Most companies chasing AI value are hitting a wall. Their data doesn’t seem clean enough, their ERP isn’t integrated enough, and their systems aren’t quite “ready.” In the interim, they form AI committees, launch data-cleaning projects, and wait for the “right” foundation before getting started.
That approach misses a key opportunity.
The real inefficiencies in most organizations don’t live inside systems. They happen outside of them in long meetings, spreadsheets passed around via email, policy documents buried in SharePoint, and in the endless back-and-forth required to answer basic questions like “does this project fit our standards?” or “is this purchase allowed under our cybersecurity policy?”
Organizations don’t have to wait for perfect system data. They can start applying AI to inefficiencies that exist outside of systems.
When companies focus exclusively on structured data inside core systems, they’re focusing on the hardest problem first. In reality, AI agents don’t exclusively work inside systems. They can provide value in other areas using the unstructured data and tribal knowledge that already exists.
Think about it: a large portion of daily work involves answering questions, checking compliance, evaluating initiatives, or routing approvals. These activities rely on policies, procedures, past decisions, and institutional knowledge, not on transactional data sitting in an ERP. What if those activities could be streamlined?
Here are a few areas to start:
Create process decompositions. Use AI to map out the company’s actual business processes by industry and function. Tie those processes to departments, roles, and existing policies. This creates a living framework that AI agents can reference.
Connect policies and procedures. Most organizations already have hundreds of policies and procedures stored in SharePoint or other repositories. AI can index and link these documents to the process maps to create a searchable knowledge base.
Build decision-support agents. Once the knowledge base exists, agents can answer practical questions without touching core systems. Does this initiative align with our internal audit standards? Does this software purchase meet our cybersecurity requirements? How does this decision impact our COSO framework? These agents don’t need ERP access. They just need access to the company’s documented standards, policies, and leading practices.
Generate leading practices and ROI frameworks. AI can analyze existing documents and industry benchmarks to propose leading practices the company should consider. These can then serve as the foundation for building business cases and calculating value on new initiatives.
Most organizational friction happens in the space between meetings, approvals, and manual workflows. By focusing AI efforts here first, companies can deliver quick wins, build organizational confidence in AI, create some momentum for more sophisticated system-integration work down the road.
Using the “our data isn’t ready” excuse just prevents organizations from starting at all. That’s a mistake. Perfect system data isn’t a prerequisite for AI progress. Start with the manual, knowledge-intensive workflows that happen outside systems, and start gaining traction with AI before more time passes.
Trenegy helps organizations cut through the noise around AI and focus on practical ways to drive real value. If you’re ready to start progressing more with AI, email us atinfo@trenegy.com.
Trenegy is a non-traditional consulting firm based in Houston, Texas.
