AI at Work: The Human Factor
Problem framing, input intelligence, results assessment, and clear communication - these essential skills put human judgment at the center of deploying AI effectively and responsibly.
Another Post on AI? Why You Should Keep Reading
There’s so much information and so many opinions on AI. Some predict that in ten years AI will be so advanced we won’t need to work, and we’ll spend our days eating bonbons. Maybe. But in the meantime, there’s a gap in the conversation: how humans actually shape and deploy AI at work, and how we make responsible decisions based on what it produces.
Our goal at We Dig Data is to build your confidence and savvy around data and related technology so you can ask sharper questions and make better decisions. AI is a collection of building blocks powering ever more sophisticated tools. To use them effectively and responsibly, you need more than curiosity. You need real understanding, judgment, and skill.
In this 5 part series, we’ll focus on grounded ways to put AI to work, set limits where it falls short, and strengthen the judgment that matters most. Expect practical advice and simple frameworks to guide your thinking.
You Do Not Need to Freak Out. But You Do Need to Learn.
Every wave of technology sparks anxiety and speculation before it settles into the everyday. While technological shifts are disruptive, what endures isn’t the tool itself - it’s the people who know how to use it well.
You don’t have to be an AI expert to put it to work. You don’t need expensive training or certification.
What you need is a solid foundation: familiarity with key concepts to ask sharp questions, judgment to assess when AI is useful (and when it isn’t), and the ability to work with technical partners. You’ll need openness to change - and the discernment to say no when an AI tool won’t deliver value.
In short, you need to be human. To effectively deploy AI and harness its potential, we need to embrace the roles both technology and humans play.
The Human Factor: Four Essential Skills
Plenty of voices hype AI’s future. This series of posts is about something else: the skills and judgment people need right now to make it work. Each post will dive into how to deploy AI at work effectively by leveraging one of these core human skills:
Problem Framing: Identify the real challenge you’re trying to solve, then decide if it’s an AI problem at all. If it is, frame the problem in a way that guides AI toward the most useful, accurate, and actionable results.
Input Intelligence: Data and context matter. Look closely at what’s going in, because that’s what shapes the output. Strong data inputs can create a competitive advantage, but also have gaps, biases, and assumptions.
Results Assessment: With clearly framed problems and a solid understanding of inputs, critically evaluate whether results make sense, align with your goals, and reveal valuable insights.
Communication & Influence: Interpret, share and act on outputs effectively. Explain complex information clearly. Be prepared for disruptive technologies to prompt emotional reactions and manage appropriately.
These skills keep teams and organizations resilient and effective. AI doesn’t replace people - it makes them more important.
First Things First: What is AI?
The term “Artificial Intelligence” (AI) was coined in the 1950s to describe machines that could mimic human thinking. Today, it covers a range of technologies like machine learning, natural language processing, and computer vision that enable computers to learn, predict, and create.
At work, people are using AI technologies to improve productivity, decision-making or their products and services. You’ve seen it already: email drafts that write themselves, customer service chatbots, product recommendations, medical image analysis, and predictive models that spot fraud or forecast demand.
AI isn’t a single thing, and it didn’t appear overnight. Decades of research in logic, statistics, and computing paved the way for today’s generative tools like ChatGPT.
You don’t need to predict the future of AI. You just need to build the skills to use it wisely today. That’s what this series is about, and it starts with a critical step: framing the problem.
Next up in the series: Problem Framing. Why the way you frame the challenge determines whether AI delivers value - or just noise.
You’ve got this. And we’ve got you.
This is the first in a series of six posts focused on AI at Work: practical guidance for moving from AI experimentation to workflow integration. Check out the next two posts: AI at Work: Frame the Problem and AI at Work: Why Data Inputs Matter
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