01
Stage
Pilot
Description
Technical proof created in a bounded environment.
Common Failure Point
Success metrics fail to predict production outcomes.
Enterprise AI Scaling Research 2026
Pilot to Production Is Broken
Most enterprises can launch AI pilots.
Few can scale them into governed, production-grade systems.
Stratify is conducting research to identify the organizational, operational, governance, and investment conditions that determine whether AI creates sustainable value at scale.
Research Progress
Updated Weekly
Interviews
7Executive interviews completedIndustries
4Industries representedCountries
2Countries representedOrganizations
25Target organizations7 / 25 Organizations Interviewed28%
Interviews are underway with enterprise leaders responsible for AI deployment, governance, transformation, and investment decisions. Progress indicators will continue to update as additional conversations are completed.
Why This Research Matters Now
- AI adoption is accelerating across the enterprise.
- Agentic systems are increasing operational complexity.
- Executive teams are demanding measurable ROI.
- Governance models are struggling to keep pace.
- Most organizations still cannot consistently scale successful pilots.
The challenge is no longer experimentation. The challenge is scaling responsibly.
Flagship model
The Enterprise AI Scaling Gap
Successful pilots rarely fail for a single technical reason. They stall when authorization, governance, ownership, and production support do not mature at the same pace as deployment ambition.
02
Stage
Authorization
Description
Decision rights determine whether expansion should proceed.
Common Failure Point
Business case approved without operational readiness.
03
Stage
Governance
Description
Controls, oversight, and risk ownership are established.
Common Failure Point
Policies and controls lag deployment.
04
Stage
Operational Ownership
Description
Teams accept responsibility for production outcomes.
Common Failure Point
Ownership becomes fragmented.
05
Stage
Production
Description
AI becomes a governed, supported operating capability.
Common Failure Point
Systems lack reliability, monitoring, or scale readiness.
The Problem We Are Studying
Enterprise AI adoption has accelerated rapidly.
Yet many organizations continue to struggle moving successful initiatives from pilot environments into governed, production-grade systems.
The challenge is rarely model performance.
The challenge is coordinating governance, ownership, investment discipline, and operating capacity as deployment expands.
This research examines the conditions that separate experimentation from enterprise-scale impact.
What we are studying
Enterprise AI Scaling Framework
Our research examines the critical factors that determine whether AI initiatives can move from pilot to governed, production-grade outcomes.
01
Pilot-to-Production Barriers
Investigating the organizational, technical, and operational dependencies that prevent successful pilots from reaching governed production environments.
02
Governance & Deployment Controls
Examining how oversight, accountability, and controls are designed and executed as AI moves into production workflows.
03
Portfolio Prioritization
Understanding how leaders choose which initiatives receive capital, attention, and operating capacity.
04
Capital Discipline
Exploring how organizations sequence investment and ensure funding aligns with value realization.
05
Operating Model Design
Assessing ownership, roles, decision rights, and cross-functional coordination required to operate AI at scale.
06
Production Readiness
Evaluating the people, platform, data, and process capabilities required for reliable, responsible AI in production.
The data is clear
Widespread Adoption.
Limited Enterprise Impact.
The challenge is no longer adoption.
The challenge is operationalizing AI at enterprise scale.
88%
use AI
79%
use GenAI
7%
have fully scaled AI
Who we are interviewing
Senior leaders responsible for AI deployment, governance, and operating decisions in complex enterprise environments.
Executive roles
- CIOs
- Chief Digital Officers
- Heads of AI
- Innovation leaders
- Enterprise architects
- Transformation executives
- AI governance leaders
Industries
- Airlines and transportation
- Telecom
- Financial services
- Insurance
- Government and public sector
- Healthcare
- Industrial enterprises
Early Observations
Patterns emerging from ongoing conversations with enterprise leaders.
Observation #01
Initiative selection remains unclear
Organizations struggle to determine which initiatives deserve broader deployment.
Observation #02
Pilot success is overinterpreted
Pilot success is often mistaken for production readiness.
Observation #03
Governance trails deployment
Governance structures frequently lag deployment activity.
Observation #04
Accountability fragments at scale
Ownership and accountability become more fragmented as AI initiatives expand.
These observations represent emerging themes and will continue to evolve as additional interviews are completed.
Emerging Research Themes
Structural patterns under investigation as the interview program continues.
Organizations don't know where to start
Many leaders struggle to determine which AI initiatives deserve broader deployment.
Pilot success is being confused with production readiness
The conditions that support experimentation often differ from those required for enterprise-scale operation.
Governance frequently lags deployment
AI initiatives often expand faster than governance structures evolve.
Ownership becomes fragmented at scale
Responsibility shifts across teams, creating coordination and accountability challenges.
Contributor Access
Executive leaders contributing to the program receive research outputs and peer operating context.
- Early access to Enterprise AI Scaling Report 2026
- Benchmark context from peer organizations
- Executive summary of emerging operating patterns
- Invitation to practitioner research briefings
- Visibility into how enterprise AI scaling practices are evolving
Research deliverable
Enterprise AI Scaling Report 2026
The Enterprise AI Scaling Report 2026 will synthesize findings from participating organizations to identify the operating conditions, governance models, deployment practices, investment approaches, and organizational structures most associated with successful enterprise AI scaling.
The objective is to create one of the most comprehensive practitioner-driven examinations of why enterprise AI initiatives succeed, stall, or fail to scale.
Publication target: Q4 2026
Request Executive InterviewCore research questions
Questions guiding the interview program.
- 01
What prevents AI pilots from reaching production?
- 02
Who decides which AI initiatives should scale?
- 03
How do organizations maintain governance while moving quickly?
- 04
Where does capital discipline break down?
- 05
What operating model is required for AI to scale responsibly?
- 06
What separates AI experimentation from enterprise AI production?
Published research context
This program extends Stratify's published work on deployment supportability, the pilot-to-production gap, and organizational strain under AI dependency. Prior research establishes the operating lens applied in this interview initiative.
Why Enterprise AI Scaling Fails
Category thesis on the operating layer behind pilot success and production instability.
Read the researchAI Project Failure Rate
Structural analysis of why most enterprise AI initiatives fail after pilot, before the model layer.
Read the analysisThe Organizational Reality of AI Scaling
Executive briefing on deployment supportability and organizational absorption under AI dependency.
Read the briefingResearch participation
Help Define How Enterprise AI Scales
If your organization is navigating AI deployment, governance, pilot-to-production challenges, or enterprise AI operating models, we would value your perspective.
This is a 30-minute executive interview focused on real-world experience and emerging operating practices.
Non-sales conversation. Individual comments are not attributed without permission. Participants may choose whether their organization is named.