Whitepapers & eBooks
Jun 10, 2026
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With 80% of AI projects failing to return business value, your team has an urgent opportunity to overcome the underlying challenges and build an AI foundation. This whitepaper explores the inherent problems with applying standard operating procedures to agentic operations and what to do instead to build AI agents that actually work in production.
Too often, organizations build AI workflows that automate work the way it's supposed to be done instead of how it actually happens. In reality, human operators use undocumented workarounds, especially when dealing with edge cases and exceptions. Without this operational context or the ability to audit AI decision-making, these organizations won’t be able to build AI agents that can handle real workloads reliably and efficiently.
That means CIOs, CTOs, COOs, and heads of transformation need to define the requirements for their AI foundation long before AI pilots are rolled out.
This practical guide outlines how missing context, governance, and measurement capabilities are setting up your AI initiatives for failure, as well as how you can start investigating the gaps in your AI foundation with Skan AI's BASE framework: a systematic approach to rolling out agentic operations that deliver returns on investment in weeks instead of years.
Key Takeaways:
Organizations need to fill context, governance, and measurement gaps to build and scale agentic AI successfully
Agentic AI can't succeed when it's built on the wrong foundation. When organizations rely on standard operating procedures and even event logs for operational context, they’re supplying agents with an incomplete picture of their operations. This context gap, combined with insufficient governance and measurement capabilities, prevents your team from building agents that can handle the full range of situations they encounter in production.
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Provide AI agents with complete operational context based on observed reality
Agents built on documentation and systems fail to capture the operational complexity of real-world work. Instead, agents should be trained on observed work so they’re able to handle edge cases and exceptions. That’s why establishing an operational baseline before exploring and deploying agentic pilots is essential. -
Ensure every AI-driven outcome is explainable, auditable, and traceable
Understanding how work actually gets done isn’t enough. Organizations should be continuously fixing and streamlining their processes, even after they’re automated with AI. Having work observation and governance capabilities built into your AI foundation ensures that your team is able to take and event-to-end view of agentic operations and optimize them over time. -
Define AI performance frameworks to prove impact and justify further investment
Deploying AI agents in production and achieving high internal adoption rates doesn’t mean an AI initiative is effective. To ensure your AI projects are not part of the 80% that fail to deliver business value, you need to clearly define what success looks like. That means building a framework grounded in metrics that show how your AI strategy and rollout have cut costs, increased efficiency, accelerated innovation, and driven revenue.
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