On January 22nd 2026, I was lucky enough to attend the very first Data & IA Camp Lyon, brilliantly co-organized by Mehdi Oudjida and Lionel Cherpin 📊, two very active members of the AADF (the French-speaking Digital Analysts Association). The AADF also organizes MeasureCamp Paris every year, as well as regular meetups for the French-speaking digital analytics community across Europe.
It was a fantastic way to start the year, with one central question: Which AI tools will Digital Analysts really need in 2026?
The discussions were rich, pragmatic, and sometimes challenging – and you’ll find a summary of those exchanges below.
Workshop during the Data & IA Camp in Lyon
This event drew strong inspiration from the “unconference” format popularized by MeasureCamps around the world. It took place as an evening meetup – from 7:00 pm to 10:30 pm – with four session slots. I’ve written a full recap in French in this article.
I led a session whose goal was not to present a “miracle tool”, but to provide a very concrete state of play:
- which AI tools are already being used today by data teams,
- at which stages of the job,
- with what real benefits… and what limitations.
🔄 The Digital Analyst role: transformation, not disappearance
The discussions I’ve been facilitating over the past few months (Paris, London, Brussels) all lead to the same conclusion:
👉 the Digital Analyst role is not disappearing,
👉 but it is changing profoundly.
The role is evolving towards:
- more steering and orchestration,
- more supervision of AI agents,
- more governance, data quality, and critical thinking.
AI is not here to replace the analyst, but to augment them.
📊 Everyone uses AI… but still rarely at the core of data tasks
In the room, 100% of participants already use an AI tool at least once a day in their data tasks (tracking, reporting, analysis, QA).
Tools mentioned spontaneously:
- ChatGPT
- Claude
- Gemini
- Copilot
- Cursor / Claude Code
But in practice:
- for most teams, less than 25% of data tasks are actually assisted by AI,
- usage often remains ad-hoc or exploratory.
🧩 The framework: Collect → Report → Analyze → Optimize
To structure the discussion, I relied on the classic Digital Analytics and Optimization lifecycle:
- Collection
- Reporting
- Analysis
- Optimization
At each step, we identified the AI tools actually used and their real added value.
🧱 Collection: KPIs, tracking plan, code, QA
On the collection side, AI is already very useful across four key areas:
🔹 KPI definition
LLMs (ChatGPT, Claude, Gemini) are often used to:
- suggest KPI ideas,
- challenge an existing framework,
- accelerate business scoping (e.g. e-commerce, retail, food…).
Tools mentioned:
- ChatGPT / Claude / Gemini
- Jetmetrics (e-commerce KPI repositories)
🔹 Tracking plan & naming conventions
LLMs can help generate a first draft of a tracking plan, but: ⚠️ human validation remains essential.
A specialized tool mentioned: Avo for nomenclature management, cross-team consistency (web/app), tracking plan generation and validation.
🔹 Code (JS / SQL / tracking)
On the technical side, gains are often immediate:
- JavaScript code generation,
- SQL queries,
- GTM configurations.
Tools mentioned:
👉 The gains are especially significant at scale (e.g. many GTM containers).
🔹 Quality Assurance (QA)
For QA, several tools can automatically detect tracking anomalies or risks (e.g. sensitive data).
Tools mentioned:
Goal: detect early rather than fix too late.
📈 Reporting: explore, narrate, automate
In reporting, AI is used at several levels:
🔹 Data exploration
AI mainly helps speed up initial exploration and structure a first version of a report more quickly.
🔹 Narrative & writing
LLMs are widely used to:
- turn numbers into text,
- structure analytical commentary,
- produce a first draft of deliverables.
Tools mentioned:
🔹 Alerts & insights
For alerting and anomaly detection:
- Google Analytics
- Adobe Analytics
- Piano Analytics
We are gradually moving from simple thresholds to automated insights.
🔹 Automation
To industrialize recurring tasks:
Typical use case: ➡️ retrieve analytics data, ➡️ send it to an LLM for a first analysis, ➡️ generate and distribute a report or recommendations.
🧠 Analysis: intelligent assistance, not full delegation
In analysis, two approaches coexist:
🔹 Native analysis within tools
- Google Analytics
- Piano Analytics
- Microsoft Clarity
- Contentsquare
These tools increasingly embed AI to detect patterns or frictions.
🔹 “DIY” analysis with LLMs
(Anonymized) data exports sent to:
👉 AI helps explore, formulate hypotheses, and structure thinking… 👉 but it does not replace analytical reasoning.
🎯 Optimization: where AI is already very mature
In optimization, AI is sometimes invisible… but everywhere:
🔹 Media & bidding
🔹 Testing
🔹 Personalization & search
🔹 Predictive
- Google Analytics (predictive metrics)
- Google BigQuery + Gemini
🧪 Always Be Testing: the real key skill
My conclusion is deliberately simple:
👉 test continuously,
👉 compare multiple LLMs,
👉 understand their strengths and limitations.
The Digital Analyst of tomorrow is less a “producer” and more a conductor of hybrid intelligence (human + artificial).
And in a world where everything evolves every 2–3 months, not testing already means falling behind.
A huge thank you to all the participants for making this session so interactive, engaging, and thought-provoking, as well as to ⚡️Laure de La Faye for the pictures!
