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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.

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.

Watch the recording here 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.