Unlocking the Power of Data and AI Maturity in Composable Commerce
Discover how to move through key stages of data and AI maturity to boost efficiency, customer satisfaction, and long-term growth in a composable commerce landscape.
Data and AI maturity are essential for brands aiming to leverage technology effectively, especially within the composable space. Progressing from manual, fragmented processes to an optimized, AI-powered system allows businesses to operate efficiently, make data-driven decisions, and deliver meaningful customer experiences. But to progress along that journey, it’s important to know where you stand today. And for many businesses, assessing current data and AI capabilities is a difficult, even overwhelming task.
This article explores the stages of data and AI maturity across eight core capabilities and highlights steps brands can take to advance on this journey.
What is the Data & AI Maturity Model, and Why Does it Matter?
The Data and AI Maturity Model provides a structured path for evaluating and understanding AI-readiness for both B2B and B2C businesses. It offers a framework for organizations at any stage, from initial data handling to a fully integrated, AI-optimized system, and clarity about where portions of a business may be ahead of or lagging in comparison to the business as a whole when it comes to data and AI maturity.
Unlike a one-size-fits-all approach, this model evaluates maturity across eight key capabilities: Data Integration & Accessibility, Event-Driven Architecture & Real-Time Data, Customer Insights & Analytics, AI-Driven Personalization, AI-Powered Search & Discovery, Operational Efficiency, Data Governance & Compliance, and Innovation & Experimentation.
Brands can progress at different rates across these capabilities, aligning investments with their strategic priorities. Each capability provides specific benefits as data maturity increases, which collectively enhances AI’s effectiveness.
Here’s how each capability supports a more robust, AI-driven environment:
Data Integration & Accessibility: Fully integrated data across ERP, CRM, and inventory systems enables AI to operate on consistent, accurate data, enhancing decision-making and personalization.
Event-Driven Architecture & Real-Time Data: Event-driven architecture allows AI to make real-time adjustments in response to data from key areas like inventory and customer behavior, ensuring that decisions are timely and relevant.
Customer Insights & Analytics: AI maturity allows brands to analyze data to predict customer needs, optimize marketing, and drive proactive engagement, resulting in better customer relationships and more strategic decision-making.
AI-Driven Personalization: Personalized experiences become increasingly dynamic and precise as brands mature, with AI adapting offers, recommendations, and content based on real-time, cross-channel data.
AI-Powered Search & Discovery: An advanced AI search experience provides contextual recommendations, supporting users in quickly finding relevant products, increasing conversion rates, and reducing friction in the buying journey.
Operational Efficiency: Automated forecasting and inventory adjustments through AI reduce manual work, aligning resources with real-time demand, and improving overall operational efficiency.
Data Governance & Compliance: With maturity, brands enhance data security and compliance protocols, ensuring data privacy and regulatory adherence, which is critical for customer trust and risk management.
Innovation & Experimentation: Advanced maturity allows brands to continuously test and scale AI-driven innovations, which drive long-term differentiation and adaptation to emerging technology.
Together, these capabilities create a foundation for AI to deliver timely, relevant insights and customer experiences across the business.
Here’s how brands can approach this progression:
Assess Maturity by Capability
Evaluate the current stage for each capability, identifying strengths and areas needing improvement. This helps pinpoint where investments will have the most impact.
Align with Business Goals
Connect capability improvements with business objectives. For example, if customer experience is a priority, advancing in AI-Driven Personalization and Customer Insights & Analytics should be prioritized.
Focus on High-Impact Capabilities
Immediate Needs: Emphasize capabilities like Data Integration & Accessibility to ensure foundational data consistency.
Accelerated Growth via Partnerships: Use consulting or SI partners to fast-track complex areas like Event-Driven Architecture & Real-Time Data, or AI-Powered Search & Discovery.
Future Development: Identify lower-priority capabilities, such as Innovation & Experimentation, for later phases.
Aim for Incremental Improvement
Start with foundational data integration and governance, then progressively add real-time data capabilities, predictive analytics, and personalized AI functions.
Practice Ongoing Evaluation
Regularly reassess each capability to ensure alignment with business needs and to integrate new insights and technologies.
Stages of Data and AI Maturity: Capability-Based Progression
Each capability follows a distinct maturity path, allowing brands to tailor their progress to business needs. Here’s an overview of the stages:
Stage 0 - Ad Hoc
Data is siloed, and processes are manual. There is little to no integration across systems, resulting in inefficiencies and minimal AI utilization.
Stage 1 - Basic
Basic automation and integration exist within select systems, allowing for some data consistency. AI use is rule-based, limited to simple tasks, and lacks personalization.
Stage 2 - Managed
Key systems are integrated, covering operational areas like supply chain and CRM, which allows for improved data flow. AI starts to streamline repetitive tasks but operates within specific departments rather than across the organization.
Stage 3 - Standardized
Data integration is comprehensive, and real-time updates are available across functions, supporting AI-driven insights. Personalization, search, and operational efficiency benefit from this full integration, enhancing the customer journey and internal processes.
Stage 4 - Optimized
AI-powered, fully integrated systems provide predictive analytics and real-time insights across all areas, including supply chain, customer interactions, and marketing. This level of maturity supports agile decision-making and highly personalized, consistent experiences across channels.
This capability-based progression allows brands to prioritize investments according to each area’s unique maturity path, maximizing the impact of AI across the business. Achieving higher maturity across capabilities requires assessing each capability individually and aligning efforts with strategic priorities.
Measures of Success for Data Maturity
As brands advance in their data and AI maturity, they can assess their progress across several impactful dimensions that directly reflect on their strategic objectives and return on investment. A key area to monitor is the time to value and time to learning—as data and AI processes mature, the time required for AI to produce actionable insights decreases. This acceleration enables teams to react more swiftly to shifts in market conditions, enhancing agility and responsiveness.
Another critical factor is the precision and relevance of AI insights. Improved data quality and accessibility boost the accuracy of AI-generated recommendations, which in turn elevates customer satisfaction and informs better decision-making. As brands mature, they also benefit from a reduction in project failure costs. With more robust data governance and integration, the risk of costly failed initiatives decreases, mitigating the financial risks often associated with technology investments.
Efficiency gains are another measurable outcome of maturity, as resource allocation improves with automation. As repetitive tasks are automated, teams can shift focus to high-value strategic initiatives rather than routine data handling. At more advanced stages, brands can also assess the scalability of innovation. Successfully tested AI innovations can be rolled out across operations or customer experiences, generating sustainable improvements in performance and customer engagement.
Lastly, as data and AI systems mature, brands see a tangible impact on customer satisfaction and revenue growth. Enhanced personalization and optimized operations contribute to higher customer retention and improved revenue, linking maturity in data capabilities to essential business outcomes. Together, these metrics create a comprehensive view of how each stage of maturity drives a brand’s broader goals and competitive edge.
Data and AI maturity is an ongoing journey, but one that brings substantial benefits as brands progress. The capability-based maturity model allows for strategic, prioritized investments across areas that matter most to the business. By advancing along this model, brands can unlock more value from data, enhance AI applications, and ultimately improve agility and customer satisfaction—critical differentiators in the composable commerce landscape.
Everett Zufelt
VP, Strategic Partnerships & Emerging Technology, Orium
As VP Strategic Partnerships & Emerging Technology at Orium, Everett leverages his extensive technical background and over a decade of experience in headless and composable commerce to lead the development of Orium’s offerings. He guides the go-to-market strategy and supports his teams in crafting solutions that enhance the digital capabilities and operational efficiency of scaling commerce brands.