Is Your AI Stuck in First Gear?

Your unique data, embracing the learning curve

Jun 03, 2025

"Effective AI isn't just about algorithms; it's about quality information and smart human partnership."— Nadina D. Lisbon

Hello Sip Savants! 👋🏾

AI is a hot topic, and many are exploring its potential. But as people are discovering, just using a standard AI tool doesn't always bring the improvements you hope for. In fact, surveys show many leaders are concerned that poor data quality can hold back their AI efforts. So, what’s the key to making AI truly useful? It often comes down to the quality of your company’s own information and a clear understanding of what AI can realistically do.

3 Tech Bites

📄 Your Company's Everyday Info
- Think about all the information your company creates and uses daily—things like emails, contracts, meeting notes, and various digital files. This is the key to making AI tools truly knowledgeable about your specific business. When AI learns from your company's unique information, it understands your products and services better, is less likely to invent details, and can bring genuine financial benefits.
🔗 Smart Systems
- There’s a common way to help advanced AI setups use your company's specific documents and information to give much better and more relevant answers. It's a practical approach to connect the AI's general capabilities with your team's internal expertise, especially since trying to feed large volumes of documents directly into an AI all at once isn't always feasible or cost-effective.
❓ It's Okay for AI to Be Unsure
- AI can sound very confident even when it's working with incomplete information or essentially guessing. This can be risky, particularly for important tasks like research for new medicines or ensuring safety in areas like self-driving car technology. New tools and methods are being developed to help AI setups indicate how sure (or unsure) they are about the answers they provide. This kind of openness helps people spot unreliable information before it leads to bigger problems.

5-Minute Strategy

🧠 AI Data Reality Check
- Pinpoint One AI Use Case
  - Select a single, specific business problem AI could solve.
- Identify Essential Data
  - List the exact internal documents (contracts, logs, reports, etc.) this AI application needs.
- Rate Data Quality
  - Accuracy & Currency: Is it correct and current? (Yes/No/Partially)
  - Completeness: Are there critical gaps? (Yes/No)
  - Clarity: Is it clearly written or confusing? (Clear/Confusing)
- Assess Data Accessibility
  - Are these documents centrally stored and easy to find, or scattered and hard to access? (Easy/Hard)
This check quickly reveals if your data is a solid foundation or needs significant work before AI deployment.

1 Big Idea

💡 AI Works Best with People: It's a Team Effort, Not Just Tech
- As AI becomes a bigger part of our work, it’s tempting to see it as some kind of all-knowing digital brain. But AI gives the best and most useful results when it works with people, especially when it's using your company’s unique information and experience. Just turning on an AI and hoping for the best doesn’t usually lead to great outcomes. A lot of the actual work in an AI project – perhaps up to 80% of the time – is spent getting the data ready and making sure it's right. This isn't just a technical job; it often highlights where company processes and human systems could be improved.
- So, instead of just asking, "What impressive things can AI do for us?", we should also be asking, "How can AI help our employees do their jobs better and more easily, and what changes do we, as people, need to make so this partnership really works?" This means more than just tidying up digital documents. It means helping everyone who creates or uses information understand how important their role is in making sure that information is good quality, clearly written, and useful. The real goal isn’t just "more advanced AI," but more effective teamwork between people and AI, building trust and understanding along the way. How can we best prepare and empower our teams to be active guides in how AI uses information, rather than just being passive receivers of what the AI produces?

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