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Agentic AI automation could usher in a new era of process excellence
The intelligent automation landscape in Europe continues to evolve rapidly in technological sophistication, competitive intensity and the pace of service and solution development. Within the intelligent automation space, several key trends can be identified:
The rise of agentic automation:
While generative AI (GenAI) still plays a significant role in intelligent enterprise automation (IEA) services and solutions development, provider attention has now shifted toward the emergence of AI agents and multiagent models for new and better forms of automation. Most leading players have invested heavily in AI agents, AI-powered bots that have a high degree of contextual awareness and that can act autonomously and proactively to carry out complex workflows and business actions. Although providers have so far focused on single-agent models, some have introduced multiagent models where agents with specialized roles such as data gathering, workflow automation and customer communications work together to achieve particular business goals or outcomes. Agentic AI enables enterprises to move beyond the piecemeal, incremental RPA of old toward a much more streamlined, end-to-end automation of enterprise business functions. While agentic-powered IA is still in its early days, provider agentic AI-powered solutions are now moving beyond proofs of concept and pilots into production-ready applications. Agentic IA automation use cases have mushroomed, with horizontal use cases now observed in areas such as zero-touch AIOps, ITSM, customer service interactions, HR support and invoice processing.
Intelligent automation morphing into process excellence:
Businesses are ultimately concerned with process excellence; they aim to achieve end-to-end efficient and accurate processes that will optimize not just ROI but also customer, user and employee experiences. More and more providers now emphasize intelligent automation as just one part of an overall approach to operational or process excellence, combining AI, automation, business process management tools, lean management principles and process expertise in a structured way. Data-driven discovery tools such as process mining can shed light on hidden or inefficient processes, paving the way for automation or restructuring of work. Providers also increasingly emphasize process management platforms that aim to orchestrate the activities of AI agents, RPA tools and human workers to achieve process excellence.
Sovereign AI becomes sovereign IA:
For various regulatory, geopolitical and data privacy and security reasons, enterprises in Europe are seeking IEA solutions that are delivered within a sovereign environment. While lacking a precise definition, sovereign IA solutions typically involve extra controls on data residency and the operational environment where data for intelligent automation services is stored or processed. These solutions may involve using an enterprise’s private cloud or a provider’s sovereign cloud platform (if they have one) in Europe.
From LLMs to SLMs:
Large-language models (LLMs) serve as the brains for most GenAI and agentic AI applications. Although providers can choose from a large array of open-source LLMs such as OpenAI, Anthropic, Cohere and Meta, to name but a few, some consider the LLM market somewhat commoditized. They find that the choice of LLM for automation as less of a competitive differentiator for IEA solution providers. Consequently, provider attention is now pivoting to small language models (SLMs), which use LLM capabilities but enrich these with enterprise- or domain-specific contextual data. SLMs not only avoid the hallucination issues that affect LLMs but also enable enterprises to maintain data and policy compliance control over their data and automation processes.
Responsible AI looms large: With the rise of agentic AI systems with a high degree of autonomy in decision-making and action, the role of responsible AI in IA has taken on a new level of importance for enterprises. How agentic IA systems make decisions, the accuracy of such systems, the role of humans and the consequences of mistakes are questions that loom large for enterprises and service providers. Most leading providers, therefore, strongly emphasize their responsible AI tools and credentials, focusing on governance frameworks for ethical AI adoption and usage, policy and regulatory compliance guardrails, and tools and frameworks to detect bias and improve the explainability of AI decision-making.
Spotlight on experiences: In the past, RPA projects often had limited scope and lifecycle and usually aimed at reducing costs and giving a quick boost to ROI. With the many possibilities now opening up with agentic AI, enterprises recognize that IEA can be a powerful driver for organizational efficiency and top-line growth. IEA is now seen as a powerful way to improve CX, using AI-powered customer agents, recommendation engines and workflow orchestrators. AI agents can organize travel bookings, rapidly process insurance claims, onboard new banking customers, or look for signs of customer churn and proactively offer upgrades and promotions. They can autonomously field customer queries within contact centers or rapidly summon customer information and recommendations for human agents.
Greater focus on human-machine interactions:
Agentic AI-powered automation promises a new path toward human-machine interaction, one in which hybrid-human teams work in tandem to deliver business outcomes. However, the path toward these new teams will likely be rocky, requiring training, new roles and new ways of working. Effective change management will be critical. Most leading providers, therefore, heavily emphasize their change management and user-centric design capabilities, offering structured methodologies, frameworks and communication methods. Some of these rely on top-down communications, yet others seek to instill change at the grass roots level, often through nudge-based incentives for behavioral change and adoption of new technologies.
The European market for IA solutions and services remains buoyant, with most providers reporting steady or increasing demand. Enterprises in Europe are generally more risk-averse and cautious (than their U.S. counterparts) when investing in IEA services. The demand for IEA in Europe is heavily driven by factors such as regulatory compliance, sustainability, ESG, green AI and a desire for sovereign data controls. Agentic AI-led automation is also seeing growing demand, with more projects moving from pilot to scaling phases. The demand for IEA is relatively broad-based by industry, including public services, manufacturing, energy, banking, healthcare and utilities. Enterprises in Europe often prefer more flexible pricing for IEA projects, with pay-as-you-go models gaining popularity. A consistent theme noted by providers is that European enterprises generally favor a strong compliance and ethics-driven approach to IEA, coupled with an emphasis on a human-centric approach to automation.