform models are reshaping routes…
Andreas Storz

Andreas Storz - Senior Research Manager, EMEA Partnering Ecosystems

Analyst Profile
Andreas Storz is senior research manager for IDC’s Europe, Middle East & Africa (EMEA) Partnering Ecosystems program. Based in the US, Andreas focuses on the evolution of go-to-market models, new digital value chains and the wider impact on partner ecosystems,…

Digital sovereignty is becoming a strategic priority across EMEA, reshaping how governments and enterprises choose infrastructure and network partners. This blog explores what the shift means for telcos, sovereign cloud, and AI infrastructure. 

Around the early 2010s, data residency was already part of the policy debate, but the infrastructure landscape was still more fragmented, and the issue had not yet become as central to cloud, AI, and national digital strategy as it is today. Telcos, regional ISPs, and a long tail of independent providers ran most of the hosting. The policy debates of the time already touched on carrier-neutral internet exchange points, peering, net neutrality, and data residency – how traffic moved between networks, where data was hosted, and who had jurisdiction over it. 

That picture has since changed twice over. 

First, market gravity shifted. A handful of hyperscalers and major social platforms absorbed most of the workloads, the storage, and the user attention. Hosting that used to sit inside national operators’ data centers consolidated into a few global clouds. 

Then policy caught up. Across EMEA, governments and regulators have moved data privacy, residency, and infrastructure control out of the compliance file and into national and regional strategy, with AI sovereignty layered on top, and geopolitical sovereignty sitting above all of it. 

For telcos, this is no longer a niche or optional conversation. It is actively shaping how enterprises and governments choose their network and infrastructure partners. 

How digital sovereignty is influencing buying decisions in EMEA 

The signal is clear. IDC’s EMEA Enterprise Communications and Collaboration Survey 2025 shows that, in response to geopolitical uncertainty, 28% of organizations are now more likely to use network service providers based in their own region, 27% are increasing their use of sovereign network services, and 26% are diversifying their network providers. Network infrastructure sits at the center of this shift, 70% of organizations cite sovereign controls over network infrastructure software as the most important component of technical sovereignty.  

The shift is showing up in budgets too. IDC’s 2025 Future Enterprise Resiliency & Spending Survey shows that nearly 30% of telcos plan to migrate applications from public cloud to country sovereign cloud infrastructure in 2026, with cybersecurity, regulatory compliance, and operational resilience among the top drivers of increased telco spending. 

Where AI and digital sovereignty converge 

AI has become a central thread in sovereignty conversations. The connection between cloud and AI needs has tightened, and sovereignty is now a recurring factor in both. IDC’s Worldwide AI and Generative AI Spending Guide Forecast (August 2025) projects AI and GenAI spending in EMEA telecommunications growing at a CAGR of 32% between 2024 and 2029, with telco AI spending set to treble by 2028. On the demand side, 53% of EMEA governments plan to increase their use of sovereign cloud for AI solutions, putting telcos squarely in the frame as infrastructure partners in sovereign AI ecosystems. 

The infrastructure shift is concrete. Among telcos, expanding data center capacity, AI inferencing (58%) and LLM training (54%) are the workloads driving most of the new build. Where these facilities sit, who certifies them, and who governs them is becoming a first-order strategic question. 

The role of telcos in sovereign cloud and AI ecosystems 

Governments across EMEA are pushing sovereign cloud and AI initiatives to take greater control of digital infrastructure and compute, and that is sharpening what buyers expect from their providers. 

Enterprise and public sector buyers are increasingly evaluating providers based on capabilities such as: 

  • In-country or in-region data centers  
  • Country-level certifications  
  • Freedom from lock-in 
  • Solutions to support operational resilience 
  • Sovereign controls of infrastructure  

Telcos are well placed to answer this list. National operators already own most of the underlying assets: in-country and in-region data centers, a regulatory and certification footprint, established government and enterprise relationships, and the connectivity layer itself. This is where telcos hold something cloud providers don’t; sovereign control over data in transit and the network layer itself. The bigger opportunity isn’t supplying pieces of someone else’s sovereign build. It’s pairing with sovereign cloud providers to deliver an end-to-end sovereign stack, data at rest and in motion, that neither side can credibly offer alone. 

What sovereignty looks like across Europe, the Middle East, and Africa 

Sovereignty is not a single play. The operator playbook looks different by sub-region. 

In Europe, regulation matters; and most of it now sits at EU level rather than national, but the top driver has shifted to protecting against extra-territorial data requests. Operators with strong domestic positions and certified infrastructure are best placed for both. 

In the Middle East, sovereignty is being driven top-down as national strategy. Governments are pairing sovereign cloud and AI ambition with serious capital, often through national champions, with major operators positioned as primary infrastructure partners. Established data localization regimes in some markets give operators a head start on dedicated capacity. 

In Africa, the story is data localization meeting infrastructure scale. National data protection frameworks are increasingly pushing data inside national borders, and pan-regional operators are expanding capacity that doubles as a sovereign-cloud foundation. 

Across all three, the playbook converges on regional infrastructure, certifications, simpler portfolios, and active ecosystem participation. Operators are also working with fewer, more strategic partners, ones that can take end-to-end accountability. 

Why sovereignty is becoming a default expectation 

Sovereignty has moved from a compliance topic to procurement criterion. It now sits alongside performance, cost, and scalability. AI and platform-based models only sharpen the demand for control, transparency, and resilience. 

For telcos, this isn’t a niche compliance discussion. It’s a strategic play, redefining what credible infrastructure looks like across EMEA, and where operators sit in the sovereign cloud and AI stack. 

Explore the broader telecom trends shaping 2026 
 
Sovereignty is one of several trends shaping the telecom market. In the IDC eBook State of the Telco Market 2026, you’ll find detailed data, forecasts, and analysis on topics including sovereign AI, infrastructure investment, and evolving business models. 

Download the eBook to explore the data behind these developments and better understand how the telco landscape is evolving. 

If you’re currently evaluating how sovereignty requirements will impact your network, infrastructure, or partner strategy, our experts are happy to exchange perspectives. Whether you’re at an early stage or already executing, we welcome the conversation. Get in touch with our team to continue the discussion. 

Tolga Yalcin

Tolga Yalcin - Research Director, Telecoms and ICT Regulations, IDC Middle East, Turkey, and Africa (META)

Analyst Profile
Tolga oversees the telecommunications and ICT policy and regulations side of IDC’s syndicated research, custom consulting, and advisory services in the Middle East, Turkey, and Africa region. Tolga plays an integral role in all IDC telecommunications-related initiatives in the Middle…

The EMEA IT market continues to grow in 2026. But the real story is not growth alone. It is how that growth is evolving under pressure. 

In our latest State of the Market webinar, IDC analysts Andrea Siviero, Stephen Minton, Ewa Zborowska and Lapo Fioretti explored how AI acceleration, geopolitical developments and rising resilience priorities are reshaping IT spending across the region. 

Here are five key takeaways that define where the EMEA IT market is heading next. 

1. IT spending in EMEA is growing, but becoming more selective 

IT spending across EMEA remains on a growth path, supported by strong momentum in software and infrastructure. At the same time, the market is becoming more selective. Devices are expected to decline in 2026, while software, infrastructure and IT services continue to benefit from changing enterprise priorities and AI-related demand. 

This reflects a broader shift in how organizations are allocating budgets. Growth is still there, but investment decisions are being made with more scrutiny. Geopolitical pressure, regulation and economic uncertainty are no longer background factors. They are directly shaping where technology spend goes and which initiatives get prioritized. 

2. AI is accelerating and reshaping IT spending across EMEA 

AI is now one of the clearest growth engines in the market. In EMEA, AI spending is expected to reach $319 billion in 2026, growing 19.2% year over year and expanding more than three times faster than total IT spending. 

But the significance of AI is not only its growth rate. It is how deeply AI is starting to influence budget allocation, vendor strategies and enterprise priorities. Organizations are no longer asking whether AI matters. They are asking where it creates measurable value and how quickly it can be embedded into the business. 

3. The shift from AI pilots to AI at scale is underway, although slow with AI Value blocked by Execution, not Interest 

The AI conversation in EMEA is moving from experimentation to execution. Organizations are becoming less focused on launching new pilots and more focused on improving, scaling and operationalizing the AI initiatives they already have in place. 

This is where the market becomes more complex. Nearly 48% of organizations are prioritizing investment in customized AI agents to automate business processes. But scaling AI requires more than enthusiasm or access to tools. It depends on infrastructure, data readiness, governance and skills. The bottleneck is no longer AI ambition. It is execution capability. 

4. AI Focus Still High on Efficiency, with Rising Expectations for Innovation and Growth 

For many organizations, the first wave of AI value is still rooted in efficiency. AI is being used to automate workflows, improve productivity and reduce operational friction. 

But the opportunity is expanding. 93% of organizations now see AI as a source of new revenue, not just efficiency gains. That shift matters because it moves AI from a cost-saving discussion into a growth discussion. The next phase will be defined by organizations that can turn AI into new products, services, business models and customer value. 

5. Resilience, governance and AI sovereignty are shaping IT strategy 

Resilience is becoming a central filter for technology investment in EMEA. It is now the number two business priority for CEOs in the region, second only to growth. 

That has major implications for AI. As organizations scale AI, they need stronger governance, clearer data control, better infrastructure resilience and trusted deployment models. AI sovereignty is also moving higher on the agenda, especially as organizations consider where AI systems are built, hosted, governed and operated. 

The message from the webinar was clear: AI scale is not just a technology challenge. It is a trust, governance and resilience challenge. 

What this means for technology providers in EMEA 

The EMEA IT market is entering a more demanding phase. Growth opportunities remain strong, but they are increasingly tied to execution readiness. 

For technology providers, this means helping customers move from AI experimentation to AI at scale. It means supporting data, infrastructure and governance readiness. And it means positioning AI not only as an innovation investment, but also as a resilience investment. 

In 2026, competitive advantage will come from helping organizations turn ambition into operational impact. 

Watch the webinar on demand 

If you want to go beyond the headlines, the full webcast offers a deeper dive into the data, regional dynamics and real-world examples discussed by our analysts. 

You will get a clearer view of where growth is actually materializing, how AI maturity is evolving across EMEA, and what is separating organizations that are scaling successfully from those that are not. 

Watch the recording here. 

And if you would like to explore what these trends mean specifically for your business, our experts are always happy to continue the conversation. Simply reach out via the contact form. 

Stephen Minton - Group Vice President, Data & Analytics - IDC

Analyst Profile
Stephen Minton is a group vice president with the IDC Data & Analytics group, focusing on ICT spending and macroeconomics. Mr. Minton is responsible for Worldwide ICT Spending programs, including the Worldwide Black Book, Worldwide 3rd Platform Spending Guides, and Worldwide Telecom Services Tracker. Mr. Minton's research expertise includes global ICT and economic analysis, and he tracks market data across hardware, software, services, telecom and emerging technologies. He is the author of papers that focus on the economic impact of IT, and is a regular speaker on the subject of IT spending. In 2002 he addressed the United Nations in New York, speaking to UN ambassadors on the subject of the Information Society. Mr. Minton previously worked with Digital Equipment Corporation (DEC), before joining IDC in 1998. Originally from Hartlepool in the North of England, he graduated from the University of Salford in 1995. He has also worked in the field of consumer market research with Millward Brown International.

Andrea Siviero - Senior Research Director, MacroTech, Digital Business, and Future of Work - IDC

Analyst Profile
Andrea Siviero leads IDC's European Digital Business and Future of Work Research group. The group provides market research insights to foster a purposeful and fair adoption of technologies supporting digital societies, businesses and workforce and empower tech providers in strategic decision making, planning and go-to-market activities. Siviero also co-leads the IDC Worldwide MacroTech Research program, focused on the intertwined connection between the Economical and Digital worlds - analyzing the impact key MacroEconomic factors have on the digital landscape and viceversa, how technologies are impacting economies around the world.

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.

Lapo Fioretti - Senior Research Analyst - IDC

Analyst Profile
Lapo Fioretti is a Senior Research analyst in IDC Digital Business Research Group, leading the European Emerging Technologies Strategies research. In his role, he advises ICT players on how European organizations leverage new technologies to create business value and achieve growth and analyzes the development and impact of emerging trends on the markets. Fioretti also co-leads the IDC Worldwide MacroTech Research program, focused on the intertwined connection between the Economical and Digital worlds - analyzing the impact key MacroEconomic factors have on the digital landscape and viceversa, how technologies are impacting economies around the world.

AI is not just changing job descriptions; it is actively rewiring how work is coordinated, controlled, and created, and it is doing so on multiple fronts at once, inside the same organization.

AI Is Transforming Work on Multiple Fronts Simultaneously

Some of our IDC Future of Work predictions bring this into sharp focus: by 2027, 40% of current job roles in large organizations will be redefined or eliminated, accelerated by GenAI adoption. At the same time, by 2030, around 70% of new job roles in Europe are expected to be directly enabled by AI technology. This is not a neat “old jobs out, new jobs in” swap. It is a systemic reconfiguration of how value flows through the enterprise. Yet most leadership frameworks still present AI scenarios as if they were mutually exclusive: automate to cut headcount, augment to boost productivity, redesign work for agility, or push toward autonomous operations.

When Automation, Augmentation, and Autonomy Collide

On the ground, those dynamics do not arrive one by one; they collide. In the same business unit, you may be cutting FTEs as routine tasks are automated and taken over by “digital colleagues,” while simultaneously hiring AI orchestrators, prompt engineers, and automation product owners to keep up with demand for AI-adjacent skills. You may be tearing up long-standing workflows as agentic systems reshape a significant share of knowledge work, at the same time as parts of your operation drift toward near-autonomous execution, powered by employees building personal agents and conversational workflows that quietly absorb whole segments of the process. These are not options on a slide; they are concurrent forces acting on the same organizational fabric. Treating them like menu choices is not workforce planning. It is misdiagnosing an organizational phase transition, a fundamental shift in the underlying architecture of how work happens.

From Role-Based Models to Capability-Based Architectures

The uncomfortable truth is that many leaders are still planning for roles, new and “to be eliminated,” while AI is reshaping the landscape at the level of capabilities and architecture. You can see the tension in three simple signals. A clear majority of European organizations have already deployed or are piloting automation to offset chronic labor shortages. A growing share of executives openly discusses replacing positions with automation, and many plan to substitute a measurable portion of their workforce with “digital colleagues.” Meanwhile, by the end of this year, a meaningful slice of frustrated knowledge workers with no formal development background will be building their own agentic workflows to change how they work, regardless of what HR’s role catalog says. When people can spin up an agent in a week, any static role taxonomy you publish today is out of date tomorrow. The center of gravity moves from “what roles do we have?” to “what capabilities can we compose, and how fluidly can we recombine them as AI matures?”

Why Traditional Role Models No Longer Hold

Role-centric models allow for some seriously wrong assumptions: that tasks are stable enough to bundle into jobs, that jobs are stable enough to plan around for three to five years, and that hierarchies are stable enough to govern how value flows. Agentic AI quietly breaks all three. Tasks fragment, recombine, and migrate between humans and machines in near real time. Work starts to look less like a tidy org chart and more like a living graph of capabilities, human, machine, and hybrid. In that context, planning headcount against static job descriptions is like trying to architect a cloud-native platform using only server rack diagrams.

Architecture Determines the ROI of AI

However, IDC’s Future of Work research also shows that when enterprises invest in digital adoption and automated learning technologies, they can unlock substantial productivity gains. The pattern across these findings is consistent: it is the architecture that determines the yield of AI, not just the tools themselves. If your workflows are fragmented, AI struggles to “see” the end-to-end journey it needs to transform. When critical data is locked in legacy systems, it cannot provide the rich, contextual recommendations you were promised. When governance is tuned for stability rather than experimentation, it throttles the learning cycles AI needs to be useful. Layer on top the reality that many organizations openly acknowledge they lack the capability support to implement automation effectively, and a clear picture emerges.

AI Amplifies Existing Organizational Weaknesses

In that environment, throwing more AI at the problem does not fix anything. It amplifies what is already there. Bad processes simply run faster. Poor decisions scale further. Shadow automation blooms in the gaps, as frustrated employees script around the constraints of the operating model. AI becomes an accelerant, not a cure.

Reframing the Strategic Question for Leaders

This is why the strategic question has to change. Instead of asking, “Which jobs will we automate?”, leaders need to ask, “Is our organization structurally able to absorb intelligence at scale?” Answering that requires moving from headcount planning to capability mapping, designing work around the interplay between human strengths, judgment, domain expertise, relationship-building, and machine strengths such as pattern recognition, generation, and orchestration. It means treating architecture as a product: standardizing interfaces, workflows, and data contracts so AI can plug into work without bespoke integration every single time. It means tracking how many workflows, decisions, and customer journeys are genuinely enhanced by AI, not just how many licenses have been bought. And it means steering reduction, augmentation, redesign, and autonomy as one coherent portfolio of change, not four disconnected projects.

Conclusion: The Real Stress Test Is Your Operating Model

AI is already changing jobs. The real test is whether your operating model can evolve quickly enough to harness that change, or whether AI will simply accelerate you toward the limits of the system you already have.

If you would like more information, drop your details in here.

Meike Escherich - Associate Research Director, European Future of Work - IDC

Analyst Profile
Meike Escherich is an associate research director with IDC's European Future of Work practice, based in the UK. In this role, she provides coverage of key technology trends across the Future of Work, specializing in how to enable and foster teamwork in a flexible work environment. Her research looks at how technologies influence workers' skills and behaviors, organizational culture, worker experience and how the workspace itself is enabling the future enterprise.

AI adoption is accelerating across EMEA, yet many organizations struggle to translate investment into measurable business value. This blog explores the structural challenges behind stalled AI initiatives and what differentiates organizations that successfully scale.

AI Adoption in EMEA: High Investment, Limited Business Value

AI adoption across EMEA has progressed significantly over the past 12–18 months, with organizations moving beyond experimentation into broader deployment phases. However, progress remains uneven.

IDC research shows that a substantial share of organizations are slowing down, scaling back, or refocusing their AI initiatives. This reflects a shift in priorities rather than a decline in interest. As macroeconomic pressures, regulatory complexity, and competing IT investments intensify, organizations are increasingly challenged to execute AI initiatives while demonstrating measurable business outcomes.

Why AI Projects Fail: The Execution Gap

The challenges that limit AI impact are consistent across industries, but particularly pronounced in EMEA.

According to IDC research, organizations continue to face difficulty in quantifying and demonstrating AI-driven ROI, alongside competition for resources and increasing regulatory uncertainty. According to IDC research, only 9% of EMEA organizations have been able to deliver measurable business outcomes from most of their AI-related projects over the past two years (Source: IDC Future Enterprise and Resiliency Survey, Wave 1, March 2026), At the same time, resistance to process change remains a persistent barrier, especially where AI requires cross-functional alignment and new ways of working.

These factors rarely cause projects to fail outright. Instead, they contribute to a gradual loss of momentum, where initiatives remain in pilot phases or are scaled selectively without broader organizational impact.

AI ROI: Why Proving Business Value Remains So Difficult

A central issue in AI adoption is the ability to measure value consistently.

IDC research highlights that AI impact extends beyond direct cost reduction to include indirect benefits such as productivity gains, revenue enablement, and risk mitigation. This makes it difficult to capture value using traditional ROI models.

As a result, many organizations lack a standardized approach to evaluating AI initiatives. This leads to fragmented decision-making, where use cases are assessed in isolation and scaling decisions are not consistently aligned with business priorities.

Without a clear framework for value measurement, AI initiatives often struggle to move beyond experimentation.

Scaling Enterprise AI: Why Moving Beyond Pilots Is So Hard

Scaling AI requires more than successful use cases. It requires integration into core business processes and operating models.

IDC research indicates that organizations face increasing challenges when moving from pilot to scale, particularly in relation to budget allocation, operational complexity, and governance requirements. While initial projects are often funded as innovation initiatives, scaling requires sustained investment in infrastructure, data, and ongoing operations.

This transition exposes structural gaps. Organizations that lack alignment between business strategy, data architecture, and execution models often struggle to scale beyond isolated successes.

AI Governance and Regulation in EMEA: Barrier or Opportunity?

Regulation is a defining factor for AI and broader technology adoption in EMEA.

According to IDC research, regulatory requirements around data protection, AI, and cybersecurity are significantly shaping how organizations approach AI deployment. While compliance increases operational and infrastructure costs, it is also driving more structured approaches to governance.

At the same time, organizations report benefits such as improved resilience, stronger ESG performance, and increased customer trust. This suggests that regulation is not only a constraint, but also a catalyst for more sustainable and trusted AI adoption.

Organizations that integrate governance early are better positioned to scale AI effectively.

AI and Workforce Transformation: Why the Human Factor Matters

AI transformation is not purely a technology challenge. It is fundamentally an organizational one.

IDC research emphasizes the importance of aligning AI initiatives with workforce capabilities, culture, and leadership. This includes reskilling, change management, and building trust in AI-driven processes.

Organizations that fail to address these elements often encounter slower adoption and limited impact. In contrast, those that integrate the human factor into their AI strategy are better positioned to realize long-term value.

The Evolving Role of the CIO in AI-Driven Organizations

As AI becomes central to business strategy, the role of the CIO continues to expand.

IDC research shows that digital leaders are increasingly expected to drive business value, support growth, and strengthen resilience. For instance, 42% of EMEA C-Suite leaders expect their CIO role to lead digital and AI transformation with a major focus on specifically creating new revenue streams (Source: IDC Worldwide C-Suite Tech Survey, September 2025). This requires a shift from a technology-centric role to a more strategic position aligned with business outcomes.

CIOs and digital leaders are therefore playing a critical role in connecting AI initiatives with measurable impact and ensuring alignment across the organization.

From AI Strategy to Execution: What Differentiates Leading Organizations

The current phase of AI adoption in EMEA is defined by execution.

Organizations that successfully scale AI tend to take a more structured approach, linking initiatives to business objectives, embedding governance early, and aligning technology with organizational change.

However, many organizations are still in transition. Key questions remain:

  • How can AI ROI be measured consistently across different use cases?
  • Which frameworks support scaling AI at the enterprise level?
  • What changes are required to align workforce and operating models?

How should the role of digital leaders evolve to effectively support AI-fueled business transformation? These questions will be explored in more detail in the upcoming webinar.

Drawing on insights from the IDC EMEA Digital Leader Playbook, the session will provide a practical perspective on how organizations across the region are approaching AI strategy and value realization.

Join the Discussion

For organizations seeking to move from AI experimentation to measurable business impact, understanding these dynamics is critical.

Register for the upcoming IDC webinar on May 28 to gain deeper insight into how leading organizations in EMEA are turning AI into real business value.

Martina Longo - Research Manager, Digital Business - IDC

Analyst Profile
Martina Longo is a research manager in the IDC Digital Business Research Group. In her role she advises ICT players on how European organizations create business value using digital technologies. She also leads IDC European Digital Native Business research, focused on those enterprises born in a modern technological world in a mix of start-ups, scaleups, and more mature digital natives. Within the European Digital Business Research, the European Digital Native Business, Start-ups and Scale-ups theme advises technology suppliers on the market dynamics and segmentation, business priorities, tech buying patterns and go to market approaches (sell to/sell with) needed to engage digital native organizations in Europe.

Hannover Messe 2026 ran from April 20 to 24 in Hannover, Germany, and it delivered. Under the theme “Think Tech Forward”, the show brought together over 130,000 visitors from more than 150 countries, 4,000 exhibitors, and 300+ start-ups across industrial automation, software, and hardware.

Brazil was this year’s partner country, and the event itself got a makeover: a new hall layout, a revamped thematic structure, and a brand-new Defense Production Park zone, reflecting just how much the scope of industrial technology has shifted.

Here are the Top 10 things I’m taking home, and yes, I’m happy to be challenged on any of them.

The user attention battle is quietly beginning

My deepest feeling coming out from the #HMI26 floor was to be the witness of the first deployments of the armies fighting for who controls the factory of the next decade. Most demos at Hannover Messe 2026 I was exposed to started with a chat box prompting the users. The question is how many of them can co-exist in a factory setup. My answer is as little as possible. The battle for the factory UI has hence started. It can turn out this way: one system as the front-end workers actually use, the others as solid back-end.

Context is the new competitive asset. Whoever owns it, then owns the process. And physics-aware data fabrics are the competitive moat

The differentiating capability in industrial AI is not model quality, but it is contextual depth. A physics-aware industrial data fabric that connects real-life physics, process history, sensor telemetry, operational and operator knowledge provides more competitive advantage than any algorithm running on top of it. Hopefully, manufacturers will define a technology journey built around data first, then context, then impact, but I fear the need to rush the deployment of industrial AI apps may result in missed opportunities in building the critical industrial model foundation.

MES stands for “Must Evolve Soon”

This application is the spine of the plant (because it acts as both the system of engagement and the system of record). But process flexibility is now its hardest test… Why? First, top-down. Advanced Planning and Scheduling applications are seeing accelerated adoption, driven by a new generation of algorithms capable of delivering real-time, context-rich, executable plans. As APS systems push dynamic re-sequencing into execution, MES must evolve fast enough to receive and act on what APS produces, or risk being seen as the weakest link. To this, it directly follows… the bottom-up pressure. Unstructured production cells (i.e. multifunctional robots, wireless machines, AMR-driven object routing) are going to be gradually replacing fixed lines. Customer requests are shifting toward rapid configuration, faster changeovers, and multifunctional automation. MES must evolve to accommodate less deterministic workflows, or lighter tools will fill the gap.

Forget upskilling. The connected worker is all about context generation and retention

The ability to bring anybody “to speed” has been so far one of the typical selling points for connected frontline worker platforms so far. But this is barely scratching the surface. The combination of AI-first vision systems, IIoT, RFID, RTLS, and mobile or wearable devices creates an ultra-visible data substrate that makes the factory transparent. On top of it, the layer of human-process interaction managed through connected worker platforms enables unprecedented levels of visibility on how people interact with process execution steps. This is truly the best material for AI-driven process improvement. This data gold mine is not just in the machine data. It is the analysis of what happens between the worker and the process.

The industrial metaverse is developing as a hyper-contextual decision-making environment

The exponential growth in data availability, combined with falling costs of modelling and representation, is unlocking use cases that were economically impossible two years ago. Hence, we can say that the “VCR” moment has arrived. Now we have the full capability to “zoom in and zoom out” and as well as “fast forwarding” the process for continous multi-scenario process planning and simulation, as well as “rewind” or playback the process for traceability and analysis.

Right-size AI now or face the potential consequences

The differentiating capability will be the agentic continuum, i.e. the unbroken intelligent chain across production execution. But building that chain responsibly requires confronting infrastructure and cost realities that vendor marketing may be now underplaying. Right-sizing AI and matching model scale and infrastructure to actual operational demand is a business continuity decision. The question is not “what is the most powerful model?” but “ do we need AI at all for this, and if the answer is “yes”, then “what is the appropriate model for this decision/process automation, in this operating environment?”

Manufacturing runs on deterministic sequences. Agentic AI is inherently non-deterministic. Reconciling these two realities is the governance challenge

Two distinct scenarios define the governance challenge. In the first, the desired output is well understood, and users can accept or reject an AI result without a care in the world about inspecting the internal process. In the second, the correct answer is uncertain, and full transparency into how the model generated its output is required before the result can be trusted. The challenge is how to gradually hand over large bits of process control to an agentic software layer that is stochastic in nature. Most manufacturing companies today are only comfortable approving small, incremental AI-driven changes, not because AI is incapable of more, but because the accountability and auditability frameworks for automating larger decisions do not yet exist.

So what?

What does this mean in practice? Three implications stand out.

Survive to Scale: Link the technology curve to the organisation curve

Technology is advancing faster than most organisations can absorb. The strategic risk for many manufacturers is not deploying too slowly, but it is scaling before the organisational substrate is ready.

Bring in the Naysayers: Organisational buy-in requires involving sceptics early, not convincing them late

There is a very nice saying that goes more or less as “Don’t let people saying that it can’t be done disturb the people who are already doing it.” But in this new venture, bringing the contrarians will be important. Creatin forums where sceptics stress-test plans with the utmost ferocity (before the market does it!) will be key.

Complexity demands simplicity: Focus on fundamental problems, not exhaustive use-case catalogues

Technology is evolving faster than any list can stay current. Vendors and manufacturers alike should resist chasing every new capability appearing on the horizon, and rather concentrate on first principle-based, core solutions that foster data integration for autonomy and decision-making improvement.

For a deeper look into Lorenzo’s research, visit our website. If any of these perspectives challenge your thinking or connect to your priorities, we would be glad to continue the discussion via our contact form.

Lorenzo Veronesi - Associate Research Director, IDC Manufacturing Insights - IDC

Analyst Profile
Lorenzo Veronesi is an associate research director for IDC Manufacturing Insights EMEA. In this role, Veronesi leads the Worldwide Smart Manufacturing research program and supports all the IDC MI research services for EMEA, by looking at Digital Transformation drivers in multiple manufacturing industry sub-verticals. He is also often involved in consulting projects across the world for end-users, IT vendors and public authorities. During the last decade his research has focused across key processes such as manufacturing operations management, supply chain management, and product lifecycle management in multiple manufacturing verticals, including - among others - automotive, aerospace, machinery, high-tech, chemicals, CPG, and fashion. Before joining IDC, Veronesi worked as analyst in multiple projects including research in the industrial logistics sector and as advisor for public authorities in Italy. Veronesi holds an MSc Degree in Regional Science at the London School of Economics and Political Science and has graduated cum laude at the Bocconi University in Milan.

Why scaling AI and proving ROI are now the real challenge for European organizations.

What comes next is far less straightforward.

For some time, the European AI narrative was fairly comfortable: lots of enthusiasm, plenty of pilots, and just enough regulatory drama to keep things interesting. Companies could experiment broadly, point to a few wins, and call it a strategy.

IDC’s recent research, based on a survey of 200+ European organizations conducted in late 2025, tells a story that is a tad inconvenient for anyone still in “innovation exploration” mode: more than half of European companies report that over 50% of their AI projects have already delivered measurable business outcomes. This is no longer a single pilot result; it is becoming a pattern. And patterns have a tendency to change expectations.

Europe is past the “AI is interesting” phase, but not quite at “AI is effortless” either. Most organizations are somewhere in the messy middle: proof points, momentum, but still unable to explain why that momentum is not turning into something more systematic. Nearly 9 in 10 say their ability to scale AI has improved. And yet, a large portion is operating with what you might call partial discipline. They are moving forward, but without the playbooks, governance structures, and execution models that make scaling feel less like controlled improvisation.

The technology was never the hard part of AI scaling
European organizations are not struggling to build AI. They are struggling to absorb it. When asked what most prevents them from realizing the full potential of their AI investments, the top answers were competition with other transformation priorities, regulatory uncertainty, resistance to process change, difficulty proving ROI, and budget pressure. None of these are technology problems. The blockers are organizational, political, and structural. Throwing more engineering at them will not help.

This is, in fact, a sign of progress. Europe’s AI constraints have shifted from technical feasibility to enterprise commitment, which means the technology has largely done its job. The hard part now is everything surrounding it: sponsorship that survives the next budget cycle, processes redesigned after years of inertia, and ROI demonstrated clearly enough to compete with every other initiative in the budget allocation process. AI is now being tested as a business program, and business programs depend on organizational discipline.

But can organizations measure AI ROI and business impact?

European organizations are no longer just tracking model performance or project completion. Operational efficiency, user adoption, business KPIs, and financial outcomes are all on the scorecard now. This removes a certain flexibility that AI teams might have previously enjoyed. A technically elegant deployment that nobody uses is no longer a qualified success. It is simply not a success.

The encouraging news is that many organizations are starting to respond, with a clear move toward formal business metrics and ROI logic built in from the start.

The gap is widening

Europe’s AI market is entering a separation phase. This is the point where the gap between organizations that can operationalize AI and those still generating isolated use cases starts to widen. The organizations pulling ahead are building the necessary connective tissue: prioritization discipline, outcome measurement, and governance that works at speed. Meanwhile, those still in exploration risk producing impressive narratives about their AI journey while actual business outcomes remain limited.

For enterprise leaders, IDC research is clear about what separates the scalers from the stragglers:

  • Stop treating AI as a project portfolio. Projects create motion; systems create lasting value.
  • Build measurement in from day one, not just as good practice, but because organizations that cannot prove value will lose internal budget competition to those that can.
  • Treat governance as a speed advantage. Organizations that build compliance into reusable controls will move faster, not slower, than those handling it case by case.

For vendors and service providers, the message is equally clear: more features are not the answer to executive skepticism. Proof of business impact is becoming a primary buying criterion. The ability to show how value will be measured, attributed, and reviewed matters more than model benchmarks.

Want to go deeper?
These dynamics are part of a broader shift shaping IT investment across EMEA in 2026. In our recent webcast, IDC analysts explored where growth is materialising, how AI maturity is evolving from pilots to scaled deployment, and what separates organisations that are successfully operationalising AI from those that are not.

If you missed it, the session is now available on demand. Watch it here and get the full data-driven perspective for your strategy.

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.