And what is the goal? Employee productivity, where AI is already making a difference (US Federal Reserve Bank of St. Louis). Over time, output per employee is the key driver of profitability, economic growth, and improving living standards worldwide.
Larridin has created an AI Adoption Guide to help you implement AI effectively at your company, helping you to achieve measurable ROI, compounding over time, with your investment (Larridin’s 2026 State of Enterprise AI Report). This blog post provides an introduction to the topic and highlights from the report.
Access Larridin’s AI Adoption Guide.
AI adoption has shifted from a technology initiative to a strategic imperative. The world’s most valuable companies are no longer asking whether employees should use AI—they are mandating it, incentivizing it, and tying it to performance.
Meta now evaluates every employee on “AI-driven impact” as part of formal performance reviews, with top performers earning bonuses of up to 200%. NVIDIA’s CEO has directed that every task possible should be automated with AI. Zapier achieved 97% company-wide adoption through bottom-up culture-building. Microsoft, Google, and Amazon have all sent the same signal: AI is no longer optional.
Yet despite this urgency, the accountability gap is stark. Only 1 in 5 AI investments delivers measurable ROI (Gartner), and 56% of CEOs report getting “nothing” from their AI adoption efforts (PwC’s 2026 Global CEO Survey). The gap between AI spending and AI outcomes is a measurement problem—and organizations that cannot measure adoption cannot close it.
If your leadership cannot answer key questions, such as: “How deeply has our organization adopted AI. Where are the gaps? How do we know?”—you have a strategic blind spot.
Enterprise AI in 2026 is not one tool. It is an ecosystem of foundation models, AI-first products, AI-augmented features, vertical solutions, and homegrown systems. Making sense of this landscape requires classifying tools along three axes:
The classification matters because an organization where 80% of employees use only ChatGPT has a fundamentally different—and weaker—adoption profile than one where 60% use a diverse portfolio across autonomy levels, modalities, and scopes.
Measuring AI adoption effectively requires moving beyond login counts. Larridin’s framework operates across four progressive layers:
Each layer adds depth. Usage alone is dangerously incomplete; segmentation transforms adoption data from a dashboard metric into a management tool.
Not all usage is equal. Your organization’s employees distribute across a spectrum:
Understanding this distribution is actionable. If 70% of your organization is stuck at “explorer,” you have a habit formation problem, not a deployment problem. Power users and AI-native employees are your champions—the internal advocates who can accelerate adoption for everyone else.
“AI adoption = how many people use ChatGPT (or Copilot).” This is the most widespread misunderstanding. Measuring a single tool gives you a vendor-specific view, not an enterprise view. Your employees are using more AI than any single dashboard reveals—and the tools they use beyond your primary platform may be where the most value is created.
“We bought 10,000 licenses, so we’ve adopted AI.” License counts measure procurement, not adoption. A 10,000-seat Copilot deployment with 15% weekly active usage is not an adoption success—it is a spend optimization problem. Adoption is about active, sustained engagement, not seat allocation.
“We measured adoption last quarter, so we’re covered.” Adoption is a dynamic, evolving metric. Measuring it once or quarterly misses the trajectory entirely. Weekly and monthly trends reveal whether adoption is accelerating, plateauing, or declining—and whether your interventions are working. Treat adoption measurement as continuous infrastructure, not a point-in-time exercise.
"Agentic AI is all that counts." This is untrue, but directionally valuable. Agentic AI can handle entire workflows, and it should be an important part of your AI mix. At the same time, each type of AI has a valuable role in your repertory of solutions. Look at common workflows within each of your teams to see which can be automated entirely with (usually, agentic) AI.
AI adoption does not exist in isolation. It is the foundation layer that connects to several related concepts across the AI execution intelligence landscape:
For a comprehensive treatment of adoption strategy, measurement frameworks, and organizational maturity models, see the full AI Adoption Guide.
Larridin is the AI execution intelligence platform that gives enterprises complete visibility into AI adoption, proficiency, and impact across every tool, team, and employee. If your leadership cannot answer “where are we on AI adoption?” with data, Larridin can fix that.