In 2018, I gave my first conference talk on AI to the Azure Montréal user group — a snapshot of a moment before the generative AI wave reshaped everything.
Looking back from 2026, much of what I predicted about investment and democratization came true — but the how changed dramatically with large language models (LLMs), copilots, and tools that didn't exist when I stood at that podium.
This post preserves that moment. For how I advise organizations today, see progressive AI adoption for SMBs.
At a glance
- 2018 marked a burst in AI investment — especially in Montréal's research ecosystem
- Microsoft Cognitive Services made AI integration accessible with minimal code — a preview of democratization
- The talk predicted broader AI adoption; LLMs (large language models) accelerated that timeline beyond what most expected
- The human-first lesson holds: start with concrete use cases, not hype cycles
Key takeaways from the 2018 talk
- 2018 would show a burst in AI investment — especially for Montréal. The ecosystem delivered.
- Integrating AI in applications was becoming easy — often with only a few clicks using Microsoft Cognitive Services (vision, speech, language APIs).
- 2018 marked the beginning of AI democratization — moving AI from research labs toward everyday applications.
What changed since 2018
| Then (2018) | Now (2026) |
|---|---|
| "How do we integrate AI?" | "How do we adopt without chaos?" |
| Cognitive Services APIs for specific tasks | LLMs (large language models), agents, and RAG (retrieval-augmented generation) |
| Custom models required more ML expertise | Fine-tuning and RAG accessible to smaller teams |
| AI projects often R&D-led | AI pilots start from operations pain points |
| Democratization via cloud APIs | Democratization via chat interfaces and embedded assistants |
The through-line in my work hasn't changed: progressive adoption, human validation, operations first. The tools got faster; the discipline didn't get optional.
In 2018, the question was "How do we integrate AI?" In 2026, it's "How do we adopt without chaos?" That's the shift my consulting work reflects — fewer impressive demos, more measured pilots anchored in real workflows.
Where you are
You're in Perspectives, part 3 of 4. Previous: 25 years in tech. Next: Microsoft AI research labs (BBC) — where the hype came from, and what still holds.
Exploring AI for your organization in 2026? Let's talk about one concrete use case — not a replay of 2018 hype.