According to McKinsey's 2026 AI Trust Maturity Survey (published March 2026), responsible AI maturity is improving overall, but governance and agentic AI controls remain the biggest laggards: only about one-third of organizations have reached a mature level in those areas, even as adoption accelerates. The constraint was never a shortage of AI use cases.
At Lingaro, we've helped global organizations deploy AI-powered supply chain, commercial excellence and revenue growth management solutions on platforms including Databricks and we've seen the same pattern repeatedly: organizations struggle to generate value from AI when business users don't fully trust the data, the governance behind it, or its recommendations. Modernization builds that trust. Trust drives adoption. Adoption turns a working model into a business outcome. And AI is no longer just a CIO concern. It’s a C-suite priority.
Data readiness: The foundation of trust
AI systems are only as trustworthy as the data behind them, yet many organizations still run on disconnected supply chain, inventory and commercial systems. A Fortune 100 consumer packaged goods company faced exactly this problem: inventory data was scattered across distribution centers, warehouses, and retail locations, limiting visibility and slowing decisions. Combining supply chain and commercial domain expertise with a modernized lakehouse platform, the company built a trusted data foundation for real-time operations and enterprise AI. Lingaro has delivered a 20% reduction in inventory costs and a 30% reduction in working capital tied up in inventory.
Governance builds confidence, not just compliance
Many organizations still govern AI the way they governed static reporting: limited oversight, periodic review, no real-time visibility. That model breaks down once AI recommendations influence pricing and forecasting at scale; the gaps become business risk and users who stop trusting the system quietly route around it. Untrusted data is a bit like a car from 1984: it might still run, but nobody wants to depend on it for anything that matters.
Governance is what lets people and AI systems operate from the same business context. A shared business ontology, role-based access control (RBAC) and a semantic layer defining what “revenue” actually means create the consistency a recommendation needs to be trusted. Data governance builds trust in the data; model governance keeps the AI within business-defined boundaries – guardrails Lingaro embeds directly into solutions using tools like Databricks' Unity Catalog, rather than layering them on after deployment. A global CPG company needed exactly this to drive adoption of a new revenue growth management program: guardrails aligned recommendations with real business constraints from day one, delivering faster adoption and 7% year-over-year revenue growth.
Adoption is the real measure of success
Even the most sophisticated AI model creates no value if people don't use it. Adoption is consistently the difference between a program that succeeds and one that quietly fades: the revenue growth management engagement above delivered its growth as much through user testing and workflow integration as through the underlying analytics. Tools like Databricks Genie now let business users query governed enterprise data directly, in natural language, without waiting for a technical team. The goal was never simply to deploy AI. It's to embed data-driven decision-making into how the business runs.
The Design x Domain x Adoption approach
Lingaro addresses this through our Design x Domain x Adoption (DDA) framework: the same three gaps this article walks through, made operational. Design makes systems simple for people and consumable by agents: agent-ready architecture, machine-friendly interfaces, redesigned workflows. Domain codifies business knowledge into governed, responsible-AI models agents can act on, not just reference. Adoption treats agents as both tools and entities in their own right: onboarded, permissioned and monitored like any user, with value attributed to the outcomes they drive.
The gap holding most enterprises back isn't a shortage of ideas. It's unresolved challenges in data trust, governance maturity and adoption and the organizations that close them are the ones that convert AI experimentation into measurable outcomes.
Conclusion
Lingaro was on the ground at the Databricks Data and AI Summit 2026, where our team sat down with senior data and AI leaders navigating this exact transition. The result is Lingaro's Agentic Readiness 2026 report: real conversations, not a vendor survey. The findings tell the same story at scale: average AI readiness sits at just 2.75 out of 5 and while 73% report they're actively scaling AI in production, only one in five feel confident their governance framework can audit what their AI agents do. The gap isn't ambition. It's execution and it's closable.
Read the full findings in Lingaro's Agentic Readiness 2026: From AI Pilots to Scaled Execution report.
