Making the impact of artificial intelligence real and secure at enterprise scale
AI agents autonomously execute workflows and services to unlock measurable business value.
Autonomous artificial intelligence (AI) is no longer a future vision. As AI technology continues to accelerate, enterprises that recently relied on rules-based automation have leapt forward and are implementing goal-based autonomous agents that operate with a human-in-the-loop approach or even independently, to deliver sustained enterprise impact.
According to a recent Capgemini Research Institute prediction, 38 percent of CxOs will use AI actively for strategic decisions within the next three years, whereas agentic AI systems with autonomy are gaining traction, with six in 10 organizations currently exploring their applications, as highlighted in this 2026 AI research brief. As a transformation partner with ServiceNow, Capgemini has already teamed up with a number of organizations across sectors to transform agentic AI into responsibly governed and value-driven autonomous IT, driving measurable value at speed and at scale.
Beyond scripted chatbots to fully autonomous agents
Simply put, the real shift in AI is from automating tasks to delegating outcomes. For instance, chatbots are programmed by humans to take specific steps and automate interactions or processes with predefined answers or solutions. This “first wave” AI helped to streamline workflows and eliminate rote tasks, but these automated systems lack agency as they often just show what has to be done. Many organizations get stuck at this stage, failing to turn proofs of concept and pilots into real, measurable business outcomes, never mind reach enterprise scale.
Autonomous agents work differently from the get-go. Instead of laying out automation steps, users give the multi-agent AI system a goal, using natural language, like asking it to write a program that will achieve X, Y, and Z outcomes. Now imagine a series of agents –e.g., project manager, programming, user interface, and testing – working collaboratively to create a minimum viable product (MVP). Agents can access data, develop new processes, adapt to new situations, and operate independently.
Humans in the loop, from operators to orchestrators
Now, autonomous agents might sound too unstructured, especially since previous approaches for IT and process automation typically involved humans to design, trigger, and monitor automation. With autonomous systems, oversight is still mission critical, but people who were previously operators of these systems now become orchestrators of the various agents, providing end-to-end business intelligence to inform decision-making.
For autonomous systems to work as intended in alignment with ServiceNow, Capgemini’s AI Resonance Framework comprises three interconnected dimensions that must be addressed together:
- Access. Establish the technical foundation through high-quality data, AI platforms, and secure infrastructure.
- Adapt. Align AI initiatives with business priorities through governance, operating models, and value-driven decision-making.
- Adopt. Enable human-AI collaboration by building trust, usability, and sustained behavioral change.
In an autonomous model, AI agents are given goals, authority, and all-important guardrails. While micromanaging is no more, human oversight – as orchestrator and reviewer – is part of AI transformation, which begins at the core of an organization and radiates outward to generate continuous waves of value, a concept Capgemini refers to as resonance.
This can be explained in the context of the three dimensions outlined above, which were employed when our client, a grocery chain, sought a solution for its malfunctioning point of sales (PoS) system. Along with severe operating delays and high maintenance costs, its employees were spending their time focusing on the technology, instead of on customers. Our solution employed a multi-AI-agent approach, first leveraging the ServiceNow knowledge base (access) to automatically identify the common root causes, then log and finally resolve PoS issues (adapt), significantly reducing the manual work required and building trust with human counterparts (adopt).
Transparency, not black boxes
Adopting autonomous systems requires responsibility, especially with enforcement of the European Union’s Artificial Intelligence (AI) Act transparency rules beginning on August 2, 2026. Again, Capgemini’s threefold framework sets the standard for creating safeguards aligned with business goals. It’s essential to have the right platform, the right skills, and the right data foundation to make sure governance is secure, ethical, scalable, and reliable before it’s adopted.
ServiceNow’s AI Control Tower provides that transparency and control, through a centralized view of AI risks as well as benefits. It even shines a light on shadow AI systems – AI tools, systems, or workarounds that have been implemented without approval or oversight from an organization’s IT or security teams – that can create vulnerabilities such as data leaks, security risks, or compliance concerns. The scalable platform integrates into company’s workflows, such as AI strategy, governance, and security, monitoring exactly what the agents are doing. Boundaries are set for agent behavior, but there’s also a “kill switch” that makes it easy to quickly disable an agent if required. An overview of the tangible outcomes in form of measured business-oriented KPIs is also available, as highlighted in the following use case.
A large financial institution we supported with ServiceNow employed two agents: one customer-facing and the other an interim employment-support agent. We introduced agentic AI with clear governance and continuous monitoring, which resulted in the organization improving its user intent recognition accuracy by seven percent – a massive improvement in a high-volume environment (37 billion transactions in banking agents per year in 2025) – which translates into far more reliable interactions and higher trust from users. ServiceNow made the value measurable by bringing agent performance and business KPIs together in one place.
It’s clear that agentic AI is already transforming enterprises, bringing intelligence to autonomy and effectively transforming industries across sectors by creating real value and opening up new opportunities.
See also: https://www.capgemini.com/insights/expert-perspectives/scaling-agentic-ai-for-autonomy/