New research from Riviera Partners identifies three distinct stages of AI execution maturity and the organizational characteristics that define each one.
| At a Glance |
| The AI Execution Maturity Model categorizes organizations into three stages: Emerging (25%), Developing (57%), and Advanced (19%) |
| Maturity is measured across three dimensions: technology structure, leadership engagement, and governance integration |
| Advanced organizations report measurable business impact from AI at nearly double the rate of Emerging organizations |
| Most organizations sit in the Developing stage, showing progress but facing inconsistency at scale |
The question most technology executives are asking in 2026 is not whether to pursue AI. It is whether their organizations can actually execute on it.
To answer that, Riviera Partners developed an AI Execution Maturity Model as part of its 2026 Future of Tech Leadership research, based on responses from 958 technology leaders across North America and Europe. The model draws on both the survey data and Riviera’s experience placing hundreds of technology executives across AI, ML, Data, and Engineering, giving the firm a ground-level view of the organizational and leadership patterns that distinguish high-execution organizations from those still working to close the gap.
The model categorizes organizations into three stages based on three measurable dimensions: technology structure, leadership engagement, and governance integration.
Here is what each stage looks like, and the signals that indicate where your organization stands.
Stage One: Emerging 25% of organizations surveyed
Emerging organizations have AI initiatives underway but face consistent barriers to moving them into production and scale.
Defining characteristics:
- Technology functions remain largely siloed, with Product, Data, Engineering, and Governance operating independently
- Leadership sets direction but has limited hands-on involvement in execution
- Governance enters the process late, typically immediately before deployment or on an ad hoc basis
- AI strategies rely heavily on SaaS-first approaches
The outcome: 27% of Emerging organizations report measurable business impact from AI. Just 23% move more than 60% of initiatives into production.
Stage Two: Developing 57% of organizations surveyed
Developing organizations show meaningful progress but face inconsistency at scale. Most of the market sits here.
Defining characteristics:
- Technology structures are partially unified, with some functions working together and others remaining separate
- Leadership engagement is increasing, though involvement in execution varies
- Governance integrates during development phases, not at initial design
- Hiring priorities are shifting toward technical execution roles
The outcome: 40% of Developing organizations report measurable business impact. 36% move more than 60% of initiatives into production.
Stage Three: Advanced 19% of organizations surveyed
Advanced organizations consistently move AI from concept into production, scale it across the business, and translate it into measurable outcomes.
Defining characteristics:
- 92% operate with highly unified technology structures
- More than half rely on player-coach leaders who stay actively engaged in architecture, governance, and delivery
- 87% integrate governance during the initial design phase
- Hiring targets the builder layer: individual contributors and AI-relevant technical roles below the VP level
The outcome: 44% of Advanced organizations report measurable business impact, nearly double the rate of Emerging organizations. They are also more than twice as likely to scale nearly all AI initiatives across the business.
What Determines Maturity
The research shows a consistent progression across all three stages. As organizations mature, technology structures unify, leadership moves closer to execution, governance moves earlier in the development process, and hiring shifts toward builders over strategists.
No single factor determines maturity. Organizations that advance improve across all three dimensions together.
Using This Framework
The AI Execution Maturity Model gives technology leaders a practical reference for assessing their current position and identifying where organizational changes are most likely to improve execution outcomes.
Riviera Partners has placed hundreds of technology executives across AI, ML, Data, and Engineering at some of the industry’s most consequential organizations. The 2026 Future of Tech Leadership Report reflects both the survey findings and the practical realities Riviera sees across its client base every day.
The full report includes detailed findings by ownership structure, leadership archetype, governance timing, compensation, and hiring trends.
Related Research
- What the 19% Get Right: New Research on AI Execution Maturity
- Governance Timing and AI Execution: Why Earlier Integration Leads to Faster Outcomes
- How Technology Structure Shapes AI Execution Outcomes
- Building the AI-Ready Organization: What New Research Says About Tech Hiring Priorities in 2026
About the Research
The 2026 Future of Tech Leadership Report is based on a survey of 958 technology leaders across North America and Europe, conducted in 2026. Respondents include CEOs, CTOs, CIOs, Chief AI Officers, and senior technology executives across organizations of varying size, ownership structure, and industry. The AI Execution Maturity Model was developed by Riviera Partners to categorize organizations into three stages, Emerging, Developing, and Advanced, based on technology structure, leadership engagement, and governance integration. Riviera conducts this research annually to track shifts in technology leadership priorities, organizational structure, and talent strategy across the market.
Frequently Asked Questions
What is the AI Execution Maturity Model?
The AI Execution Maturity Model is a research-based framework developed by Riviera Partners to categorize technology organizations into three stages of AI execution capability: Emerging, Developing, and Advanced. It evaluates organizations across three dimensions, technology structure, leadership engagement, and governance integration, based on data from 958 technology leaders surveyed in 2026.
What are the three stages of AI execution maturity?
The three stages are Emerging (25% of organizations), Developing (57%), and Advanced (19%). Emerging organizations face consistent barriers to moving AI into production. Developing organizations show progress but face inconsistency at scale. Advanced organizations consistently move AI from concept into production, scale it across the business, and realize measurable business impact.
How do I know what stage my organization is in?
Three dimensions signal execution maturity: how technology functions are organized (unified vs. siloed), how actively leadership engages with execution (player-coach vs. strategic direction only), and when governance integrates into the development process (initial design vs. pre-deployment). Organizations with siloed structures, strategy-led leadership, and late governance consistently fall into the Emerging stage. Organizations with unified structures, engaged leadership, and early governance characterize the Advanced stage.
What is the difference between Developing and Advanced AI execution maturity?
The most significant differences are governance timing and organizational structure. 87% of Advanced organizations integrate governance at the initial design phase, compared with 35% of Developing organizations. 92% of Advanced organizations operate with highly unified technology structures, compared with 51% of Developing organizations. The outcome gap reflects this: 44% of Advanced organizations report measurable business impact versus 40% of Developing organizations, and Advanced organizations are far more consistent at scaling AI across the business.
What organizational factors determine AI execution maturity?
The 2026 research identifies three primary factors: technology structure (whether Product, Data, Engineering, Security, and Governance work in a unified model), leadership engagement (whether leaders remain actively involved in architecture, decisions, and delivery), and governance timing (whether cybersecurity, legal, and compliance integrate at design or at deployment). Hiring strategy also plays a role. Advanced organizations invest in builder-layer talent rather than relying on SaaS-first approaches.
How can an organization move from Emerging to Advanced AI execution maturity?
The research points to four organizational levers: unifying technology structures so cross-functional teams can make decisions together, keeping leaders actively engaged in execution rather than delegating it entirely, integrating governance earlier in the development process to reduce rework and delays, and building the technical talent capacity required to implement and scale AI systems. Organizations that advance do so by improving across all three maturity dimensions simultaneously, not by optimizing one in isolation.