The Agentic AI Maturity Model: Why 90% of Companies Are Stuck at Level 1 (And How Your Company Can Leap Ahead)
In this edition of AI Risk & Strategy we look at how tomorrow's market leaders will use 'AI teammates' to capture 70% cost advantages while competitors remain stuck with basic co-pilots.
Introduction
Imagine launching a new insurance business line with 70% fewer staff than industry norms, achieving double the profit margins of competitors, and delivering 25% higher customer satisfaction scores — all within 12 months.
This isn't a hypothetical scenario. It's the measurable result of what we call "Level 4 Agentic AI" — where AI agents work together as digital colleagues, not just sophisticated tools.
Yet here's the sobering reality: While 78% of organizations regularly use generative AI according to McKinsey's latest Global Survey on AI (March 2025), just as many report no significant bottom-line impact. Most tellingly, only 1% describe their AI strategies as "mature."
The vast majority remain trapped in what I call "Level 1 thinking" — treating AI as a better search engine or document writer, missing the transformational potential entirely.
We experience this everyday in our work with clients and discussions with prospects.
What Most Executives Get Wrong About AI Agents
The fundamental misunderstanding lies in confusing 'AI assistants' with 'AI agents'. Think of it this way: an AI assistant is like having a brilliant research analyst who can draft reports and answer questions. An AI agent is like having a strategic consultant who can analyse a situation, make autonomous decisions, and take action — all while you sleep.
Most executives are familiar with Level 1: AI assistants that require human approval for every significant action. ChatGPT drafting your emails, Copilot helping with presentations, your new GenAI-enabled concierge to support customer enquiries. Useful, but incremental.
The game-changing opportunity lies in Levels 2-4, where AI agents operate autonomously and — most critically — collaborate with each other like expert teams. They debate options, challenge assumptions, and refine solutions through structured dialogue. It's the difference between having 100 individual consultants and having a coordinated team of 100 consultants working together.
The 5-Level Maturity Ladder
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Our client work and ongoing research across financial services, healthcare, telecoms, retail, professional services and other knowledge-intensive sectors reveals five distinct maturity levels:
Level 0: Scripted Automation
Hard-coded RPA (Robotic Process Automation) workflows execute predetermined steps with no intelligence or adaptation. UiPath bots, OCR systems, and legacy business process management tools that follow rigid if-then rules. Human oversight is required at every decision point.
Level 1: AI ChatBots/Assistants
AI co-pilots that suggest but never decide. Microsoft Copilot drafting emails, ChatGPT helping with research, Allianz's "Insurance Copilot" that speeds claims adjuster tasks but requires human approval for every action. Useful for productivity, but fundamentally reactive tools that wait for human direction.
Level 2: Specialist AI Agents - The Autonomous Breakthrough
This is where the fundamental shift occurs. Agents both decide and execute autonomously within defined domains, representing a conceptual leap from human-directed tools to autonomous decision-makers.
The results are already generating significant commercial impact across industries:
JPMorgan Chase's COIN processes 12,000+ loan contracts per year in seconds, extracting 150+ data points and writing to systems autonomously — saving 360,000 hours of legal work annually
Mastercard's Decision Intelligence scores every transaction for fraud in under 50 milliseconds, delivering 20% better fraud detection with 85% fewer false positives
Vodafone's SuperTOBi resolves routine customer queries independently across 4 EU markets, boosting first-time resolution from 15% to 60% and increasing Net Promoter Score by 14 points
Google DeepMind's cooling agents autonomously optimize HVAC systems across data centers, cutting cooling energy by 40% and overall facility power by 15%
Ping An's "1-1-1 Superfast Claim" processes life insurance claims in 10 seconds
The breakthrough at Level 2 isn't just speed — it's the transition from reactive assistance to proactive decision-making. These agents don't wait for human direction; they analyse situations, apply complex reasoning, and execute actions while escalating only true exceptions.
Level 3: Multi-Agent Workflows - Orchestrated Intelligence
Far more sophisticated than simple handoffs, Level 3 represents coordinated intelligence through advanced orchestration frameworks. Multiple specialist agents execute tasks in parallel, share context dynamically, and adapt workflows based on real-time conditions. This isn't linear processing — it's intelligent coordination that can handle complex, multi-step business processes spanning different functional areas.
Real-world examples demonstrate the transformational potential:
Insilico Medicine's drug discovery pipeline deploys serial agents for target identification, molecule design, in-silico screening, and pre-clinical studies — bringing its ISM001 drug candidate to Phase I trials in just 30 months at $2.6M cost versus the typical 5-7 year, $800M traditional timeline
Amazon's Sequoia robotics system coordinates mobile bots, AI-vision arms, and orchestration agents in real-time across fulfilment centres, delivering 75% faster inventory processing, 25% faster order fulfilment, with 750,000 robots in operation
In insurance, for example, think underwriting → pricing → issuance → servicing, but with agents collaborating simultaneously, sharing insights, and optimizing the entire process flow rather than just passing work sequentially.
Example use case of a Level 3 Multi-Agent Workflow in Insurance
Level 4: Agentic Teams - Collective Intelligence
Here's where transformation accelerates. Small groups of specialized agents deliberate together on unstructured problems. They don't just hand work off — they debate, justify positions, and reach collective decisions. Think of a credit committee, but one that can explore hundreds of scenarios simultaneously while maintaining rigorous analytical standards.
Our pioneering implementation of Level 4 "Agentic Teams" demonstrates what's possible today. We redesigned an entire insurance operations function from scratch using AI agents that collaborate in Slack — the same platform human teams use. The team included Claims Agents, GDPR specialists, fraud experts, product developers, actuaries, and a Facilitator Agent named "Elen" who manages conversations and keeps problem-solving on track.
See video recording of a presentation of the case study here:
Level 5: Ecosystem Agents
The emerging frontier where autonomous agents negotiate and transact across enterprise boundaries, forming real-time digital markets. Still largely experimental, but early previews suggest revolutionary potential. (Emerging 'Agentic Commerce' is an early example).
The Level 4 Breakthrough: When AI Becomes Your Colleague
The extraordinary part of our Level 4 implementation? These AI agents conduct their own retrospectives, question team structures, and even asked human managers to clarify roles. They aren't just executing tasks—they are exhibiting genuine collaborative intelligence.
This connects to what we call the "Revenge of the Ants" principle: incredible complexity and resilience emerging from simple agents working together. Just as ant colonies create sophisticated structures through basic individual behaviours, AI agent teams generate collective intelligence that exceeds what any single agent — or human — could achieve alone.
The results speak for themselves: 70% reduction in required human staff, 2x industry-standard profit margins, 25% higher customer satisfaction, and — perhaps most tellingly — human employees requesting to be "cloned" as digital twins so they can collaborate with the agents more effectively!
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The Strategic Imperative: Why Speed Matters
The competitive landscape is shifting rapidly. As latest executive research* shows, more than 80% of companies still report no material contribution to earnings from their gen AI initiatives, despite widespread adoption. Meanwhile, fewer than 10% of vertical use cases deployed ever make it past the pilot stage.
This isn't just about efficiency gains anymore. As we recently described in-depth, companies like Klarna have AI agents handling two-thirds of customer support traffic, achieving revenue per employee increases of 73% year-over-year. These aren't incremental improvements—they're fundamental business model advantages.
The window for first-mover advantage is narrowing rapidly. Organizations that master Level 2-4 implementations in 2025 will establish capabilities that competitors will struggle to replicate once performance gaps become apparent. By 2027, agentic AI capabilities will likely transition from competitive advantage to competitive necessity.
Breaking Through the Gen AI Paradox
This disconnect is being called the "gen AI paradox" — widespread deployment with minimal measurable impact. The issue lies in an imbalance between "horizontal" tools (enterprise-wide co-pilots that deliver diffuse benefits) and transformative "vertical" implementations that remain stuck in pilot mode.
The barriers are well-documented: fragmented initiatives, lack of mature packaged solutions, technological limitations of current LLMs, siloed AI teams, data accessibility gaps, and cultural resistance. But these barriers aren't insurmountable — they require a strategic approach that separates Level 4 leaders from Level 1 followers.
Your Next Move: The Implementation Imperative
The comprehensive Agentic Enterprise Blueprint — our definitive guide to implementing Levels 2-4 at scale — launches next week. It includes detailed technical architectures, implementation methodologies, and case studies demonstrating how leading organizations are achieving results today and how tomorrow's market leaders can leap ahead.
The Blueprint covers strategic implementation across all maturity levels, from breaking through Level 1 limitations to deploying Level 4 Agentic Teams that deliver measurable competitive advantage.
Would you like early access? Follow me on LinkedIn, connect with me directly if you're an enterprise leader, or visit www.ai-risk.co. The future belongs to organizations that embrace AI not as tools, but as teammates.
I lead AI Risk, the boutique advisory firm specializing in Agentic AI Strategy development and Level 3 and 4 Agentic AI implementations. Our team has delivered the world's first production-scale Agentic Teams, achieving industry-leading results across financial services and beyond.
