Few patterns I see for 2026: ❌ AI stops being exciting ⚡️And starts being useful It moves from chat interfaces into infrastructure, supports decisions (not replaces them), and prioritizes reliability, cost, and accountability.

Working in #AI sometimes feels like playing chess during an earthquake. In the past 2 years alone, we’ve had to: 🧨 Rethink our roadmap 🧨 Pivot from general KM to engineering tools 🧨 Rebuild around #LLMs & digital twins 🧨 Redefine “value” — again and again

One of the biggest misconceptions in AI adoption: “Start with the most technical team. They’re the most ready. Not always. Some of the biggest wins I’ve seen came from non-technical teams… while technical ones stalled or resisted.

The teams most ready for AI: ✅ Feel the pain (manual, repetitive work) ✅ Open to experimentation ✅ Have clear but flexible use cases ✅ Willing to change workflows Tech skills help, but mindset matters more.

For years, the “standard” way to make documents usable has been the same: 1️⃣ Split text into snippets 2️⃣ Embed them 3️⃣ Pray your assistant can stitch it all back together We tried that too. And we kept hitting the same wall: snippets don’t understand relationships.

That’s why last week we open-sourced Graph Builder — the part of ContextClue that transforms messy documents into structured, queryable knowledge graphs: github.com/Addepto/graph_bui…

AI moves fast. The only constant is change. We built ContextClue at @addepto as a knowledge management tool. Then the LLM boom hit. Overnight, semantic search was everywhere - for free. Our product felt obsolete. We had 2 options: push forward or pivot fast.

We chose to pivot. From general-purpose KM → semantic search for engineering: digital twins, system structures, virtual commissioning. That meant a new roadmap, new domain, and rebuilding sales from scratch. Painful, but it kept us alive.