How to use this

Headcount is the one expense line where Finance, Talent, and HR each hold a piece of the decision, and none of you holds the whole thing. This document works the same way. Every section ends with a short breakdown for Finance, Talent, and HR, so you can read your part fast or read it all for the full picture.

Why this planning cycle is different

You are being asked to design a workforce, not forecast one

Every planning cycle before this one asked the same question: how many people, and at what cost. 2027 asks something harder. Which work needs a person, which work needs a person with better tools, and which work needs neither. Nobody in your company has answered that at scale before, and the answer changes org charts, job descriptions, and career paths. Treat this as the first step of a workforce redesign that runs across the company, because that is what it is. The measurement problems below are real, but they are not the hard part.

Boards now review headcount the way they review capital

That tolerance disappeared with cheap money. Boards want to know which roles were approved, by whom, at what cost, and what happened to the ones that were funded and never filled. Those are controls questions, and most companies cannot answer them from a system.

AI spend has outgrown tracking and now has to be planned

Enterprise AI spend is growing fast enough that it can no longer sit outside the budget conversation. AI-native application spend rose 108 percent year over year across all company sizes, and 400 percent at organizations above 10,000 employees. Vendors are already converting seat pricing into token and action based pricing. Gartner recorded the demand side response: CFO headcount growth expectations falling from 6 percent to 2 percent between 2025 and 2026, which its analysts describe as a structural pivot from labor expansion to automation, not a side experiment.

Other companies have already started restructuring

This is not only a TeamOhana argument. Atlassian recently gave its Chief People Officer responsibility for AI enablement, expanding her remit from a 700 person HR organization to a 3,500 person function. Their stated reason: cultural and technological transformation cannot be separated if the change is going to stick. Do the same at your company. Name one executive who owns the mixed workforce decision, and do it before planning opens. Atlassian also reports that 85 percent of knowledge workers now use AI, but only 29 percent have actually changed how they work. That gap, access without redesigned work, is exactly where planning has to do the heavy lifting. It will not close itself.

Headcount and AI spend are now one question, not two

A real share of 2027 work will not be done by an employee. Contractors, outsourced functions, and AI agents are absorbing work that used to become a req. Companies that cannot control their own headcount today have little chance of controlling a mixed human and AI workforce in 2027. The control layer has to exist for people before it can extend to agents, which is the practical argument for fixing this now rather than after the plan locks.

The plan you are about to build has a new shape

Planning conversations still start the way they have for a decade: what do we want to spend, and on how many people.

That question is no longer complete. A hiring plan used to have one shape: hire, or do not hire. It now has three shapes, and all three come out of the same budget.

1. Fund a new hire, who arrives with a salary and, increasingly, a real AI cost attached.
2. Give more AI capability to someone already on the team, raising what they cost without raising headcount.
3. Stand up an agent that does the work instead, with its own budget and, in most companies, no approval process built for it yet.

Those three are substitutes for each other, and in most companies right now they are three separate decisions made in three different places. KPMG frames the same underlying choice as buy, build, borrow, or bot: hire talent, develop it internally, use contingent labor, or use AI. Whatever you call the four options, the discipline is the same: price them consistently, or someone will guess.

Finance approves the hire. Someone in engineering or IT approves the AI seats. Nobody approves the agent, because there is no form for that yet. The 2027 planning problem is not forecast accuracy, but whether your plan can price a hire, a capability upgrade, and an agent against the same dollar and let someone choose.

Five ways the 2027 plan breaks

The first four are the ones that have always broken a headcount plan. The fifth is new, and it is growing faster than the other four combined.

1. Drift: roles get opened that nobody approved

A hiring manager has leftover budget and a recruiter has capacity, so a req gets posted with no gate between intent and action. The org finds out when an offer needs signing, or when Finance closes the quarter.

A Talent leader at an advanced manufacturing company described what turned up in their legacy planning spreadsheet:

“There’s an enormous backlog of headcount that no one had ever accounted for. That’s ridiculous.”

The good news: a TeamOhana customer does not run into this blind. Docker stopped six figures of unapproved monthly spend once every request had to map to an approved position, and SeatGeek prevented 200,000 dollars of overspend in a single cycle.

2. The reconciliation tax: Finance rebuilds the forecast by hand

Every month, someone exports the HRIS, exports the ATS, opens the plan, and manually rebuilds the number so all three agree. Mid market Finance and Talent teams burn 60 to 120 hours a month on this.

Greenhouse cut that to roughly two hours a week. Metronet saw a 7x return, the equivalent of at least three full time roles reclaimed from reconciliation work.

3. The approval black hole: no trail, no defensibility

A role was approved, probably. The evidence is a memory and a thumbs up in a Slack channel.

A Finance leader at a life sciences company described their current audit trail for headcount approvals:

“Finance ends up actually manually rebuilding the whole headcount forecast, and you’re pointing back to a thumbs up in a Slack channel.”

This is a slow cost until it is a sudden one. It shows up hardest when a board asks who approved a position, or when a company heads toward an audit or a financing event.

The good news: SeatGeek replaced exactly this kind of operational chaos with one system of record Finance and Talent both trust.

4. Timing blindness: the variance nobody models

The plan is on track in headcount and off track in dollars, because a handful of start dates slipped a quarter and nobody repriced the plan when they did.

IonQ identified 594,000 dollars in annual savings once timing was visible in dollars rather than headcount.

5. The token blind spot: AI spend nobody can attribute

This failure mode did not exist three years ago. It is now often the fastest growing number in the company, and it shows up the same six ways almost everywhere we have looked closely.

• Finance owns the AI number by hand, in a spreadsheet reconciled weeks after the money is already gone.
• The heaviest individual spenders are invisible, because their usage sits inside an enterprise plan while tracking only covers the API console.
• Contractor and intern API keys map to nobody, because contingent workers are frequently missing from the headcount system entirely.
• Autonomous agent spend hides on a cloud bill nobody in Finance owns or sees.
• The same dollars get counted twice across two different reporting sources.
• There is no baseline for normal, so a healthy engineering team’s heavy usage and one runaway account look identical in a raw total.

Deloitte treats the token as the basic unit of AI cost and value, and recommends real time monitoring and forecasting discipline, the same rigor Finance already applies to headcount. McKinsey goes further: the unit that should be governed is the completed business outcome, not the raw token count.

The good news: this is exactly what Token Spend Management is built to close, every AI dollar attributed to a person, team, or agent, inside the same system that holds the headcount plan.

What planning season actually costs

To size these in your own numbers: count unapproved reqs from last year, tally reconciliation hours across Finance and Talent Ops, time how long it takes to answer who approved a role, compare planned to actual start dates, and compare per-head AI spend to what is currently budgeted. The table below separates the general benchmark from what a specific customer did about it, since those are two different kinds of evidence.

The line that should worry Finance most: business units, not IT, now control 81 percent of software spend, against IT’s 15 percent. AI is landing on someone’s expense report inside a business unit before any central function sees it. By the time it reaches a budget review, it is already a run rate, not a decision.

The new planning question

Annual planning has always been about deciding what to fund. What changed is that funding now splits three ways, and all three draw from the same pool of money.

That last column is the whole problem. One of the three options runs through an approval workflow. The other two do not. If your planning process cannot price all three against the same budget, your leaders are not making a tradeoff between them. They are guessing, and Finance reconciles the guess after the money is spent.

The data already shows it. Gartner’s CFO survey found headcount growth expectations falling from 6 percent to 2 percent between 2025 and 2026, a pivot their analysts describe as structural. KPMG makes the same point from the workforce planning side: the useful classification is not "will AI take this job," it is whether a task is human-led, AI-assisted, AI-automated with oversight, or not appropriate for AI at all.

That classification is worth running task by task inside the Build phase of your own sequence, in Part 8, rather than deciding it once at the role level.

TeamOhana Computer: your AI coworker for planning

A chatbot waits to be asked. TeamOhana Computer connects directly to your ATS, HRIS, and finance data, already inside your access controls, and reasons through multi-step planning requests instead of one-shotting them. By connecting your systems to TeamOhana, it acts as your analyst: ask it a question, drop in your own file, or assign it a task, and it shows its work, asks when it is unsure, and remembers your corrections.

Why a generic AI agent is not the same tool

Most teams have already tried wiring workforce data into a general purpose AI agent. It usually works, for one person. One team built their own agent so capable that, in their own words, only one person could use it, it was too powerful. A raw agent does not know your org chart, your approval chain, or who is allowed to see what, so the choice becomes either lock it away or hand out access nobody can safely audit.

Atlassian’s own research is a useful check on this: 85 percent of knowledge workers already use AI, but only 29 percent have changed how they actually work. Access to a chat window is not the same as a coworker embedded in the workflow with permission to act. That gap is exactly what a generic agent leaves open and what TeamOhana Computer is built to close.

The trust model

For any action that changes a real record, the agent runs a dry run first and asks for confirmation before anything actually changes. Nothing updates by accident, and every action leaves a record of who, or what, made the change. Teams hand it real work because of that dry run, not only questions.

What Finance is already asking it to do

“I need to find $1M in savings within our hiring plan with the least impact to our goals. Model these scenarios and rank them by savings versus business impact: pushing target start dates later, deprioritizing non-revenue-generating roles, leveling down open roles, and moving roles to lower-cost regions.”

“Generate a dashboard that compares my May 1 headcount forecast snapshot to my July 1 forecast snapshot, and include recommendations.”

“Run an analysis for all hires this year comparing their budgeted compensation to their target compensation and what we ultimately paid them. Any trends worth noting?”

What Talent is already asking it to do

“Show me all open headcount with a target start date in the next 90 days that don’t have a recruiter assigned.”

“Which recruiters should I assign to the open headcount based on their capacity and specialization?”

“What is our time from approval to fill, by role and department, for all hires this year?”

What HR and People Ops are already asking it to do

“Generate an org design dashboard I can filter by division that assesses span of control, org layers, and key ratios including AE to Sales Ops and AE to Solutions Engineer.”

“Show me our average attrition by department and tenure, and what are the top five causes?”

“Build a change log of every employee hired, promoted, transferred, or terminated last quarter, grouped by change type.”

What executives are already asking it to do

“Build a workforce health dashboard including average tenure, year to date attrition, headcount growth versus the same period last year, and average comp ratio. Then analyze the hiring plan: expected growth by department, the biggest risks to hitting it, and 3 to 5 concrete recommendations.”

“Build an end of quarter workforce summary structured as a report for the CEO: headcount bridge, plan versus actual by department, budget versus approved spend, and a forward look at open reqs and top risks.”

Introducing the ledger for AI spend

Token data shows what was spent, but only the employee record explains who spent it and why. Most companies hold those two facts in different systems, so AI spend gets reported weeks late instead of planned in advance.

The idea worth taking into 2027 planning, regardless of which tool gets you there: one ledger for both. Every AI source, from Anthropic and OpenAI to Cursor and AWS Bedrock to seat based tools, consolidated with your HRIS and ATS data in one place. Pairing them makes five things possible.

• Every dollar attributed to a person, a team, or an agent, with an owner to contact when something spikes.
• No double counting, so when two sources report the same spend, you keep one clean number instead of two.
• Outliers measured against a team’s own baseline, not a raw total, so one person spending ten times their team’s average surfaces in days instead of at year end.
• Fully loaded cost per employee: compensation plus AI spend on one line, rolled up by team and by org.
• Forecasting at the point of the headcount request itself, so a planned hire carries expected token spend alongside comp, budgeted before the offer goes out, not discovered after.

Visibility and planning are not the same thing. McKinsey argues the unit that matters is cost per completed business outcome, not cost per model call. A report tells you what already happened. A plan has to price what has not happened yet.

“The big thing is having all token spend in one place, so we can actually analyze what’s going on. A new engineer next year adds incremental payroll and incremental AI spend. Budgeting for that is exactly where we’re going.”

Elisa Folden, Sr. Manager, Business Systems, SeatGeek

The one prerequisite that determines whether any of this works

None of the above works if your contractors and interns are not in your headcount system with a consistent worker type. They are typically the heaviest unattributed AI spenders and the population most likely to be missing entirely, because a sync filter written years ago to exclude consultants often catches fixed-term and intern workers by the same rule. You cannot attribute a dollar to someone your systems never loaded. Fixing this is the actual first step, before any ledger or dashboard.

The planning sequence

Twelve weeks, six phases, three owners, with an AI checkpoint built into every phase. Every phase below comes from how the fastest planning teams run it. Twelve weeks is the pace shown here; a smaller company can compress it to six to eight weeks, a larger one may need sixteen. The order and the ownership matter more than the exact calendar. On a calendar fiscal year, a 12 week sequence starting in early September has you board ready by the end of November.

The critical design choice is in the last phase. Most companies stop at approval. This sequence ends at load and lock, where the approved plan, including its AI spend, becomes the operating constraint in the systems where work actually happens.

Three rules that make it work

1. Give departments dollars, not headcount. A body count invites level inflation. A dollar envelope, inclusive of expected AI spend, forces the requester to make the tradeoff themselves.
2. Never let AI spend live in a separate system from the headcount plan. If Finance is reconciling two forecasts by hand, the plan is already out of date the day it is approved.
3. Never end at approval. If phase six does not happen, phases one through five were a forecasting exercise, and next spring will prove it.

See a demo of Token Spend Management.

The readiness score

Fourteen questions. One point for each yes, where yes means the answer comes from a system in under five minutes and Finance, Talent, and HR would all give the same answer. Score the two sections separately, headcount out of 8 and AI out of 6. A blended number out of 14 hides the gap you are looking for, because almost everyone is strong on one and weak on the other. Run this out loud, together, before you build a single line of the plan. Disagreement about the answers is the most useful thing that happens in the meeting.

Headcount authorization

1. You can state today’s approved headcount and today’s actual headcount, and the gap between them, without opening a spreadsheet.
2. Every open req maps to a specific approved and funded position.
3. A req cannot be opened without that mapping existing first.
4. For any position, you can name the approver, the date, the approved level, and the approved cost.
5. Your plan is expressed in dollars by start date, not only headcount by quarter, and reprices automatically when a start date moves.
6. Every backfill required an explicit decision to refill, not an automatic one.
7. Finance, Talent, and HR pull the same headcount number from the same place.
8. You could hand an auditor a complete approval trail for your largest expense line without a project to assemble it.

AI and agent readiness

1. Every contractor and intern who holds an AI tool account exists in your headcount system with a consistent worker type.
2. You can state fully loaded cost per hire, comp plus expected AI spend, for any role in the plan.
3. Every planned hire carries an AI spend estimate alongside comp, before the offer goes out.
4. When a leader trades a hire for an agent, that decision is recorded somewhere, not just discussed.
5. Your AI spend by person or team is measured against a baseline, not just a raw total.
6. You know today whether an autonomous agent is running on a cloud bill nobody in Finance can see.

What changes when this is one system

What this looks like in practice

These are outcomes from companies that brought headcount and AI spend into one system. Full stories are linked below.

• Scale AI: built a headcount planning engine to 10x its hiring velocity.
• IonQ: 594,000 dollars in annual savings identified through timing and level visibility.
• SeatGeek: ended operational chaos, 23 hours a week saved.
• Docker: six figures of unapproved monthly spend stopped.
• Metronet: 7x ROI, the equivalent of at least three full time roles saved.
• Greenhouse: reconciliation cut to roughly two hours a week, three teams aligned.
• Rad AI: scaled to 220 employees on a single source of truth.
• Cedar: freed up 1.5 million dollars by turning backfills into decisions.