"Business handover" and "Introducing generative AI into business" are similar topics

Recently, while leveraging LLMs in my work, serving as a training instructor, and listening to clients' concerns about adopting generative AI, I've been struck by a certain thought.

"This is remarkably similar to the kinds of struggles people face when working with other humans."

Among all the parallels I've noticed, the ones I find most striking are the challenges around "handing off work to someone else" and "asking generative AI to produce deliverables."

Having repeatedly wrestled with why things don't go smoothly — whether taking over someone's work or dealing with mediocre output from generative AI — I've come to the conclusion that these two situations are structurally similar.

In this article, I'd like to explain the structure I've identified between these two points.

Why the Challenges of Work Handoffs and Generative AI Adoption Are Similar

First, have you ever felt that a handoff just didn't go well? Even though you explained the project properly. Even though you walked them through the workflow.

And have you ever run into a situation where, despite trying to incorporate generative AI into your work, employees couldn't quite reach the point of improved efficiency? Even though you wrote the prompts properly. Even though you provided the structure.

At first glance, these two cases appear to be separate problems — but my conclusion is that they share the same underlying difficulty.

Let me explain why in detail.

Putting Siloed Knowledge Into Words Is Hard — and Tedious

Work, by nature, doesn't proceed exactly as manuals or design documents prescribe.

It almost always depends on the knowledge, skills, and accumulated experiences of the person doing it. Exception handling and small judgment calls that aren't written in any manual are unconsciously filled in by the person responsible, drawing on their past experience. That's precisely why "that particular person" can get the job done — and siloed, person-dependent knowledge isn't the result of negligence; it arises naturally from the very nature of work.

So why does siloed knowledge go undocumented? I think there are two main reasons.

• The first is that articulating it is inherently difficult
  • Tacit knowledge is hard even for the person who holds it to consciously recognize, and putting "I somehow just make this judgment" into words is far more laborious than it sounds.
• The second is that the effort required to articulate it is greater than expected
  • Finding a sustained block of time to create documentation, amid the busyness of day-to-day work, is no easy feat.

As a result, documentation gets pushed back, and work stays siloed.

Understanding Background and Context Takes Time

I'm not saying the fault lies solely with the person doing the handoff. The person receiving the handoff — and the AI — also face their own barriers.

No matter how structured and high-quality the information provided, understanding the context that cuts across individual pieces of information — the circumstances and background that led to the current state of affairs — takes time.

Accepting this fact is important. Understanding takes time, and there are real barriers to reaching it. If you skip over that and assume "I got the documents, so I'm fine," you will absolutely, certainly, without a doubt, 100% stumble later. (Perhaps an exaggeration.)

At the same time, those giving the information — whether handing off work or issuing instructions to AI — must also understand this difficulty. Before blaming the other party with "but I gave them the documentation" or "but I wrote it in the prompt," keep in mind that there is a separate wall called understanding context.

What Matters Is "Handing Over the Work Itself," Not Just "Instructions and Information"

Taking everything so far into account, one conclusion comes into view. For the person handing off, it's about "passing along instructions and information"; for the person using AI, it's about "passing along a prompt." But neither of these marks the end of the job.

What matters is holding the awareness that you are handing over your actual work to someone else.

In concrete terms, this means building in time upfront to let the other person retrace the background and journey you've traveled; holding repeated meetings rather than handing things off once and calling it done; and including checks to gauge the other person's level of understanding.

These kinds of efforts become necessary. The same applies to AI — giving context in stages, verifying output, and refining prompts iteratively. The key is not treating the handoff as a one-and-done event.

Both Handoff Recipients and Generative AI Are a "Mirror" of Your Own Work

I hope that by now you have a reasonable sense of the common ground between the challenges of work handoffs and generative AI adoption.

So how can we hand work over successfully?

I believe the key lies in viewing both handoff recipients and generative AI as a "mirror of your own work."

The Quality of Output Is Determined by the Match Between "How You Hand It Over" and the Recipient

When the output from a successor or an AI falls short, it's hasty to dismiss it as "the other party lacked ability." That said, swinging to the other extreme and blaming yourself entirely — "my handoff must have been bad" — is equally unbalanced.

What matters is whether the other party's capabilities and characteristics are matched to the way you hand things over. Even with the same handoff approach, results will differ depending on the successor's skill set or the AI's model and areas of strength. Adjusting your approach to suit the recipient is where the skill of the person doing the handing over truly shows.

This is also what "mirror" means here. What's reflected in the output isn't only the other party's ability. It also reflects how well you understood them and how you chose to hand things over — that shows up in the results.

The Recipient's "Way of Asking" Also Shapes the Outcome

That said, it isn't solely the responsibility of the person handing over. The recipient's own attitude also greatly influences the outcome.

Don't leave uncertainties unaddressed — ask freely whenever something is unclear. That in itself is a perfectly valid approach.
And equally important is clearly communicating "where your current understanding stands." What do you understand, and what don't you understand yet? By making that visible to the person handing over, they can determine what to supplement next.

This applies to interactions with AI as well.
Including instructions like "please ask if anything is unclear," or having the AI summarize what it has understood to confirm its current grasp — the structure is the same as with a human counterpart.

Both Humans and Generative AI Require Dialogue Design

Whether it's a work handoff or a generative AI adoption, the core difficulty lies in "handing work over to someone else."

If that's the case, treating the "handoff" as a one-time event is the wrong approach. Passing along documents and calling it done, or throwing a prompt and calling it done, won't work.

Hand it over. Have the recipient reflect back where their understanding currently stands. Fill in the gaps and hand it over again. Designing this loop from the outset — that is "dialogue design."

With humans, it might look like regular check-in meetings or creating an environment where asking questions feels easy. With AI, it might mean confirming summaries of understood content or iteratively refining prompts. The form differs, but the structure is the same.