Orchestrating the Future: Designing a Human + AI Operating Model for the Age of Agentic Intelligence

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Presented by Myridius

in Collaboration with Everest Group

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Orchestrating the Future: Designing the Human and AI Operating Model

Artificial intelligence is moving from isolated pilots into the core of how enterprises work, compete, and grow. The question is no longer if you should use AI, but how you will orchestrate people, processes, data, and technology so that humans and AI work together in a scalable and trusted way.

This executive summary distills key insights from the Everest Group report, sponsored by Myridius, on how to design a human and AI operating model and what it takes to become an AI native enterprise. It provides leaders with a practical blueprint that connects strategy, governance, technology, and talent so AI creates real and repeatable business value. Use this page to understand the core ideas and frameworks, then download the full report for deeper analysis, data, and industry-specific examples.

What Is a Human and AI Operating Model?

A human and AI operating model defines how people, AI systems, data, and processes interact to deliver business outcomes. It covers who does what, how decisions are made, what is automated, and how value and risk are managed.

In a traditional operating model, humans sit at the center of most decisions and workflows, supported by systems of record and basic automation. In a human and AI operating model, AI systems become active participants in the workflow. They analyze data at scale, recommend actions, automate tasks, and sometimes act autonomously, while humans provide oversight, judgment, and accountability.

Key Characteristics

  • Humans and AI are explicitly assigned roles and responsibilities
  • Workflows are redesigned so that AI is embedded into journeys and processes, not bolted on
  • Governance and risk management account for AI-driven decisions and outcomes
  • Data, platforms, and integration patterns support repeatable AI use at scale

Why Enterprises Need a Human and AI Operating Model

Most organizations have many AI pilots but only a few scaled, measurable wins. The gap is rarely technology alone. It is usually an operating model issue.

Common Challenges

  • Fragmented pilots that never scale beyond a single team
  • Limited trust and adoption from business users
  • Inconsistent governance and unclear ownership
  • Data silos and technical debt
  • Talent gaps in AI literacy, engineering, and change management

A clear human and AI operating model helps enterprises:

  • Align AI investments with business strategy and value pools
  • Standardize how AI is designed, deployed, and monitored
  • Build trust with transparency and accountability
  • Accelerate scale through reusable platforms and shared components

What is an AI Native Enterprise?

Definition

An AI native enterprise embeds AI into its strategy, operating model, and technology stack so that AI is a default capability, not a special project. AI influences how the organization designs products, serves customers, optimizes operations, and empowers its workforce.

AI Maturity Journey

  1. Ad hoc and experimental — Isolated pilots, limited governance
  2. Program driven — Central sponsorship, early standards
  3. Scaled and industrialized — Reusable platforms, MLOps, consistent governance
  4. AI native enterprise — AI integrated into strategy, culture, and operating model

The Six Dimensions of a Human and AI Operating Model

1. Strategy and Value

  • Define where AI will create the most value
  • Prioritize use cases based on impact and feasibility
  • Establish clear value hypotheses and KPIs

2. Governance, Risk, and Ethics

  • Clarify decision rights and ownership for AI systems
  • Set policies for transparency, fairness, safety, and explainability
  • Align with regulatory requirements
  • Review AI performance and incidents regularly

3. Data and Technology Foundation

  • Build a unified data foundation for cross-enterprise use
  • Standardize AI platforms, tools, and services
  • Implement MLOps and monitoring practices
  • Create reusable integration patterns

4. Ways of Working and Process Design

  • Redesign processes so AI is embedded in the flow of work
  • Define human-in-the-loop vs fully automated decisions
  • Use Agile and product-centric methodologies
  • Introduce feedback loops to improve AI over time

5. Talent, Skills, and Culture

  • Build multidisciplinary teams
  • Raise AI fluency across the organization
  • Equip leaders to guide AI investments and risk
  • Promote a culture that sees AI as a partner in value creation

6. Operating Economics and Performance Management

  • Define cost and funding models for AI initiatives
  • Track value realization at each stage of deployment
  • Use metrics that reflect efficiency and growth
  • Continuously adjust based on performance

How Humans and AI Work Together

Common Collaboration Patterns

  • AI as advisor — AI recommends actions while humans decide
  • AI as co-pilot — AI drafts content or next best actions for human review
  • AI as agent — AI performs tasks autonomously within guardrails
  • AI as orchestrator — AI coordinates multiple steps or systems while humans handle exceptions

Leadership Playbook: How to Get Started

1. Align on Vision and Value

  • Define what AI native means for your organization
  • Identify value pools and priority domains
  • Secure executive sponsorship

2. Assess Current Maturity

  • Evaluate your standing across the six operating model dimensions
  • Identify strengths and gaps
  • Benchmark against industry patterns

3. Design the Target Human and AI Operating Model

  • Clarify human and AI roles in priority workflows
  • Define governance and decision rights
  • Outline the target state for data, platforms, and teams

4. Build the Foundation

  • Invest in shared AI platforms and data foundations
  • Establish deployment and monitoring standards
  • Raise AI fluency across the business

5. Orchestrate a Portfolio of Use Cases

  • Create a balanced mix of quick wins and strategic bets
  • Use cross-functional teams for end-to-end delivery
  • Apply consistent patterns for design, testing, and scaling

6. Scale and Improve

  • Expand successful AI use cases across the enterprise
  • Monitor value, adoption, and risk indicators
  • Continuously refine models and processes
Get the Full Guide

Download a PDF version of the Everest Whitepaper by filling out this form.

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