Answer extracted from the Unprompted: Real AI in IC podcast — listen to the full episode below.
Should you define what you want from AI before diving into data analysis?
Yes—you absolutely need to know what you want to achieve before using AI for data analysis. A clear objective allows AI to help you find correlations and compare data points across different lenses, a task that would have required extensive manual analytics work and significant time investment in the past.
Starting with AI and letting it lead the analysis typically produces unfocused results. The most valuable approach combines your own work and insights with AI enhancement, rather than asking AI to find patterns in raw data from scratch. Amanda Atkinson, Communication and Culture Leader at Science Based Targets Initiative, describes this as the critical difference between exploratory floundering and purposeful analysis.
The distinction matters because while there is merit in exploring what AI tools can do, exploration without direction wastes both time and computational resources. As discussed in the episode, the real power of AI in data analysis emerges when you pair it with a human point of view—your hypothesis, your business question, your desired outcome—and then let the tool accelerate the correlation-finding and cross-lens comparison work that would have previously required dedicated analytics support.
"Where I start with AI, I find myself just getting a mess. Where I start with my own work and then use AI to enhance and challenge, that's where it works best for me."
Amanda Atkinson — Communication and Culture Leader and Internal Comms Person at Science Based Targets Initiative. Amanda has built her career in internal comms across several industries, always seeking ways to leverage technology and automation. She spent seven years at Salesforce leading internal comms for EMEA, where she developed a deeply tech-driven approach to her work. She describes herself as an AI intrepid explorer, enthusiastically investigating AI applications while maintaining a grounded acknowledgment of the learning curve ahead.
For a practical example of how this plays out, Amanda used AI to analyze feedback from the Science Based Targets Initiative's first all-staff off-site in Amsterdam, a meaningful detail expanded further in a dedicated segment of the podcast.
Why clarity of intent matters more than tool capability
The temptation to let AI-driven exploration lead your analysis is understandable; the tools are powerful and appear to offer unlimited analytical possibility. However, boundless exploration without a hypothesis typically produces noise rather than insight. AI is most effective as an accelerator of your thinking, not as a replacement for it.
When you arrive at AI with a clear question—such as "What themes emerge most frequently in our team feedback?" or "Which departments report the highest engagement?"—the tool can compare data points across multiple dimensions far faster than manual analysis would allow. This transformative speed gain only materializes when you know what question you are asking.
See also
What is a concrete strategic use case for AI in internal communications?
Amanda used AI to analyze feedback from Science Based Targets Initiative's first all-staff off-site in years held in Amsterdam. She combined overwhelmingly positive feedback with deeper pattern recognition to uncover actionable insights for her internal comms strategy.
How should internal communicators approach using AI for writing and content creation?
Amanda recommends not using AI to start from scratch for writing, as that tends to produce poor results. Instead, she uses AI to scale and refine existing work—a principle that extends beyond writing to all AI-assisted tasks.
What is 'bot sitting' and how widespread is it among workers using AI tools in 2024–2025?
According to a study surveying 6,000 people across the US, the UK, and Australia—'bot sitting' refers to the time workers spend waiting for or actively monitoring AI tools to complete tasks, a widespread inefficiency in current AI adoption workflows.
Key takeaways
- Define your analytical objective before involving AI—vague exploratory prompts produce unfocused results and wasted effort.
- AI excels at accelerating work you've already started: finding correlations, comparing data across dimensions, and surfacing patterns that manual analysis would take weeks to uncover.
- Pair AI tools with your own hypothesis and business question; let the tool enhance your thinking rather than replace it entirely.
- Clear intent transforms AI from a time sink into a productivity multiplier for data-driven decision-making in internal communications and beyond.