A Copilot license here, a ChatGPT subscription there, and voila: AI is switched on for the enterprise. Mission accomplished?
Not so fast. At this point, every organization has the same AI models available, the question is how it can be shaped to deliver meaningful outcomes for your organization. To achieve transformative outcomes, intelligence needs to be embedded where the action happens: the frontline processes and workflows that are the backbone of every company.
The key is to design AI systems that intelligently combine accurate, timely data with grounded, reliable digital workers that can deliver great enterprise outcomes. At Striim and causaLens, we take a different approach to AI adoption that delivers on these promises.
Maturity to Meet the Challenge
The complexity of an AI system needs to match the need at hand. Many organizations have a lot of human labor expended on “Level 1” use cases: operational use cases that are mostly about moving routine tasks forward. These are ripe for automation.
As Level 1 use cases are increasingly smooth and automated, Level 2 and 3 use cases arise to infuse analytical and strategic digital labor into all facets of the business.
No matter what level the AI is operating, the key is up-to-date, accurate data. That’s where MCP-ready architectures come in. With ready-to-go data, it’s possible to automate operational tasks and free up humans for deeper thinking, or even to design digital workers to take on more analytical, specialized processes.
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Working with sophisticated, governed agents, armed with real-time data, enterprises can achieve more than operational efficiency. Analytical insights and strategic support are within reach if agents are given secure, trusted access to the context that makes them effective.
Trust as the Non-Negotiable
AI initiatives can only succeed when there’s a high degree of trust in the reliability of the digital workers. causaLens is pioneering agentic causal reasoning to dramatically improve the trustworthiness of digital workers by ensuring they act responsibly, adapt reliably, and adhere to predetermined logic. And their novel System of Work ensures it’s easy to monitor the operations, cost, and performance of large fleets of digital workers.
In addition, agentic digital workers are hungry for data, and getting them the right data at the right time is crucial for good outcomes. For enterprise AI to be both reliable and useful, thye need data that is:
- Accurate: Data must be correct and free of duplication or drift, or agents will make the wrong calls.
- Compliant: Sensitive data must be masked, encrypted, or excluded so agents cannot expose PII.
- Resilient: Agent traffic must not degrade production systems; replicas and staging layers are essential.
To build trust in AI initiatives, enterprise leaders need solutions that combine agentic frameworks that are reliable and grounded with data access patterns that include masking, protection, and in-flight de-risking, so it lands in its destination in a clean, AI-ready format. Only with both these components can digital workers meet the needs of modern enterprises.
Agentic AI in Action
Here are a few examples of organizations that have managed to deploy reliable, trustworthy digital agents that combine trustworthiness and timely, accurate data for real world success.
How UPS protects packages
UPS embraced agentic AI to optimize one of the world’s most complex logistics networks. By unifying real-time fleet, package, and customer data, UPS empowers its AI assistant to recommend optimal routes, anticipate bottlenecks, and cut operational waste. The result is faster deliveries, lower fuel consumption, and significant cost savings at scale. This shift drives efficiency while strengthening trust in UPS’s ability to deliver reliably for its customers.
How a leading clinical research firm accelerates innovation
One of the world’s leading global clinical research organizations relies on agentic AI to accelerate drug development and trial management. By streaming operational and clinical data into Databricks, they enable AI systems to run simulations, forecast trial outcomes, and spot risks earlier in the process. This has shortened study timelines while ensuring compliance with strict regulatory frameworks. The outcome is a more agile, data-driven R&D operation that improves patient outcomes and speeds life-saving treatments to market.
How Cisco navigates supply chain complexity
Cisco has reimagined supply chain forecasting with AI agents that can think and act like seasoned analysts. By embedding causal reasoning into agentic workflows, Cisco’s data science team is scaling demand forecasting across 10,000+ products, 10 business units, and a multi-billion-dollar global supply chain. These agents can analyze, explain, and deliver forecasts with business-ready narratives that build trust across technical and non-technical stakeholders. The result is faster model development, broader insight coverage, and a more resilient forecasting process that helps Cisco navigate global complexity with confidence.
Ready to Operationalize Agentic AI?
Leading enterprises are proving that agentic AI can scale when it’s built on real-time, trusted data and causal reasoning. Striim and causaLens together provide the foundation and intelligence to make this possible: Striim streams, transforms, and governs enterprise data in real time, while causaLens agents apply proven AI workers to deliver safe, explainable outcomes.
If you’re ready to move beyond pilots and put agentic AI to work in your business, connect with us and causaLens to learn more.
