Here's what the numbers tell us: 96% of companies have integrated AI into their core business processes. 52% describe their AI efforts as "significantly successful."
Here's what the numbers don't tell us: only one in four AI initiatives actually deliver their expected ROI.
I see this disconnect everywhere. Product-driven companies in CPG, Automotive, and Software are rushing to deploy AI technology while their most critical asset remains largely inaccessible to these systems.
The issues aren’t AI capability. The issues are with data access and how seamlessly AI’s power is threaded into key workflows and scenarios.
Only 38% of IT leaders have achieved full data access for their AI initiatives. Just 9% report that all their data is both accessible and usable for AI.
Why? IT teams are emphasizing governance controls over maximum context for AI systems. Additionally, most tools require too much setup or awkward process changes to get wide adoption.
Take a common scenario: an IT team prevents their organization's AI meeting assistant from capturing meetings with outside suppliers. They're protecting data flow, which makes sense from a security standpoint.
But they're also stifling natural workflows and depriving the organization of critical context. Supplier conversations that could surface supply chain risks, highlight key project dependencies, or signal relationship problems never make it into the organizational knowledge base.
Here's what happens when official channels are too restrictive: people find workarounds.
There's an explosion of tools that let users record and generate meeting notes without conspicuous AI assistants on calls. Devices like Plaud allow people to inconspicuously record everything.
The irony is striking. The very controls meant to protect the organization create blind spots. Hidden recording methods only benefit the single note-taker and generate meeting notes that are often out of reach of the same IT teams trying to control the data.
About 42% of organizations lack access to sufficient proprietary data for their AI systems, yet employees are creating more data silos through shadow AI usage.
I believe transparency creates better organizational outcomes than control.
Security and compliance matter enormously. But the solution isn't restricting data access. It's building systems that create an AI-ready project context using role-based controls and a security-first posture.
Organizations need to enable teams to work with each other and outside partners, vendors, agencies, and suppliers in an ecosystem that builds full project context for AI. I’m not proposing that an organization opens up everything in its organization. Access control is important. Teams need to have the ability to easily and safely add context to the projects they are working on so that it is optimized for AI. This will drive incredible efficiency, alignment, and effectiveness.
The difference in our approach at Empwr.ai comes down to architecture. Most organizations store flat data that lets AI retrieve and summarize. But a knowledge graph lets AI reason, infer, and coordinate.
This turns disconnected project data into a living system of context that supports proactive insights, automation, and collaboration across teams.
When AI systems can reason through structured project knowledge, they detect conflicts before they happen. Two projects competing for the same resource get flagged early.
They suggest missing connections. An action item with no owner gets automatically linked to the team responsible for the related deliverable.
They anticipate outcomes. Delays in one task trigger alerts about downstream impacts on dependent projects and milestones.
This is the difference between basic AI assistance and transformative AI capability.
Knowledge graphs are emerging as the key technology for making enterprise data truly accessible to AI systems. Gartner predicts GraphRAG will reach maturity in two to five years.
The companies getting this right aren't waiting. They're deliberately making project conversation data from meetings, documents, and tools accessible as usable context for AI systems.
They're building transparent systems where notes benefit all attendees and stakeholders, not just individual note-takers.
AI technology has matured. The question now is whether organizations will build the data infrastructure to match it.
Because without that bridge, all those AI success stories might just be fiction.
At Empwr.ai, we’re building a system for teams to create project context that compounds in value, is designed for enterprise-grade security and privacy, and most importantly, fits within natural workflows to encourage wide adoption. Book a demo if you’d like to learn how Empwr.ai can help you actually win with AI.
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