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

Presented by Myridius

in Collaboration with Everest Group

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Most organizations assume that scaling AI requires more data infrastructure. In reality, the greatest barrier to enterprise AI is often something less visible. Across large enterprises, data exists across hundreds of systems owned by different teams, governed inconsistently, and described using conflicting terminology.

When organizations attempt to build advanced analytics or AI on top of these environments, they frequently encounter a structural limitation: teams cannot confidently determine what data exists, what it means, or whether it can be trusted. For highly regulated industries such as pharmaceuticals, this challenge becomes even more significant.

AI initiatives require not only access to large volumes of data but also clear governance, lineage, and contextual understanding. A global pharmaceutical leader encountered this challenge while scaling AI and machine learning initiatives across its enterprise operations.

Impact Summary

Unified 10,000+ datasets across 20+ domains and reduced data discovery time by 40–60%, accelerating enterprise AI readiness

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Industry

Pharmaceutical

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Employee Count

10,000+

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Myridius Service Offering

Data & AI Strategy

Overview

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.

Key Takeaways

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.

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 It Matters

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

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

What is an AI Native Enterprise?

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
Get the Full Guide

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

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With deep industry expertise and experience across enterprise ecosystems, we help organizations align people, processes, data, and AI to deliver scalable solutions, accelerate innovation, and achieve measurable business outcomes.

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