AI Over 40 Series - Week 7: The Multi-AI Approach

How a forecasting nightmare led to my biggest AI breakthrough

In Week 1, I shared how a frustrating forecasting exercise pushed me onto this AI journey. This week, I’ve come full circle: the same challenge showed me why relying on a single AI platform is too limiting—and how a multi-AI approach can transform not only forecasting but any complex business problem.

The Forecasting Challenge

When ArcherPoint became part of a larger, private equity–backed organization, I faced monthly forecasting responsibilities that felt impossible to tackle. Each month was like shaking a Magic 8-ball: equal parts guesswork and hope.

I knew there were advanced methods—regression analysis, data modeling—that could ground forecasts in patterns, but I wasn’t a data scientist. I don’t code in Python or R. I needed a bridge between financial knowledge and data science expertise. That bridge turned out to be AI.

The Problem with One-Platform Thinking

Early on, I leaned heavily on Microsoft Copilot. Useful, yes—but limited. Each AI platform has strengths and weaknesses, and no single tool could handle the full scope of forecasting.

So, I brought my whole AI “advisory board” to the problem: Claude, ChatGPT, Gemini, and Copilot. What emerged was a playbook for how different AIs can complement one another.

• Initial Analysis: I asked each platform to interpret my messy, non-technical explanation of what I needed. All agreed: I had to normalize my data.
• Data Transformation: ChatGPT outperformed the others when it came to wrangling my Excel reports into usable formats.
• Strategic Framing: Claude provided the clearest big-picture view of the challenge and maturity path for forecasting.
• Blind Spots: As AI worked through my files, it uncovered hidden rows and columns I hadn’t noticed—errors that would have undermined any analysis.

Together, these platforms gave me clarity I never would have achieved alone.

Bridging Finance and Data Science

The process highlighted a larger issue: finance and data science often talk past each other. Finance leaders know the business, while data scientists know the methods. But translating between the two worlds is difficult.

AI became my translator. It helped me shape what I now call an AI Ready Data Model™—a framework that structures financial data in ways that data science tools can use effectively.

Pushing Beyond Comfort Zones

Even with the right platforms, limitations showed up. Claude was strong on strategy but weak on output files. ChatGPT worked well with data, but not perfectly.

So, I did something I never thought I’d do: I installed Python. Guided by AI-generated scripts and step-by-step instructions, I ran models that transformed my data. I’m still not a data scientist, but now I have a practical way to access advanced tools without waiting for outside help.

The Transformation

The difference between “Magic 8-ball” forecasting and data-driven forecasting is night and day. With my multi-AI approach, I now have:

• Normalized financial data that surfaces patterns.
• Regression models that ground forecasts in math.
• A framework for repeatable analysis.
• A new capability—Python—that extends what I can do on my own.

Most importantly, I have forecasts I can trust.

Why This Matters

The lesson is bigger than forecasting. It’s about how to use AI effectively. Different platforms excel at different tasks—analysis, data manipulation, strategic framing, or code generation. Treating them like interchangeable tools misses the point.

Think of it like hiring consultants: you wouldn’t expect one person to be your lawyer, accountant, and marketing strategist. Why expect one AI to solve every problem?

Week 7 Challenge: Build Your Multi-AI Approach

1. Pick a complex problem that has multiple components.
2. Break it down into the types of expertise it requires.
3. Assign AI specialists—use Claude for strategy, ChatGPT for data transformation, or whatever mix works best.
4. Cross-pollinate outputs—feed insights from one AI into another.
5. Document your learnings so you build a “directory” of which AI does what best.
6. Push your comfort zone. Try a step (like Python for me) that stretches your toolkit.

The Real Revolution

My breakthrough didn’t come from finding the “best” AI. It came from combining platforms strategically—treating them like a team of advisors with different strengths.

That’s the real revolution: moving from single-tool thinking to a multi-AI mindset. Complex business problems demand multiple perspectives, and now, with AI, we can assemble an advisory board instantly.

Your Magic 8-ball moments don’t need to stay that way. Stop expecting one platform to be everything. Start building your own AI team.

This post is part of our “AI Over 40” series. It first appeared on LinkedIn: AI for the Over 40 – Week 7: The Multi-AI Approach.

Next Week: How to build your personal AI User Manual – the secret to making any AI platform work better for you, faster.

Read more AI and Copilot blogs.

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