📚 A Framework to Decide which AI Tools to Buy vs Build

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Hello AI Enthusiast,

Today we have a different kind of newsletter. Rather than exploring a practical "how-to", we're sharing a strategic piece written by our founder Gianluca Mauro on how to define your organization's AI tool stack.

If you're a leader in an organization and find this framework helpful and would like some help applying it to your own case, reply to this email and we'll be in touch to schedule a chat!

The Real Question Nobody's Asking

How many people have tried to sell you an AI tool this week?

Every company has decided they need AI, but what that actually means isn't often clear. Here's what nobody talks about: which tools to buy and what to build?

Your Three Options

1. Generic AI tools (ChatGPT, Claude, Gemini, Copilot) - Swiss army knives that are super flexible but leave all responsibility to users to figure out what to use them for and how.

2. Specialized AI tools - Just like you'd buy a CRM instead of managing sales in Google Sheets. Examples: Legora for lawyers, Jasper for marketing, Epiphany for instructional design.

3. Custom tools - AI agents you build yourself. Modern no-code tools like Zapier, Make and n8n have lowered the bar by 100x.

The Framework: Two Axes That Change Everything

Gianluca developed a simple framework to guide these decisions. For each use case in your organization, rank it across two axes:

• Business specificity: Is this task unique to your company, or something every company does?

• Value: Is this task critical to business performance, or just a small productivity boost?

When you plot your use cases on these axes, you get four quadrants with clear recommendations:

Low specificity / Low value → Use generic AI tools like ChatGPT. Think summarizing meeting notes or auto-formatting reports.

Low specificity / High value → Buy specialized tools. Important tasks common across industries justify investing in purpose-built solutions (contract review, marketing, copywriting).

High specificity / Low value → Build as an experimentation sandbox. Perfect for your team to learn no-code and build internal AI confidence without much risk.

High specificity / High value → Build! These areas are too important and too unique to rely on off-the-shelf tools. This is where competitive advantage lives.

A Real Example

Stroeer, a German advertising company with 13,000+ employees, needed to give design feedback to customers for billboards. This task had extremely high value for their business and was very specific - they had their own "10 golden rules for effective designs". No off-the-shelf solution existed, so they built a custom AI prototype that became a successful product after customer validation.

If you're navigating AI strategy decisions for your organization and want help applying this framework to your specific situation, just reply to this email. We'd be happy to schedule a conversation.

We'll be back with more AI insights next week!

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We'll be back with more AI tips soon!