Enterprise AI Scaling Research 2026
Enterprise AI Scaling Research Program
Investigating why some enterprise AI initiatives scale successfully while others stall after the pilot stage.
Enterprise AI adoption continues to accelerate, yet production-scale deployment remains uneven across industries. This research program examines the organizational, operational, governance, and investment conditions associated with successful AI scaling.
We are currently interviewing leaders responsible for enterprise AI deployment, governance, portfolio management, and scaling decisions. Relevant whether your organization is still expanding pilots, actively scaling AI deployments, or refining production governance.
No preparation is required. The interview is a confidential 30-minute conversation about your organization's experience moving AI initiatives from pilot environments toward production deployment. Findings are reported in aggregate.
The scheduling link opens a private calendar page. If none of the listed times work, request an alternate time.
What Participants Receive
Participants receive early access to findings and opportunities to compare perspectives with other leaders responsible for enterprise AI deployment and scaling.
Early Access to Findings
Receive research insights before public release.
Benchmarking Perspectives
Understand how peer organizations approach AI scaling decisions.
Executive Roundtable Invitations
Participate in future private discussions with enterprise AI leaders.
Research Report Access
Receive advance copies of Enterprise AI Scaling reports.
Framework Development Input
Help inform emerging research frameworks for evaluating enterprise AI scaling conditions.
Participant information
Research Confidentiality
Individual responses remain confidential.
Findings are reported only in aggregate form and are used to identify patterns across organizations rather than attribute views to specific participants.
Participants are encouraged to speak candidly about challenges, deployment experiences, governance approaches, and scaling decisions.
Contribute Your Perspective
30-minute confidential research interview. No preparation required.
Contribute Your PerspectiveCurrent Research Cohort
Completed
7Executive Interviews CompletedIndustries
4Industries RepresentedCountries
2Countries RepresentedTarget
50+Target ParticipantsThe research cohort is expanding across industries, geographies, and operating models to identify recurring patterns associated with successful enterprise AI scaling. Current participation represents an initial cohort and will continue growing throughout the research program.
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.
- Successful pilots still do not consistently become production-scale capabilities.
Experimentation remains necessary, but enterprise impact depends on whether organizations can scale AI responsibly under real operating conditions.
Who We Are Interviewing
This research focuses on leaders responsible for enterprise AI deployment, governance, portfolio management, and scaling decisions.
Participants do not need to represent organizations with fully scaled AI programs; the study includes organizations at different stages of enterprise AI deployment.
Areas of Responsibility
- Enterprise AI portfolios
- AI deployment decisions
- AI governance models
- AI operating models
- AI transformation programs
- Enterprise technology strategy
Typical Participants
- Chief AI Officers
- Heads of Enterprise AI
- AI Transformation Leaders
- CIOs
- CTOs
- Enterprise AI Program Leaders
Topics We Are Exploring
Research interviews explore how organizations evaluate readiness for scale and navigate the transition from pilot initiatives to production deployment.
Deployment Decisions
- How organizations determine which AI initiatives deserve broader deployment
- Deployment authorization processes
- Governance and oversight models
Portfolio Management
- AI investment prioritization
- Resource allocation decisions
- Portfolio performance evaluation
Operational Readiness
- Capabilities required for production deployment
- Operating model challenges
- Organizational dependencies
Scaling Outcomes
- Measuring deployment success
- Post-deployment performance
- Long-term value realization
Participant information
Research Interview Details
- Duration
- 30 minutes
- Format
- Virtual interview
- Recording
- Optional with participant permission
- Confidentiality
- Individual responses remain confidential and findings are reported in aggregate
- Participation
- By invitation or approved request
What the interview covers
- Current AI deployment portfolio
- Pilot-to-production decision process
- Governance and ownership model
- Scaling constraints and operating lessons
Participant Perspectives
The observations below reflect themes emerging from early research conversations. Individual participant identities remain confidential.
Emerging Observation
Several leaders described having dozens of pilots without a consistent way to determine which initiatives deserve broader deployment.
Head of Enterprise AI
Global Financial Services Organization
Emerging Observation
Early conversations suggest that resource concentration, rather than model development, is becoming one of the harder scaling decisions.
AI Transformation Executive
Fortune 500 Enterprise
Emerging Observation
Participants continue to note that production readiness requires capabilities that are often not evaluated during the pilot.
Technology Executive
Global Enterprise
About the Research
Tom Williams leads the Enterprise AI Scaling Research Program.
The research focuses on understanding why some enterprise AI initiatives scale successfully while others stall after the pilot stage.
The goal is to identify the organizational, operational, governance, and investment conditions associated with successful enterprise AI scaling.
The research draws on experience working with large enterprise technology organizations and ongoing analysis of enterprise AI deployment practices.
Attribution is never made to an individual or organization without explicit permission.
Research participation
Contribute Your Perspective
Organizations navigating AI deployment, governance, pilot-to-production challenges, or enterprise AI operating models can contribute practical evidence to the research program.