Resource Regions: Western Europe

In two earlier posts I argued that SaaS is being disrupted rather than killed, and that the per-seat revenue model is on its way out. The dramatic decline in market capitalization of SaaS vendors over the past 9 months, sometimes referred to as the SaaS-pocalypse, is partly about investors pricing in the fear that SaaS applications will recede behind an AI agent layer. This would make the SaaS applications become invisible “featureware,” with the agent capturing the user relationship, the workflow, and, eventually, the budget. 

This post will show that enterprise buyers now expect their software vendors to supply the agents, and to serve as the trusted source of data and context for custom-built and third-party agents. This is the natural next progression for SaaS vendors and represents an attractive market opportunity. 

To avoid confusion, here are IDC’s AI-related definitions. An AI assistant is conversational and works as a tool for a human. An AI agent is autonomous, uses other tools, and carries memory and context across tasks. An agentic workflow is a business process that an agent executes end-to-end with limited human intervention. 

SaaS vendors are faced with a dual threat, hence SaaS-pocalypse 

The first threat is that organizations will simply write their own applications instead of buying standard SaaS applications. This threat triggered the dramatic devaluation of SaaS stock in February as Anthropic released its Code capabilities. Conversations with CIOs in enterprises partly validate this fear. While they are not contemplating creating core accounting, HRIS or workforce management applications, they might build rather than buy auxiliary applications in areas such as planning, performance management, compensation management, logistics planning, etc. Application areas where customer requirements vary widely and where legal complexity is low are moving gradually from buy to build. 

The second threat is that agentic overlays will increasingly handle the user interaction while AI agents access SaaS applications via API interfaces to carry out transactions on behalf of the human user. The implication is key price justifiers of SaaS applications, such as the interface, the feature breadth, and the brand gradually disappears from the users. This also implies that the per-seat user pricing stop making sense, when users are no longer the primary users of SaaS applications. 

AI agent adoption is already past the tipping point 

In IDC’s April 2026 Future Enterprise Resiliency and Spending survey of organizations with 500 or more employees, 74% had already deployed at least one agent, 15% were piloting, and only 1% reported no use and no plans. The same respondents expect the number of agent types in production to roughly triple, from 24 in March 2026 to 62 by 2027. Buyers are not evaluating whether to enter the agentic era. They are deciding which parts of their operation to hand over first, and operational and core business data are their top target. 

The big question is who will deliver these AI agents. Most organizations have deployed standard AI tools and run pilots, but few have redesigned core processes to use AI at scale. The survey results shows that most organizations deploy ready-made agents wherever they can, rather than build their own. 

Where Vendor-Supplied Agents Win 

IDC distinguishes four kinds of agent by who builds them and how the buyer obtains them. In-application agents come packaged inside the application and the buyer simply adopts them. Low-code / no-code agents are configured by the buyer in a visual builder the vendor provides. Standalone agents are third-party products the buyer implements alongside existing applications. Custom-built agents are assembled by internal teams using full-stack orchestration frameworks. 

The survey data, which focuses on AI agent quantities as opposed to spend, points in one direction. The two types that vendors supply directly, in-application agents and low-code or no-code builders, are growing fastest in numbers and from the largest installed base. Custom-built agents show the slowest growth, because the orchestration they require is more difficult and require more inhouse skills to build. Buyers prefer to adopt or configure an agent that already understands their data and respects their permissions over building one from scratch. 

SaaS applications as trusted data and context for custom-built agents 

IDC also sees a massive demand for custom-built AI agents, especially for core business processes that are unique to an industry or an organization. Standalone products and custom-built agents will operate inside the same enterprise, and they will need data and context that lives inside your application. 

A custom-built or third-party agent that accesses data “from anywhere” still needs a place where the data is correct, the process is compliant, the permissions are enforced, and the transaction is guaranteed to execute. A SaaS vendor can offer vetted business processes, governed data, and audit trails for custom AI agent consumption. This is, in IDC’s view, a key future role for today’s SaaS applications. 

What the AI-pivoted SaaS application looks like 

The interface stops being a single screen and becomes several modes serving the same processes: the traditional UI, a conversational UI, a flow-of-work UI embedded where the user already operates, and machine interfaces so external agents can call the application directly and safely. 

The AI-pivot requires SaaS vendors to rethink the workflow from the ground up, which touches the full stack: foundation models, an embedding layer, a vector database, retrieval-augmented generation, an orchestration layer, guardrails, monitoring, and version management. The vendor also has to give buyers an agent toolkit of their own, so that the low-code and no-code configuration buyers increasingly demand happens inside the vendor’s governed environment rather than outside it. 

European vendors carry an additional set of requirements that, handled well, become a selling advantage. Compliance with GDPR, NIS2, and the EU AI Act (still there despite the recent delay), data residency and sovereign cloud guarantees, genuine multi-language model performance, and transparency in how the AI reaches a decision are all conditions of sale to compliance-sensitive European buyers. 

SaaS is not dead, and the incumbents are not doomed. But the asset that justifies a vendor’s existence is shifting from the screen the user looks at to the agents the vendor supplies and the governed data those and other agents rely on. Your customers already expect you to be their agent supplier. The only question is whether you are ahead of that shift or reacting to it. 

So what do you actually do with this? There are three concrete moves software vendors need to make in the near term. They are specific, they are sequenced, and the window to move first is closing. I will walk through all three in a focused 25-minute webcast, grounded in IDC survey data from more than 1,000 enterprise organisations: where your customers sit on the AI maturity curve, why vendor-supplied agents will dominate enterprise deployments through 2030, and which ERP and SaaS processes are attracting the most AI investment right now. Secure your spot here. 

Artificial intelligence has been part of the telecom conversation for years. What is changing now is its role and scale. 

AI is no longer limited to isolated use cases or innovation initiatives. It is increasingly influencing core infrastructure decisions, from data center strategy to network architecture and investment priorities. 

Across EMEA, telcos are moving from experimentation to more structured, large-scale adoption. 

AI investment in telecommunications is accelerating across EMEA 

AI and generative AI spending in telecommunications is growing rapidly. In EMEA, spending is expected to increase at a compound annual growth rate of 31.8% between 2024 and 2029. 

Most telcos are already using, or planning to use, AI and machine learning to optimize network operations, improve customer experience, and explore new revenue streams. 

At the same time, operators are becoming more pragmatic. There is increasing focus on aligning AI ambitions with what existing data, cloud, and operational capabilities can realistically support. 

AI is reshaping telco infrastructure priorities 

As AI adoption scales, it is beginning to reshape how telcos think about infrastructure. 

Data center investments are being driven by workloads such as AI inferencing and large language model training. These require high-performance, low-latency environments and are pushing telcos to rethink how compute, storage, and networking are designed and deployed. 

This is reflected in several shifts: 

Closer collaboration with hyperscalers and ecosystem partners  

Expansion of colocation and edge deployments  

Greater focus on GPU-intensive infrastructure  

Infrastructure is becoming more closely aligned with the need to support real-time, distributed AI workloads. 

How short-term ROI is shaping AI adoption in telecom 

While investment is increasing, telcos are prioritizing use cases that can deliver measurable value in the near term. 

Employee productivity, customer experience, automation, and operational efficiency are key focus areas. These areas offer clearer paths to return on investment compared to more experimental AI initiatives. 

This creates a balance between near-term impact and longer-term transformation goals, such as autonomous networks and more adaptive service models. 

Data and cloud foundations remain a key challenge for AI 

Despite strong momentum, many telcos are still working to align their underlying capabilities with AI ambitions. Strong security and data privacy protections are fundamental to ongoing telecom investment in AI capabilities. If a capability or vendor partner is not trusted it won’t be implemented in production. 

Challenges around data quality, data management, and cloud readiness can also practically limit the speed and scale of AI adoption. As a result, AI strategy is closely linked to broader transformation efforts, including modernization of data platforms and investment in hybrid infrastructure. AI capabilities often act as multipliers of existing capabilities. The better the foundation the greater the impact and ability to scale. 

What this means for telecom operators 

AI is becoming a cross-functional priority that connects network, IT, and business strategy. 

Infrastructure planning, partner selection, and operating models increasingly need to reflect AI requirements. At the same time, telcos are working more closely with partners to access capabilities, accelerate adoption, and manage complexity. 

As AI becomes more embedded, it is also influencing how services are designed and delivered, and how operators position themselves in the market. 

Download the full analysis 

AI is one of the defining trends shaping the telecom market. In the IDC eBook State of the Telco Market 2026, you’ll find detailed data, forecasts, and analysis on how AI investment, infrastructure, and operating models are evolving. 

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

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

Most EMEA organisations have the intent to scale AI. What they are missing is a way to execute. On May 28, 2026, IDC’s EMEA Digital Leaders Hub brought together Martina Longo, Daniel Saroff, and Giulia Carosella for a live session drawing on 12 months of conversations within the Hub and fresh IDC research. Below is a brief overview. The full recording is available on demand. 

AI maturity in EMEA in 2026: why execution, not intent, is the real gap 

IDC’s latest MaturityScape Benchmark (EMEA, N=583) tells a clear story: 63% of EMEA organisations are still in the two lowest AI maturity stages. Fourteen percent are ad hoc: scattered initiatives, no coherent strategy. Forty-nine percent are opportunistic, running pilots but without the repeatability needed to scale. Just 2% are effectively scaling AI and Agentic AI initiatives across their organizations, including unlocking AI-driven revenue growth. 

The journey maps from the (Gen)AI Scramble (fragmented, investment-heavy experimentation) through the AI Pivot (structured scaling) to the Agentic Organisation (AI embedded across operations). Most EMEA organisations are stuck in the transition between the first and second stage. The blocker is almost never ambition. It is the ability to execute. 

Why AI adoption in EMEA is stalling: five challenges organisations need to address 

Notably, 49% of EMEA organisations have already shifted their focus from launching new AI pilots to improving existing initiatives The experimentation phase is peaking, and EMEA organizations are no longer seeking new tools but instead focusing on making current AI work effectively first. But five structural challenges continue to slow progress: 

Competition for resources among digital initiatives 

Regulatory uncertainty slowing deployment decisions 

Resistance to process change within the business 

Difficulty quantifying and demonstrating AI ROI to the board 

Lack of executive sponsorship or organisation-wide buy-in 

These are not isolated problems. They compound each other. Without a shared language for value, the resource conversation is difficult to win. The webinar addressed all five, and the one that generated the most discussion was ROI. 

Measuring AI ROI: why cost savings are not enough 

The most common reason AI initiatives stall is not technical. It is that no one agreed upfront on what success looks like. IDC’s AI Business Value Benefit framework maps nine dimensions where AI creates measurable impact, spanning Revenue Generation and Customer Experience through to Sustainability, Time to Market, and Business Resilience. Most organisations are measuring only one or two of these dimensions and are therefore systematically underselling the value they already have. 

“Know what you want to achieve and how you will measure it” 
  — Alex Catmur, Commercial Director Digital, AtkinsRéalis 

Three practices that came up consistently in the session: 

anchor every initiative to a specific business outcome before selecting any tool: Start with value drivers, not technology 

if no executive has a stake in the metric, the initiative will eventually stall: Align to KPIs executives already own 

productivity gains are visible; resilience and trust are harder to quantify but equally real, and the framework accounts for both: Separate direct and indirect value 

The session also walked through a detailed case study of a global professional services firm that went from no shared ROI lens to confident scale decisions. If that is where your organisation is at present, it is worth watching the recording to hear how they structured the turnaround. 

How the CIO role is evolving in the age of AI 

IDC’s WW C-Suite Tech Survey (EMEA, N=300) makes the expectation clear: 42% of the broader C-suite now expects the CIO to lead digital and AI transformation with a major focus on creating new revenue streams. That expectation is growing faster than the formal authority that would make it achievable. 

The digital leader of the future, as IDC frames it, is an architect of three things: Workforce (orchestrating AI-fuelled change management), Resilience (modernising IT for strategic alignment), and Value (demonstrating what digital technologies actually deliver for the business). 

Three steps for digital leaders looking to prepare for this shift: 

not called in to implement choices that have already been taken: Get in the room before decisions are made 

accountability for results, not just go-live dates: Own the business outcome, not just the delivery 

design AI deployments to grow and withstand failure, not just to ship: Architect for scaling and recovery from the start 

The session went into considerably more detail on each of these steps, including the structural and political dynamics that make them harder in practice than they appear on paper. 

A practical AI transformation playbook: four steps that matter

The session closed with a synthesis that cuts through the complexity. Leading AI-fuelled business transformation comes down to four sequential actions: 

modernise architecture and data before scaling AI; pilots built on brittle infrastructure do not survive production: Fix the foundation 

anchor every initiative to a KPI an executive already owns; no metric, no mandate: Define the value 

redesign workflows and roles alongside the technology; AI layered onto old processes delivers expensive old results: Change the model 

run experiments to disprove bad assumptions quickly; promote only what survives to a funded pilot with a scale plan: Scale what works 

Straightforward to state, harder to execute. The webinar covers what this looks like in practice, including the Q&A that followed. 

► Watch the full recording on demand 
 

The complete recording covers the full AI Business Value Benefit framework across all nine dimensions, the case study of a professional services firm moving from pilot to scale, the detailed best practice sessions on ROI measurement and the evolving CIO mandate, and the Q&A with the IDC analysts. If the topics covered are relevant to your organisation’s AI journey, IDC’s EMEA Digital Leaders Hub offers advisory support, benchmarks, and peer roundtables for CIOs and digital leaders navigating this transition. Reach out via the contact form to continue the conversation. 

AI is redrawing the rules of the partner ecosystem faster than most organisations can adapt. Last week, Stuart Wilson, IDC’s Senior Research Director for Partnering Ecosystems, and Andreas Storz, Senior Research Manager in the same practice, shared IDC’s latest research on what that means in practice for vendors, partners, and distributors operating across Europe and beyond. Drawing on survey data from more than 1,000 established partners, direct feedback from IDC’s European Partner Advisory Board, and real-world vendor examples, they made the case that the ecosystem is not simply evolving: it is being structurally reset. Here is a brief overview. The full recording is available on demand. 

How AI is eroding traditional partner revenue streams 

Stuart opened with a finding that will resonate with anyone tracking partner economics right now: AI is systematically compressing the lifecycle phases where partners have historically earned the most. Implementation, integration, and basic support are not disappearing, but they are becoming thinner and, in a growing number of cases, absorbed directly into vendor platforms. IDC has documented specific examples of how this compression is already playing out at scale, with leading vendors publicly committing to timelines and automation levels that would have seemed ambitious just 18 months ago. 

What makes this moment different from previous platform shifts is the speed and simultaneity of the impact. AI is hitting vendor economics, partner margins, and customer expectations at the same time. Partners who are waiting for the dust to settle before repositioning are likely to find the window has already closed. 

Where partner value is growing: advisory, AI governance, and outcome-based services 

The compression of execution-heavy activities does not mean the overall ecosystem opportunity is shrinking. IDC’s data points clearly to a redistribution of spend toward higher-order roles: AI solution design and agent creation, governance and compliance services, industry advisory, data engineering, and reusable marketplace IP. These are areas that reward deep domain knowledge and customer trust rather than delivery capacity. 

Customers are also changing how they expect to be served. Rather than relying on a single partner to cover the full lifecycle, they increasingly want a coordinated network of specialists. That shift has direct implications for how vendors structure their ecosystems and how partners think about collaboration rather than competition. The recording covers the full breakdown of where IDC sees demand growing and shrinking, and what partners are doing today to get ahead of it. 

Agentic marketplaces and the new partner go-to-market playbook 

Andreas Storz walked through a structural shift in how technology solutions are discovered and bought. Marketplaces are moving inside products, and IDC is seeing real evidence that customers are making decisions before a formal procurement process ever begins. This compresses buying cycles, introduces new buyer personas including business users and domain specialists who are not traditional IT buyers, and moves partner influence upstream into phases where most partner programs have little presence today. 

Partner Advisory Board members were frank about what this looks like from the front line. One dimension that generated particular discussion was the growing scrutiny customers apply to every new AI investment: 

Co-sell models are adapting accordingly. The shift away from field-led selling toward digital and telemetry-driven motions is not a future state: it is already shaping how the most forward-leaning vendors are structuring partner engagement today. The recording covers what that looks like in practice and what partners need to do to remain visible in these new buying journeys. 

Why vendor partner programs need a fundamental redesign for the AI era 

The structural conclusion Stuart and Andreas reached is that most partner programs in operation today were designed for a world that is rapidly ceasing to exist. The incentive structures, metrics, and engagement models built around resale transactions and implementation milestones are misaligned with where ecosystem value is now being created. Vendors that do not address this gap will find themselves losing the partners best positioned to deliver AI-driven outcomes to customers. 

The research points to a dual imperative: accelerating existing partners toward AI-centric delivery models while simultaneously cultivating a new generation of AI-native partners who bring differentiated industry IP and a very different set of expectations around how vendor relationships should work. How to run both strategies in parallel, without letting either undermine the other, is one of the more complex programme design challenges IDC is helping clients navigate right now. 

The Q and A that followed also surfaced sharp questions on how AI model providers are disrupting established alliance hierarchies for global systems integrators, and whether the net effect of all this change will be a more consolidated or more fragmented ecosystem. Stuart’s answer to the latter was more nuanced than a binary either/or, and worth hearing in full. 

►  Watch the full recording on demand 

The complete recording includes data from the IDC EMEA Partner Survey data (N=1,001), a detailed breakdown of the dual partner strategy framework, and a live Q and A with both analysts. If the topics covered resonate with your ecosystem strategy, IDC’s Partnering Ecosystems practice offers advisory support, custom research, roundtables, and strategic workshops tailored to vendors, distributors, and partners navigating this transition.  
 
Our experts are always happy to continue the conversation. Simply reach out via the contact form.  

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. 

Start by listening. Assess what employees actually need through surveys, one-on-ones, and functional and contextual evaluations.  

Review your technology stack. Evaluate hardware, software, productivity suites, and assistive tools for compatibility and gaps.  

Embed accessibility early. Integrate accessibility into design and procurement, turning standards such as WCAG and EN 301 549 into mandatory checkpoints.  

Establish governance. Define clear roles, responsibilities, escalation paths, and cross-functional ownership.  

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.