Accenture champions responsible AI, gains faster time to market with Azure AI Foundry

The desire to help enterprise companies reinvent their operations through technology and deep industry insight has long guided Accenture’s approach to business and innovation. With more than 800,000 employees serving clients in every major industry worldwide, Accenture recognized early on that generative AI is more than just a technological advancement—it’s a fundamental turning point in how enterprises think, operate, and innovate. 

The promise of generative AI can come with significant challenges, though. Deploying generative AI at scale requires addressing complex questions around accuracy, security, governance, and performance to meet demanding enterprise standards.

“There is no GenAI without responsible AI,” says Nayanjyoti Paul, Associate Director and Chief Azure Architect for Gen AI at Accenture. Paul emphasizes the critical need for trustworthy solutions, explaining, “Our clients were ready to move past proofs of concept. They needed real, production-grade solutions—but they also needed assurance that those solutions could be trusted.”

Initially, many clients approached Accenture primarily to “test the waters,” according to Revathi Kottaru, AI/Machine Learning Engineering Manager at Accenture. As their understanding and comfort with the technology matured, their expectations evolved significantly.

Clients began probing deeper, asking essential questions such as: How can generative AI scale reliably across diverse operations? How do we ensure solutions comply with regulatory, ethical, and specific business standards? “Clients don’t just want working demos anymore,” Kottaru explains. “They want solutions that are explainable, compliant, and production ready.”

Recognizing the limitations of ad hoc development processes, Accenture identified the need for a centralized, enterprise-grade capability to manage the entire life cycle—from evaluation through deployment. The vision: a comprehensive solution that embeds the principles of responsible AI at every stage. One that not only meets operational needs but also aligns with critical ethical and compliance requirements.

A responsible and purpose-built solution

One team within Accenture worked with Microsoft to develop a repeatable, scalable, and highly secure generative AI solution using Azure AI Foundry. It’s purpose-built for orchestrating agent-based workflows, evaluating generative outputs, and maintaining robust observability throughout the life cycle of each AI application. 

“Azure AI Foundry gives us a unified view across all our applications. Instead of stitching together a dozen tools, we had a single SDK to track safety, accuracy, and performance,” says Anoop Gopinatha, Managing Director of Industry Gen AI at Accenture. Using Azure AI Foundry, developers assess and improve AI models before deployment. 

Building on its unified foundation, Accenture can design, customize, manage, and support applications with technical consistency and business confidence. The architecture includes Azure AI Search for retrieval-augmented generation (RAG) scenarios where enterprise-specific knowledge is indexed, vectorized, and made available to large language models in real time. This ensures grounded responses that reduce hallucination and maintain contextual relevance.

Azure Functions plays a critical role in persisting application state, session metadata, and agent memory. Combined with Azure App Service, Azure Functions supports modular, event-driven workflows that interoperate with both internal systems and third-party APIs. When clients need fine-tuned control over conversational data or hybrid deployments with PostgreSQL databases, the flexible multilayer interoperability of Azure makes it seamless.

Evaluations and layers of safety

Security and compliance are enforced through multiple layers. Azure AI Content Safety is embedded directly into each application’s response pipeline to detect and filter personal data, offensive language, and policy violations. These filters operate alongside custom-built guardrails and moderation layers. 

For highly sensitive environments, Accenture added cascading safety nets—both pre- and post-response—to ensure nothing slips through undetected. Kottaru explains, “We were able to establish two layers of filtration. Even if one misses something, the second catches it. That’s peace of mind at scale.”

Evaluation is a cornerstone of Accenture’s model of responsible AI. Using the integrated evaluation suite in Azure AI Foundry Observability, the team could assess outputs across dimensions such as groundedness, coherence, fluency, content safety, and jailbreak. These evaluations are not static. Developers can run iterative AI red teaming simulations and A/B comparisons against golden datasets. The process is continuous, enabling each deployment to improve with feedback. “We’re not just launching applications—we’re measuring them, tuning them, and improving them with every cycle,” says Gopinatha.

This Accenture solution relies on Azure Monitor and Application Insights to provide a real-time observability layer. Every model call, user interaction, and agent decision can be logged, visualized, and traced. This transparency makes debugging faster and ensures traceability—critical for industries bound by regulatory standards. Gopinatha says, “Instead of piecing together telemetry from a dozen dashboards, we can get a full view of system behavior in one place. That shortens resolution times and increases reliability.”

Azure Machine Learning was used for custom model training, and models in Azure AI Foundry Models were used for fine-tuning, which in this solution supported Accenture’s need to adapt foundational models to a specific client domain. With built-in version control, deployment pipelines, and model assurance testing, Machine Learning plays a key role for Accenture, delivering AI that is powerful and tailored to meet the unique needs of their clients.  

The team also worked with Microsoft engineering teams to anticipate and prepare for the future. Accenture is already testing the Microsoft AI Red Teaming Agent, which simulates adversarial prompts and detects model and application risk posture proactively. This tool will help validate not only individual agent responses, but also full multi-agent workflows in which cascading logic might produce unintended behavior from a single adversarial user.

“We want to stress-test these systems, not just certify them,” says Paul. “Red teaming lets us simulate worst-case scenarios before they ever hit production. That changes the game.”

No longer reinventing the wheel

Early results have been positive. In this scenario, Accenture can reduce the time to build AI applications by up to 50%. It sees the potential for a 30% increase in overall efficiency and a potential for a 20% reduction in costs. “Azure AI Foundry can accelerate our time to market,” says Gopinatha.

Accenture has also deployed more than 75 generative AI use cases across clients, with over 16 solutions in full production. The use cases span industries like energy, healthcare, and financial services, and range from knowledge agents to regulatory compliance systems.

As an example, a water services company used Accenture’s innovation to deploy a multi-agent solution that reimagined its business processes from end to end. Built-in safety switches, toxicity filters, and branded response layers ensured every output met strict regulatory standards.

Paul highlights one case where a client requested 14 use cases over eight months. With the solution’s speed and flexibility, Accenture delivered 17 use cases in just four months. “That’s the power of having a unified, Azure-native foundation,” Paul says. “We’re no longer reinventing the wheel each time.” 

Azure AI Foundry Observability also helped streamline post-production monitoring. Instead of manually checking metrics across disparate services, the team could now observe performance, detect drift, and proactively adjust responses—all in one place. Kottaru adds, “We’re building confidence, not just capability. When clients see that their models are measurable and improvable, it shifts the conversation.”

Looking ahead, Accenture is focused on scaling responsibly. The interoperability of the AI Red Teaming Agent will introduce proactive threat modeling, while enhancements to vector search and multimodal workflows promise to expand the breadth of enterprise-ready solutions. “Azure AI Foundry lets us design with safety from the start, but the AI Red Teaming Agent will help us build systems that adapt and evolve responsibly over time,” explains Kottaru.

As AI accelerates, Accenture is leading by example. “Responsible AI isn’t a checkpoint—it’s a mindset,” says Paul. “And with the right architecture, it becomes a strategic advantage.”

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