How StereoLOGIC Accelerates and Secures Agentic AI Success

Poor Data Quality and Governance: Agentic AI relies on high-quality, structured data to make autonomous decisions. Yet, many enterprises struggle with ungoverned or unstructured data in legacy systems, spreadsheets, or hidden applications, leading to unreliable AI outputs.

Overcoming Data Quality Issues Agentic AI thrives on clean, contextual data. StereoLOGIC’s Automated Data Discovery identifies and organizes both structured and unstructured data across applications, ensuring AI systems have the reliable inputs they need.

This capability aligns with Gartner’s 2024 emphasis on continuous data governance as a prerequisite for AI readiness (Gartner, Quick Answer: What Makes Data AI-Ready?).

StereoLOGIC Case Study. Canadian Specialty Insurer:
Saved $3M in two months by eliminating 49 FTE worth of manual inefficient tasks, such as collaboration via email, unstructured data management in office apps, and report drafting.

Lack of Operational Visibility: Without a clear understanding of "as-is" processes - what employees do, how long tasks take, and where inefficiencies lie - AI deployments often target the wrong or misunderstood processes, resulting in low ROI or outright failure.

Enhancing Operational Visibility StereoLOGIC’s ADTO provides a 360-degree view of processes, revealing what employees do, how long the tasks take, and where delays or errors occur. This eliminates the guesswork that causes 70% of transformations to fail (McKinsey).

This visibility helps the companies to navigate agentic AI implementations and target the high-value processes, avoiding wasted automation efforts.

StereoLOGIC Case Study. Major Canadian Bank: Reduced customer service errors by 95% and saved $15M annually by using StereoLOGIC to monitor and refine employee operations at branches nation-wide using a new loan origination platform.

Workflow Integration: Agentic AI requires seamless integration into reimagined workflows, not just existing ones. Misalignment can cause process breakdowns, with 90% of high-impact AI use cases stuck in pilot mode (McKinsey, June 2025).

Streamlining Workflow Integration: By generating detailed process maps and documentation (exportable to Visio/BPMN), StereoLOGIC enables enterprises to redesign workflows for agentic AI integration.

This aligns the re-engineered workflows with the capabilities of AI, reducing the risk of pilot-phase stagnation.

StereoLOGIC Case Study. Global Healthcare Group: Achieved $5.7M in saving within six months by outsourcing low-skill tasks, optimizing nurse staffing, and standardizing the process, with $10M in additional future savings identified.

Employee Trust and Adoption: Resistance to autonomous systems and lack of trust impede the adoption, particularly when employees are unaware of how AI aligns with their daily work.

Boosting Employee Trust and Adoption: StereoLOGIC’s non-intrusive monitoring and clear visualizations (e.g., process maps, time metrics) help employees understand how AI will enhance their work, fostering trust.

Transparent insights bridge the gap between leadership and employees.

StereoLOGIC Case Study. Global Healthcare Group: Achieved $5.7M in saving within six months by outsourcing low-skill tasks, optimizing nurse staffing, and standardizing the process, with $10M in additional future savings identified.

Leadership and Strategy Misalignment: Executives cannot accurately assess the AI usage in the company. Typically, less than half of employees use generative AI and only for 30% of the tasks. Only 20% of executives can accurately gauge employees’ AI usage, leading to strategic missteps (McKinsey, January 2025).

Driving Strategic Alignment: StereoLOGIC’s real-time analytics and process instrumentation empower leaders to prioritize AI use cases with high-ROI.

This data-driven approach ensures leadership decisions align with operational realities, accelerating AI maturity.

StereoLOGIC Case Study. Large Health Insurance Company: Identified a pipeline of improvement opportunities with total savings of $100M by analyzing 2,500 employees across five business units, establishing a scalable process improvement practice.

Uncover Hidden Data and Processes: Automatically records employee activities across all applications, including legacy systems and spreadsheets, generating a Master Data Dictionary that maps data usage, frequency, context and associated processes.

Provide True Operational Visibility: Produces auto-generated process maps, step-by-step operational manuals (SOP), workforce analytics, and detailed reports on time, frequency, errors, and inefficiencies. This, in turn, enables companies to find opportunities for Agentic AI adoption and accurately predict the ROI.

Enable Rapid Deployment of Agentic AI: Requires no desktop installations and delivers results within days, not months, ensuring immediate insights without disrupting employee workflows.

Support Platform-Agnostic Integration: Works with any IT system, making it a universal solution for diverse industries like banking, insurance, healthcare, and manufacturing.