The artificial intelligence boom was expected to benefit the companies building the models. Recent earnings suggest otherwise for Europe's largest established technology firms. SAP, Capgemini, Sopra Steria and OVHcloud have all reported stronger demand, faster growth or upgraded outlooks as businesses move from experimenting with AI to deploying it across operations.
In the process, organisations are discovering that making AI productive inside a complex enterprise is harder than gaining access to the technology itself. Large organisations are unlikely to rely on a single AI provider; they expect to use different models for different tasks depending on performance, security and regulatory requirements. The challenge increasingly lies not in choosing a model, but in making AI work with the software, data and business processes companies already use.
Integration becomes the battleground
"AI applications are the battleground, and that is where most value will be created," UBS said in a recent note. That plays directly to the strengths of Europe's established software, consulting and infrastructure groups. Many of those firms built their businesses helping large organisations integrate complex technologies long before generative AI emerged.
Most large organisations do not start with a clean technological slate. AI systems must work with software built up over decades, fragmented databases, customised applications and increasingly complex governance requirements. They must also access live company information while respecting permissions, preserving audit trails and fitting into workflows employees already use.
The complexity of that task is becoming one of the biggest constraints on AI adoption. Boston Consulting Group said deployment was advancing faster than companies' ability to manage it, with more than 70% of investors expressing concern about whether organisations have the technical and operational capabilities needed to succeed with AI. As companies move from experimentation to application, spending on implementation, integration and governance is becoming an increasingly important part of the AI value chain. This is where established firms like SAP, Capgemini and Sopra Steria are well positioned - skills that are directly relevant for professionals exploring AI for IT & Development.
Earnings reflect the shift
SAP's cloud backlog rose 26% at constant currencies to €22.9 billion as companies continued moving critical finance, procurement, supply-chain and human-resources systems onto platforms that serve as the foundation for AI deployment. The company's acquisitions of data specialist Dremio and AI company Prior Labs underline the growing importance of making enterprise data accessible to AI applications.
Capgemini raised its annual growth target after bookings climbed 9.2%, while Sopra Steria upgraded its outlook after organic growth accelerated to 5.3%. Both Paris-listed companies are benefiting from the work that follows AI adoption: integrating models into workflows, managing data and building governance systems. That work is particularly valuable in sectors such as defence, aerospace, healthcare and critical infrastructure, where AI must be fitted around specialist software and tightly controlled operational processes.
Demand for control boosts incumbents
Publicis Chief Executive Arthur Sadoun has said clients increasingly want advanced AI models operating within environments where they retain control over their technology and their data. The preference is strongest in defence, aerospace and critical infrastructure, where concerns over sovereignty, security and compliance are particularly acute.
Airbus' decision to use Scaleway - owned by French telecoms group Iliad - for sensitive industrial and defence applications, alongside AI tools developed with Mistral, reflects that shift. Airbus expects around 70 critical applications to run on Scaleway by the end of 2028. OVHcloud's public-cloud revenue rose 20.2% in its third quarter, providing early evidence that demand for European-controlled AI infrastructure - not exposed to extraterritorial laws such as the U.S. Cloud Act - is beginning to translate into commercial growth.
Europe's technology incumbents still need to prove that AI-driven demand can be sustained and that margins can withstand the automation of lower-value consulting and software work. Recent results, however, suggest the biggest beneficiaries of AI may not be limited to those building the models. Increasingly, they may be the companies that make those models usable inside the world's largest organisations.
Why this matters for finance professionals
The shift from model-building to integration and implementation means the most profitable opportunities in AI may now lie in established enterprise software, consulting and infrastructure providers. For finance professionals tracking AI investments, the earnings signals from SAP, Capgemini, Sopra Steria and OVHcloud suggest that value is migrating to companies that can solve the hard operational problems of making AI work inside complex organisations. Understanding who controls the data, governance and workflow integration - not just the model - will be key to evaluating which AI-related bets are likely to deliver sustained revenue growth. For senior leaders making strategic decisions about AI adoption, resources such as AI for Executives & Strategy can help frame these considerations.
Your membership also unlocks:
