There Is a Structural Lie Inside Every Job Description
Pull apart a job description and you will find something the document never mentions: a significant portion of this person's actual time goes to checking spreadsheets.
What HR presents to a candidate, and what that candidate's day-to-day time looks like after they join, are often two entirely different things.
The JD format is familiar. Responsibility 1, Responsibility 2, Responsibility 3 — each line written as if it were a strategic mandate. "Responsible for brand media strategy formulation and execution." "Lead integration of multi-channel media resources." "Drive data insights to support business decisions." Reads cleanly. Looks complete.
What the document does not tell you is how this person actually spends their hours. A significant portion of the time goes to checking spreadsheets.
This is not an indictment of any particular company's management, and it is not a comment on any individual employee's capability. It is a structural limitation of the job description as a tool. JDs describe responsibility boundaries, not time distribution. They tell you what this role is accountable for. They do not tell you how the person filling it actually spends their day.
Before AI arrived, this gap was a management precision problem — some organizations tracked it, many did not, and not tracking it rarely cost anything visible. The gap existed like a layer of ambient tolerance inside the organization, broadly accepted and mostly ignored.
After AI arrived, that gap started getting expensive.
The reason is straightforward: AI does not enter work at the role level. It enters at the level of specific work actions. It can take over the spreadsheet checking. It can take over report generation. It can take over parameter compilation. It can take over first-draft aggregation. But it cannot enter the negotiating table. It cannot enter the reasoning process behind "should we increase this channel's budget this month?" And it cannot enter the structure where, if something goes wrong, there has to be a person to answer for it.
If a manager only looks at the JD and not at the time distribution, they cannot see where AI has already entered — and they cannot see which portions of a person's time are genuinely irreplaceable.
This is why role unbundling has acquired new urgency in the AI era. Not because AI is about to replace everyone, but because the job description provides an analytical resolution that is too low — too coarse to see what AI is actually changing, and too coarse to see where the human's core value sits.
You Are Asking the Wrong Question
When most organizations discuss AI transformation, the question they ask is: will AI replace this role?
Will it replace the media buyer? Will it replace the finance specialist? Will it replace the content editor? Every time a new tool appears, the question circulates through the company and leaves behind a fog of unresolved anxiety.
That question is wrong because it treats the role as the unit of analysis.
Inside a media buying role, there are fragments where AI has a significant speed advantage in certain contexts, and there are fragments where AI has no entry point at all. If you only ask "can AI replace a media buyer?", the only possible answer is "it can replace some of it" — which has no operational value, because you do not know which part, you do not know the proportion, and you do not know how to restructure what remains.
The right question is: within this role, which fragments are butter-cutting, and which are the real work?
Butter-cutting is the term I use to describe a certain quality of work. It refers to the fragments inside a role that are repetitive, standardizable, and produce information-organization outputs rather than judgment outputs — like cutting butter with a knife, the motion is regular, the result is predictable, there is no craft involved. The real work is the opposite: it requires situational judgment, depends on relationship and trust, has no standard answer, and carries real cost when it goes wrong.
Butter-cutting fragments: AI enters first. Real work fragments: humans hold their ground.
The JD describes responsibilities. It does not say which are butter-cutting and which are real work. But without making that distinction, the organization has no way to design AI's entry path — and no way to answer the more important question: once AI absorbs the butter-cutting, where does this person's time get reallocated?
This is why the role is not the right unit of analysis. The work fragment is. Without unbundling the role, you cannot see where AI has genuinely entered. Without unbundling, you cannot see where humans are genuinely holding their ground.
Five Fragment Types, One Map
Unbundle a role into five types of work fragments and you can see where AI has actually entered. Without that unbundling, managers typically end up stuck asking "should we reduce headcount?" — permanently politically charged. After unbundling, the question becomes "if this task goes to AI, where does this person's time get redirected?" — that is a management question, not a political one.
Data fragments are work whose primary actions are collecting, organizing, locating, and reporting information. The core output is information in motion, not judgment. Checking a spreadsheet, compiling a media performance report, aggregating competitor pricing across channels — these are data fragments. Their defining characteristic: the action can be described precisely, whether the result is correct is relatively easy to verify, and errors can be redone.
Task fragments are work that executes specific actions to produce specific deliverables. There is a defined action sequence and a concrete output. Writing a first draft of a media plan, uploading creative assets to a platform, configuring ad placement parameters — these are task fragments. Task fragments can be broken into steps and partially standardized, but they vary widely in complexity: simple task fragments can be handed almost entirely to tools; complex task fragments still require human intervention at key nodes.
Judgment fragments are work requiring cognitive decisions. There is no single correct answer — selection requires drawing on current information, experience, and understanding of the objective. Deciding whether to increase spend on a channel this month, determining which creative direction fits this campaign node — these are judgment fragments. AI can offer reference points, but the call still belongs to a human, because it depends on contextual understanding that cannot be compressed into directly callable rules.
Relationship fragments are work that creates value through interpersonal connection. The core is conversation, trust, and shared history. Negotiating procurement terms with a media representative, reporting campaign progress to a client, coordinating internal resources to resolve a sudden conflict — these are relationship fragments. Relationship fragments are nearly impossible to standardize because they depend on the specific trust between two specific people. Hand them to AI and the issue is not that execution degrades — it is that the thing itself loses its meaning.
Accountability fragments are work about risk ownership and result backstop. The core is not action but authority — when something goes wrong, who answers for it. Signing the final contract, making a disposition decision on a major campaign incident, accepting responsibility for budget outcomes — these are accountability fragments. AI can participate in decisions, but accountability itself cannot be held independently by AI, because the accountability system presupposes a specific person who can be held to account.
These five types offer a practical classification starting point that captures the primary character of most work inside a role. Some work combines characteristics of more than one type. The purpose of unbundling is to find the dominant character, not to require that every minute be cleanly assigned to a single category — the framework is an analytical instrument, not an exhaustive taxonomy.
Once the unbundling is complete, the manager has a far clearer map: how this role's time is distributed, which fragment types AI has already entered, which fragment types represent the human's core value, and if AI absorbs some of the butter-cutting, what the freed-up time can now go toward.
AI Does Not Enter Work Randomly
The sequence in which AI enters work fragments is not random.
If you only ask whether AI can do something, the question gets fuzzy — AI can write, query data, generate plans, assist with judgment, it touches nearly everything at some level. But in actual organizations, what determines where AI enters first is two overlapping conditions: how standardizable the fragment type is, and how low the cost of an error is.
High standardizability means the fragment's action sequence can be described, there is a relatively stable relationship between input and output, and the work does not depend on large amounts of contextual information to complete. Low error cost means that even if AI gets it wrong, the consequences are manageable — checkable, redoable, not irreversible.
These two conditions together describe data fragments. Checking prices, compiling reports, aggregating data — this work can be fully described as rules, errors can be rechecked, consequences are limited. AI enters this fragment type fastest, and in some contexts delivers meaningful speed gains.
Task fragments follow closely, but with wide variation. Standard task fragments like filling purchase orders and uploading materials are similar to data fragments; but writing a creative brief that requires judgment about the target audience's psychology — even though the final output is a document, the task-type surface conceals a judgment-type core. AI can assist, but the human intervention point cannot be removed.
Judgment fragments: AI can offer reference, but not verdict. Whether to increase or reduce spend on a given media channel this month depends on an integrated reading of market rhythm, client expectations, and historical data. That reading is difficult to compress into directly callable rules. AI's role here is support, not substitution.
Relationship fragments: AI does not enter. The trust on the negotiating table, the emotional management in a client presentation, the interpersonal calibration in a resource coordination conversation — the value of these things exists precisely because they happen between two specific people. Remove that and the thing loses its meaning.
Accountability fragments: AI cannot hold them independently. The organizational accountability system presupposes a specific, traceable person. AI can participate in decisions, but when results come in, the person who signed, who approved, who owns the consequences — that can only be a human. This is not a technical limitation. It is how accountability systems are designed to operate.
Understanding this pattern lets a manager explain a common puzzle: why do some roles that have introduced AI tools show little efficiency gain? The most likely explanation is that the tools entered judgment-type or relationship-type fragments rather than the data-type or task-type butter-cutting. Nothing wrong with the tools — placed in the wrong position, they do not deliver what they could.
What a Media Buying Role Looks Like Unbundled
I have unbundled a media buying role.
What the analysis revealed is that this person's time divides into two fundamentally different categories of work.
The first is the spreadsheet-and-report work. Every week, multiple media pricing tables to check, historical performance comparisons across channels to run, data pulled from scattered platform dashboards to merge into a consolidated summary, then formatted for reporting. These actions are regular. The steps can be reproduced. The output is information, not judgment. This is the butter-cutting — repetitive, time-consuming, low judgment content, stable motions, predictable results.
The second is where this person's actual expertise lives: human-to-human communication and connection. Negotiating procurement terms with a media representative — what the rep says on a call is not just price, it is also which placements have seen traffic shifts this quarter, which periods have inventory available to capture — information that accumulates through years of relationship trust, not through email inquiries. Reporting campaign progress to a client — not just reading numbers, but when the client says "the results feel underwhelming," reading whether that is an information asymmetry problem or an expectation mismatch problem, then deciding how to adjust the communication approach going forward. Deciding whether to increase a channel's budget this month — grounded in a feel for market rhythm that cannot be read out of a table.
That is the real work. Relationship, judgment, trust — these fragments do not repeat in identical form, cannot be standardized into a set of steps, and when they go wrong there is no simple "redo" option.
After unbundling, the question changes. The old question — should this media buying role be replaced by AI? — is unanswerable because it has no correct answer and no operational exit.
The new question: what tools exist today to take over the spreadsheet and report work? Once they do, how many hours per week does that free up? Can that freed-up time go more toward maintaining media relationships and reading campaign rhythm?
That is a question with operational value. The butter-cutting goes to tools. The person's time gets reallocated to the real work. The role does not disappear, but the center of gravity of the work shifts.
One clarification: this case illustration is a methodological demonstration, not a statistical finding. The proportion of butter-cutting to real work in a media buying role varies across companies — sometimes considerably. The value of unbundling is not arriving at a fixed ratio. It is giving a manager clear sight of two fundamentally different categories of work within the role, so that the next design step can proceed from that clarity.
How to Find Your Butter-Cutting
Knowing the five fragment types, the next practical question is: how do you locate the butter-cutting in a specific role?
Butter-cutting has four directly checkable characteristics. First, it recurs — this work happens weekly or daily, it is not occasional. Second, it can be standardized — its action sequence can be written as steps, and if someone else follows those steps the result does not differ much. Third, the primary output is information organization, not judgment — what is delivered afterward is data, a table, a report, not a decision or the core judgment inside a plan. Fourth, the result can be verified in some form — when done, there is a way to check it, errors can be corrected, it does not leave irreversible consequences.
The characteristics of real work are the inverse: it does not repeat in the same form every time; it cannot be standardized into a set of steps — the critical element is judgment; the output is a directional decision or a relationship grounded in trust; and when it goes wrong, the cost is often more than a redo — it might be a lost client, eroded team trust, or a flawed decision that ran for a period before being caught.
Scanning a specific role with both sets of characteristics lets you go through the work item by item: which strongly matches butter-cutting characteristics, which is mixed (some stages butter-cutting, some judgment), and which does not match at all (relationship, judgment, and accountability are the substance). Items that strongly match butter-cutting characteristics are the priority candidates for AI tool coverage — look immediately for existing tools that can handle them. Mixed items are suited for AI to handle the butter-cutting portion while humans focus specifically on the judgment portion, rather than handing off entirely or keeping it entirely. Items that clearly do not match — AI tools can at most assist with information gathering, but the substance must be human-led.
One practical approach worth trying: ask employees to keep a time log for one week. Write down each piece of work and mark which fragment type it mainly belongs to. Employees themselves are often the most accurate reporters of where their time goes. Sometimes they will notice on their own that they are spending large amounts of time on butter-cutting — and the feeling they report tends to be "tedious, repetitive, not meaningful." That is exactly where AI tools should enter.
Identifying butter-cutting is not about replacing employees. It is about helping employees release their time from low-judgment-content work and place it in work only they can do. Done well, this improves the employee's work experience, increases the role's value, and reduces the organizational waste of applying human effort to work that tools can handle.
The Manager's First Move
Where does role unbundling begin?
Not with purchasing AI tools. Not with updating job descriptions. Definitely not with launching an "AI transformation task force." None of those are wrong in themselves, but done without first genuinely understanding where time actually goes in this role, the typical result is: tools purchased, JDs revised, task force met several times — and the person who was spending significant time on spreadsheets every week is still spending significant time on spreadsheets.
The first step is getting the time distribution right.
Select a role that is already using AI tools, or is being asked to push forward with AI adoption. Ask the person in that role to keep a time log for one week — not "what are your main responsibilities" (that is the JD), but "where do you actually put your time?" Record each piece of work and how long it took. Then match each item against the five fragment types and mark the primary category.
This is not complicated. But in the cases I have encountered, managers often oversee a role without knowing how that role's time is distributed — not because they do not care, but because the JD has been providing a sustained illusion of "I know what this person is doing."
Once the time log comes back, the manager needs to answer two questions. First: how much of this person's time goes to data and task butter-cutting? Second: once tools take over the butter-cutting, where does this person's time go — is there somewhere worth going?
The second question is the critical one. If the butter-cutting gets absorbed but there is no plan for where this person's time goes next, absorbing the butter-cutting only produces idle time, not a genuine role upgrade. In that situation, the manager will quickly face a harder question: what do I need this person for?
So the real first move is not "what tools do we buy?" It is: within this person's time, which parts are butter-cutting and which are real work — and once the butter-cutting is absorbed, is there enough real work to sustain the value of the role? Only after answering that question does the next step become clear — whether the path is introducing tools, reallocating responsibilities, or redefining the role itself.
The concrete action a manager can take away: select one role, run a one-week time log, mark each work item by fragment type, and count how much time data and task butter-cutting accounts for in total. That number is the actual starting point for AI transformation in this role. Not the number of tools purchased. Not the number of meetings held. That number.