Data team design

How to design the data team for AI-assisted analytics

A data team set up for AI-assisted analytics has two common designs: a lean team that uses an off-the-shelf AI tool, or a full team with an AI engineering role and custom-built tools. Both work well. The right choice depends on your size and how much you want to customize. In both, the data analytics team is the foundation: it cleans the data, defines the metrics and builds the standard tables everyone else uses.

Model A

Lean team with an off-the-shelf AI tool

A smaller team: data engineering, data analytics, and a ready-made AI tool the analysts use.

Lean team: an off-the-shelf AI tool supports data analytics Data engineering sends raw data to the data analytics team in the center. An off-the-shelf AI tool with a built-in chatbot supports the analytics team. The analytics team feeds the tool and serves data science. The tool includes a built-in chatbot that serves business stakeholders. Questions and data issues flow back to analytics. New questions and data issues flow back Raw data Data engineering Off-the-shelfAI tool+ chatbot Data analytics Cleans the data Defines the metrics Builds the tables Checks quality Data science Business stakeholders The same foundation, a smaller team.
What it takesNo AI engineer. Analysts use a ready-made AI tool, with its built-in chatbot for the business, and keep the tables and checks in order.
What you getA faster, cheaper start and an excellent working model of AI-assisted analytics.
Watch out for
  • Less customization. Many functions out of the box, but limited ability to shape them to your data and way of working.
  • Vendor lock-in. Once your team depends on one tool, switching is costly, and price increases are hard to resist.
  • A young technology. Expect some small kinks, and uneven results unless context and quality checks are strong.
Model B

Full AI-assisted team

An AI engineering role builds custom AI tools around the analytics team's work, often together with data science.

Full AI-assisted team: AI engineering sits next to data science, and supports data analytics and the chatbot Data engineering sends raw data to the data analytics team in the center. AI engineering and data science sit side by side, because they can be one team and their functions can overlap. AI engineering supports data analytics and the chatbot. The analytics team's standard tables go to data science and to the chatbot, which serves business stakeholders. Questions and data issues flow back to analytics. New questions and data issues flow back Data engineering Raw data Data analytics Cleans the data Defines the metrics Builds the tables Checks quality Chatbot Self-serve Business stakeholders Data science AI engineering One definition per metric. Same question, same answer.
What it takesA small AI engineering role (or a part-time one). In the diagram it sits next to data science because the two can be one team, and their functions can overlap.
What you getTools built around your data and your way of working, including your own chatbot.
Best forLarger or more complex data estates, and teams that want to go furthest.

Lean team or full team: how they compare

Model A: lean teamModel B: full team
RolesData engineering, data analyticsData engineering, data analytics, AI engineering (can be combined with data science)
AI toolingOff the shelfCustom-built around your data
Business accessChatbot built into the toolSelf-serve chatbot you build
Start-up effortLowerHigher
CustomizationLimited: many functions, less tailoringHigh: built around your data
Vendor dependenceLock-in risk, with possible price increasesLow: you own the setup
MaturityNewer technology: expect small kinksDepends on what you build
Common to bothA data analytics team that cleans the data, defines the metrics and builds standard tables, with quality checks

Both models follow the same ten-stage Roadmap, and the AI Foundation Track and AI Implementation Program support either one.

Without this foundation: every team builds its own tables, the numbers disagree, and AI just produces wrong answers faster. That is why the Roadmap starts with governance and documentation, before any AI tool.

Which model fits your team?

A 2-minute self-check against the 10 stages of the Roadmap, or a free 30-minute call.

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