The exposure heatmap is a starting point, not an answer
Your department heatmap is finished. Cognitive routine scores are high in accounts payable. Social/judgment scores are high in account management. The board deck is due Thursday, and somewhere between the red cells and the green cells sits the question every HR strategy director eventually has to answer out loud: now what?
An exposure score tells you where task content overlaps with what generative AI and automation currently do well. It does not tell you what to do about it. That gap — between a heatmap and a decision — is where most workforce planning efforts stall. Gartner's 2024 research found that 86% of HR leaders have not implemented strategic workforce planning at all, and a separate Gartner survey found 66% of organizations say their workforce planning is effectively limited to headcount counting, with leaders struggling to demonstrate its ROI to the business. A list of exposure scores, however accurate, risks becoming one more report that confirms a problem without pointing toward a plan.
This article is about the second half of the exercise: turning a Review or Redeployment Candidate finding into a concrete, O*NET-grounded workforce redeployment plan — one you can defend to a CFO, a works council, or an individual employee who wants to know what comes next. If you haven't yet built the exposure picture this plan depends on, our guide to running a company-specific AI workforce exposure assessment walks through that first step in detail.
Why redeployment beats displacement, financially and organizationally
Before getting into method, it's worth being explicit about why redeployment is the default posture WorkforceAnalysis is built around, rather than headcount reduction.
The financial case starts with what replacement actually costs. SHRM's 2024 analysis puts the cost of replacing an employee at roughly 50% to 200% of that employee's annual salary, depending on seniority and role complexity, once recruiting, onboarding, lost productivity, and ramp time are counted. SHRM's benchmarking research separately found an average cost per hire of $4,129 across employers. Internal moves largely avoid both of those costs, and the Josh Bersin Company's 2023 research on internal talent mobility found that external hiring typically costs three to five times more than filling the same role through an internal placement. None of this is a claim about any specific redeployment saving money in every case — it is the direction the independent research points, and it's the reason redeployment deserves first consideration before a role is treated as a net headcount reduction.
The organizational case is about time. The World Economic Forum's Future of Jobs Report 2025 estimates that 39% of the skills used in today's jobs will be transformed or rendered outdated by 2030, and that employers see skill gaps as their single biggest barrier to workforce transformation, cited by 63% of respondents. Lightcast's 2025 research on the pace of skill change found that the skills required in an average job changed by 32% between 2021 and 2024, and that a quarter of jobs saw at least 75% of their required skills turn over in that same window. Skills are moving faster than most job architectures can track manually. A role that looks fully exposed today may share most of its underlying skill content with an adjacent occupation that isn't — but only a systematic, task-level comparison will surface that adjacency before the decision gets made informally.
That's the redeployment argument in one sentence: workforce redeployment is not a soft alternative to a hard decision. It is frequently the financially and operationally rational path, and it only becomes visible when exposure findings are paired with a structured map of where skills transfer.
How WorkforceAnalysis finds adjacent occupations
WorkforceAnalysis builds its redeployment logic on top of the same foundation it uses for exposure scoring: the ONET database maintained by the U.S. Department of Labor's Employment and Training Administration. ONET currently describes 1,016 occupational titles and 923 data-level occupations spanning more than 55,000 individual jobs in the U.S. economy, each characterized by roughly 277 descriptors covering tasks, skills, knowledge areas, work activities, and work context. The database is updated on a rolling basis, with a primary annual update typically released in the third quarter. This article references O*NET database version 30.3; confirm the version loaded in your account matches the current production release before relying on any specific adjacency figure.
This site incorporates information from ONET. Used under the CC BY 4.0 license. ONET is a trademark of USDOL/ETA.
Every occupation in O*NET carries a structured task list, each task rated for its importance to that occupation on a standardized scale. This is the same importance-weighting mechanism WorkforceAnalysis uses to produce a role's exposure score — high-importance tasks contribute more to the score than incidental ones — and it's also the backbone of the redeployment engine. Instead of asking "how exposed is this occupation," the redeployment step asks a structurally similar question: "which other occupations share a high proportion of this occupation's important tasks and skills?"
Concretely, the process runs in three passes:
- Task and skill vectorization. Each occupation's O*NET task statements, skill ratings, and knowledge-area ratings are represented as a structured profile, weighted by importance.
- Overlap scoring. A candidate occupation's profile is compared against the exposed role's profile, producing a skill-overlap percentage — the share of importance-weighted skill and task content the two occupations hold in common.
- Adjacency ranking. Candidate occupations are ranked by overlap percentage, filtered to those requiring a comparable or reachable level of the SOC-classified skill, education, and experience typically associated with the role — reflecting the Bureau of Labor Statistics' 2018 SOC structure of 867 detailed occupations grouped into 459 broad occupations, 98 minor groups, and 23 major groups.
The output is not a single "correct" landing role. It's a ranked shortlist of adjacent occupations, each with a transparent overlap percentage and the specific shared tasks that produced it — the same level of task-by-task transparency the exposure score itself is built to provide, so a People Analytics Lead can trace exactly why an occupation appears on the list rather than treating it as a black-box recommendation.
A worked example: from Review to Redeployment Candidate
To make this concrete, walk through a simplified example using round numbers designed purely to illustrate the method — not a claim about any real occupation's actual score.
Suppose a mid-market insurer's exposure assessment scores a claims-processing role across the four WorkforceAnalysis dimensions, each measured 0–100:
- Cognitive routine: 78
- Physical routine: 15
- Social/judgment: 40
- Creative: 20
Task-importance weighting (drawn from the O*NET importance ratings for the matched occupation) pulls the blended exposure score toward the cognitive-routine figure, since data entry and standardized document review make up the bulk of the role's high-importance tasks. The role lands in the Review band — high enough to warrant active planning, not so concentrated in a single narrow task type that redeployment is the only option on the table.
The redeployment engine then compares this occupation's task-and-skill profile against neighboring O*NET occupations and returns three candidates:
- Claims adjuster, examiner, or investigator — 71% skill overlap, driven by shared judgment-based tasks around loss evaluation and documentation review, but requiring stronger negotiation and investigative skill development.
- Insurance underwriter — 58% skill overlap, sharing structured-data-review tasks but requiring materially different risk-assessment training.
- Customer service representative, insurance — 64% skill overlap, sharing documentation and case-management tasks, with a shorter reskilling path but a smaller compensation band.
Each of these is a Redeployment Candidate pathway, not a guarantee — the overlap percentage tells a planner where skill transfer is highest and where the reskilling investment will be smallest, but the decision about which pathway fits a given employee still depends on their specific experience, aptitude, and interest. This is the same discipline that governs exposure scoring: the tool surfaces the input; a person makes the call.
Building the reskilling roadmap: what closes the gap
An adjacency shortlist with an overlap percentage is only half of a redeployment plan. The other half is naming, specifically, what closes the remaining gap between the current role and the target occupation.
Because the comparison is task-and-skill-level rather than job-title-level, the gap is visible in the same terms as the overlap: the specific O*NET skill or knowledge descriptors present in the target occupation's profile but absent or underweighted in the source role. In the claims-processing example above, moving toward the claims adjuster pathway means the reskilling roadmap should center on negotiation and investigative interviewing — not a generic "communication skills" line item, but the specific descriptors the overlap analysis flagged as the delta.
This specificity matters because of how the WEF's Future of Jobs Report 2025 breaks down what actually happens to workers whose roles are undergoing transformation: of every 100 workers needing training, the report estimates 59 will need it, 29 will be upskilled within their current role, and 19 will be reskilled or redeployed into a different role internally — leaving 11 unlikely to receive the training they need at all. A redeployment roadmap built on a vague skills gap is far more likely to land its target employee in that last group. A roadmap built on specific, importance-weighted skill deltas gives a learning-and-development team a concrete curriculum to build against, and gives the employee a concrete, honest picture of what the move actually requires.
Practically, a workforce redeployment roadmap built this way should specify, per candidate pathway:
- The target occupation and its SOC/O*NET code, so it can be tracked consistently across HR systems.
- The overlap percentage and the specific shared tasks driving it.
- The named skill or knowledge gaps, drawn from the O*NET descriptor comparison, not a generic competency framework.
- A realistic timeline and delivery method for closing each gap — internal mentorship, formal training, a stretch assignment — decided by the L&D team, not generated automatically.
- The compensation and level implications of the move, reviewed by HR and finance together.
This is also where a structured internal mobility process differs from an ad hoc one. Internal mobility software and workforce-planning tools generally track open requisitions and employee profiles; what they don't typically provide is a defensible, O*NET-grounded reason a specific employee in a specific exposed role is a strong match for a specific open pathway. That reasoning is the piece a redeployment plan needs to survive scrutiny from a skeptical manager or an anxious employee.
Governance: who owns the redeployment decision
A common failure mode is treating the exposure-to-redeployment pipeline as something HR can run alone, quietly, and present as a finished plan. That approach tends to produce plans that look complete on paper and fall apart in execution, because the people who actually know whether a redeployment pathway is realistic — line managers, the receiving department's leadership, and often the employee — were never consulted.
A workable governance model gives three parties a defined role:
- HR strategy / people analytics owns the exposure and adjacency data, and is responsible for making sure every Redeployment Candidate finding comes with a transparent overlap percentage and task-level rationale — not a black-box score.
- The receiving department's manager validates whether the adjacent occupation reflects real, current work in their team, and flags where the O*NET occupational description has drifted from how the role is actually staffed at this company.
- The employee, wherever process allows, is brought into the conversation before a pathway is finalized — both because aptitude and interest are not captured in a skill-overlap percentage, and because a redeployment plan imposed without input tends to produce the attrition it was meant to avoid.
This governance structure also reinforces the core constraint that should run through every stage of this work: an exposure score, and the redeployment pathways derived from it, are inputs to a human decision. They are not a verdict on any individual's employability, and they are not a substitute for a manager's judgment about a specific person doing a specific job.
Getting started this quarter
If your organization already has department-level exposure findings sitting in a heatmap, the redeployment step doesn't require starting over. It requires running the same task-and-skill data through an adjacency lens, one exposed role at a time, starting with the roles your exposure assessment flagged as Review or Redeployment Candidate.
WorkforceAnalysis's pricing tiers include the redeployment-mapping capability alongside the core exposure scoring, so the adjacency shortlist and overlap percentages generate directly from the same role data you've already uploaded — no separate data pull required. If you'd rather work through the mechanics on paper first, the Role Redeployment Planning Workbook in our store walks an HR team through the same task-comparison logic manually, occupation by occupation, before committing to a software workflow.
And if you're earlier in the process — still building the case internally for why a structured, O*NET-grounded approach beats a spreadsheet exercise or a costly one-off consulting engagement — join the waitlist to be notified as new capabilities, including expanded redeployment pathways, roll out. The rest of our blog covers the exposure-scoring methodology in more depth, for teams that want to understand the full pipeline before bringing it to their board.