Real AI Transformation Starts with a Dynamic Source of Truth
Moving from knowledge storage to fact-based operations is the key to unlocking AI’s full business value.
There are lots of things about AI that are a mystery at present, but one thing we can be sure of: AI is only as good as what it knows.
Like any team member, without proper training and context, AI will fail. And unfortunately, right now, most business AI is working with stale, scattered, and often contradictory information. Companies still rely on static knowledge bases, bloated documentation, and legacy file structures to “teach” their systems what’s true— assuming the knowledge even lives somewhere accessible in the first place. The result? Misinformed agents, fragmented workflows, and decision-making that lags behind the business. It goes beyond a technical shortfall into the realm of strategic liability.
The organizations reaping real value from AI, by contrast, aren’t just feeding it more data. They’re building what we call a Dynamic Source of Truth (DSOT)— not a warehouse of documents, but a structured, updatable layer of business facts. This layer becomes the foundation for how AI systems understand, reason, and act. It’s what allows them to move from passive knowledge retrieval to agentic execution, operating in lockstep with the business rather than five steps behind it.
DSOTs align your assistants, workflows, and teams around what’s actually true in the business at any given moment. They ensure every AI interaction—from summarizing a meeting to updating a pricing model—is rooted in accurate, current context. And unlike traditional knowledge systems, DSOTs are designed to evolve continuously, reflecting every change in your business with precision and speed.
Static Knowledge, Sluggish AI
Legacy knowledge systems like wikis, PDFs, and SharePoint folders weren’t designed with AI in mind. They were built for human reference, not machine action. These systems are slow to update, hard to standardize, and impossible to query reliably at scale. Even the most advanced LLMs struggle when the source material is outdated, inconsistent, or locked behind pages of prose.
The implications of this are serious. Out-of-date facts lead to misaligned recommendations, broken workflows, and customer experiences that erode trust. When your AI assistant pulls last quarter’s pricing or references an old compliance policy, the downstream effects ripple fast. What’s worse: there’s often no clear owner or process for correcting these facts across systems.
Importantly, it’s not just about having more information. More data, in more places, only makes the problem worse. That’s why vector databases are gaining traction— they store meaning, not just words. Instead of matching exact terms, they let AI find information based on similarity in context. This means a question like “How do I refund an order?” can surface the same answer as “What’s the return policy?” even if the phrasing is different. It’s a foundational shift that helps AI move from passive lookup to active reasoning.
All of these hidden, outdated, and inaccessible knowledge silos create friction. While companies experiment with AI at the edges, their underlying information architecture keeps pulling them back. It’s ultimately too fragmented, too rigid, and too slow to support the systems they’re trying to build.
What a Dynamic Source of Truth Actually Looks Like
A DSOT is not a file system or a set of notes. It’s a structured, machine-readable layer of business facts—entities, attributes, relationships—that reflect the current state of your operations. These facts can be queried, updated, and reasoned over by both humans and machines. They’re version-controlled, traceable, and composable.
For example, instead of referencing a policy document, your DSOT contains a live representation of the policy itself: its scope, effective dates, owners, and dependencies. Change one element, and every connected workflow, agent, or content asset updates automatically. No more manual syncing. No more version drift.
In practice, DSOTs often draw from multiple systems (e.g., ERP, CRM, HRIS), and unify them into a coherent ontological layer. But unlike data warehouses, they aren’t focused on analytics. They exist to support operations, making sure that every AI-augmented action is based on what’s actually true now.
Why This Matters for the Business
The strategic value of a DSOT is simple: it keeps your business aligned, fast, and resilient. When facts change—for example, when a product is deprecated, or a regulation shifts, or a pricing tier is updated—that change propagates instantly. Everyone and everything, from employees to AI agents, works from the same playbook.
This kind of alignment doesn’t just reduce mistakes, it accelerates execution. Marketing can launch campaigns without waiting on product updates. Sales can quote faster with confidence. Customer service can resolve issues without escalation. AI becomes a partner in speed, not a source of confusion.
Just as importantly, a DSOT reduces risk. It ensures that AI outputs are trustworthy and auditable. You know where a fact came from, when it was last updated, and who approved it. That traceability is crucial for industries with compliance and regulatory pressures, and it’s becoming non-negotiable as AI takes on more mission-critical roles.
Building Toward a DSOT
You don’t need to start with the whole business— in fact, it’s inadvisable. Biting off more than you can feasibly chew is a sure-fire way to lose momentum, stall out, and never see the ROI for your efforts. We recommend beginning by focusing on a few high-leverage domains: pricing, policies, product taxonomy, or support procedures. Early on, much of the work is surfacing key knowledge still buried in people’s heads, scattered across chat threads, or locked in hard copy documents. The goal is to extract the facts from where they currently live, define them structurally, and create a shared system of record.
This usually involves a combination of subject matter experts, structured data tools, and AI agents trained to update and query the DSOT. Over time, more domains get added. The ontology grows. The business gets faster.
As with almost any change in a business, the bigger shift is mental. Building an effective DSOT means treating facts as infrastructure, not afterthoughts.
That also means assigning clear ownership. Just as you’d staff a cloud platform or an integration layer, someone needs to be responsible for designing and maintaining the context that powers AI systems. In many organizations, a new kind of role is emerging here—sometimes called a Context Engineer—focused on curating business knowledge, defining how it’s retrieved, and ensuring every AI interaction is grounded in current, usable truth. It moves the work beyond training models to instead focus on engineering the connective tissue that makes AI work across the business.
Beyond the Stack: A New Operating Model
Digital leaders have spent the past decade building composable stacks. DSOTs are what make those stacks intelligent. They act as the connective tissue between systems, teams, and AI agents, allowing the entire organization to move with clarity and precision.
The companies that embrace this shift won’t just be more efficient. They’ll be fundamentally more agile— able to adapt, automate, and align in real time. And that’s what AI-native business actually looks like.
Not document-driven. Fact-powered.
Leigh Bryant
Editorial Director, Composable.com
Leigh Bryant is a seasoned content and brand strategist with over a decade of experience in digital storytelling. Starting in retail before shifting to the technology space, she has spent the past ten years crafting compelling narratives as a writer, editor, and strategist.