Editorial
Walk into almost any organization today and you’ll hear two conversations happening at once. One is about data: architectures, pipelines, fabrics, lakes, flows and so forth. The other is about people: engagement, experience, communication, culture and connection.
The problems may sound different, but they’re more connected than you think.
Both are about how information flows through your organization. Whether it flows through APIs or all-hands meetings, through dashboards or daily stand-ups, the challenge is identical for you as the leader.
How do we make sure the right signals reach the right people at the right time so that we can make better decisions?
You need to answer that question before you can lay any kind of technology on top of it, whether it’s advanced analytics, AI or a new meeting schedule.
As organizations race to get AI-ready, it’s worth a reminder that building an AI-enabled enterprise is far less of a technical project than it is an organizational design project. The same principles that make a data fabric work, like shared standards, interoperability, feedback loops and cross-functional integration, are what make people work well together, too.
The Parallel Architectures of Work
A data fabric connects disparate systems into a unified, intelligent layer so that information can move freely across the enterprise. It’s how your data “talks.” You use it to weave together different sources, clean them up and provide context so analytics tools, agents or other automation can reason across them.
Now look at your organization. Departments often act like independent data systems. Finance doesn’t always talk to HR, Operations doesn’t sync with IT, and comms teams often learn about change after it’s already happened. So what would the architecture of a “data fabric” for information flow across your people look like?
- Shared data standards should look a lot like shared vocabulary, an agreement about what things mean before you act on them.
- Interoperability looks a lot like cross-functional collaboration, where your systems and teams can work together without translation errors.
- Feedback loops are pretty much the same, communication channels that listen, measure and adapt rather than the one-way broadcasts our communication can sometimes be.
When we talk about getting our data fabric right, the same principles apply to our human fabric.
Shared Standards
Ask a data architect what kills most AI projects, and they’ll tell you it’s inconsistent data standards. If every dataset defines an employee differently, your models won’t work. When working with the Army, we realized upon combining systems that we had 37 different ways to list an employee’s name.
The same thing can derail your human systems. If every department has different definitions of engagement, performance or innovation, under different authorities, it fragments your business processes and your culture.
Shared standards create the conditions for trust and scalability in both your data and your human use cases. When teams share a common language and business processes, they can integrate faster, just as when systems share a common schema, your tools can operate effectively across them.
It’s not a coincidence that both use cases require data governance and change management, either. Governance ensures consistency and change management ensures adoption. Together, they make shared meaning possible.
One of the most powerful things your internal comms team can do in an AI-driven organization is to set the semantic standard. When communicators define, translate and reinforce shared terms, they’re performing essentially the same function a data dictionary does in data architecture.
Interoperability
If shared standards define what things mean, interoperability defines how they work together.
Interoperability in your data architecture means systems can exchange and use information seamlessly. No manual handoffs, no translation layers that lose fidelity. In your human use case, interoperability means that departments, disciplines and teams can collaborate without (or with minimal) friction.
The modern workforce runs on interoperability. Hybrid work environments where humans and machines depend on seamless handoffs between humans and digital systems. AI-enabled workflows depend on reliable, cross-functional data flows.
The problem happens when organizations build their technical interoperability without considering their human one. We build APIs before we build relationships, and then miss functional interoperability, as well as the benefits of shared standards.
The most effective AI transformations start by mapping workflows at both levels. What systems and datasets need to talk to each other? Who owns those systems, and how do they coordinate decisions? And most of all, what decision am I supporting with this workflow?
You can automate a process, but if the teams responsible for maintaining that automation aren’t interoperable, you’ve just automated a bottleneck.
Cross-Functional Teams
Data fabrics thrive on nodes and connections, just like organizations. They’re effectively a technological social network map. Every system, dataset and model becomes a node in a larger connected mesh. The more you connect them, the more value you unlock.
Should sound a lot like your organization!
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AI-enabled work demands mesh teams: small, cross-functional groups that combine domain expertise, data literacy and change capability. Mesh teams function like micro-architectures inside the larger enterprise, bridging gaps between data scientists, technologists, HR, comms and line leaders.
AI becomes practical this way, as it coordinates dozens of workflows that depend on humans and machines sharing context and insight. Cross-functional teams are where that context lives. In many ways, they are the middleware between data and decision-making.
Feedback Loops
AI systems learn through feedback and reinforcement, the same way humans do — except their reinforcement is functions of ones and zeroes and not praise or rebuke. Supervised or unsupervised, machines adjust based on outcomes they are “taught” are positive or negative, the same way humans adjust when we get timely, actionable feedback.
But in some organizations, these feedback loops are broken or slow. Annual surveys replace real-time sentiment data, and project reviews lag behind the pace of change.
Modern data architecture prioritizes real-time streaming, the constant ingestion of and response to current data. The equivalent in your organization is continuous communication. Internal comms teams play a huge role here in closing the loop between leadership decisions and employee experiences. The human signals they provide guide better action.
Imagine combining those human feedback loops with data ones, which is largely what our employee experience systems do. Engagement metrics, collaboration patterns, workflow analytics — you can theoretically create a singular learning system so that all your workflows, whether human or machine, become teammates.
Designing Trust
Every architecture, data or human, lives or dies on trust. If employees don’t trust how their data is used, they’ll withhold it. If leaders don’t trust the insights AI generates, they’ll ignore them. Trust is the bridge between collection and action.
This means designing transparency into every layer. In technical terms, this looks like explainable AI, robust data governance and clear data lineage. In human terms, it looks like communicating why data is collected, how it’s used and what the employee gets in return — the foundations of informed consent.
When internal comms and data governance align, trust scales. Your organization can experiment and trust that the data you’re getting is what you need, your automation runs a reduced risk of alienating people rather than earning adoption, and you can personalize your systems without feeling like you’re invading privacy.
Transparency and trust accelerate AI adoption at every step, and also make a much more efficient and adaptable organization.
A Learning Fabric
“We are surrounded by data but starved for insights.” — Jay Baer, marketing and customer experience expert
When your data fabric and your human fabric both function, your organization becomes a learning network. Each interaction generates a signal, and those signals get interpreted, fed back into the network, and used to improve the next decision.
That’s the essence of an AI-enabled enterprise, continuously learning and adapting.
Leaders can build toward that by asking:
- Where are our isolated systems, both data and human?
- Do our communications channels mirror our data pipelines: fast, transparent and two-way?
- Are we governing our people and organizational data with the same care we apply to our financial or operational data?
- Are our employees literate in how AI uses their inputs and empowered to shape this?
When the answers to these questions are yes, you’ve achieved something that’s more powerful than a data strategy or a people strategy: an organizational intelligence strategy.
I can’t overemphasize the importance of internal comms in all of this. Often treated as the soft side of change, internal comms is infrastructure. It’s your interface with the workforce, whether human or digital. It translates strategy into meaning and connects disparate data points into narrative.
As AI systems generate more insights, employees need comms systems that contextualize those insights. Without that translation, it’s just noise.
Humans and Machines in Conversation and Cohesion
We talk about AI as if it’s a separate entity, but it ultimately is an extension of our collective intelligence. AI learns from our data, from our decisions, from us. And because of that, it can surface insights. But except for random emergent behaviors, it won’t build greater meaning or build our culture for us. That’s our job.
When we design systems that let our data talk clearly and import those systems into our human systems, we create space for our people to collaborate meaningfully. The organizations that thrive into the next decade are those that master both digital and human architectures with the knowledge that they are one and the same.
Further Reading:
- Keeping the Human Experience at the Forefront of HR — A human-centered employee experience is built on three pillars. In the Army, we look at the combination of: Quality of Life, Quality of Work and Connection.
- The Silent Exit of Employee Experience: A CIO's Call to Action — An open letter to CIOs on the role they play in protecting EX as a strategic priority — and why it matters for the long-term of their own functions.
- Work Design Is How Performance, Engagement and Well-Being Scale — When leaders organize work around how people actually collaborate, they'll drive performance and help workers finish their days with energy, not exhaustion.
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