Answer extracted from the Unprompted: Real AI in IC podcast — listen to the full episode below.
How does layered data improve internal communications strategies with AI?
Individual data points are useful, but combining multiple sources—internal surveys, employee satisfaction data, event feedback, and external research indexes—creates exponentially richer insights for AI-powered communications decisions. This layered approach gives your AI agents and human team the full context they need to craft more effective, targeted messaging.
The power of layered data lies in depth. Rather than relying on a single metric or feedback channel, Rebecca Lindsay compiles a personal internal comms brain that pulls from multiple simultaneous data streams. This includes hard metrics like internal comms survey results and employee satisfaction scores, alongside qualitative inputs like event feedback and benchmarked research from reputable sources.
As detailed in this episode of Unprompted: Real AI in IC, external research frameworks like the Institute of Internal Comms index become particularly valuable when layered with your own organizational data. These indexes reveal macro trends—such as the finding that people are increasingly time poor—which you can then cross-reference with your own employee feedback to understand how that pressure manifests in your specific culture.
Why a single data point falls short
One survey result, one satisfaction score, or one event feedback comment tells you what happened, but not why it matters or how to act on it. Layering changes that calculus entirely. When survey data aligns with event feedback and external benchmarks all point to the same tension, your AI agents have unambiguous direction on where to focus comms efforts.
Rebecca emphasizes that richer context enables richer output from both AI tools and human judgment. Instead of defaulting to generic messaging, you can craft internal communications that directly address the specific pressures and priorities your people face. As discussed in the full podcast episode, this principle applies whether you're designing an AI agent or writing a strategic brief for your leadership team.
"We want to be giving you the tools to do great comms yourself, and that is precisely what AI can enable us to do at scale."
Rebecca Lindsay — Internal Comms Manager, Hyper Exponential. At Hyper Exponential, a tech-focused organization with an AI-maximalist leadership culture, Lindsay has pioneered the use of AI agents and tools to solve real internal communications challenges. Her work spans tone of voice agents, emotionally intelligent communications checkers, and employee enablement systems—all grounded in rigorous data layering and behavioral design principles.
The practical implication is clear: before you build an AI agent or refresh your comms calendar, audit your data sources. Look for gaps where you're flying blind, then systematically layer what you learn across all future strategy. This layered approach is what transforms AI from a content-generation shortcut into a strategic decision-making engine for internal communications.
Key takeaways
- Multiple data sources combined—surveys, satisfaction scores, event feedback, and external benchmarks—deliver far deeper insights than any single metric alone.
- External research indexes like those from the Institute of Internal Comms reveal macro trends that, when layered with your own data, unlock actionable context for comms planning.
- Richer data context enables both AI agents and human communicators to craft more targeted, effective messaging aligned to real organizational pressures.
- A personal internal comms brain that systematically layers data sources becomes a strategic asset for all future communications decisions.
See also
How does an emotionally intelligent communications checker agent work and what are its limitations?
Rebecca created a rudimentary agent that reviews communications against key principles of emotionally intelligent communication using a scoring rubric, helping ensure messaging resonates on both rational and emotional levels.
What prompt engineering techniques help ensure better AI agent outcomes?
Rebecca recommends asking the AI tool to clarify your requirements before running the prompt, saying you don't know what you don't know. Starting with this dialogue helps surface hidden assumptions and improve agent performance.
What are the key steps for implementing and rolling out an AI agent in an organization?
The three steps are: first, identifying the need through data or observed friction; second, creating the agent itself; third, behaviorally designing the rollout to drive adoption and demonstrate value to users.