Organizational approach

When implementing AI in your organization, one of the first decisions is choosing how to structure your adoption journey. The approach you select will shape everything from team composition to success metrics and workshop structure.

Overview

There are two primary organizational approaches to AI adoption with Intric:

1. Use-case-driven approach: Start with specific problems or processes, then recruit people to solve them
2. Person-driven approach: Identify AI champions first, then let them discover high-value opportunities

Neither approach is inherently better—the right choice depends on your organization’s current situation, goals, and AI maturity level.

The Use-Case-Driven Approach

In a use-case-driven approach, you begin by identifying concrete processes, pain points, or automation opportunities within your organization. Once these specific use cases are prioritized, you recruit and train creators specifically to address these challenges.

When It’s Suitable

This approach works best when your organization:

• Has clearly defined priorities: You’ve already identified specific processes that need improvement or automation
• Faces urgent operational needs: There’s pressure to deliver measurable results quickly on known problems
• Operates in a structured environment: Your organization prefers planned, top-down initiatives with clear scopes
• Has limited AI experience: Starting with a focused, concrete goal can be less overwhelming than open-ended exploration

Pros and Cons

Advantages	Considerations
Clear success metrics: You know exactly what problem you’re solving and can measure results directly	May miss hidden opportunities: By focusing on predetermined cases, you might overlook innovative applications
Easier stakeholder buy-in: Leadership can see the direct connection between AI investment and business outcomes	Requires upfront analysis: You need to invest time identifying and validating use cases before implementation begins
Focused resource allocation: Training and effort are directed toward specific, predetermined goals	Can limit creativity: Creators may feel constrained to work only on assigned problems
Lower risk: Starting with well-understood processes reduces uncertainty	Slower organizational learning: AI competence develops in isolated pockets rather than broadly

Example in Practice

A municipality knows their procurement team spends hours manually reviewing supplier contracts against invoices. They identify this as a priority use case, recruit creators from the procurement department, train them on Intric, and task them with building an assistant to automate contract verification.

The Person-Driven Approach

In a person-driven approach, you appoint “AI Ambassadors” or creators from various departments without predetermined use cases. These individuals are given training, resources, and the freedom to identify high-value opportunities from within their own domains.

When It’s Suitable

This approach works best when your organization:

• Seeks innovation and discovery: You want to explore AI’s potential without limiting it to known problems
• Needs broader AI literacy: Raising general AI competence across the organization is a priority
• Has uncertainty about priorities: You’re not yet sure where AI will deliver the most value
• Values bottom-up innovation: Your culture supports employee-driven initiatives and experimentation
• Has time to explore: You can afford a longer discovery phase before requiring specific ROI

Pros and Cons

Advantages	Considerations
Uncovers unexpected opportunities: Ambassadors often identify valuable applications that leadership hadn’t considered	Less predictable outcomes: You can’t guarantee which use cases will emerge or how quickly
Builds organizational capacity: AI competence spreads more broadly and organically	Requires strong support structures: Ambassadors need guidance, community, and regular touchpoints to succeed
Increases engagement: Employees feel empowered when they can solve problems they care about	Harder to measure initially: Success metrics may be fuzzy until concrete applications emerge
Creates advocates: People who discover value themselves become enthusiastic champions	Demands patience: Leadership must accept an exploration phase before seeing clear ROI
Flexibility: The organization can pivot quickly as high-value use cases emerge

Example in Practice

An organization selects ambassadors from HR, communications, finance, and operations—each with curiosity about AI but no specific assignment. After training, the HR ambassador builds a policy Q&A assistant, the communications lead creates a template generator, and the finance ambassador develops a report summarizer. Each solves a problem they intimately understood but leadership hadn’t explicitly prioritized.

Making Your Choice

Before selecting your approach, discuss these questions with your team:

Do we have clearly prioritized processes that need improvement?

• ✓ Yes → Use-case-driven is more suitable.
• ✗ No → Person-driven allows for discovery.

What’s our timeline for demonstrating value?

• Urgent → Use-case-driven delivers faster on known problems.
• Flexible → Person-driven allows for deeper exploration.

What’s our primary goal?

• Solve specific problems → Use-case-driven.
• Build AI competence → Person-driven.
• Both? → Consider a hybrid approach.

How comfortable are we with uncertainty?

• Low → Use-case-driven provides more control.
• High → Person-driven embraces emergence.

What resources can we dedicate?

• Limited → Use-case-driven focuses effort.
• Substantial → Person-driven benefits from broader investment.

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Remember: The approach you start with isn’t permanent. Many organizations begin with one method and evolve as they learn. The key is to start deliberately, measure consistently, and adjust based on what you discover.