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professional-services · People ops · AssistBuy it

AI employee onboarding assistant

01 — The scenario

A professional-services business where this lands in HR.

The friction
onboardingknowledgeramp-timeinternal-helpdesk

02 — What is actually going wrong

Every new hire spends their first fortnight asking colleagues questions that are already answered somewhere in the intranet. The answers are inconsistent because they come from whoever was free, and the senior people being interrupted are the expensive ones.

How it works

An assistant grounded in your actual policies, system guides and process documentation answers the new starter's questions in context, with a link to the source document. What it cannot answer, it routes to the right person — and logs, so you learn what your documentation is missing.

What good looks like

New starters get consistent answers immediately instead of inconsistent answers eventually. Ramp-to-productive shortens, and the gap log quietly becomes the best documentation backlog the firm has ever had.

03 — The problem, in money

$3,459$1,015,531/ yr at stake
Modeled
  • New hires per month6 hires100 hiresPlaceholder — not a validated benchmarkStarting point
  • Onboarding hours per hire8 hours41 hoursPlaceholder — not a validated benchmarkStarting point
  • Share automated0.4 ratio0.5 ratioPlaceholder — not a validated benchmarkStarting point
  • Blended blended_hourly_cost cost40 USD110 USDPlaceholder — not a validated benchmarkStarting point
  • Share of freed time redeployed0.5 ratio0.5 ratioPlaceholder — not a validated benchmarkStarting point

04 — The options

Buy$150–$400 / moOff-the-shelf tooling, live sooner.
Bin$0Do nothing — and accept the leak.

05 — The call

Buy it

Buy it. An off-the-shelf tool wins this one — it gets you live faster, and the math does not justify building from scratch.

06 — How this goes wrong

  • Grounding it in documentation that is out of date. The assistant will confidently repeat a superseded policy, and new hires have no way to know.
  • Including HR material with access rules — salary bands, performance notes, anything personal — in the same index as general policy.
  • No escape hatch. A new hire stuck in a loop with a bot on day three learns that the firm does not want to be asked things.
  • Measuring deflected questions instead of time-to-productive. Deflection is easy to game and is not the outcome you want.

07 — How you would actually do it

  1. 01Audit what documentation actually exists and how stale it is. This usually reveals the real problem.
  2. 02Scope the index to general policy and process; exclude anything with per-person access rules.
  3. 03Require a source link on every answer so the new hire can verify.
  4. 04Add a one-click handover to a named buddy.
  5. 05Log unanswered questions and work that list monthly.
  6. 06Measure time-to-first-independent-delivery, not chat volume.

08 — Tools worth looking at

Glean / DashworksEnterprise search across the tools you already use, with permissions respected.
Notion AI / Confluence AINative option where the documentation already lives in one wiki.
Claude ProjectsCheapest credible start for a firm under fifty people.

Named for orientation, not endorsement. No affiliate arrangements, and nothing here has been paid for.

09 — Run it on your numbers

Start from industry averages, then drag the inputs to match your business. The number moves live.Drag each input to your number — the starting points are neutral midpoints, not industry data. The number moves live.

Worth to your business
$3,459per year
Modeled — industry averages
Revenue$0Modeled
Cost savings$0Modeled
Hours / week4Modeled
Industry averages
6 hires
Industry avg: 6 hiresPlaceholder — not a validated benchmark
8 hours
Industry avg: 8 hoursPlaceholder — not a validated benchmark
0.4 ratio
Industry avg: 0.4 ratioPlaceholder — not a validated benchmark
40 USD
Industry avg: 40 USDPlaceholder — not a validated benchmark
0.5 ratio
Industry avg: 0.5 ratioPlaceholder — not a validated benchmark

10 — The evidence · what others report

No hard numbers reported.

Reported numbers are third-party claims, source-attributed — not verified by me, and never blended into the model above.

11 — Questions owners ask

Every figure is Modeled — computed from industry-average benchmarks against the scenario’s formula. Move the sliders to your own inputs and the numbers recalculate live. Nothing here is a promise; it is a starting estimate you can pressure-test.
The library is organized by department, outcome, effort and readiness so you can find what fits. Add scenarios to your plan, tune the inputs, and see the combined picture on your plan dashboard.
Want this to be a Projected number? That's what the Audit produces — my forecast on your real data, with my name on it.
Build means a custom system is the right move; Buy means an off-the-shelf tool wins; Bin means the math does not justify doing it at all. Binned scenarios can be favorited for reference but never enter a plan.

Is this your business?

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