AI Maturity Requires More Than Technology Adoption
The Data + AI Summit is Databricks’ annual conference for customers and others. The event draws thousands of data, analytics and IT leaders, developers, data scientists and business executives to discuss emerging trends in artificial intelligence, data management and machine learning. Databricks, a data and AI platform provider, helps organizations unify data engineering, analytics, machine learning and AI workloads on a single platform used by enterprises worldwide.
Sutara and Botha outlined three stages of AI maturity that the platform commonly observes among its customers.
The first group, which the company calls “scalers,” are focused for now on tactical productivity improvements. These organizations automate repetitive tasks, streamline workflows and use AI to augment existing processes without fundamentally changing how work gets done. Databricks estimates that approximately 60% of organizations currently fall into this category.
Among them: a government agency in the Philippines that reduced dashboard creation times from 90 days to 30 days while saving approximately 8,000 labor hours. Another government agency in New Zealand used generative AI-assisted development to create data pipelines in two weeks instead of an entire quarter.
A smaller group of “reinventors” comprise about 30% of Databricks’ customers, they said. These organizations have begun redesigning workflows, goals and operating models around AI capabilities. Rather than simply automating existing processes, they ask how work would be structured if it were designed today using modern AI tools. An example includes athletic apparel giant Adidas, which has explored using Databricks’ agent technology to help executives access business insights directly, reducing reliance on analysts and enabling leaders to answer operational questions through self-service AI tools. The result was not simply faster reporting but also a redesign of decision-making workflows.
The remainder are “native AI operators.” This group of no more than 5% of organizations are redesigning organizational structures around agentic systems and, in some cases, treating AI agents as workforce participants that are managed and measured alongside human employees. An example here is Workday: Rather than using AI solely for incremental productivity gains, Workday has re-examined entire business processes and deployed AI agents in production, demonstrating how enterprises can redesign operations around agentic systems.
Agentic AI Demands Organizational Reinvention
Sutara and Botha emphasized that successful AI transformation requires organizations to rethink how people and AI systems collaborate. Botha pointed to convenience retailer 7-Eleven as an example. The company initially deployed a basic retrieval-augmented generation chatbot to support marketing activities but found that the system struggled to capture the brand’s voice and creative requirements.
Rather than abandoning the effort, the company redesigned the workflow around multiple specialized AI agents, including creative design, copywriting and supervisory agents that work together as a coordinated system.
The example underscored a broader lesson: AI transformation is often iterative rather than revolutionary.
“It's not an all-or-nothing,” Botha said, adding that organizations frequently move from one narrowly defined use case to another, gradually building capabilities that ultimately support larger operational changes.
As organizations look to move beyond pilots and proofs of concept, Sutara urged leaders to focus less on permission and more on experimentation. “Stop asking for permission,” she said. “Just go do something. Go build something. Take the ownership, find the problem, do the solution, and build it out.”
