Understanding AI adoption maturity models.
Organizations across industries are racing to harness AI's potential, but many struggle to measure progress or identify next steps. Most traditional models treat maturity like a linear ruler—a simple race from start to finish. However, true AI readiness is multi-dimensional.
The Workday approach to AI maturity acts less like a simple ruler and more like an organizational X-ray machine. It doesn't just show that you are "behind"; it pinpoints the "broken bone"—whether it's weak leadership alignment, poor data quality, or a missing ethics framework—enabling precision treatment rather than just generic solutions.
Key takeaways:
- Move beyond the linear: AI maturity isn't a single score; it is a journey across five critical organizational drivers.
- Data-driven insights: Based on a survey of 1,000 global organizations, Workday focuses on the Five Drivers Framework for AI Maturity, consisting of strategy, executive sponsorship, organizational knowledge, operational readiness, and governance and risk.
- Precision over generalization: Identifying your specific "cohort" enables tailored AI adoption roadmaps rather than a "one-size-fits-all" AI implementation.
- From assessment to action: Moving from "The Measured" to "The Unified" requires a shift from disconnected pilots to a cohesive, platform-wide strategy.
AI maturity: A strategic approach to enterprise transformation.
For most leaders, AI maturity is about your level of development, adoption, and integration of Workday AI within your operations and culture.
While you may believe you are lagging, our research shows that maturity is rarely a straight line. By using a diagnostic framework, you can move away from “random acts of AI” toward a strategic approach that drives competitive advantage, boosts operational efficiency, and improves the employee experience.
The five drivers of AI maturity.
To gain a clear “diagnostic” of an organization, we evaluate five core areas:
- Strategy: Is there a clear vision for how AI creates business value?
- Executive sponsorship: Do leaders actively champion and fund AI initiatives?
- Organizational knowledge: Does your workforce have the literacy and skills to work alongside AI?
- Operational readiness: Is the data infrastructure and tech stack capable of supporting AI?
- Governance and risk: Are there frameworks in place for ethical, secure, and compliant AI use?
The advantages of a diagnostic maturity model.
Unlike generic models that offer vague “levels,” a diagnostic approach delivers a precise view of where you are today.
Primary Metric
Traditional Linear Maturity Models
Overall "Score" (1-5).
Workday’s Diagnostic Model
Performance across 5 Key Drivers.
Analogy
Traditional Linear Maturity Models
A ruler (How far are you?).
Workday’s Diagnostic Model
An X-ray (Where is the specific gap?).
Outcome
Traditional Linear Maturity Models
Generic "level" description.
Workday’s Diagnostic Model
Tailored recommendations and cohort-specific roadmaps.
Focus
Traditional Linear Maturity Models
Technology adoption only.
Workday’s Diagnostic Model
Strategy, people, data, and governance.
Understanding the five AI maturity cohorts.
Through extensive research, we have identified five distinct archetypes of AI maturity. Each cohort has a unique profile characterized by their strongest and weakest drivers.
1. The Measured (the safety inspectors).
- Profile: These teams prioritize risk mitigation above all else
- Top driver: Governance and risk | Bottom driver: Strategy
- Real-world example: Many healthcare systems fit this profile. While they lead in security and ethical safeguards for patient data (Governance), they often lack a unified AI business vision (Strategy), resulting in administrative AI tools that remain siloed from core clinical innovation
- The path forward: Move from defensive governance to offensive strategy by identifying low-risk, high-impact pilots.
2. The Resourceful (the self-starters).
- Profile: Motivated teams experimenting with AI in pockets, but without top-down support.
- Top driver: Strategy | Bottom driver: Executive sponsorship
- Real-world example: Large retailers and manufacturers often fall here during the “shadow AI" phase. Departmental teams deploy specialized AI tools for route optimization or marketing (Strategy), but because these projects lack C-suite backing (Sponsorship), they fail to scale enterprise-wide.
- The path forward: Empower executive champions to turn grassroots experiments into enterprise standards.
3. The Systematic (the architects).
- Profile: A planned approach with leadership support, facing challenges from legacy technical debt.
- Top driver: Executive sponsorship | Bottom driver: Operational readiness
- Real-world example: Zillow faced a high-profile challenge in this cohort. While leadership was fully committed to its “iBuying” AI vision (Sponsorship), the underlying data foundations were incomplete and couldn't handle real-world market complexity (Operational Readiness), leading to a $500M loss.
- The path forward: Invest in a unified data platform to provide the “fuel” for AI initiatives.
4. The Rigorous (the engineers).
- Profile: Strong technical foundations, but the workforce is being left behind.
- Top driver: Operational readiness | Bottom driver: Organizational knowledge
- Real-world example: Global logistics and industrial firms often invest heavily in GPUs and clean data (Operational readiness), yet an EY survey found firms lose 40% of AI productivity gains because only 5% of employees have the literacy to use the tech in transformative ways (Knowledge).
- The path forward: Focus on change management and upskilling the workforce to work alongside AI.
5. The Unified (the master builders).
- Profile: High performance across all five drivers; AI is business-critical infrastructure.
- Top driver: Strategy and executive sponsorship | Bottom driver: Organizational knowledge (continuous learning gap)
- Real-world example: Walmart serves as the gold standard here. It has unified disconnected tools into a “super agent” framework and recently hired an EVP of AI acceleration to ensure AI transformation is led directly from the C-suite.
- The path forward: Continue your evolution through autonomous agents and advanced reasoning systems.
How customers can improve upon their AI maturity.
To increase your AI maturity, Workday has established three pathways for FY27 to help organizations take action:
- AI advisory services: Multi-day intensive workshops to deliver concrete organizational roadmaps
- Professional services: A portfolio of AI services covering strategy, operational readiness, and feature adoption
- Prescriptive guidance: Like a digital coach, a self-service program allowing customers to engage with content related to the Five Drivers at their own pace
Ready to accelerate your AI maturity journey?
Let's talk.