Transforming an Enterprise with Agentic AI and Intelligent Data Agents
A global business and technology services provider adopted an agentic AI approach to build conversational AI tools that help employees at all levels retrieve, interpret, and act on real-time data—enabling more intuitive, role-based access to insights. The AI-driven platform improved productivity and empowered technical and non-technical teams with reliable decision support across their enterprise areas. Results have also supported further exploration of multi-agent frameworks.
Orbis adopted an agentic AI approach to achieve greater insights, faster decision making and scalable data-driven operations.
Generating actionable insights through agentic AI and enterprise data
The company is a global technology services provider serving clients across more than 50 countries. With a full spectrum of business consulting and technology services, the organization supports digital transformation across complex industries, from financial services and manufacturing to healthcare and government.
- Time to market at least 50% faster for new data solutions.
- Improved productivity across HR, operations and sales workflows.
- Data products scaled quickly and safely, including for non-technical users.
- Less operational friction from development and deployment through to adoption and governance.
What stood in the way
The potential for tremendous gains through AI has always been clear. The challenge is actually deriving that value. In one instance in particular, the company wanted to get insights from data faster than existing solutions—reporting and visualization tools, automation platforms and robotic process automation (RPA)—could deliver.
Like many global enterprises, the company’s data was both deep and widespread. The question was how to best access, organize and derive maximum value from the data.
Legacy dashboards were too rigid and presented static views, often falling short in providing the actionable insights needed to make informed decisions. The company wanted to shrink the time to market for solution development and leverage its data more optimally so functions including HR, sales, and marketing could mine data at speed and scale.
That vital gap—between the potential of AI and the reality of current mainstream approaches—kickstarted a shift in strategy. The company turned to AI agents to reduce friction, accelerate decision making, and deliver insights tailored to the needs of a wider range of users across business functions.
It was the start of a new approach to interacting with enterprise data and empowering the organization to create and use domain-specific data agents that function as data experts—in other words, self-service data insight engines.
How Orbis got to work
Orbis combined enterprise data with agentic AI to build intelligent systems capable of providing role-specific insights and supporting users in working directly with business data.
The approach focused on moving beyond simple chatbots toward autonomous agents capable of understanding enterprise data, reasoning over it, and helping users take action.
The solution, Orbis DataPilot, provided the conversational layer for interacting with enterprise data, allowing users to ask personalized questions and uncover insights based on their role or function. Under the hood, Orbis used data agents as domain experts and combined them with an agentic AI framework to accelerate deployment and continuously improve performance.
One of the earliest and most productive deployments was in the company’s HR operations. Enterprise systems tracking workforce labor data were analyzed to provide insights into staffing, chargeability, and productivity. These data sources powered a suite of HR-focused agents, helping users interact directly with real-time data. In parallel, back-office functions were enhanced with similar agents that monitor key performance indicators and surface operational inefficiencies.
But the objective went beyond insights—the company wanted action. The integration of Orbis DataPilot with Orbis AgentCore, an agentic AI orchestration platform, made it possible to build intelligent agents that not only understand and reason over unstructured data, but also plan and execute tasks using structured and semantic data from across the enterprise data environment.
Crucially, these solutions integrated with tools the company was already using. The enterprise data estate was connected through a unified data layer, while the agentic AI framework offered interoperability across business units and technical teams. That tight integration accelerated time to value and reduced the friction of onboarding new users.
The company also developed an AI-powered sales coach, a multi-agent tool that prepares representatives for meetings, drafts proposals, and uncovers client-specific insights—all using secure, enterprise-grounded data. By embedding AI into business processes—not just apps—the company demonstrated how organizations can move from passive data consumption to proactive decision-making.
The result delivered
The initial outcomes have proven both directionally and operationally successful. Combining conversational access to data with intelligent automation, the company is transforming how teams work, how decisions are made, and how AI agents can create value across the enterprise.
The company effectively became Customer Zero in proving the efficacy of solutions that can later be taken to market. Efficiency is the big outcome. Data products can now be scaled quickly and safely, including for non-technical users.
Time to market is now at least 50% faster, while the new solutions provide analysis and insights that support decisions beyond what dashboards alone could deliver. The unified structure allows the organization to scale faster and with less operational friction.
With AI now embedded across HR, operations, and sales workflows, the company is measuring higher productivity, better alignment, and a stronger return on its data investments.
As the framework expands, the company expects to see significant opportunity for compound ROI from the aggregated efforts and outcomes. This productive new approach provides flexibility, scalability, and advanced multi-agent patterns that can enhance enterprise-wide productivity and profitability in even the most complex scenarios.
