What data do we already have that, if connected to AI, would make it dramatically more useful?

Every dataset, data dictionary, and semantic mapping you build is context that makes AI smarter about your business. The most common missed opportunity is data that exists but is not connected to the AI tools that need it.

When we ask AI for help today, does it know anything about our specific business situation?

Without context, AI gives textbook answers. With your financial data, industry benchmarks, operational history, and org structure, the same question gets a specific, actionable answer grounded in your reality.

What would we need to document or structure to give AI the context a new employee would need to be effective?

The onboarding test is a good proxy — if a new employee needs six months to become effective, that six months of absorbed context is what AI also needs. Structuring it benefits both human onboarding and AI performance.

Using AI in Your Work

Why context determines AI quality

By Mark Ziler · Last updated 2026-04-05

Context is everything the AI knows about your situation when it tries to help you. Without context, asking AI "what should we do about our margin problem?" gets you a generic textbook answer. With context — your financial data, your industry benchmarks, your operational history, your org structure — the same question gets a specific, actionable answer grounded in your reality. This is why the data layer matters so much. Every dataset, every data dictionary, every semantic mapping you build is context that makes AI smarter about your business. The more context, the better the answer. The better the data foundation, the more context the AI has.

Go deeper

You ask an AI: 'How should we handle our margin problem?' It gives you a five-paragraph essay about cost reduction strategies pulled from an MBA textbook. Useless. Now imagine the AI already knows: you run a 90-location behavioral health network, your margin compression is driven by a 14% increase in per-diem staff costs since Q1, your best-performing locations maintain margin by keeping per-diem below 20% of total labor hours, and your three worst locations are all in markets where hiring full-time clinicians takes 90+ days. Same question, radically different answer — one that actually tells you which locations to focus on and what lever to pull.

The trap most companies fall into is keeping their data in silos that the AI cannot reach. The financial data is in one system, the HR data in another, the operational data in a third. The AI can only be as contextually aware as the data it can access. If it only sees financials, it gives you a finance answer. If it sees financials, staffing, and operations together, it gives you the cross-functional insight that actually explains what is happening. Context is not just a nice-to-have — it is the difference between generic advice and specific intelligence.

Questions to ask

How many of our data sources are currently connected in a way that AI can access them together?
When we ask a strategic question, does the AI have visibility into financial, operational, and HR data simultaneously?
What is the most important cross-system insight we wish we had — and what data connections would it require?