The FAT Lens: Concentric Maturity Model for AI Integration

AI’s transformative power is often likened to earlier step-change innovations: the printing press democratised knowledge, the steam engine industrialised production, the internet connected humanity, and AI is now augmenting human decision-making across every sector.
Yet the speed at which AI capabilities evolve—and their very newness—leave many organisations struggling to integrate them coherently.
Another stumbling block is up-skilling the workforce. Harvard Business School’s Karim Lakhani famously said, “AI won’t replace humans, but humans with AI will replace humans without AI.” The aphorism is compelling, but for most teams, the real question remains: how do we help everyone at the company “know AI”?
We propose the Concentric AI Maturity Model offers a pragmatic roadmap for evolving from simple AI automation to fully agentic orchestration. It guides CTOs, CIOs, and newly minted CAIOs through three concentric “circles,” each representing a broader scope of AI capability and complexity. The model is assessed through the FAT lens—Familiarity, Autonomy, and Trust—which tracks how teams grow in understanding, confidence, and governance as AI takes on a greater role.
The visual model above illustrates the Concentric AI Maturity Model, a framework for how organizations expand their use of AI across three stages—Deterministic Workflows, Hybrid/Constrained Agents, and Fully Autonomous Systems. Each stage radiates outward with increasing capability and autonomy, and is assessed through the FAT lens: Familiarity, Autonomy, and Trust.
Let’s now explore each circle in depth—starting with the Inner Circle, where teams begin their AI journey through structured automation and human-guided workflows.
 

The Inner Circle: Structured AI Automation

This initial phase focuses on building explicit flowcharts where every branch is pre-defined, targeting high-volume, well-understood cases using the Pareto principle. The goal is to establish a solid foundation with no-code enabled workflows that handle the bulk of routine operations.

Familiarity:
This is where the journey of "knowing AI" begins. Teams learn AI model strengths and limitations, when to escalate to humans, and how to co-create automation workflows. By having non-tech and tech teams collaborate on these deterministic flows, everyone develops hands-on experience with AI's capabilities and boundaries. They see firsthand how AI can handle human-dependent simple tasks like ticket classification, data extraction from invoices, or summarizing documents, empowering them to identify new automation opportunities.

Autonomy:
AI autonomy is intentionally constrained to simple human-dependent tasks within larger workflows. The AI is not allowed to branch on its own. This initially limits the scope of workflows that can be automated, but that's by design—organizations need to learn walking before running. If it encounters low confidence or ambiguous inputs, the workflow automatically triggers human review. It’s a powerful assistant, not an agent.

Trust:
Trust is rooted in traceability. Every route in the flow is visible and explainable. Users can confidently explore how a decision was made and why AI deferred to a human. This transparency eliminates black-box anxiety, encouraging further experimentation.

Behind the scenes, the tech team sets up critical infrastructure—APIs, ETL, dashboards, model integrations, and logging. The human-in-the-loop process creates a discovery loop that helps improve current implementations and uncover unhandled scenarios, paving the way for greater AI autonomy.

The Center Circle – The Hybrid / Constrained Agent

As organizations mature, they're ready to sandbox an agent inside existing flows to handle long-tail, ambiguous, low-frequency cases. Previous workflows continue running, but now an agent dynamically handles edge cases that fall outside traditional deterministic paths.

Familiarity:
Teams graduate from understanding structured workflows and AI steps to experimenting with agentic AI. They now begin to see the real power of AI augmentation. They learn advanced skills including troubleshooting, defining tools and guardrails, implementing tracing and observability, and mastering prompt patterns. Through continued collaboration, they become adept at mapping complex business logic to agentic approaches, diving deeper into prompt engineering techniques like ReAct and Chain of Thought.

Autonomy:
AI gains significant autonomy through dynamic planning and tool selection. For unhandled scenarios, an AI agent is invoked with context and pre-approved MCP tools, dynamically creating plans to resolve issues. However, this autonomy remains constrained within a sandbox, with human supervision ensuring responsible operation. 

Trust:
Trust is growing, with teams focused on improving agentic handling and implementing robust guardrails. As the system effectively handles complex scenarios and guardrails prove their worth by correctly escalating issues, confidence in the agent's reasoning increases. Human feedback on agent decisions is stored, creating a feedback-rich learning loop.

The infrastructure becomes more sophisticated with guardrails, MCP tools, observability and audit processes, vector stores for feedback, and hallucination checks. A powerful discovery loop emerges: human-approved plans are saved and replayed for semantically similar cases, and when patterns repeat frequently, successful plans are promoted to new reusable tools.

The Outer Circle – Dynamic Agentic Orchestration

Having developed processes to make agentic approaches reliable, teams are prepared to unleash fully autonomous AI orchestration. This represents full AI autonomy, where agents compose entire workflows on-demand to tackle novel, complex, multi-domain, or unprecedented tasks. The system achieves end-to-end autonomous execution while leveraging existing workflow portions as reusable MCP tools.

Familiarity: 
Teams master the most advanced AI capabilities, learning to specify metrics, design evaluations, build exhaustive validation datasets, trace agentic decisions, troubleshoot complex issues, run structured experiments (like prompt variants and tool explanations), provide meaningful feedback, and implement comprehensive monitoring. This represents the pinnacle of AI literacy within the organization. The organization shifts from building workflows to evaluating and steering agents.

Autonomy:
AI achieves full autonomy by decomposing complex objectives, selecting and sequencing MCP tools, reflecting on and revising its own outputs, and evaluating various AI generated outputs using LLM-as-a-judge approaches. Agents can compose whole workflows on the fly, adapting dynamically to unprecedented challenges.

Trust:
At this stage, teams become cautiously optimistic—teams recognize impressive potential while acknowledging fragility in edge cases. Trust is reinforced through robust monitoring, validation datasets, escalation paths, and structured feedback mechanisms. Organizations develop sophisticated mechanisms to validate AI decisions while maintaining appropriate skepticism.

The infrastructure reaches maximum sophistication with robust evaluation pipelines, comprehensive tracing and monitoring tools, multi-agent governance systems, context-aware logging, extensive prompt management and experimentation tools, and detailed feedback dashboards. The discovery loop explores several candidate solution paths in parallel and logs which ones users prefer. It estimates confidence by checking how often those independent paths converge; when they diverge—or confidence falls below a threshold—the case is automatically escalated for human review and the user can be alerted to the uncertainty. Paths that receive consistent positive feedback are replayed for semantically similar future problems, but the number of edge-case scenarios grows quickly at this stage. To keep up, the organization periodically fine-tunes its models with Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimization (DPO), further sharpening both reasoning quality and tool selection over time.

To complement the conceptual model above, the following table summarizes how organizations evolve through each concentric stage of AI maturity. It breaks down the transformation across goals, workflows, AI capabilities, team behaviors, infrastructure, and learning loops—making the progression more operationally tangible.

This side-by-side view makes it clear: advancing in AI maturity isn’t just about increasing autonomy. It’s about deepening team familiarity, building the right infrastructure, and establishing trust mechanisms that support responsible, high-impact AI deployment at scale.

Real-World Examples: From Automation to Agentic AI

To bring the Concentric AI Maturity Model to life, let’s explore how teams in Marketing and Finance evolve through each phase. These domain-specific journeys illustrate how the principles of Familiarity, Autonomy, and Trust translate into meaningful business transformation.

Example 1: Marketing Workflow Automation to Agentic Orchestration

To illustrate how organizations progress through the Concentric AI Maturity Model, consider the journey of a marketing team evolving from routine automation to full agentic orchestration. Each phase builds trust through transparency, capability through learning, and value through measurable business impact.

Circle 1: Structured AI Automation

Scenario: Weekly campaign performance reporting across all channels

Workflow:

• Scheduled data extraction from Salesforce (leads), MixPanel (engagement), and ad platforms (spend)
• AI classifies campaign types and flags performance anomalies
• AI generates executive summaries of key trends
• Summaries are routed to the marketing manager for final review and distribution

Why it works:

The team can trace every step, clearly understand AI’s role, and trust the process. With up to 70% time savings, they start recognizing AI’s strengths in pattern recognition and text generation.

Key takeaway: Transparency builds early trust; AI handles repeatable, structured tasks.

Circle 2: Hybrid / Constrained Agent

Scenario: Troubleshooting complex campaign underperformance

Evolution:

• Standard workflows continue for typical reports
• For edge cases, an AI agent receives context and access to tools: MixPanel query builder, Salesforce analytics, Snowflake custom queries, competitive intelligence
• The agent plans an investigation:
• “Check user journey in MixPanel → Analyze lead quality in Salesforce → Run attribution analysis in Snowflake”
• Agent executes the plan and produces specific recommendations
• Human reviews and approves recommendations before action

Business value:

AI now handles the 20% of complex, ambiguous cases that used to require data science support. Marketing teams begin to understand agentic reasoning and where human oversight is still essential.

Key takeaway: AI becomes a trusted analyst for complex, low-frequency tasks—with humans in the loop.

Circle 3: Dynamic Agentic Orchestration

Scenario: Autonomous campaign optimization and strategic planning

Capabilities:

• Given a high-level goal like “maximize Q4 customer acquisition ROI,” the AI agent orchestrates a full analysis:
  • Reviews past performance
  • Identifies opportunities
  • Adjusts Braze email timing, Salesforce lead scoring, and MixPanel tracking
• Offers strategic recommendations, backed by transparent reasoning and predictive insights

Transformation:

The team shifts from reactive reporting to strategic planning. AI handles operational optimization, while humans focus on creative direction and long-term strategy.

Key takeaway: Full autonomy is possible when trust, tooling, and feedback systems are mature.

Example 2: Intelligent Finance Operations through Agentic Maturity

While the marketing team shows how AI enhances strategy and customer engagement, finance illustrates how AI supports operational scale, compliance, and risk management. This example follows the same three-phase journey.
 

Circle 1: Structured AI Automation

Goal: Automate 80% of high-volume, routine invoice processing with explicit, rule-based flows.

Finance Scenario:

• Invoice ingestion via email, SFTP, or direct upload
• Data extraction (vendor name, invoice number, total, etc.)
• Validation against vendor master data
• Categorization into correct GL accounts
• Approval routing based on thresholds and roles

Key takeaway: Teams gain confidence as AI handles predictable tasks with accuracy and traceability.

Circle 2: Hybrid / Constrained Agent

Goal: Handle the 20% of edge cases that involve ambiguity or complex routing logic.

Finance Scenario:

• Parsing unusual invoice formats from new or international vendors
• Resolving PO-invoice mismatches and proposing fixes
• Managing conditional approval flows across projects or departments
• Drafting vendor communication for discrepancies or delays

Key takeaway: AI becomes a problem-solving assistant in edge cases—helping humans, not replacing them.

Circle 3: Dynamic Agentic Orchestration

Goal: Enable agents to autonomously orchestrate cross-functional financial operations.

Finance Scenario:

• Executing complex financial closings by coordinating across teams and systems
• Leading fraud investigations with real-time evidence gathering and response triggers
• Responding to new regulations by analyzing impacts, proposing workflow changes, and implementing updates autonomously

Key takeaway: AI shifts from helper to orchestrator—driving adaptability, compliance, and proactive problem-solving.

Final Thoughts

These examples show that AI maturity isn’t just a technological evolution—it’s an organizational one. The Concentric AI Maturity Model provides a pragmatic roadmap for evolving from narrow automation to dynamic agentic orchestration. It’s not just a technology maturity model—it’s a people maturity model for AI. It recognises that the path to autonomous agents isn’t only about better models, but about building shared understanding, growing confidence, and developing collaborative tooling between humans and machines.
While AI systems become increasingly capable, it’s the teams that must mature in Familiarity, Autonomy, and Trust to unlock that potential. These attributes aren’t just technical milestones—they reflect how effectively humans and AI can partner at each stage.
Ultimately, the future of work isn’t about AI replacing people—it’s about people who know how to work effectively with AI.


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