How to Effectively Delegate to AI (And Know When Not To)

As we are asked to do more and more, it is imperative that we find ways to work smarter and not harder. A big part of this is learning how to delegate. Delegation allows you to multiply your capacity by focusing on the higher-level strategy and relying on someone or something to execute the details. The trick is figuring out which tasks can be delegated and by how much. This often depends on the skill level of the person or thing you are delegating to. As AI tools continue to evolve, it isn’t always clear what you can rely on them to do well and what things are better done by a human. Delegation loops are a helpful way of determining this.

Delegation loops

A delegation loop is the full cycle of handing a task to AI, reviewing the output, and closing the loop — either by accepting the result, sending it back for revision, or taking it back yourself.

The core loop has four steps:

1. Delegate — You hand the task to AI with enough context, constraints, and a clear definition of done.

2. Review — You evaluate the output against your intent. This requires knowing what "good" looks like.

3. Correct or Accept — You either refine your prompt and re-delegate, edit the output directly, or approve and move forward.

4. Apply — The output gets used, published, or acted on in the real world.

Start small. Think of a task you've done before that you already know the answer to. Use it as a test. Because you know what correct looks like, you can meaningfully evaluate AI's output — and learn where it falls short before the stakes are high.

Why loops matter

• Delegation without review is abdication. If you never close the loop, errors compound and quality drifts over time.

• Review without clear criteria is just vibes. You need to know what you're checking for, or the loop becomes a rubber stamp.

• Tight loops build trust. The more you delegate, review, and correct, the better your prompts get, and the more you learn where AI can be trusted to run more autonomously.

Is a task ready to delegate to AI?

Not every task is equally safe to hand off. A few things worth considering before you delegate:

• Reversibility — Can mistakes be undone easily? Lower reversibility → more human involvement.

• Stakes — High-consequence decisions (legal, financial, medical, reputational) warrant higher human control.

• Novelty — Routine, well-defined tasks are safer to delegate fully than ambiguous or one-of-a-kind situations.

• Verifiability — Can a human meaningfully evaluate the AI's output? If not, "human-approved" becomes a rubber stamp rather than a real check.

What makes loops fail & how to improve them

The AI tool lacks the context or capability to do the task well

Often, AI fails because it lacks sufficient information. This is a prompt and setup problem, and it's usually fixable. Give it background on your situation — who you are, what the output is for, what good looks like, and what to avoid. The more specific you are, the better the result.

That said, sometimes the task is genuinely beyond what AI can do reliably. No amount of better prompting will fix this. An important part of working with AI is learning to recognize the difference — and knowing when to take a task back.

A few things that help:

• Give background on your situation — your role, your organization, the audience the output is for, and the broader goal it's serving.

• Clarify the task precisely — describe what a good output looks like, not just what you want. Specify format, length, tone, and what to leave out.

• Provide the right raw material — paste in relevant documents, data, or prior work rather than describing them. Include previous outputs from the same loop, so the AI has continuity.

• Set the standard for success — tell AI how you'll evaluate the output. Describe what failure looks like so you can avoid it.

Humans lack the expertise to evaluate the AI tool’s output

Many times,  AI will generate something that looks good at first glance but, upon deeper inspection, proves to have flaws. Yet without the right subject-matter expertise, it is difficult to spot errors and assess quality. A common example is a PM reviewing AI-generated code. Without the expertise, a person may defer to the AI’s output simply because they have no basis to challenge it.

A few things that help:

• Bring in the experts to spot-check: AI tools may make us feel like we can do it all - research, write requirements, design, and write code, but without expertise in these areas, it is impossible for us to evaluate whether these things are actually being done WELL by AI. This is why it is so important to include ALL the functions of our Product org – UX, Product, and Engineering – in the ideation and evaluation process.

• Define explicit criteria for evaluating success before delegating: If you wait until you see the output to decide what you think of it, you risk anchoring to what the AI gave you. You might unconsciously adjust your standards to match the output rather than evaluating the output against your standards. Setting criteria first keeps you honest.

• Use a second AI pass as a lightweight check — asking it to critique or find flaws in the first output. The most useful mental model is to treat the second pass as a junior reviewer — good at catching obvious gaps and inconsistencies, but not a replacement for an expert eye when it really matters.

Feedback is too slow to catch errors before they compound

AI can produce output far faster than humans can review it. A human agent working slowly gives you natural forcing functions to check in. AI removes that friction, which means errors can travel much further down the pipeline before anyone notices. The speed that makes AI valuable is the same thing that makes slow feedback loops dangerous.

Consequences could look like:

• A flawed assumption in a PRD gets carried into design, then into engineering, then into QA — by which point unpicking it is expensive

• An AI summarizes customer feedback with a subtle bias, that summary informs a roadmap decision, the roadmap shapes a quarter of work, and the error isn't caught until a launch underperforms

• Code generated with a structural flaw gets extended over several sprints before the underlying architectural problem becomes apparent

A few things that help:

• Start with low-stakes tasks - When delegating something new to AI, begin with tasks where a slow feedback loop doesn't matter much. Build confidence in the output quality before letting it run further upstream in your workflow.

• Shorten the loop cadence - Review outputs more frequently, especially early in a new delegation relationship with an AI tool. Catching one bad output early tells you a lot about where the failure pattern is.

• Build checkpoints into the workflow - Don't let outputs automatically feed into the next stage. Insert a deliberate pause — even a lightweight one — before an AI output becomes an input to something else.

• Make errors visible early - Define what an early warning sign looks like before you delegate. If you know what a flawed output tends to look like in its early form, you can catch it before it compounds rather than after.

• Reduce the blast radius - Structure work so that an error in one AI-generated output can't automatically corrupt everything downstream. Modular, reviewable steps limit how far a mistake can travel before it's caught.

The bigger picture

The goal isn't to use AI for everything. It's to figure out exactly which tasks it can handle, at what level of autonomy, and how to catch it when it drifts. Every loop you close — every output reviewed, corrected, and applied — makes the next one better. Your prompts improve. Your criteria sharpen. You build a clearer picture of where AI can run on its own and where it needs a human in the loop.

That's what working smarter actually looks like.

Insights from Blink UX Leadership Exchange

Last night, I attended a panel discussion hosted by Blink UX on leadership in UX during the AI explosion. Two of the quotes that stuck with me the most were:

1. The importance, now more than ever, to be able to articulate and defend why a design works.

As rapid prototyping tools make it easier for everyone to produce design artifacts, what sets designers apart is our expertise when it comes to quality, human psychology, and understanding of what works and why. Being able to understand, explain, and defend a design rather than saying "AI said so" is crucial. Not only is this a crucial skill for designers trying to earn a seat at the table, but it is also something they can do better than AI. Being able to understand and articulate what good looks like will set designs and businesses apart from the homogeneous AI-generated styles we are increasingly seeing.

2. High-fidelity design leads to low-fidelity feedback

Ben Shown shared the statement "low-fidelity design leads to high-fidelity feedback. And the opposite is true for high-fidelity design. Meaning, when we share an artifact that looks polished and "final", people are more hesitant to share feedback, feeling like the design is too far along for a pivot in thinking. Also, seeing something polished limits diverse thinking because people become anchored to the presented design. Conversely, if we show a sketch or a wireframe to communicate our thinking, it communicates that we are early in our process and open to ideas. It also allows people to be more interpretive about what's presented to them and to share how they think it could evolve, which may be a more interesting approach than you or AI might have taken.

One designer on my team has intentionally trained Claude to create black-and-white prototypes with limited fidelity for this reason. It helps focus stakeholder feedback on the concept rather than getting locked into or distracted by the visual treatment. It also helps communicate that the project is still in the early stages and not all the details have been set.

It is interesting to see how some AI tools are starting to create tools to address this. I was delighted to see the wireframe option in Figma's First Draft tool, which lets you create an intentionally sketchy-looking mockup with prompting. As Figma describes, it is an option "to help you sketch out less opinionated, lo-fi primitives." It gave me flashbacks to the days of Balsamic, which was often used for similar reasons.

Final thoughts

My big takeaway from the discussion was that the UX profession will need to evolve, but we possess a unique set of skills that will set us up for success in this evolving approach to rapid product development. A few of the panelists enforced this idea, saying:

"Designers are faster problem solvers and better able to able to see opportunities" ~ Celeste Bernard

"Big thinkers with good judgment and the ability to collaborate will do well." ~ Bill Flora

The Mindset Shift That Changed How I Lead

In an uncertain time, it is easy to think you have no control over anything. Yet you may have more control than you think. 

Years ago, I read “7 Habits of Highly Effective People” by Stephen Covey – a book that had a big impact on my thinking and leadership. One of the ideas he introduces is the concept of the circle of control, the circle of concern, and the circle of influence. He describes how proactive people tend to focus on what they can do and influence, whereas reactive people focus their energy on things beyond their control, leaving them feeling powerless. As Covey puts it, "I am not a product of my circumstances. I am a product of my decisions."

Here's how the three circles break down:

Circle of Concern

This is the biggest outermost circle. It includes all the things you are concerned about, such as the economy, the job market, your company, the prospect of AI taking your job, etc. While your list may vary, the key thing to remember is that you have control of only a small fraction of these things.

Circle of Control

The smallest innermost circle represents the things you can actually control - this includes your thoughts, emotions, decisions, actions, and behaviors. In most cases, you have control over what you wear, what you spend money on, what you do with your time, what you say, etc. While this circle may seem small compared to the circle of concern, you may realize you actually have more control than you think once you start listing things out.

Circle of Influence

This circle in the middle of Control and Concern is key. This is how you amplify your control to make an impact on the things that concern you. While you may not be able to change something on your own, you can influence others to help. The more you do this, the more your circle of influence will increase. As your influence grows, your power to make change will increase.

Again, the key is to focus on the things you can control and influence. When you fixate on what you can't change, it doesn't just drain you — it makes you seem negative, and people stop wanting to work with you.

Applying this to your work

A couple of times a year, my company would send out an engagement survey to measure employee satisfaction, likelihood to recommend the company, intent to stay, etc. My team’s scores were consistently low. They were doing great work, but their outlook was bleak. The qualitative researcher in me wanted to know what was beneath these scores.

I ran a brainstorming session where I listed out the biggest detractor themes from the survey and gave everyone space to submit anonymous feedback. After grouping the feedback into themes, I introduced the circles to my team, and we discussed the difference between what we can control and what we can influence. There was some feedback that each individual had the autonomy to act on, like cutting out unnecessary meetings and setting better boundaries with their time. There was other feedback that I, as their leader, could affect: adding more clarity to the career ladder and getting them access to the tools they needed. Lastly, there were things beyond both our control, but we could certainly influence at the company. A major theme we discussed was helping our partners better understand the value of UX beyond the UI, so we could ensure our team had a real seat at the table when strategic product decisions were made.

While the discussion didn’t solve all their problems, it helped them see that they had more control and influence than they thought. It also gave me a clearer narrative to bring back to my leaders to garner support.

I encourage you to try this exercise either with your team or on your own. I’m sharing my Figjam template in case it is useful to others. Let me know how it goes.