Enhancing IT ops with a multi-AI agent approach

Enhancing IT ops with a multi-AI agent approach

Dnyanesh Joshi

September 15, 2025

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Across the enterprise, departments are placing increased demands on their organization's data to enable multi-AI agents. It's the IT operations (IT ops) department's challenge to deliver the optimal environment for agentic AI to eventually bring business value.

Enterprises are grappling with a volatile, uncertain business climate - and to address this, they are increasingly turning to their data to draw actionable insights that enable competitive advantages through agents.

As networks grow more complex and the demands on them increase, IT ops departments need to develop better tools, including multi-AI agent systems to enhance the decision-making process by making recommendations aligned to set business goals.

Properly designed and implemented agentic AI solutions are game-changers - but IT ops must be prepared to take advantage of these powerful tools, which requires a well-crafted plan and a partner that can deliver more than just the technology.

The IT ops imperative

In conversations with IT professionals, my Capgemini colleagues and I have identified a number of common challenges for IT operations at enterprises across all sectors. Simply stated, IT professionals are under pressure to boost service performance while reining in costs - including operating expenses and costs for infrastructure and cloud services. They're also under pressure to better identify, provision, and deploy the solutions required to allow other departments to take advantage of emerging technologies such as agentic AI.

The organization's own data is an important source of the information required to help IT professionals achieve these goals. Unfortunately, legacy business intelligence systems often fail to satisfy their needs. There are several reasons for this:

• Analytics systems rarely support strategic foresight and transformative innovation - instead providing business users with yet another dashboard.
• The results are often, at best, a topic for discussion at the next team meeting - not sufficient for a decision-maker to act upon immediately and with confidence.
• Systems typically fail to personalize their output to provide insights contextualized for the person viewing them - instead offering a generic, unsatisfying result.
• Systems often aggregate data within silos, which means their output still requires additional interpretation to be valuable.

In short, many legacy systems miss the big picture, miss actionable meaning, miss the persona - and miss the point.

Based on my experience, I recommend an organization address this through multi-AI agent systems.

With the introduction of Gen AI Strategic Intelligence System by Capgemini, this could be the very system that bridges the gap between the old way, and a value-driven future. This system converts the vast amounts of data generated by each client, across their enterprise, into actionable insights. It is agentic: it operates continuously and is capable of independent decision-making, planning, and execution without human supervision. This agentic AI solution examines its own work to identify ways to improve it rather than simply responding to prompts. It's also able to collaborate with multiple AI agents with specialized roles, to engage in more complex problem-solving and deliver better results.

How would organizations potentially go about doing this?

Define the technology and business KPIs

First, organizations must establish well-defined KPIs and associated roadmaps to take full advantage of agentic AI recommendations - KPIs that align technology with business objectives.

This starts by identifying the end goals - the core business objectives and associated KPIs relevant to IT operations. These represent the IT operation's key activities that support other departments as they contribute to the organization's value, and strengthening them is always a smart exercise. The good news is that even small improvements to any of these KPIs can deliver enormous benefits.

The roadmap should leverage pre-existing AI models to generate predictive insights. It should also ensure scalability, reliability, and manageability of all AI agents - not just within the realm of IT operations, but throughout the organization. And it should be designed to leverage domain-centric data products from disparate enterprise resource planning and IT systems.

Finally, the roadmap must identify initiatives to ensure the quality and reliability of the organization's data by pursuing best-in-class data strategies. These include:

• Deploying the right platform to build secure, reliable, and scalable solutions
• Implementing an enterprise-wide governance framework
• Establishing the guardrails that protect data privacy, define how generative AI can be used, and shield brand reputation

Choose a partner that delivers more than tech

Second, the organization must engage the right strategic partner. While innovative agentic AI systems are essential, that partner must also be able to support the IT team with business transformation expertise and industry-specific knowledge.

Capgemini leverages its technology expertise, its partnerships with all major platform providers, and its experience across multiple industrial sectors to design, deliver, and support agentic AI strategies and solutions that are secure, reliable, and tailored to the unique needs of its clients.

Capgemini's solution draws upon the client's data ecosystem to perform root cause analysis of KPI changes, and then generates prescriptive recommendations and next-best actions - tailored to each persona within the IT department. The result is goal-oriented insights aligned with business objectives, ready to help IT empower the organization through actionable roadmaps for sustainable growth and competitive advantage.

*Meaningful, measurable benefits

Capgemini estimates that with the right implementation and support, the potential benefits include augmenting the IT workforce through autonomous processing, touchless data crunching, improved data and systems integrations, continuous monitoring of controls and compliance, and real-time access to reports and insights.

The potential for IT operations to translate these internal gains into meaningful advantages for other departments across the enterprise means that leveraging agentic AI for its own strategic insights cannot be ignored.

^*Results based on industry benchmarks and observed outcomes from similar initiatives with clients. Individual results will vary.

The Gen AI Strategic Intelligence System by Capgemini works across all industrial sectors, and integrates seamlessly with various corporate domains. Download our PoV here to learn more or contact our below expert if you would like to discuss this further.

Large Deals Advisory, AI/Analytics/Gen-AI based IT/Business Delivery oriented Deals Shaping Leader

Dnyanesh is a seasoned Large Deals Advisory, AI/Analytics/Gen-AI based IT/Business Delivery oriented Deals Shaping Leader with 24+ years of experience in Large Deals Wins by Value Creation through Pricing Strategy, Accelerator Frameworks/Products, Gen-AI based Strategic Operating Model/Productivity Gains, Enterprise Data Strategy, Enterprise, Data Governance, Gen-AI/ Supervised, Unsupervised and Machine Learning based Business Metrics Enhancements and Technology Consulting. Other areas of expertise are Pre-sales and Solutions Selling, Product Development, Global Programs Delivery, Transformational Technologies implementation within BFSI, Telecom and Energy-Utility Domains.