AI at Work: Translating AI Results into Decisions and Action

The crucial step that turns AI-assisted work into trusted practice.

Before your AI-assisted project can become part of how your organization works, you have to do two things that only people can do: translate what AI produces and share it so others can understand and trust it.

Up to this point in the AI at Work Series, we’ve focused on preparing the ground:

• Framing the problem so you know what you’re solving.

• Providing quality inputs so the AI has context.

• Evaluating results to decide what to trust, adapt, or discard.

Now we turn to integration, communicating how AI fits in the flow of work:

• Where it enters: the task or stage it supports.

• What happens around it: the human context, checks, and edits that make it reliable.

• What follows: the decisions, actions, or communications it enables.

When you make that flow visible - how AI fits between clear human inputs and accountable human outcomes - people can see its value and trust its place in the workflow.

That’s what this post is about: how to translate and share AI-assisted work so it’s understandable and actionable, bringing an AI experiment into everyday operations.

Show Where AI Fits

Before you can communicate what AI helped produce, build the big picture of how it fits into your workflow - where it comes from, where it’s going next, and how people shape it along the way.

Integration starts with visibility: demonstrating how an AI-generated analysis or draft passes through human decisions, checks, and refinements before it becomes part of real work.

The first step in making AI a reliable partner is showing how it supports and complements human effort.

For instance, in a state agency evaluating community feedback, AI might be used to summarize hundreds of open-ended survey responses. The model can group comments into themes such as access to services, staffing, or digital resources, surfacing what’s most frequently mentioned.

Human reviewers take that raw analysis and strengthen it - verifying that categories align with local program structures, adding context from field staff, and refining insights so they’re accurate, relevant, and ready to inform policy decisions.

Translating your AI-project is showing how an AI task connects to the work around it. This builds credibility. It demonstrates both governance and intent. And when people understand where AI fits, they’re far more likely to trust it - not because the technology changed, but because they can see its place in your process and the system of human judgment that surrounds it.

Build on What You Have Documented

Now that you’ve translated where AI fits, refine the record of your work to capture how it came together. Here, your documentation to date turns experimentation into evidence. Your goals, prompts, datasets and validation steps form the backbone of credible communication, and help others see how your result came to be.

That record matters. It’s how you demonstrate diligence when questions arise around using AI, the basis for AI inference and what and how outputs were verified.

The art and skill is in the balance: communicate enough detail to inspire confidence, but not so much that you overwhelm your audience. Transparency isn’t about showing everything - it’s about being ready and able to show your reasoning when it counts.

Make AI Results Decision-Ready

Every AI-assisted result is only as valuable as the decision or action it enables. To move from experimentation to integration, your work must guide what happens next.

Take your translation and documentation, and begin to craft them into your overall communication story.

When sharing results, be sure to explain:

1. Why we did this: What problem or goal were we solving?

2. What we did: What approach and data did we use?

3. How we did it: Which tools, prompts, or processes shaped the result?

4. How we’ll use it: What decisions or next steps will this inform?

5. How we’ll know if it’s working: What feedback or evidence will show success?

Clear explanations not only bring your audience along, but help them use the new workflow and turn your success into a playbook for the next project.

What’s At Stake

At this stage, your role is communication - making AI’s contributions clear, relevant, and ready to adopt. Whether you are informing a decision, designing a product, or analyzing data, what and how you communicate will determine whether your AI project becomes a useful part of the workflow or remains a novelty.

To do that, your communication needs to bridge three key gaps for your audience:

• Comprehension: Your audience wasn’t part of the process and may not understand what was done or why. Tell the story clearly without drowning them in detail.

• Context: AI doesn’t know your organization’s policies, priorities, or nuances. It’s up to you to connect results to what matters locally.

• Trust: People are cautious, often for good reason. To earn their confidence, show that you’ve set clear standards for how AI is used, reviewed, and validated.

Adapt to the Audience You Have

Communication is the responsibility of the communicator.

Accuracy matters, but relevance is what earns trust. Each audience brings its own priorities and comfort level with AI, so your communication needs to meet them where they are.

• Technical teams want to understand data sources, thresholds, and model performance.

• Leaders focus on what decisions can now be made, by whom, and how much faster.

• Public or policy audiences look for reassurance that privacy, fairness, and accountability have been handled responsibly.

AI can help you reformat and simplify your communications, but only you know what each stakeholder group values. Your job is to shape the message - what to emphasize, what to simplify, and what to hold back for deeper discussion.

Stakeholder uncertainty is normal. The key to adoption is to show your audience where the AI technology operates in the workflow, and where people remain firmly in control.

Build the confidence of your audience using practical techniques:

• Illustrate the process of reviewing and refining inputs, prompts, outputs and so on by showing before and after examples.

• Invite input. Ask “Does this interpretation align with your experience?”

• Check across audiences. Can someone outside the project understand your summary?

Clear, tailored communication does more than inform; it earns trust and reinforces that human judgment still anchors the process.

Summary: Turning Your AI Experiment into Practice

Translating and sharing AI-assisted work is what turns testing into trusted practice. It’s where human judgment makes AI results credible, meaningful and ready for use.

Before any AI-assisted result can guide action, your communication has to answer five questions clearly for your audience:

1. Why we did this

2. What we did

3. How we did it

4. How we’ll use it

5. How we’ll know if it’s working

If you can explain those points, you’ve already built the foundation for responsible AI usage and integration.

Show where AI fits, record what it does, and communicate how it’s used - that’s how experiments become everyday work.

Communication is what completes the chain: connecting analysis to decision, experimentation to integration, and innovation to impact.

As you move forward, stay structured, stay transparent, and keep showing your work. That’s how you transform exploration into sustainable capability - and prepare your organization for what comes next.

Wrapping Up the Series

This post concludes our AI at Work series. We’ve explored what it takes to move AI from experimentation to integration - to make AI a practical, trusted part of everyday work.

We began with the human skills that make AI effective: framing the problem, providing quality inputs, evaluating results, and now, communicating those results clearly and credibly. Each step reinforces the same principle: AI can successfully elevate your work when people bring structure, judgment and intent to how it’s used.

Even the best process won’t sustain itself without people who understand it.

Integration of new technologies means learning new approaches, building new skills, and redeploying talent as part of a shared, evolving cycle. Strengthening existing capabilities, developing new ones, and continuing to experiment are what keep both people and processes moving forward.

Thank you for following along with the AI at Work series. We hope it’s helped you move from experimenting with AI to integrating it with confidence.

The AI at Work series provides practical guidance for moving from AI experimentation to workflow integration. Check out the other posts: The Human Factor, Frame the Problem, Why Data Inputs Matter, and When to Trust, Adapt, or Toss AI Outputs.

If these posts sparked ideas or helped you think differently about how AI fits into your work, share it with a colleague or drop us a note. We’d love to hear what you’re trying next!