Operating model & engagement

Talent and capability pathway

Choose how to close each capability gap and inspect the modeled readiness timeline.

Current decision

6 gaps have no pathway

Current coverage 48%; modeled coverage after selected pathways 48%. Outcomes are projected, not achieved.

Choose build, hire or partner per capability; review the latest completion date against the pace of change.

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

Readiness now

48%

Average current capability score

Readiness after plan

48%

After selected pathways

Open gaps

6/6

No pathway chosen

Time to ready

N/A

Stack moved in 18 mo

Capability gap · current → target

Prompt & context engineering

55 → 85 · gap 30

Agent orchestration (MCP / A2A)

30 → 80 · gap 50

Eval & observability for LLMs

40 → 85 · gap 45

LLM Ops / deployment

50 → 80 · gap 30

AI governance & risk

45 → 90 · gap 45

Domain × AI translation

65 → 85 · gap 20

Tick = target. Build 8mo (cheap, permanent) · Hire 4mo (permanent, costly) · Partner 2mo (fast, rented).

The stack moves faster than the team

Stack went agentic

18 mo

Choose a pathway for all 6 open gaps to calculate team readiness. No completion date is assumed.

6 capability gaps have no plan

Orchestration and eval are the widest gaps and the newest skills, build only there takes eight months. Mix in partner for speed on the critical path and build for what must live in house.

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

The call

Build, hire, or partner for each capability gap based on urgency, permanence, and cost.

Expected lift · illustrative

Aligns team readiness with the pace of the AI platform roadmap.

How you'd measure it

Capability coverage, time to ready, open gaps, pathway cost, productive capacity.

Steering committee takeaway: The AI stack may change in 18 months. Teams often take longer. Start the people plan before the platform plan becomes urgent.

Resume echo, team capability building across delivery portfolios.

How this is built

Each capability has current coverage vs an agentic era target; gap = target − current. A pathway (build 8mo / hire 4mo / partner 2mo) closes it; team time to ready = the slowest chosen pathway, compared against the 18-month stack shift.

Stack: Next.js (static) + shared design system; deterministic client side.

Limitations: this is a modeled capability planner. Real workforce planning would require role inventory, skills assessment, hiring market data, vendor strategy, budget, and manager validation.

Method, evidence and limitations

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

Problem

Agentic and enterprise AI work requires new combinations of context engineering, orchestration, evaluation, LLMOps, governance, and domain translation. Training alone does not close every gap, and some gaps require hiring or partnership.

Approach

The planner assesses current and target capability coverage, exposes gaps, and models build, hire, or partner pathways with time to ready implications.

Why this way

This connects AI strategy to workforce planning, capability maturity, delivery risk, budget, and operating model change.

The metric

Skill gap per role; time to productive per pathway.

The trade-off

Building is slow but sticky; hiring is fast but costly; partnering is quick but external.

Outcome

The build/hire/partner pathway per role, with time to productive.