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In the early 2020s, most IT dashboards looked deliciously green – until you cut them open. That “watermelon problem” summed up the gap between what SLAs said and how people actually felt at work: 99.8% uptime on paper, but slow logons, clunky multi-factor authentication, and chatbots that couldn’t understand what anyone really wanted. Experience was an afterthought, AI was a sideshow, and creativity was nowhere to be found in the contract.​

When SLAs ruled the world

Back then, three things defined the status quo. AI was narrow and local, sitting on the edge of workflows answering FAQs or routing tickets rather than orchestrating work. Experience measurement lagged reality, with annual or quarterly surveys surfacing issues long after the damage was done. And creativity simply didn’t exist in the metrics; contracts cared about uptime, not whether people had the cognitive space to experiment or innovate.​

The result was a strange split-screen. On one side, leaders proudly cited their SLA success. On the other, employees wrestled with friction that didn’t fit any KPI: context-switching between tools, re-entering the same data, and watching “helpful” chatbots miss the point. XLAs were occasionally piloted  (an NPS here, a satisfaction score there) but rarely changed actual design or investment decisions.​

Now: XLAs as control towers for human-AI work

Fast forward to 2026, and AI is no longer the sidekick; it is the backbone of digital work. GenAI assistants, low-code agents, and orchestration platforms now sit inside service desks, digital workplace platforms, and line-of-business apps. XLAs have emerged as the language that decides whether all this AI is genuinely helping humans do better work or just adding more noise.​

Three big shifts define the “now.” Agentic AI makes XLAs real-time and contextual, correlating technical signals like latency and crashes with human signals such as sentiment, task completion, and time to productivity. It can trigger automated remediation, from self-healing endpoints to conversational agents that guide users through fixes, and spotlight experience hotspots for specific personas or workflows. IDC’s 2025 Future of Work survey shows 79% of organizations now actively measure the relationship between employee and customer experience, with two-thirds having proof of causal linkages, while 94% of AI-enabled work adopters report productivity gains and over half see significant improvements.​

Making creativity a measurable outcome

The most interesting XLAs no longer treat creativity as a fuzzy aspiration. They track uninterrupted focus time per persona, link AI automation to freed-up hours, and measure innovation throughput:  ideas submitted, prototypes built, experiments completed. Instead of only asking if AI is fast or accurate, organizations track “human-plus” metrics: how much better decisions, proposals, and options become when humans and AI work together.​

Governance grows up

This evolution is forcing governance structures to grow up fast. AI-focused Centers of Excellence increasingly use XLA dashboards as strategic instruments, challenging deployments that look great on technical metrics but poor on human outcomes. They prioritize changes that build trust and agency, such as better explainability, robust feedback loops, and human override capabilities, and retire tools that consistently score badly on ease of use or learning curve.​

Metrics are diversifying accordingly: about 69% of organizations use productivity scores such as task-based speed and throughput to assess AI, while 42% also track employee satisfaction and 44% monitor skills proficiency. XLAs have become a proxy for hard questions: Are we making it easier for people to solve novel problems? Are AI tools empowering experts or boxing them in? Where is digital friction quietly killing initiative?​

Tomorrow: XLAs as the OS for co-creation

Looking ahead, XLAs are set to become the operating system for human/AI co-creation. Emerging “experience-risk” indices predict burnout or disengagement, while creativity capacity scores combine focus time, use of exploratory tools, and psychological safety indicators. Agentic AI will increasingly use XLAs as experience-intent parameters  – goals like maximizing focus time for data scientists or ensuring frontline staff resolve most issues in under three minutes  – and autonomously orchestrate tools, notifications, and workflows to hit them.​

Contracts will catch up too, moving from green dashboards to models that reward innovation, protect against “experience debt,” and explicitly safeguard time and cognitive bandwidth for meaningful work. For service providers, the mandate is clear: anchor XLAs on outcomes only humans can deliver, make creativity visible on the dashboard, build strong feedback loops, and use XLAs as guardrails against over-automation. XLAs are no longer just a friendlier way to measure IT; they are becoming the central platform for keeping human potential at the center of an AI-driven future of work.

For more information see IDCs upcoming research documents: “Measuring What Matters: XLAs and the 2026 Digital Workplace” and “Control Towers for Human Potential: The Growing Importance of XLAs in the Age of Agentic AI”.

If you have a question about this or any other IDC research, drop it in here.

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

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

By Bo Lykkegaard, Associate VP for Software Research Europe with advice and review by Ewa Zborowska, Research Director, AI, Europe

Providers of SaaS solutions across the world have been through the market capitalization bloodbath during the past six months. Despite presenting solid indicators of growth and margins for 2025, almost all publicly traded companies have seen share price reductions 10% to 60% with the average reduction being in the 30-35% range.

Forget about looming trade wars, recession fears, missed revenue goals, and other conventional share price depressants. This is about AI disruption of the current SaaS user experience, licensing model, and product architecture. Investors are starting to fear that the SaaS ‘rental model for software’ will become invisible ‘featureware’ inside an AI agent layer.

What Are the Market Cap Reductions Telling Us?

We have examined the market cap reductions of public traded SaaS vendors over the past six months. Based upon this, we can make the following observations:

  • All SaaS vendors are affected across solution areas, geographies, size of vendor, recent growth KPIs, and size focus (SMB vs. enterprise). This means that investors are reexamining their assumptions related to SaaS growth prospects in general.
  • Vendors of workflow automation solutions and vendors targeting small and medium-sized businesses appear particularly exposed. Commercial workflow software is seen as exposed to replacement by new AI agent technologies. Also, vendors targeting small businesses are seen as more exposed to churn and price pressures.
  • SaaS vendors headquartered in EMEA do not appear harder hit than those headquartered in North America and the market cap correction has hit the largest as well as the smaller SaaS vendors.

Changes that All SaaS Vendors Are Facing

Firstly, the conventional SaaS user experience must change. In a conventional SaaS application, the user executes tasks manually within defined workflows. In an AI-powered application, the system adds to these structured workflows with probabilistic outputs, where it generates, predicts, recommends, or executes. Also, AI-powered applications can accept and react to all kinds of conversational user inputs. Furthermore, just like today’s LLM-based apps, business applications understand context and remember past interactions, which make recommendations and predictions more relevant and precise. Finally, AI-powered business applications are more proactive in nature and help users with monitoring tasks and relevant notifications.

Secondly, the conventional SaaS licensing model must evolve. The talk of the town these days is ‘outcome-based pricing’, i.e. the notion of pricing an application on outcomes (e.g. number of invoices issued) as opposed to number of users. If agentic workflows increasingly automate core business processes in the future, the user of a, say, financial application will be an agentic workflow as opposed to a human user. As AI agents increasingly become users of business applications, the user-based revenue model of SaaS application collapses. Investors are looking for SaaS vendors to at least align licensing better to business outcomes.

Thirdly, the conversional SaaS product architecture must be rethought. Adding AI to a conventional SaaS solution in the form of a chatbot or other form of AI-generated add-on does not make a meaningful difference. Real modernization requires rethinking the SaaS workflow from the ground up. AI changes all levels of the SaaS product stack and needs foundation model(s), embedding layer, vector database, retrieval-augmented generation (RAG), orchestration layer, guardrails, monitoring, and prompt/version management.

AI is making several other significant changes in SaaS. Development and maintenance as well as running costs have become more volatile and unpredictable. Data management requires new approaches, as application data now serves as a key source for training AI-powered SaaS solutions. Product roadmaps and release cadences are increasingly driven by AI model upgrades rather than traditional update schedules. Software vendors face new risk management challenges related to hallucinations and regulatory compliance. And both vendors and end-user organizations need to adapt their teams with new sets of skills. And most importantly, the overall competitive landscape has shifted, with AI-based startups and hyperscaler offerings emerging as new challengers.

The changes above certainly apply to SaaS vendors in Europe. However, in addition, vendors in Europe – as they adapt solutions and business models to become AI-driven – must pay particular attention to four areas in order to successfully transform.

Firstly, there is the GDPR, NIS2 and EU AI Act compliance, often accompanied by various national or industry-specific regulations. If they cannot document and showcase complete compliance to customers, they cannot sell their AI-powered solutions to compliance-sensitive European organizations.

Secondly, increasingly we see data residency requirements from customers in Europe, particularly in public services, financial services, and healthcare. Buyers in such industries can require EU-hosted data and sovereign cloud guarantees and approaches and can seek to avoid subjection to the US CLOUD Act and to exposing data for foundation model training.

Thirdly, Europe is multi-lingual and buyers require multi-language model performance. A conversational SaaS application is great but only if the conversation happens in the local European language where the application is deployed. We have seen many cases where non-English conversational capabilities are years behind English.

Fourth, European AI-powered SaaS vendors should expect higher demand for transparency and explainability. European customers have a strong preference for understanding how AI systems make decisions, a need often reinforced by regulations like GDPR and the EU AI Act. This means vendors must provide clear logic behind decision criteria, bias mitigation documentation, human oversight mechanisms, and comprehensive audit trails. Black box AI approaches such as “Pick this candidate because the recruiting application assigned a high AI score” simply will not fly in Europe, where trust is key and it heavily depends on being able to trace and justify how conclusions are reached.

Join the Conversation

At IDC, we help you navigate these changes with deep market research, robust data analytics, and tailored custom solutions. Whether you need strategic insights, benchmarking, or support in adapting your business model, our experts are ready to guide you.

Contact us to discuss your unique challenges and discover how IDC can empower your next steps in the evolving, AI-disrupted European software landscape.

Sources:

Bo Lykkegaard - Associate VP for Software Research Europe - IDC

Analyst Profile
Bo Lykkegaard is associate vice president for the enterprise-software-related expertise centers in Europe. His team focuses on the $172 billion European software market, specifically on business applications, customer experience, business analytics, and artificial intelligence. Specific research areas include market analysis, competitive analysis, end-user case studies and surveys, thought leadership, and custom market models.

AI-mediated discovery is changing how buyers find information, form opinions and build trust. For marketing leaders, this shift raises new questions about visibility, credibility and influence long before engagement begins.

For a long time, digital discovery felt predictable. Search engines, keywords, SEO and funnels shaped how buyers found information. If content was optimised well enough and distributed widely enough, it would eventually be discovered. That assumption no longer holds. 

In an AI-first economy, discovery is increasingly mediated by intelligent systems that interpret intent, assemble knowledge and prioritise answers across fragmented sources. This shift is subtle but profound. It changes not only how buyers find information, but also when relevance is established and where influence is formed. By the time buyers engage directly, their understanding of the problem space, the category and the credible players is often already shaped. 

IDC research shows that discovery is evolving from a tactical marketing activity into a strategic capability. Visibility, credibility and authority are formed earlier, indirectly and through systems that organisations do not own or control. 

From finding information to making sense of it 

Traditional discovery models are reactive. They respond to explicit queries and rely on static indexing and keyword logic. In practice, they answer what users ask, not why they are asking. 

Agentic AI introduces a different logic. Instead of retrieving information once, these systems reason about intent, refine discovery paths, validate relevance and adapt outcomes over time. Discovery becomes a continuous sense-making process rather than a single interaction. This distinction matters because most discovery is not transactional. Buyers are trying to understand problems, compare approaches and frame internal conversations long before any commercial intent becomes visible. By the time evaluation begins, discovery has already shaped expectations, trust and default assumptions. 

In this context, relevance is no longer defined by ranking alone. What matters is whether perspectives, knowledge and data can be interpreted by AI systems as credible, useful and meaningful in answering real buyer questions. 

Why discovery has become a brand issue

As AI systems increasingly summarise, synthesise and prioritise information, they also shape perception. Every generated answer reflects a series of decisions about which sources are trusted, which viewpoints are included and which providers are referenced or excluded. Over time, these decisions influence how markets understand categories, solutions and leadership. Discovery therefore becomes the new front line of authority. Even when buyers are not ready to engage commercially, early informational moments shape narratives and build trust long before demand materialises. Organisations that fail to appear in these moments risk becoming invisible, regardless of innovation or execution quality. 

At the same time, many discussions about discovery still rely on outdated assumptions. Discovery is often equated with visibility, SEO or personalisation. In reality, these are outcomes, not the system itself. Agentic discovery does not replace human judgement, nor does it automate buying decisions end to end. It reshapes how relevance is constructed, combining machine reasoning with human context in a continuous feedback loop that begins well before any buying process formally starts. This shift challenges go-to-market models that assume discovery can be compressed into campaigns, launches or early funnel stages. 

The European dimension: trust and responsibility

In Europe, discovery carries additional weight. Trust, transparency and data privacy are not only regulatory requirements, but strategic differentiators. As agentic AI matures, organisations that embed governance, GDPR compliance and transparency into how their knowledge is structured and surfaced are better positioned to build long-term credibility. 

In this environment, relevance is shaped not only by technological capability, but also by responsibility. Discovery becomes both a performance challenge and a trust challenge. 

What this means for marketing leaders

Marketing is not becoming obsolete. It is being redefined.

In an AI-first economy, discovery rewards organisations that invest in structured, machine-readable knowledge, align content with buyer intent rather than campaign logic, and connect content, data and orchestration more deliberately. Linear funnels and campaign-led journeys struggle to reflect how buyers actually navigate information today. The challenge for marketing leaders is no longer how to push content more efficiently, but how to remain relevant inside AI-mediated discovery processes they do not fully control. 

These shifts raise practical questions many teams are only beginning to confront. How do AI systems decide which sources to trust? What makes content discoverable in agent-driven environments? And how should go-to-market strategies adapt when discovery happens long before engagement? 

Ornella Urso - Research Director, IDC Retail Insights - IDC

Analyst Profile
Ornella Urso is Head of IDC's Retail Insights team and leads the Customer Experience research group in Europe. Urso conducts market research, industry analysis, and proactively contributes to the definition of thought-leadership at the intersection of businesses priorities and technology innovation in B2C and D2C strategy companies. In her role, she is responsible for the delivery of research reports, custom projects and offers strategic direction and advice to both technology providers and IT and business executives of global brands.

In January, Carla Arend, Rahiel Nasir and Luis Fernandes presented IDC’s predictions for cloud in 2026 and beyond. Below is a summary of the main points that were made in the webcast.

The need for digital resilience has never been more crucial

  • Tariffs, supply chain glitches, regulations, skills shortages… digital organisations are being assaulted from all sides.
  • For the majority of EMEA organisations, maintaining operational resilience and cyber security is the top priority.
  • To survive, organisations need to ensure their tech stack is robust and assess the strengths of their tech partner ecosystem. Adaptability and financial stability will also be key weapons to add to the armoury.

Digital sovereignty could help

  • Around half of organisations in EMEA have increased interest in implementing digital sovereignty solutions due to all the geopolitical uncertainties, such as trade tensions, regional conflicts, and regulatory shifts, witnessed in 2025.
  • Digital sovereignty solutions offer data owners complete control and autonomy over their digital assets – maintaining operational resilience is a key tenet of sovereignty.
  • Governance, risk and compliance solutions will be the key focus for organisations looking for sovereign cloud providers, especially for their AI. This will help them reassess their cloud provider options, determine the right IT venue for their workloads, and help to create a more robust tech stack.

The right venue for AI workloads

  • Enterprises are shifting to specialized AI providers and edge infrastructure to maximize performance and efficiency.
  • By 2028, physical AI use cases will experience explosive growth with cloud providers powering the bulk of these deployments at the edge with industry-specific AI agents and high-performance edge infrastructure.
  • By the end of this decade, at least 30% of advanced GPU needs will be met by specialised AI cloud providers offering true cloud features, flexible pricing, APIs, and software services (unlike GPU-only providers).

 AI and cloud modernisation

  • Cloud modernisation continues while legacy systems are re-platformed for AI, using autonomous agents to automate operations and orchestration.
  • Over the next two years, more than half of enterprise apps will leverage SaaS platforms to orchestrate predefined app functions and AI agents for real-time workflows, enabling modular and interoperable solutions.
  • By 2030, 45% will use cloud AI-infused tools to assess cost and performance metrics to optimise workload placement. Furthermore, a fifth will use AI agents to automate workload orchestration.

 Recommendations for cloud users

  • With geopolitical turmoil continuing into 2026 (and probably beyond), organisations are advised to take a risk-based approach to their cloud and AI strategies.
  • Choose the most appropriate venue for your workload. This should be supported by a hybrid and multicloud ecosystem of partners who offer services tailored to your needs.
  • The time to modernise your cloud estate to get ready for AI is now.

Watch the European cloud predictions webcast here:

For the EMEA FutureScape predictions webcast, click here.

If you would like more information on any of the above, please drop your details in here.

Rahiel Nasir - Research Director, European Cloud Practice, Lead Analyst, Digital Sovereignty - IDC

Analyst Profile
Rahiel Nasir is responsible for leading and contributing to IDC's European cloud and cloud data management research programs, as well as supporting associated consulting projects. In addition, he leads IDC's worldwide Digital Sovereignty research program. Nasir has been watching technology markets and writing about them throughout his professional life.