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In today’s technology market, certainty has become a luxury. AI adoption is accelerating, but unevenly. Partner ecosystems are fragmenting, consolidating, and recombining at speed. Go‑to‑market models are collapsing into customer‑led buying journeys, and leadership teams are being asked to make high‑stakes decisions with incomplete, fast‑aging information.

Broad market reports, benchmarks, and best practices retain significant intrinsic value as foundations for strategy. Yet decisions that are deeply contextual, ecosystem‑specific, and time‑sensitive often require additional layers of insight beyond these core inputs.

The reality is that strategy is no longer about understanding “the market” in the abstract. It is about understanding your market position, your partners, and your customers, right now. Increasingly, the most important questions leaders are asking sound like this:

  • How is AI changing buying behavior and economics in our customer base?
  • Which partners are actually driving growth, influence, and outcomes – and which no longer align with our direction?
  • How does our ecosystem strategy compare to competitors in EMEA, not just globally?
  • Where are customers genuinely willing to invest, and where are they experimenting, delaying, or pushing back?

These are not questions that generic insight can answer with confidence, because the answers depend on your installed base, your partner mix, your regional footprint, your commercial model, and your competitive posture. In short, strategy has become situational.

Faced with this uncertainty, many organizations default to gathering more data: more dashboards, more surveys, more internal analysis. But volume is rarely the issue. The real challenge is relevance. Internal data lacks external context. Global averages mask regional and sector nuance. Lagging indicators arrive after decisions have already been made. What leaders need instead is interpretation, synthesis, and external validation that is designed around the decisions they actually need to take.

This is why we see a growing shift toward custom insight. High‑performing organizations increasingly start with the decision, not the dataset. Whether the challenge is AI monetization, partner strategy, ecosystem prioritization, or route‑to‑market design, the work begins by asking what choice must be made in the next three to six months, and what evidence is required to make it with confidence. From there, insight is built backwards.

Critically, the most effective custom projects blend signals rather than relying on a single method. Partner surveys reveal capability gaps, investment priorities, and friction points across the ecosystem. Customer surveys surface willingness to pay, buying behavior, trust dynamics, and expectations around AI, services, and outcomes. Qualitative interviews add depth and context, while ecosystem and competitive analysis connects those findings to broader market forces. The value does not sit in any one input, but in how those inputs are connected and translated into strategic implications.

We consistently see customer and partner insight deliver the greatest impact when applied to a small number of high‑value areas:

  • AI and agentic AI strategy, including pricing, packaging, economics, and partner roles
  • Ecosystem and partner optimization, from role clarity to performance segmentation and investment focus
  • Go‑to‑market and route‑to‑market evolution, particularly in EMEA’s fragmented markets
  • Executive alignment, creating a shared, evidence‑based fact base for leadership teams
  • External storytelling, using proprietary insight to support thought leadership and market influence

This is where insight turns into action. In many engagements, the report itself is not the most important output. The real value is decision confidence: knowing that a strategic move is anchored in how customers and partners are actually behaving, not how we assume they are behaving.

There is also a powerful dual role at play. Custom insight supports internal strategy and decision‑making, but it can simultaneously fuel external influence. Proprietary findings can shape executive narratives, strengthen partner and customer communications, and differentiate a company’s point of view in an increasingly noisy market. When insight is designed with this dual purpose in mind, it becomes a strategic asset rather than a one‑off deliverable.

This matters now more than ever. Across EMEA, partner ecosystems are being reshaped by a set of interlocking forces: AI economics, consolidation, shifting alliance hierarchies, collapsing route‑to‑market models, sovereignty pressures, and the rise of in‑product and marketplace‑led buying. Many of these shifts are subtle in isolation but powerful in combination.

Understanding which are leading indicators, which are mid‑cycle effects, and which are lagging consequences requires more than surface‑level analysis. It requires insight grounded in real partner and customer evidence, interpreted through an ecosystem lens.

The bottom line is simple. In a market defined by AI acceleration, ecosystem complexity, and regional divergence, generic insight is no longer enough. Organizations that are pulling ahead are those that ask better questions, invest in insight tailored to their context, and use research as a decision tool rather than a reference document.

If these questions resonate, you’re not alone. Most technology leaders we work with are already grappling with how AI, ecosystem change, and buyer behavior are reshaping their growth models – and are looking for concrete, evidence‑based answers they can act on. Through bespoke research and advisory projects, we help clients translate partner and customer insight into tangible business benefits: sharper internal intelligence for decision‑making, clearer ecosystem strategy, and insight‑led assets that can be used confidently with partners and customers alike.

This perspective is also captured in our 15 Key Trends Shaping EMEA Partnering Ecosystems report, often used as a starting point for bespoke client work. Contact us to learn more about our ecosystem research, custom solutions and advisory portfolio.

IDC’s ecosystem lens

IDC’s ecosystem research focuses on value creation, margin capture, and strategic influence. We analyze how partners orchestrate outcomes, how they align with customer buying journeys, and how they evolve their business models to stay relevant. You can find more information here. 

If you have any further questions, drop them in the form here. 

Stuart Wilson - Senior Research Director, EMEA Partnering Ecosystems - IDC

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 to market and partner engagement frameworks.
Key takeaways from IDC’s Telco Forum 2026 Barcelona

On March 1, 2026, IDC brought together senior telecom leaders, vendors, system integrators, cloud leaders, partners, and media in Barcelona to examine how the industry is evolving in an AI-driven world. The discussions reinforced a clear message: telecom transformation is no longer theoretical. It is structural, financial, operational and increasingly sovereign. Drawing on insights shared across the event; this blog captures the major themes shaping telecom strategy through 2030.

The four megatrends shaping telecoms through 2030

There are 4 compounding megatrends that have been reshaping the sector since 2022. Looking back, the telco industry has moved through a rapid succession of technological focal points: Network APIs as foundational enablers for exposing network capabilities; Generative AI as the entry point for process automation; and Agentic AI in 2025, which introduced autonomous decision-making into customer experience, network management, and enterprise solutions. In 2026, the critical new frontier for AI is inferencing, the shift from model training to real-time, distributed AI workload execution, and it is this transition that is forcing telcos to fundamentally rethink their infrastructure architecture and competitive positioning. Underpinning all of this are the four defining themes of 2026:

  • Structural transformation is intensifying. Business model reinvention is not new for telcos, but the pace has accelerated. Four distinct strategic paths are now in play simultaneously: the TechCo transition (embracing network-as-a-platform models), Delayering (separating ServCo, NetCo, and InfraCo entities to optimize asset utilization), Consolidation, and a redefined form of Convergence, that focuses on bundling fixed-mobile-satellite services and designed to lock in ARPU and reduce churn.
  • Network investment is tapering. The cyclical CAPEX peak from 5G non-standalone rollout has passed in high and middle-income markets. IDC forecasts a 1.5% decline in global telecom CAPEX in 2026, bringing the total to $320 billion, with CAPEX intensity projected to fall from 22% in 2024 toward 18% by the end of the decade. The drivers are multiple: the one-off FTTP spending peak is fading, satellite partnerships are smoothing access and transport investment, and a structural CAPEX -to-OPEX shift is underway as telcos increasingly rely on ISVs and cloud providers for virtualization and AI. The freed-up cash is flowing into shareholder returns, strategic investments, and targeted digital infrastructure plays.
  • AI adoption is crystallizing around inferencing and sovereign AI. In 2025, the buzzword was agentic AI. In 2026, it is inferencing, and the telcos have a genuine structural advantage to capitalize on it. With distributed infrastructure, low latency, and deep regulatory trust in their home markets, telcos are positioned to become national AI factories, delivering sovereign AI solutions to governments, healthcare systems, and regional enterprises. IDC’s survey data shows that AI compute spending is approaching a pivot point in 2027, when inferencing will overtake training as the dominant driver of AI infrastructure investment. Telcos that expand their data center footprint and deepen relationships with co-location providers now are positioning ahead of that curve.
  • LEO satellite partnerships are becoming strategic. Starlink has established an early lead as the preferred satellite partner for telcos globally. Use cases vary significantly by geography – D2D and satellite broadband in the US and Canada’s large, underserved coverage areas, disaster recovery in Europe’s dense markets, and transport and backhaul across Asia Pacific and Australia. What is clear across all regions is that the satellite-terrestrial boundary is dissolving into a hybrid connectivity model, and the telcos that forge the right partnerships now will have a differentiated coverage story that competitors simply cannot replicate terrestrially.

Balance, pivot, revolt: the transformation imperative

Telcos’ internal transformation focus for 2026 can be framed with three sharp words: balance, pivot and revolt.

Balance is the defining tension of 2026. According to IDC’s C-Suite Tech Survey (September 2025, n=45 telecom respondents), 52% of telco C-suite leaders have AI implementation as a top three priority, but 50% simultaneously have technology modernization as a top three priority. These can be complementary investments as telcos cannot get full value from AI if they have not addressed legacy system complexity, data governance gaps, and architectural debt; but they also compete as telcos must decide between investing in new capabilities that promise significant gains vs. unglamourous IT modernization initiatives which have often been neglected for years. This is at a time with funds for transformations are finely balanced: Telecom CAPEX is declining, though IDC forecasts a modest 5.2% growth in spend on operations and monetization systems in 2026, reaching $54 billion, as telcos invest in IT systems to monetize the billions in CAPEX invested in rolling out new wireless and fixed networks. 5.2% growth is far from a blank cheque, every dollar deployed in IT must demonstrably either cut cost or support new revenue.

The autonomous networks aspiration illustrates this balancing act with particular clarity. TM Forum data from 2025 shows that only 4% of operators self-reported achieving Level 4 autonomous network status, yet 85% aspire to reach that level by 2030. That is an extraordinary gap. According to IDC’s EMEA Telco Transformation Survey (July 2025, n=150), the barriers are familiar: interoperability failures and the persistent lack of a single source of truth in network data. Notably, these are precisely the same barriers that have constrained AI adoption more broadly.

Pivot means making deliberate choices about where to invest and what to sequence. Data quality, accessibility, security are all in focus in 2026. This is represented in telcos making positive investments to overhaul their network inventory systems and updating their data governance policies and infrastructure from customer data down to the network. For autonomous networks, IDC’s research points to a more granular, domain-specific approach gaining traction: telcos are identifying specific use cases, service assurance and fault management are the top automation priorities for EMEA telcos in 2026, and targeting specific domains (IP access, RAN, and core) for Level 4 capability. This is far more tractable than a blanket push to full autonomy. On the people side, 97% of telcos recognize gaps in their talent base for developing and using AI at scale. Sixty-five percent are investing in AI-enabled learning tools, and 58% are expanding internal upskilling programs, but with only 42% currently offering skills training, there is still a meaningful gap between recognition and action.

Revolt is the urgent call to fix customer commercialization before AI finally demolishes the buying behaviour telcos have relied on for decades. For example, a UK mobile subscriber paying £15 per month for 10GB, regularly consuming just 6GB, with known Disney+ and international roaming usage, was on renewal offered to take a device upgrade, to increase their data rate to 30GB for £18 or unlimited data for £24. There was no demand signal for a device, no upsell of complementary services, and no personalization of any kind. The customer found a 40GB plan with the same mobile provider on a comparison site for £7.50, a 50% ARPU reduction and 400% value giveaway. Comparison sites have been established for well over a decade empowering consumer to find the best deal with some manual effort. Today’s consumers and enterprises, however, are already beginning to use AI to undertake similar comparisons with far less manual effort.

The point is not just that this particular offer was poorly designed. The point is that the entire commercial model relies on customer inertia, and AI is systematically dismantling that inertia. As AI agents increasingly make purchasing decisions on behalf of consumers and enterprises, operators that cannot demonstrate differentiated, personalized value in real time will find their customer bases eroding with a speed and scale unlike anything seen before.

5G: from product to platform, and the 6G horizon

The back half of the 5G lifecycle represents an inflection point, but only if operators change their frame of reference. Core mobile remains solid: IDC projects a 2.0% CAGR in global mobile connections through 2029. The world will exceed 9 billion mobile connections within the next two years, surpassing the current global population of 8.3 billion. In saturated markets, however, the growth lever has shifted decisively from subscriber acquisition to retention and value extraction — which brings the customer experience and commercialization issues directly back into focus.

The bigger opportunity lies in the shift from 5G as a product to 5G as a platform. For the first five years of 5G, operators sold speed, latency, and connection density. The next phase is less about branding a connection as 5G and more about 5G as the underlying infrastructure that enables XR, drones, V2X, private 5G, and RedCap solutions to be viable, scalable, and mobile. The challenge is that these use cases do not scale in the millions the way mobility or FWA does, they scale in tens of thousands. That requires a fundamentally different approach to network architecture, back-end systems, and, critically, business models.

Integration complexity remains the most significant brake on enterprise 5G adoption. 46% percent of enterprises cite it as the primary adoption barrier. The solution is less ego and more ecosystem: operators need to be willing to play a back-end role in partner-led solutions rather than insisting on front-facing primacy. 74% of enterprises express interest in network slicing; 49% plan to increase fixed wireless access investment; 58% say they are interested in satellite connectivity, but many still have significant misconceptions about what satellite-to-device actually delivers today. Expectation management is part of the product.

On 6G, If the industry maintains the ten-year generational cycle, 6G commercial launches would begin around 2029. Technical specifications are still in the study phase at 3GPP. The defining features of 6G, AI-native architecture enabling autonomous self-optimization, integrated sensing that turns every cell tower into a radar station, quantum-resistant security, and new terahertz spectrum, collectively point toward a network that moves AI out of the data center and into the physical world. The concept of “physical AI,” or what one operator CTO termed “kinetic tokens,” suggests that 6G will not merely support AI-driven applications but will provide the real-time connectivity substrate that makes physical AI, autonomous robots, connected vehicles, intelligent infrastructure, a viable commercial reality.

The enterprise connectivity opportunity: vast, varied, and underserved

Enterprise connectivity budgets are growing. IDC’s Future Enterprise Connectivity Infrastructure and Services Survey (August 2025, n=758) shows that 37.5% of enterprises increased their connectivity budget by more than 10% over the last two years. For 2026, that proportion rises to 44%. The primary drivers are cloud migration, SaaS usage, AI, video, IoT and device density are driving up bandwidth requirements. Four in ten enterprises saw bandwidth demands increase by more than 50% over the past year. Among organizations with over 10,000 employees, 17% saw their bandwidth demands double. Retail and financial services lead in cumulative bandwidth growth, but the opportunity is sector-wide: only 40-46% of enterprises are at an advanced or market-leading stage of connectivity maturity. The majority are still on the journey and actively looking for guidance.

The question of who captures this opportunity, however, is not straightforward for network service providers. When IDC asked enterprises which provider types they see as best and worst placed to address their future WAN requirements, cloud providers ranked first at 29%, followed by IT partners at 28%, with network service providers third at 23%. More pointedly, in the “worst placed” ranking, network service providers came second. The reasons cited: not treating customers well 35%, limited IT and network capability 28%, and difficult to work with 26%.

This is a reputational and structural challenge, not just a product one. Cloud providers are perceived as having broad network capability, even though they fundamentally depend on telco partners for last-mile delivery. IT partners are perceived as having deep industry expertise, expertise that telcos themselves possess but has not been to communicate or commercialize effectively. The gap is therefore not simply about capability. It is about perception. Perception shapes purchasing decisions, which in turn shape market reality.

Encouragingly, telcos’ “best placed” positioning has improved in recent years as operators have prioritized customer experience and simplified portfolios to deliver more flexible, scalable, and accessible services aligned with enterprise demand. Network as a Service, or NaaS, is central to this shift. NaaS is a cloud-based delivery model in which connectivity, bandwidth, security, and routing are provisioned and consumed on demand via APIs or self-service portals. It abstracts the underlying physical infrastructure and allows enterprises to scale, configure, and optimize network resources without directly owning or managing hardware. Enterprise sentiment toward NaaS remains mixed, 32% said they could make it easier or cheaper for a service provider to manage their networks and security, and 26% said they could simplify self-managed network operations. But 19% said they would not want to be locked into one service provider’s platform regardless of the benefits, and 10% remain unfamiliar with NaaS entirely. The education gap is significant and closing it will require more than technical refinement. It demands commercial clarity, stronger communication, and deeper customer relationships. Ultimately, this is not just a transformation in network architecture. It is a transformation in trust, positioning, and perceived value.

The bottom line

The IDC Telco Forum 2026 in Barcelona surfaced a market that is, in many respects, more coherent in its direction than at any point in recent years, but also more demanding of execution discipline than most operators have yet demonstrated.

The opportunity in AI inferencing and sovereign infrastructure is real and structurally aligned with telcos’ natural positioning. The satellite-terrestrial convergence is creating a coverage differentiation story that was not available five years ago. The enterprise connectivity market is expanding, budget-rich, and hungry for strategic guidance. And 5G, finally maturing beyond its early-product phase, is approaching its platform moment.

But against each of these opportunities sits a structural challenge that must be addressed in parallel: legacy system complexity is limiting AI value extraction; autonomous network ambitions are outpacing organizational readiness; commercial and CX systems are still leaving significant value on the table; and enterprise perception of telcos’ breadth and quality of service lags behind the reality.

The telcos that will win this decade are those that treat these not as separate workstreams but as a single integrated transformation, one where the investment in networks, IT modernization, talent, customer experience, and ecosystem partnerships compounds into a durable competitive position. The window is open. The question, as always, is execution.

For more information on IDC’s telecom research, including the newly launched Satellite and NTN research program, contact your IDC account manager or drop your details in here.

Download a copy of the State of the Telco Market ebook here.

Masarra Mohamad - Senior Research Analyst, European 5G Enterprise Strategies - IDC

Analyst Profile
Masarra Mohamed is a senior research analyst specializing in analysing the connectivity and communications services markets, focusing on the changing networking requirements, trends, and competitive dynamics that support enterprises in their digital transformation. She explores how enterprise network strategies evolve to enable cloud, AI, and security.

It’s a common situation we’ve being seeing: you have fixed the data pipeline, you have hired or trained the talent, you have the executive mandate. The budget. The technology. The time and dedication even! And you are still wondering: why is your enterprise AI still underperforming? Why is it not scaling? The answer, turns out, may be hiding in in people’s heads.

Enterprise AI adoption has a well-documented data problem. IDC research consistently identifies data quality, data availability, and data silos as the top barriers to scaling AI across the organization. Globally, 89% of organizations acknowledge some level of data quality problem and at the same time, 52% of companies say data quality is the most important factor for AI projects success. Only 6% of CIOs admitted they completed all data initiatives and are ready to move to the next level of AI adoption. And 7 in 10 IT and business leaders cite data silos as one of the biggest challenges for AI adoption. We can see significant budgets being invested in data lakes, data governance frameworks, or MLOps infrastructure. And yet, more than a half of AI initiatives stall after the pilot phase: succeeding  at delivering impressive demos but failing to generate enterprise-wide value.

The data problem is real. But is the problem just about data readiness? There might be another knowledge crisis running quietly and often in the shadows. Most organizations do not see it coming until something goes seriously wrong. A major restructuring or a round of layoffs happens. A reorg that lets go of the wrong people. Suddenly, things that used to just work start breaking down. Processes that ran smoothly for years  become unreliable. New hires cannot figure out how their predecessors got results. That is the moment leadership realizes something important walked out the door. And if it was never captured, it is simply gone. This kind of knowledge is easy to overlook precisely because it is invisible when everything is going fine. It only shows up in the gap it leaves behind. Or when an AI project doesn’t scale.

There are things that cannot be put into a dataset

In the 60’s, philosopher Michael Polanyi articulated something that anyone who has tried to teach a skill to a machine, or an algorithm, or even to another human, knows intuitively: “We can know more than we can tell”. This is the essence of the Polanyi Paradox. It captures the idea that much of what we know, we know through experience, practice, and intuition rather than through rules we could ever fully write down. A master chess player cannot explain every instinct that guides a move. A skilled surgeon cannot put into words every micro-adjustment she makes mid-procedure. They just know and that knowing lives in them, not in any manual or dataset. The paradox is this: the knowledge that is often most valuable is precisely the knowledge that is hardest to transfer, document, or teach explicitly. That silent knowledge is often called tacit.

Organizations are similar. Tacit knowledge is everything an organization knows but has never written down. It is the senior underwriter who can sense a bad risk before she looks at a single data point. It is the way the logistics team re-routes shipments when two things go wrong simultaneously in a process that exists nowhere in any corporate workflow diagram but their heads. It is this unspoken understanding of which stakeholder actually needs to approve something, regardless of what the org chart says. It is decades of built-up expertise, accumulated judgment, pattern recognition, and impossible to document gut feeling. And it is embedded in people and informal process, not systems and databases.

AI models, algorithms, and systems learn and reference  from data. But tacit knowledge,  by definition, never makes it into the databases, structured or not. Which means, if organizations decide to look at the end-to-end transformational AI deployments, that often are training and deploying AI using an incomplete picture. At the same time, achieving success around unique use cases, where knowledge can easily be written down and transferred.

Three more problems?

The tacit knowledge gap is structurally difficult to manage for three reasons that reinforce one another.

  • First, Polanyi himself never bothered to assess what the proportion of tacit knowledge was. Organizations do not know how much tacit knowledge they have. Is it 25% of institutional knowledge? 60%? 90%? There is no universal answer, even if some management experts try to guess, and that uncertainty is itself a strategic liability. It is impossible to close a gap that cannot be measured. We can only assume that in knowledge-intensive industries, from professional services, to healthcare, to advanced manufacturing and financial services, the proportion of expertise that lives solely inside people’s heads is almost certainly larger than leadership assumes.
  • Second, and this is where the problem compounds beyond Polanyi’s original framing, tacit knowledge is not static. It evolves as markets shift, as teams learn, and, painfully, as people come and go. Every time a senior expert retires or an experienced employee leaves, a portion of that knowledge walks out the door permanently. The institutional knowledge base your AI was designed around last year may no longer reflect today’s reality. The power of silent expertise also fluctuates, it is particularly crucial in time of sudden changes, which means it will become even more critical when an organization decides for AI transformation.
  • Third, and something I can see among many experts I meet, tacit knowledge gaps may erode trust in AI. This is perhaps the most underappreciated consequence. When experienced professionals interact with AI outputs that feel off, like answers that are technically defensible but miss something important, they often cannot articulate exactly why. The AI passed every benchmark. The data was clean. But the output does not  fit the context that only an insider would know. The result: employees either spend hours manually verifying AI recommendations, and defeating the productivity case, or they quietly stop or avoid using the tools altogether. A smooth way to prove the business case wrong, if you’re asking me.

Is there hope? I don’t know, but we can try

There probably is no one ultimate way to address the Polanyi Paradox – that is, in a sense, the point. But organizations can – and should – take deliberate steps to reduce the gap and build AI systems that are more honest about what they do and do not know.

Companies need to design AI for collaboration, not replacement. The most effective AI deployments in mature organizations use human expertise to continuously refine models, tools, or applications behavior. This can be done through feedback loops, exception handling, or human-in-the-loop review. This, if done correctly, creates a mechanism for tacit knowledge to gradually surface and be encoded over time. AI will take over much of the work that can be fully defined and encoded, but in many situations it will only handle a limited part of the overall task.

Companies can start making tacit knowledge capture a design requirement, not an afterthought. Before deploying AI in any high-stakes domain, conduct structured knowledge extraction with domain experts. Techniques borrowed from cognitive task analysis (sounds heavy, but can be really fun!), may help surface decision logic that experts themselves did not know they were applying. This is not a one-time exercise; it needs to be embedded in how teams work and how processes are documented. This process also calls for factoring in potentially high cost and resistance, and prioritizing “easier” AI use cases unless the expected return is exceptionally high.

Organization should treat employee transitions as a knowledge continuity risk. Organizations frequently invest significantly in operational continuity planning. Knowledge continuity deserves the same approach. Structured offboarding, mentorship programs designed to transfer expertise rather than just tasks, and apprenticeship models can preserve hidden knowledge before it disappears.

Organizations must aim at making AI systems transparent  about uncertainty.  When building or procuring AI tools, organizations can define confidence thresholds that trigger human review rather than automated action (might not be great for an autonomous agentic part, but we need compromises). They can also test models specifically against edge cases and domain-specific scenarios where tacit knowledge would normally kick in and use those gaps to inform where human oversight is non-negotiable. It is less about an organization admitting  AI weakness and more about an organization designing guardrails around known blind spots.

The organizations that will extract the most value from AI over the next decade will be the ones that are honest about what knowledge they have actually managed to encode, even if, yes, we all agree we can never have all the knowledge. And those trying to close that gap systematically. For your AI to succeed and scale, data is necessary, but it is not sufficient. The missing variable is knowledge and all of it, not just the part that lives in organization’s databases.

Got a question? Drop it in here.

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.