With enterprise software, consulting, and cloud infrastructure firms seeing a sharp increase in demand, Europe’s well-established tech incumbents have emerged as major AI winners.
Businesses are finding how challenging it is to integrate models into intricate legacy workflows, dispersed data systems, and strict regulatory compliance frameworks as they go from AI experimentation to full implementation.
Legacy tech companies with strong integration knowledge are reaping the financial advantages instead of new frontier model developers gaining all the value, as reported by Reuters.
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As businesses go from testing artificial intelligence to implementing it throughout their operations, SAP, Capgemini, Sopra Steria, and OVHcloud have all reported increased demand, quicker growth, or improved outlooks.
During this process, they find that it is more difficult to make AI productive within a complex organisation than it is to have access to the technology.
It is improbable that big businesses will depend on only one AI supplier. Rather, they must employ various models for various activities based on security, performance, and legal considerations. Choosing a model is becoming less difficult than integrating AI with the data, software, and business processes that businesses already employ.
“AI applications are the battleground, and that is where most value will be created,” UBS stated in a recent statement.
This immediately plays to the capabilities of Europe’s well-established software, consultancy, and infrastructure firms, many of which created their companies long before generative AI became popular by assisting major organisations in integrating difficult technology.
The majority of large organisations don’t begin with a blank slate when it comes to technology. AI systems have to cope with software that has been developed over many years, disjointed databases, customised applications, and ever-more complicated governance regulations.
Additionally, they must be able to access real-time company data while maintaining audit trails, honouring permissions, and integrating with existing employee workflows.
One of the main obstacles to the widespread use of AI is the task’s complexity. According to Boston Consulting Group, deployment is happening more quickly than businesses can handle it, and over 70% of investors are worried about whether businesses have the operational and technical capacity necessary to succeed with AI.
Spending on implementation, integration, and governance is becoming a more significant component of the AI value chain as businesses transition from experimentation to application.
At constant currencies, SAP’s cloud backlog increased by 26% to €22.9 billion as businesses kept transferring vital supply-chain, finance, procurement, and human resources systems onto platforms that increasingly provide the basis for the deployment of artificial intelligence.
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Making enterprise data accessible to AI applications is becoming increasingly important, as seen by the company’s acquisitions of data expert Dremio and AI startup Prior Labs.
While Sopra Steria improved its view following an acceleration of organic growth to 5.3%, Capgemini increased its annual growth target following a 9.2% increase in bookings.
The labour that comes after AI adoption—managing data, creating governance systems, and incorporating models into workflows—benefits the two Paris-listed businesses.
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