Dun & Bradstreet found that 97% of organizations have active AI initiatives, but only 5% say their data is fully ready for AI. That gap can block enterprise scaling, but it doesn’t have to block visibility into how AI is already being used.
Key Takeaways
- Dun & Bradstreet found that 97% of organizations report active AI initiatives, while only 5% say their data is fully ready for AI.
- Enterprise-wide data readiness is necessary for scaling AI across mission-critical workflows, but it isn’t required to begin measuring adoption, tool usage, and spend.
- Start with behavioral telemetry and targeted integrations, then add deeper outcome measurement as the relevant business data becomes available.
The 97%–5% Gap Is About Scale
The gap looks alarming, but adoption and data readiness measure different stages of AI maturity. Employees and departments can launch copilots, chat tools, and contained use cases with limited enterprise data. Scaling AI across core operations is harder.
Dun & Bradstreet’s chief strategy officer told CIO that organizations don’t need enterprise-wide AI-ready data for pilots or isolated use cases, but they do need it to scale AI reliably across mission-critical workflows and systems.
That’s where clean, interoperable, governed data becomes critical. An AI system handling customer onboarding, financial forecasting, compliance, or supply-chain decisions needs accurate data, consistent definitions, clear ownership, and reliable connections across systems.
AI measurement has a different starting point. Leaders can begin by seeing which tools employees use, how often they use them, where adoption is growing, and what those tools cost. That visibility doesn’t require the organization to finish a company-wide data modernization program first.
Why Waiting for Perfect Data Creates Another Blind Spot
Preparing enterprise data for AI is difficult because it’s scattered across legacy systems, cloud platforms, departmental tools, and acquired businesses. Definitions conflict, ownership is unclear, and quality varies across systems and workflows.
CIO reports that poor data can leave AI teams spending most of their time wrangling data, reworking pipelines, and compensating for weak inputs. In our work with enterprises, we’ve seen broader data-readiness programs stretch across 12 to 18 months.
If AI measurement waits for that work to finish, the organization can spend another year adding tools, licenses, agents, and use cases without a reliable view of adoption or cost. The data foundation may improve while the AI visibility gap gets worse.
The better approach is to treat data readiness and AI measurement as parallel tracks. Continue the data work required for reliable enterprise scaling, but start collecting the signals that are already available.
What You Can Measure Before Your Data Is Fully Ready
AI measurement can start with three practical data layers.
Behavioral Telemetry
Behavioral telemetry shows which AI tools people use, how frequently they use them, and where AI is becoming part of daily work. Browser- and desktop-level monitoring can provide this visibility without depending on a clean enterprise data lake.
Larridin’s AI Adoption dashboard uses this layer to show adoption trends, tool usage, team differences, and cost. It establishes a baseline leaders can use while deeper integrations are still being planned.
Tool and Spend Data
AI providers already generate the data needed to track licenses, tokens, API calls, and tool usage. Bringing it together helps leaders spot underused or overlapping tools, rising consumption, and spend without clear ownership.
This doesn’t prove business impact, but it answers important early questions: What are we running? Who’s using it? What’s it costing? Where is activity growing faster than governance or budget control?
Targeted Workflow Data
Organizations rarely need to clean every enterprise system before connecting one high-priority workflow. GitHub, Jira, CRM, service-management, and other productivity systems may already contain structured outcome data.
A targeted integration can connect AI usage with cycle time, throughput, quality, or revenue in a specific workflow. That produces a stronger impact signal without turning enterprise-wide data readiness into a prerequisite for every measurement decision.
Where Data Readiness Still Matters
Telemetry can tell you who’s using AI and how. Proving business impact also requires outcome data.
Larridin’s enterprise AI impact guide describes three complementary sources:
- Real-time telemetry provides the behavioral baseline.
- Business-system integrations connect AI activity with outcomes.
- Targeted surveys add employee context that the other two sources can’t capture.
Start with visibility, then expand measurement where the business case justifies more integration and data work.
A Practical Measurement-First Sequence
- Establish the baseline. Identify the tools in use, active users, adoption patterns, and spend.
- Choose priority workflows. Focus on use cases with strategic value, high cost, or clear scaling potential.
- Define the outcome. Select the business metric the AI investment should change, such as cycle time, quality, cost per outcome, or revenue.
- Connect the necessary systems. Integrate only the data sources required to evaluate that workflow.
- Use the evidence to prioritize data readiness. Invest first where better data will unlock a use case with demonstrated demand and measurable value.
This approach makes data-readiness investment more focused. Instead of trying to solve the full enterprise data estate before learning anything, leaders use measurement to decide which data problems are worth solving first.
Frequently Asked Questions
Why is enterprise AI data so hard to prepare?
Enterprise data is distributed across systems, formats, owners, and business units. Preparing it for AI requires quality controls, governance, integration, consistent definitions, and ongoing maintenance. The difficulty increases when AI needs to operate across mission-critical workflows rather than a contained pilot.
What can we measure before enterprise data is fully ready?
Organizations can identify which AI tools are in use and measure active usage, adoption by team, feature engagement, license utilization, token consumption, and spend. Targeted integrations can also connect AI usage with outcomes in selected workflows before the entire data environment is ready.
Can behavioral telemetry prove AI ROI?
Not alone. Telemetry shows what people are doing with AI. Proving impact requires connecting that behavior with workflow or business outcomes. Telemetry is the starting layer, not the whole measurement system.
How should we prioritize data-readiness investment?
Start with workflows that show meaningful AI usage, strategic importance, or measurable potential. Then invest in the data quality and integrations needed to evaluate and scale those workflows. That grounds data priorities in evidence rather than assumptions.
Start Measuring While the Data Work Continues
You don’t need perfect enterprise data to start measuring AI usage. Larridin surfaces AI adoption, usage, and cost through behavioral telemetry, then connects those signals with business outcomes as the relevant integrations become available.
Book a discovery call to start measuring what your AI program is doing now.