Is It Digital Transformation 3.0 or AI Transformation 1.0?
Summary
The article argues that AI marks a new era distinct from previous waves of digital transformation. Where Digital Transformation 1.0 focused on internal efficiency and 2.0 on digitising customer interactions, AI (Digital 3.0 / AI Transformation 1.0) shifts businesses toward human-like engagement: words become primary data, LLMs, RAG and AI agents form a new infrastructure, and companies must move from managing preferences to orchestrating intent and context.
Key Points
- AI represents a fundamental shift, not merely the next phase of digital transformation.
- Natural language becomes the primary data source — “words as data” — enabling direct customer dialogue instead of proxy metrics.
- Legacy martech stacks and outdated mindsets remain major barriers to effective AI adoption.
- New architecture relies on LLMs, retrieval-augmented generation (RAG) and AI agents, creating autonomous ecosystems.
- Organisational roles change: from operators and plumbers to choreographers and moderators of AI-driven experiences.
Content Summary
The article outlines three eras: Digital Transformation 1.0 (1990s, internal systems and efficiency), 2.0 (2000s, SaaS and customer-facing platforms), and what the author calls Digital Transformation 3.0 or AI Transformation 1.0 (>2020s). In 3.0, the emphasis is on managing intent and context through language-aware systems rather than adding more discrete tools.
It highlights five dimensions of change — purpose, process, architecture, operating model and technology — showing how each evolves from closed, predictable systems through open cloud ecosystems to autonomous, AI-centred infrastructures. The piece warns that adopting AI successfully requires cleaning data, retraining mindsets, and shifting KPIs away from adoption metrics toward outcomes driven by intent and continuous learning.
Context and Relevance
This matters because many organisations are already investing in generative AI without recognising the deeper operating and cultural shifts required. For marketing and CX leaders, the article reframes AI as infrastructure — not just a feature set — and suggests leaders rethink measurement, data hygiene and team roles to avoid repeating past mistakes.
Author style (punchy): Frans Riemersma cuts through buzz to say: this isn’t an incremental upgrade — it’s a new operating model. Read the full piece if you want to understand why your martech stack and KPIs might be the very thing stopping your AI progress.
Why should I read this?
Quick take: if your organisation is dabbling in GenAI but still measuring success by logins, feature adoption or legacy cost-savings, this article will smack you with reality (nicely). It tells you what actually changes — people, data, and mindset — and what to prioritise so AI doesn’t just become another expensive toy.