November 9, 2026

Blog: Accenture Launches Data-as-a-Service, Designed to Help Organizations Build Enterprise-Ready AI

What's New?

• Accenture has launched Data-as-a-Service, a new offering which can help organizations improve AI models using real-world business expertise. It provides access to industry experts and enterprise-focused testing environments to help organizations train, evaluate and refine AI for business-critical workflows.
• Data-as-a-Service looks to help organizations, including frontier AI labs, assess whether AI systems are ready for production through benchmarking, evaluations, and data creation where real-world scenarios and expert judgement are required.
• It is supported by Accenture's global network of experts spanning 35 industries and 73 languages, as well as proprietary demand matching capabilities and automated quality assurance processes, enabling organizations to improve AI performance.

Why Now?
Many organizations are investing significantly in AI but continue to face challenges turning that investment into business value. Data-as-a-Service gives AI models access to expert-created data and realistic business scenarios to help train AI and test whether it’s ready for real work.

As AI moves into business-critical workflows, organizations need confidence that AI and agentic systems can perform reliably in real-world environments where context, judgment and business expertise matter most. Traditional benchmarks can measure technical performance, but they often fail to capture the realities of enterprise work. Organizations don't just need to know whether an AI model can pass a test, they need to know whether it can perform real work in complex, global and multi-system environments.

At the same time, AI development is placing greater emphasis on industry and functional expertise. As organizations move from general-purpose AI applications to agentic systems designed for specific business functions and workflows, they need ways to train, evaluate and improve models using realistic enterprise scenarios and the experience of people who understand how businesses operate and make decisions.

"The AI conversation is shifting. For many organizations, the challenge is no longer gaining access to powerful models, it's ensuring those models can perform effectively in real-world business environments," said John Crosthwait, U.S. Communications, Media and Technology Lead- Accenture. "As enterprise AI use cases become more specialized, domain and functional expertise is becoming a key differentiator. Data-as-a-Service can help organizations bring that knowledge directly into how AI systems are trained, evaluated and improved, ultimately helping to provide real business value."

Helping AI Learn from Real-World Expertise
Data-as-a-Service is designed for both AI model developers and enterprise organizations. AI model developers can use training and evaluation resources developed by experienced professionals to identify weaknesses, improve model capabilities and benchmark model performance in business domains. Enterprise organizations can use Data-as-a-Service to assess whether AI systems are fit for critical workflows such as finance operations, process management, supply chain and other use cases where expert judgment and business context are essential.

Data-as-a-Service combines three core capabilities: access to verified enterprise specialists who create, label and evaluate AI training data; pre-built, quality-checked datasets developed by practitioners with deep industry and functional expertise; and reinforcement learning environments that allow organizations to test AI models against realistic enterprise scenarios, including safety evaluations and red teaming.

• For a software and platforms client that was preparing to release its state-of-the-art model, Accenture developed 500k prompts across 15 enterprise domains for supervised fine tuning.
• For a software and platform client, Accenture partnered to develop a specialized and expert review program that trained AI models prior to production to improve accuracy and throughput across 12+ product areas across 24 languages.

“As model benchmarks continue to evolve, we know more is needed for organizations to get ahead and stay ahead on real business domains,” said Kevan Yalowitz, Global Software & Platforms Lead. “Data-as-a-Service helps organizations train and evaluate AI against the complexity of real-world enterprise environments”.

As part of the launch, Accenture and Faculty are premiering EnterpriseVAL, a frontier LLM benchmark designed to evaluate model readiness for real world tasks. While most benchmarks are built for academic settings, EnterpriseVAL is expert-crafted, and built specifically for complex enterprise contexts. The first industry release is focused on the Finance and Accounting domain, where LLMs only successfully complete 35% of enterprise tasks with no critical failures across order to cash (OTC), procure to pay (P2P), record to report (R2R), and Financial Planning & Analysis (FP&A).

By combining expert talent, curated datasets and enterprise-focused testing environments, Data-as-a-Service can help organizations build AI systems that are better aligned to real-world business needs and better prepared for enterprise deployment.