Executive Summary: Intelligent Automation Services - Europe 2024

The individual quadrant reports are available at:

ISG Provider Lens™ Intelligent Automation Services - Artificial Intelligence for IT Operations (AIOps) - Europe 2024

ISG Provider Lens™ Intelligent Automation Services - Intelligent Enterprise Automation - Europe 2024

ISG Provider Lens™ Intelligent Automation Services - Next-Gen Automation - Europe 2024

Agentic AI promises to be the future of intelligent automation, but it is still in its infancy

The rise of generative AI (GenAI), the emergence of agentic process automation, increasing skill imbalances and a growing demand for responsible AI frameworks and solutions are but a few of the key trends shaping the intelligent automation services market in Europe.

GenAI — large language models (LLMs) that are capable of ingesting and analyzing vast amounts of data, understanding their probabilistic structure and generating outputs, workflows, text, code and a variety of other artifacts — is already reshaping the field of intelligent automation services and solution. Service providers have infused GenAI capabilities across a swathe of industry, functional and domain automation use cases, including intelligent finance and accounting processes, intelligent procurement, employee onboarding, contact-center agent assist and ESG compliance and reporting. Automation providers have also been actively enhancing their in-house LLM capabilities and building or fine-tuning domain-specific LLMs for the insurance industry. In doing so, they can draw on their deep understanding of individual business processes and industry- and domain-specific data. They help speed up the development of GenAI automation use cases by utilizing their accelerators, extensive use case libraries, and prebuilt templates. Many also offer structured workshops and methodologies for identifying use case, developing prototypes rapidly and progressing use cases into production.

Despite the enthusiasm for GenAI-powered automation, enterprises continue to face formidable challenges in implementing and scaling this automation. Some challenges are technical. Many enterprises struggle with integrating GenAI automation solutions across complex, sprawling technology systems that are often hampered by high levels of technical debt and a wide array of automation tools. This is where leading automation service providers play a critical role, offering their technology integration skills and extensive experience across the ecosystem of intelligent  automation ISVs and product providers. They can identify and benchmark a client’s IT and automation maturity, aligning it with the appropriate automation and technology solutions.

Another challenge pertains to data availability, quality, and governance. Effective implementation of intelligent automation solutions can be stymied by poor-quality data or organizational data silos, multiple data formats or lack of clarity around the ownership of data. Leading providers assist with data governance and modernization services, such as creating data warehouses or domain-driven data fabrics, often built on one or more public clouds.

Other challenges are organizational and cultural. Intelligent enterprise automation requires a systemic approach in which IT systems and business processes operate in harmony. This process involves a significant cultural adjustment for both IT operators and developers, who have limited experience in business processes, and process professionals,
who typically lack IT skills. While the shift toward low-code/no-code applications can help bridge the gap, the demand for service providers who can integrate IT and business processes remains very high.

Finally, the human workforce element in intelligent enterprise automation is becoming increasingly critical. With advanced GenAI capabilities, enterprises see intelligent automation less as a cost optimization play, and more as an important way to supercharge employee productivity and, ultimately, EX. However, workforce apprehension regarding
intelligent automation and its impact on roles and jobs remains an important factor limiting change. Again, intelligent automation service providers often play a key role in smoothing the transition, leveraging their expertise in user design and workforce change management methods.

A second major trend is the emergence of agentic AI and its role in intelligent automation. Not so long ago intelligent automation was largely about the automation of specific tasks through RPA, conversational AI and other solutions. This evolved into hyperautomation — connecting end-to-end workflows and business processes. More recently, we have seen the emergence of intelligent automation systems built on ML, natural language processing (NLP), computer vision and other technologies. Agentic automation, while still in its infancy, promises something completely different: LLM-powered automated agents that are context-aware, communicate with each other, autonomously initiate workflows and make context-dependent decisions with no or very little human intervention. These agents would be able to trawl knowledge databases and continuously adapt to changing business circumstances, such as daily, monthly or seasonal changes in customer demands or emerging bottlenecks in supply chains. A vast range of potential applications, such as agentic automation of customer contact centers, integrated customer support, agentic employee onboarding, and agentic procurement and supply chain, are possible.

The third major trend is responsible AI. While agentic AI may well be the future of intelligent automation, it poses some significant ethical and regulatory challenges, especially concerning trust in automated decision-making. Under what circumstances can agentic AI systems be trusted to make correct decisions? What kind of safeguards and backstops are needed? How explainable are agentic AI decisions and what kind of biases might be created? Given the growing prominence of these challenges, many leading intelligent automation providers underscore the importance of responsible AI practices and compliance across their service offerings.

The fourth big trend is a growing skill imbalance facing both enterprises and intelligent automation service providers. The growing infusion of GenAI into all aspects of IT fundamentally shifts the types of skills required: fewer professionals in operations and more with deep data and digital engineering skills. While many providers are ramping up GenAI training programs, GenAI skills are still in short supply.

In Europe, the demand for intelligent automation services is generally increasing, driven partly by a focus on operational efficiency and a rising emphasis on enhancing CX levels. Contact center transformation is a significant area of implementation. The adoption of intelligent automation is generally high in automotive and manufacturing
industries, as well as in healthcare, finance and retail. Consequently, the demand for industry-specific automation solutions is increasing. There is also a significant demand for compliance, data sovereignty and sustainability-related automation solutions. Enterprises also generally seek hyperautomation services as opposed to more traditional, piecemeal RPA implementations.

Amid a difficult economic backdrop in Europe, cost pressures on businesses remain intense, with automation  providers competing heavily on pricing and capabilities. The locus of enterprise buying power for automation solutions still resides mainly within the IT organization, but the growing adoption of process mining and automation is giving
enterprise and business leaders an increased say in automation decisions.

European enterprises are cautiously optimistic about the role of GenAI in intelligent automation services, with investment somewhat lagging behind North America. Some providers attribute this lag to a lower risk appetite, influenced by a greater focus on ethics and compliance guidelines in Europe partly driven by the EU AI Act and GDPR. 
However, many providers also noted that regulatory innovation in the EU, as exemplified by the EU AI Act, has driven a strong demand for responsible AI frameworks and solutions, and ensured a strong ethical line running through intelligent automation and GenAI implementations in Europe. In the long run, this may well work to Europe’s  advantage in global intelligent automation competition.

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