Your org chart is current. Your AI exposure picture is not
Your People Analytics Lead just finished refreshing headcount and compensation data in ChartHop ahead of a board meeting. The chart is clean, the reporting lines are accurate, and the comp bands are current. Then the board's question lands: which roles across the organization are most exposed to AI-driven task automation, and what is the plan for the people in them.
ChartHop has no answer to that question. It was not built to have one.
This is not a knock on ChartHop. It is a scope observation. ChartHop is a mid-market people-analytics platform built for org charts, headcount planning, and compensation modeling, and it publishes its packaging on its own site rather than hiding pricing behind a sales call. Within that scope, it does real work well. But mapping a role list to O*NET occupations, scoring the underlying tasks against a transparent exposure rubric, and surfacing which roles warrant monitoring, review, or redeployment planning sit outside what the product does. This article covers where ChartHop fits, where the gap opens, and what to look at — including WorkforceAnalysis — if the board question above is the one you actually need to answer.
What ChartHop actually does well
ChartHop's core strength is making org structure and people data legible and current. Dynamic org charts update as headcount changes. Headcount planning tools let HR and finance model hiring scenarios against budget. Compensation modules keep pay bands and equity data attached to the same org structure, so a VP of People can see reporting lines, cost, and planned changes in one place.
For a mid-market company whose immediate need is "get our org chart out of static spreadsheets and into something the exec team can actually use," ChartHop solves a real, everyday problem. It is also worth noting, per its own published packaging, that buyers can see list pricing without a sales conversation — a genuine point of transparency relative to some of the enterprise workforce platforms in this space. If your evaluation is broader than AI exposure specifically, our people analytics software for mid-market guide and our HR analytics tools guide cover that wider landscape, including where ChartHop and its peers sit relative to one another.
Where the gap opens: task-level AI exposure
The gap shows up the moment the question moves from "what does our org look like" to "what is our org exposed to." ChartHop's data model is built around reporting structure, headcount, and compensation — not around the tasks that make up each role, and not against any external occupational taxonomy. It does not offer O*NET integration, does not offer AI-exposure scoring, and does not offer a redeployment recommendation feature. None of that is a criticism of the product's execution; it is simply outside what the platform was designed to model.
That gap matters more than it might have two years ago. Gartner's 2024 HR Priorities research found that 86% of HR leaders have not implemented strategic workforce planning, and a separate Gartner survey found 66% say their workforce planning is limited to headcount planning or struggle to demonstrate its ROI. An org chart tool, however well executed, does not close that gap on its own — it was never built to. Closing it requires a layer that connects specific roles to the tasks that compose them, and connects those tasks to a defensible exposure framework.
How O*NET-grounded exposure scoring actually works
This is the layer WorkforceAnalysis adds, and it is worth explaining plainly rather than asserting, since the credibility of the output depends entirely on the transparency of the method.
The U.S. Department of Labor's ONET database covers 1,016 occupational titles (923 with full data coverage, spanning more than 55,000 real-world job titles) and describes each one with roughly 277 descriptors, refreshed on a regular update cycle with a primary annual update each Q3. Every occupation in ONET carries a list of specific work tasks along with an importance rating for each — a number that reflects how central that task is to the job, not merely whether it appears on a list.
WorkforceAnalysis maps a company's actual role list — job titles as they exist in your org, not generic labels — to the closest matching O*NET occupation(s). It then scores the underlying tasks against four dimensions, each rated 0–100:
- Cognitive routine — how standardized and rule-based the mental work is
- Physical routine — how standardized and repeatable the physical work is
- Social/judgment — how much the task depends on relationship management, negotiation, or contextual judgment
- Creative — how much the task depends on original synthesis or novel problem-solving
A worked example, using round, illustrative numbers only: imagine a role composed of three O*NET-linked tasks. Task A carries an importance weight of 60 and scores 80 on cognitive routine. Task B carries an importance weight of 25 and scores 40 on cognitive routine. Task C carries an importance weight of 15 and scores 10 on cognitive routine. The importance-weighted cognitive-routine exposure for the role is (60 × 80 + 25 × 40 + 15 × 10) ÷ 100 = 59.5. Run the same weighting across the other three dimensions, and the role has a full four-dimension exposure profile — not a single number, but a shape.
That shape feeds into a department-level heatmap, and individual roles land in one of three categories: Monitor, Review, or Redeployment Candidate. None of those labels is a verdict. They describe where a role sits in an analytical input to human planning — never a prediction that a specific job will be automated, and never a statement that any role is "safe." The decision about what to do with that information stays with your HR strategy team, informed by context the model does not have: budget, culture, union agreements, individual performance.
For context on why this level of granularity matters at all, McKinsey's 2023 research estimated that 60–70% of work hours could theoretically be automated with current technology, up from roughly 50% in earlier estimates — and the Anthropic Economic Index found that by 2025, 36% of occupations were using Claude for at least a quarter of their tasks, rising to 49% on a pooled basis. Those are economy-wide figures, not statements about your company. The point of task-level scoring is to translate research at that altitude into something specific enough to act on internally — which is exactly the translation an org chart tool isn't built to do.
If you want the fuller landscape of tools attempting this translation, at various levels of depth, see our AI workforce planning tools compared guide.
Comparing the alternatives for AI workforce exposure
Once you're evaluating specifically for AI exposure work rather than general org/people data, the field narrows quickly, and the useful comparison isn't "which tool is better" but "which tool was built for this specific question."
Enterprise workforce-design platforms — Orgvue, Visier, and SAP-acquired Faethm — sit at the top of this category. Orgvue offers scenario modeling and an organizational digital twin, with an AI automation-potential capability, aimed at large enterprises; it does not publish pricing publicly. Visier offers predictive people analytics and prebuilt workforce metrics at enterprise scale, also without published standard list pricing, and does not include O*NET task-level exposure scoring or a redeployment engine. Faethm, now under SAP, is positioned as an enterprise scenario-planning tool with no standalone self-serve pricing published. All three are built for organizations with the budget and internal analytics capacity to run a long enterprise sales and implementation cycle — a different buyer than the mid-market HR team evaluating ChartHop.
Free macro research — from McKinsey, Goldman Sachs, and Anthropic among others — is genuinely valuable for understanding sector- and economy-level exposure trends, and it costs nothing to read. But none of it produces company-specific, role-level output. It tells you what's happening in the labor market broadly; it cannot tell you which of your 40 department roles warrants a closer look this quarter.
Manual consulting or in-house Excel/O*NET mapping is the incumbent most mid-market teams actually use today, by default rather than by choice. It can be customized to your exact org, but it is not self-serve, not easily repeatable when the org changes, and it consumes real analyst or consultant time. Independent consultants in adjacent workforce-strategy work bill in the range of roughly $100–$350 per hour, per ConsultFees' 2026 benchmarking (median commonly cited around $150–$200/hr) — a useful reference point for what a one-off manual mapping exercise tends to cost in time, even before considering that it needs to be redone every time the org changes materially.
WorkforceAnalysis sits in the gap all three of the above leave open: self-serve rather than enterprise-sales-gated, ONET task-level rather than sector-level, and priced for the mid-market budget rather than the enterprise contract. It does not replace ChartHop's org-chart and compensation functions, any more than it replaces a full enterprise workforce-design platform's scenario modeling. It answers one specific, board-relevant question — where is task-level AI exposure concentrated in our org, and which roles warrant monitoring, review, or redeployment planning — with a transparent, ONET-grounded method behind every score.
Which path fits your team
If your immediate problem is org-chart clarity, headcount planning, or compensation modeling, ChartHop remains a reasonable, transparently-priced choice within that scope — our self-serve workforce assessment tool piece and our AI org design tools guide go deeper on how org-design tooling and AI-exposure tooling relate to and differ from each other.
If your immediate problem is the board's question — where is our organization exposed, role by role, and what's the internal playbook for it — that requires task-level scoring against an external occupational taxonomy, which is the layer ChartHop was never built to provide. Running both tools side by side, rather than treating them as substitutes, is the most common pattern among mid-market teams that have both needs at once.
Getting started
WorkforceAnalysis is priced for mid-market HR teams evaluating this specific question, not for enterprise procurement cycles. You can review current tiers on the pricing page, and teams building out a broader internal playbook around AI workforce strategy — beyond the initial exposure scoring — can also look at the HR AI Strategy Toolkit for a structured starting framework. Either way, the underlying method — O*NET task mapping, four-dimension scoring, and Monitor / Review / Redeployment Candidate outputs — stays the same, and stays visible, so you can defend every number in it when the board asks how you got there.
This site incorporates information from ONET. Used under the CC BY 4.0 license. ONET is a trademark of USDOL/ETA.