Operating model & engagement

Onboarding and knowledge transfer

Separate avoidable access delay from the ramp, and plan continuity before people leave.

Current decision

2 resources blocked on access

Average productive day 40. Pre-provisioning could reduce carrying cost by $24k.

Compare access delay above the seven-day norm with the unchanged 35-day ramp.

Same instrument · 3 industries choosing a scenario replaces its inputs; save or share your current changes first

Avg time to productive

40 d

35-day ramp + excess access delay

Blocked on access

2/6

Access > 14 days

Ramp carrying cost

$227k

Cost before productive

Compression saves

$24k

Pre provision access

Time to productive · one shared day scale

ML Engineer

Offshore

access 18d → 11d beyond the 7-day norm + 35d ramp = productive day 46

blocked

Data Engineer

Onshore

access 8d → 1d beyond the 7-day norm + 35d ramp = productive day 36

36d

Delivery PM

Onshore

access 4d → 0d beyond the 7-day norm + 35d ramp = productive day 35

35d

QA / Eval

Offshore

access 21d → 14d beyond the 7-day norm + 35d ramp = productive day 49

blocked

MLOps

Onshore

access 12d → 5d beyond the 7-day norm + 35d ramp = productive day 40

40d

Domain SME

Onshore

access 6d → 0d beyond the 7-day norm + 35d ramp = productive day 35

35d

Excess access delay Excess delay · blocked Ramp

Access is the pole nobody manages

2 resources exceed the 14-day access threshold. Pre-provisioning caps access at five days and saves $24k in modeled ramp carrying cost.

If you act on this · the call → expected lift → how you'd measure it

The call

Compress onboarding and close KT risks before they become delivery blockers.

Expected lift · illustrative

Reduces carrying cost and protects continuity when senior knowledge leaves the program.

How you'd measure it

Time to productive, blocked resources, preprovisioning savings, KT completion, bus factor risk.

Steering committee takeaway: A resource is a cost from day one and an asset only once productive. Onboarding compression is an operating lever.

Resume echo, resource lead reality of the 31 resource AMEX portfolio; onshore/offshore mobilization.

How this is built

Time to productive = ramp (35d) + access beyond a 7-day norm; blocked if access > 14 days. Carrying cost = time-to-productive × loaded day rate (onshore $1,100 · offshore $700). Pre-provisioning caps access at 5 days.

KT view scores each knowledge area by bus factor; scheduling KT adds a backup (+1). Single point of failure = bus factor < 2. Stack: Next.js (static) + shared design system; client side.

Limitations: this is a modeled onboarding and KT tracker. Real use would require access systems data, role plans, training status, manager validation, KT artifacts, and resource calendars.

Method, evidence and limitations

Prefer to read? The two minute case study · problem → approach → metric → outcome

Problem

Access delays, unclear ramp expectations, and undocumented senior knowledge create hidden cost. Technology strategy professionals need to know which roles are blocked, what preprovisioning saves, and where knowledge has a single point of failure.

Approach

The tracker models onboarding timelines, access delays, ramp time, carrying cost, and knowledge transfer coverage. It highlights blocked resources and bus factor risks.

Why this way

This connects onboarding to cost, margin, delivery continuity, vendor mobilization, and operational readiness.

The metric

Time to productive; KT items captured before roll off.

The trade-off

Speed of ramp versus the depth of knowledge transfer captured.

Outcome

The onboarding critical path plus the KT captured before the senior leaves.