Resource Regions: Western Europe

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.

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.

Digital sovereignty is moving from concept to strategic requirement. As organisations focus on managing IT risk, control, and compliance, expectations towards providers are rising. This blog explores why the “sovereign” label is no longer enough and what it takes to meet these new demands. 

Many technology providers in Europe today claim to offer “sovereign” solutions. 

But ask a simple follow-up question, what exactly makes them sovereign, and the answers quickly become less clear. 

At the same time, demand for digital sovereignty is increasing. Over the past 15 months, geopolitical and economic uncertainties have pushed the topic higher up the agenda. When asked about digital sovereignty, almost 50% of organisations globally say their interest has increased compared to the previous year. 

But focusing on geopolitics alone misses the bigger shift. 

Why digital sovereignty expectations are changing 

As interest grows, so do expectations. Digital sovereignty is no longer an abstract or purely regulatory concept. It is becoming an essential strategic requirement in IT decision-making. 

At the same time, it remains a source of confusion. Many organisations still struggle to define what sovereignty actually means in practice, what is required to achieve it, and whether they need it at all. And then you need to ask, who can you trust? And then you need to ask, who can you trust? 

This creates a gap in the market. Providers talk about sovereignty. Customers are still trying to understand it. 

What is really driving digital sovereignty adoption 

Despite the geopolitical backdrop, the main drivers are far more practical. 

Organisations are prioritising control over their data, stronger governance and compliance, and the ability to manage risk. In Europe in particular, protection against extra-territorial data requests has emerged as the highest concern. 

This is where expectations begin to change. 

More than 40% of organisations globally say they will increase the frequency and granularity of their reviews of IT vendors and platforms to better assess and manage this risk. Furthermore, when asked what was most needed to achieve data sovereignty, 85% cited enhanced tools and solutions for governance, risk and compliance as the extremely or very important. 

Thus, if digital sovereignty is ultimately about managing IT risk, it cannot be reduced to a label or a feature. It needs to be something that is tangible and can be clearly explained, implemented, and validated. 

This also changes the role of providers. They need to help organisations assess their risk appetite, manage that risk, and deliver the solutions required to meet these expectations. 

And this is where many providers are not yet aligned. 

What digital sovereignty actually requires 

Part of the challenge lies in how sovereignty is framed. It is often treated as a single capability, when in reality it spans multiple dimensions. 

One practical way to approach it is through three areas: data sovereignty, technical sovereignty, and operational sovereignty. These form the three key pillars of cloud sovereignty, which itself represents a subset of the broader concept of digital sovereignty. 

Together, these define how control is exercised across data, infrastructure, and operations. 

For providers, this raises the bar. Sovereignty is no longer something that can be communicated in broad terms. It needs to be articulated across these dimensions, in a way that is transparent and verifiable. 

Where sovereignty really matters: high-risk workloads 

It is also important to clarify where sovereignty actually needs to be applied. 

Sovereign requirements are typically focused on workloads that involve sensitive data, regulatory exposure, and or critical business processes. This increasingly includes certain AI use cases, where data control and model governance are essential. 

This is also where trust becomes central. 

Customers need confidence that sovereignty claims hold up under scrutiny, especially in high-risk scenarios. It is no longer enough to state that a solution is sovereign or to only address isolated aspects such as data residency or localisation. 

Providers need to demonstrate how sovereignty is ensured, where the boundaries lie, and what guarantees are in place. This assurance must extend across the entire partner ecosystem, from primary providers to their partners and beyond. 

From positioning to proof 

The conversation around digital sovereignty is evolving quickly. Expectations are rising, and with them, the level of scrutiny applied to providers. 

In this environment, sovereignty is no longer a positioning or marketing statement. It is something that needs to be clearly defined, agreed upon by all stakeholders, consistently implemented, and credibly demonstrated. 

For many providers, that requires a shift. From broad claims to precise explanations. From messaging to evidence. 

And ultimately, from sovereignty as a label to sovereignty as a trust model that delivers autonomy, control, transparency, and resilience. 

If you are reassessing how to position and deliver digital sovereignty, speaking to an expert can help clarify what your customers will expect next. Request a call here. 

 
All data sources: IDC Europe, Worldwide Digital Sovereignty survey 2025, July 2025 

Wholesale has traditionally been a scale-driven business focused on connectivity and volume. That model is now evolving. 

Across the telecom industry, wholesale providers are rethinking how they deliver value, moving toward more flexible, platform-based approaches that go beyond traditional network services. 

This shift is being driven by changing customer expectations, new technologies, and increasing pressure to create sustainable growth. 

Wholesale telecommunications is shifting toward platform-based models 

IDC highlights 2026 as a key moment in the transition toward more automated, API-driven, and AI-enabled wholesale models. 

Rather than offering static products, wholesale providers are increasingly expected to deliver services that are more flexible, on-demand, and easier to integrate into customer environments. 

This includes: 

Greater use of APIs to expose network capabilities  

Increased automation across ordering, provisioning, and operations  

More dynamic and usage-based pricing models  

As a result, wholesale telecommunications is gradually adopting characteristics typically associated with cloud platforms. 

Customer expectations in wholesale telecommunications are changing 

Wholesale customers are no longer only looking for access to infrastructure. They expect solutions that can adapt to their specific requirements and business models. 

Flexibility, scalability, and ease of integration are becoming key decision factors. 

This is particularly relevant as enterprise and service provider customers operate in more complex, multi-vendor environments and require greater control over how services are consumed and managed. 

Wholesale providers are responding by offering more configurable services and by simplifying how customers interact with their networks. 

Ecosystems are becoming more important in wholesale telecom 

As wholesale models evolve, the role of ecosystems is expanding. 

Providers are increasingly working with partners to extend coverage, enhance capabilities, and co-develop new services. This includes collaboration across technology vendors, platform providers, and other telecom operators. 

At the same time, there is a growing focus on standardization, particularly around APIs and emerging technologies, to enable interoperability and scale across ecosystems. 

Managing these ecosystems effectively is becoming a key capability for wholesale providers. 

Vendor strategies in telecommunications are evolving 

This shift is happening alongside changes in how telcos approach their vendor landscape. 

Operators are becoming more selective and are reducing the number of partners they work with. There is a clear move toward strategic partnerships with vendors that can deliver end-to-end capabilities and take on greater accountability. 

This reflects the increasing complexity of telecom environments, where fragmented ownership across multiple vendors can slow down transformation and increase operational challenges. 

Fewer, more integrated partners can help simplify execution and align outcomes more closely with business objectives. 

What this means for wholesale telecom providers 

For wholesale telcos, the transition to platform-based models requires both technology and organizational change. 

This includes: 

Modernizing legacy systems to support API-driven services  

Investing in automation and AI capabilities  

Rethinking product design toward more modular, flexible offerings  

Building and managing partner ecosystems more actively  

At the same time, providers need to balance innovation with the realities of existing infrastructure and customer commitments. 

Wholesale telecommunications is becoming a strategic growth lever 

Wholesale is no longer just a supporting function within telecom organizations. It is increasingly seen as an area for differentiation and new revenue generation. 
Platform-based models, ecosystem collaboration, and more flexible service delivery approaches are opening up new opportunities to monetize infrastructure and reach new customer segments. 

As these models mature, the ability to execute effectively will determine which providers can translate this shift into sustainable growth. 

Download the full analysis 

Wholesale transformation is one of several trends reshaping the telecom market. In the IDC eBook State of the Telco Market 2026, you’ll find detailed data, forecasts, and analysis on platform-based models, API strategies, and evolving telco business models. 

Download the eBook to explore how wholesale is evolving and what it means for telecom providers. 

If you’re currently evaluating how platform models or ecosystem strategies could impact your wholesale business, our experts are happy to exchange perspectives. Whether you’re just starting or already transforming your model, we welcome the conversation. Get in touch with our team to continue the discussion.