AI Should Remove Handoffs Before It Removes Jobs

Look at the Work Between the Work

When you start thinking about AI productivity, the conversation can move quickly toward jobs. Which roles can AI automate? How many hours can it save? Which tasks no longer need a person? Those questions may eventually matter, but they can distract you from a more immediate source of waste inside your organization: the work required to move information from one person or system to another.

Think about what happens after a meeting. Someone takes notes, identifies the action items, and enters them into a project management system. A customer sends a request, and someone interprets it before creating a ticket for another person. Marketing finishes a campaign brief, and someone prepares the relevant information for development. The underlying work has already happened, yet someone still has to prepare that information so the next person can use it.

These handoffs rarely look significant enough to become strategic priorities. Ten minutes here and twenty minutes there can disappear into the normal rhythm of a workday. Multiply them across your organization, however, and you begin to see how much capacity gets spent carrying information instead of acting on it.

That is a useful place to start looking for AI opportunities. Before asking AI to replace a job or redesign an entire department, look at where it can remove the administrative friction between work people are already doing.

Handoffs Create More Work Than You See

A handoff sounds simple because the word implies movement. One person gives something to another, and the work continues. In practice, information rarely moves that cleanly.

The person receiving the information may need a different level of detail. The next system may require specific fields. A technical person may need a marketing request translated into requirements. Each transition creates a small transformation of the information before the work can continue.

That transformation has a cost. Someone has to understand what came before, determine what matters next, and put the information into a form another person or system can use. When the handoff is delayed, the work waits. When context gets lost, someone asks a question and the work travels backward before it can move forward again.

The cost becomes larger when the same information moves through several layers. A customer request becomes a support note, which becomes a development ticket, which eventually becomes part of a management update. By the time the information reaches someone making a decision, several people may have spent time interpreting and repackaging the same underlying issue.

You can improve individual productivity throughout that chain and still have a slow process. Everyone may complete their part efficiently while the work itself spends too much time being prepared for the next person. That is why handoffs deserve attention as an operational problem of their own.

Find the Translation Work

One way to identify promising AI opportunities is to listen for people describing their work with words such as "copy," "summarize," and "reformat." Those verbs often signal that someone is taking information that already exists and preparing it for another destination.

You can also look at where information crosses boundaries. What happens when Sales needs something from Marketing? How does a customer issue reach Engineering? What does Finance need before it can approve a request? The friction often appears where people use different systems, terminology, or levels of detail.

Consider a website request. Someone in marketing may submit a message saying a page needs to be updated for an upcoming campaign. A project manager reads the request, asks for missing details, and turns it into a ticket a developer can use. The developer may still have questions because the original request described the marketing goal without specifying the expected behavior.

AI can help prepare that handoff. It can identify missing information, organize the request into your established ticket format, and preserve the original business context alongside the technical requirements. Your project manager can review the result instead of constructing it from scratch.

Marketing still determines what it needs. Your project manager still understands priorities and dependencies, and your developer still decides how the change should be implemented. AI reduces the translation work connecting those decisions so the request can move forward with less administrative effort.

Start With a Handoff, Not a Department

Your AI initiative can easily begin with a broad mandate: transform marketing, automate customer service, or make the company AI-first. Those ambitions can generate plenty of activity before you have established where AI can create measurable value.

A handoff gives you a smaller unit of work to improve. You can see what enters the process, what needs to come out, and who uses the result. That makes it easier to evaluate whether AI actually helped.

Imagine you spend several hours each week turning meeting notes into project updates. You do not need to redesign project management to improve that process. Give AI access to the meeting transcript and the structure of a good project update. Have it identify decisions, owners, and next steps, then let the project owner review the result before it is published.

Now you can measure whether the handoff improved. How much preparation time did you save? Did the update reach the right person sooner? How often did someone have to correct missing or inaccurate information? Those questions give you evidence about the workflow instead of a broad claim that AI made your organization more productive.

This approach also reduces the risk of automating a process you do not understand. When you work on one handoff at a time, you can see where human judgment enters the process and keep it there deliberately. You may also discover that the handoff should disappear completely. Sometimes removing an unnecessary step creates more value than automating it.

This builds on the approach I discussed in The Right Place for AI in Your Digital Workflow. AI becomes more useful when you place it inside real work with clear inputs, outputs, and review. Handoffs give you a practical way to find those places.

Preserve Context While Information Moves

Speed is only one reason to improve handoffs. You also need to consider what happens to context as information moves.

Every time information gets summarized or reformatted, something can disappear. The person preparing the next version decides what matters, sometimes without realizing that a detail will become important later. After several handoffs, you may end up with a clean, concise version that has lost the reason the work began in the first place.

You see this frequently when information crosses functional boundaries. A business goal becomes a technical requirement, but the developer never sees why the requirement matters. Customer feedback becomes a feature request, but the language the customer used to describe the problem disappears. A leadership decision becomes a task without the tradeoff that shaped the decision.

AI can help preserve more of that context because a summary does not have to replace the source. You can design the workflow to produce the concise version someone needs while keeping the original information available. The next person gets something useful to work from and can still trace an important point back to its source.

That traceability becomes particularly important when AI participates in the handoff. If AI summarizes a customer request incorrectly, you should be able to see the original request. If it turns a meeting into action items, you should be able to confirm what was actually decided. You can move information faster without asking someone to trust a summary they cannot verify.

Use AI to Prepare the Next Step

A strong handoff does more than summarize what happened. It prepares the information for what needs to happen next. That distinction can help you find more valuable uses for AI because the goal becomes moving work forward instead of simply producing another document for someone to read.

Summarizing a meeting may save you a few minutes. Turning that meeting into a draft project update with decisions, owners, and unresolved questions can remove a meaningful piece of administrative work. A customer request can arrive with the relevant account context already attached. A marketing request can be checked for missing information before it enters the development queue.

To do this well, AI needs context about the destination. It needs to know what a useful ticket looks like, which information belongs in a project update, or what you expect from a weekly report. Without that context, you may simply generate more text for someone else to sort through.

This is why workflow design matters as much as model capability. You need to understand what the next person actually needs before asking AI to prepare it. Once that expectation is clear, AI can reduce the work required to get information into a useful form and help the next person act sooner.

Keep Judgment at the Right Points

Removing handoff work does not require removing people from every transition. Some handoffs include a decision that depends on experience, accountability, or context that is difficult to capture in a system.

A useful distinction is the difference between preparing information and making a decision. AI can assemble the relevant facts for a budget approval, but you still decide whether the investment makes sense. It can organize customer feedback into themes, while you decide which problems deserve attention. It can prepare a campaign performance summary, while you determine what should change next.

That distinction also gives you a practical way to decide where review belongs. The higher the consequence of an error, the more important it becomes for someone to verify the AI's work before the process continues. This is especially important when a handoff includes a compliance or approval gate that requires human oversight before the work can move forward. You can use a lighter review for routine internal work and more scrutiny when the output affects a customer or an important business decision.

Human review should have a clear purpose. If someone has to recreate the entire task to verify what AI produced, you have moved the work instead of reducing it. A good workflow gives the reviewer enough context to evaluate the result quickly and a clear way to correct it when necessary.

The opportunity is to let AI handle more of the preparation surrounding a decision so you can keep people's attention on the parts of the process where their judgment matters.