Skill Change Index (SCI)

McKinsey's measure (GCC Philippines Summit 2026 (PHx)) of how much AI reprices the skills demanded by a role — the degree to which a given skill's relevance rises or falls as AI automates parts of the work. Computed across ~6,800 skills (sources: Lightcast, US BLS, McKinsey).

The shape (illustrative: Account Executives, median SCI 28%)

Decreasing in relevance (high automation exposure): spreadsheet software, CRM software, lead generation, sales-territory management — routine, tool-bound, codifiable work.
Increasing in relevance: influencing, negotiation, forecasting, customer-relationship management, judgment — human-, hybrid-, and context-heavy work.

The headline

"AI will not just reduce work — it will reprice skills." The Philippines must shift talent from routine execution to judgment, influence, and AI-enabled problem-solving. The winners build skill corridors — defined migration paths that move people from compressing roles into rising ones.

2026-06-13 — Agent skills are mostly re-application, not greenfield

The 7 Skills You Need to Build AI Agents (IBM Technology) (Bri Kopecki) reinforces the SCI thesis from the AI-engineering side. Of her seven production-agent skills, five (system design, reliability, security, evaluation/observability, and arguably retrieval) are pre-existing backend / distributed-systems / security disciplines re-pointed at LLM-driven systems — not net-new competencies. In SCI terms most "agent" skills score as low skill-change / re-application of existing expertise, not greenfield reskilling: the platform/SRE/security engineer already speaks the language. That sharpens the repricing story — the relevance of those backend skills rises as they get re-aimed at agents, rather than being displaced.

2026-07-05 — Second-methodology corroboration: Deloitte/LinkedIn + Gallup (via Justin Sung)

How To Become Dangerously Self Educated with AI (Justin Sung) cites Deloitte/LinkedIn and Gallup reports (both, per Justin, pre-AI-hype) reaching a directionally identical conclusion via a completely different methodology: 30–50% of workforce-relevant skills will change in the next 5 years. Justin's punchier corollary: by the time a university student graduates their degree, roughly a third of what they learned is outdated.

warning Citation traced and corrected (web verification, 2026-07-07) The attribution doesn't check out as stated, but the direction survives with better anchors: LinkedIn Work Change Report (Jan 2025) — "By 2030, we expect 70% of the skills used in most jobs will change, with AI emerging as a catalyst" (report); WEF Future of Jobs 2025 — employers expect 39% of workers' core skills to change by 2030 (down from 44% in the 2023 edition) — the figure that actually falls inside Justin's 30–50% band. No Deloitte skill-change-magnitude stat was found (Deloitte's only nearby "30%" is an adoption stat), and no corroborating Gallup skill-change figure was found. Justin most likely misattributed WEF's 39% and/or LinkedIn's 70%.

Two ways this matters for the SCI page:

Independent triangulation. McKinsey's 6,800-skills instrument (SCI) and the employer/skills surveys are entirely different measurement stacks. The verified second-stack anchors are WEF's 39%-of-core-skills-change-by-2030 and LinkedIn's 70%-of-skills-used-in-jobs-change-by-2030 (see callout above) — converging on "a large fraction of skills reprice this decade" is stronger evidence than either alone. The specific "Deloitte/Gallup 30–50%" version of the claim did not survive verification and should not be quoted onward.
The rising-in-relevance side maps directly onto Higher-Order Thinking. Justin's frame — the space AI is architecturally weak at is contextual, multifactorial, high-conditionality problem-solving — is a different vocabulary for the SCI's "influencing, judgment, forecasting, customer-relationship management" bucket. Two independent framings landing on the same "up" list.

The specific Deloitte/LinkedIn and Gallup reports are not identified on the source; flagged as a follow-up on the source page confidence block — locating the specific reports would let this page anchor SCI from a second methodological angle at citation-grade quality.

Connections

The talent-side mechanism behind the Headcount-to-Value Pivot and the Frontier GCC talent pyramid. Directly feeds the user's Designing IT Roles for an AI Era (Talent Strategy POV) and DRAG for AI Upskilling at Manila IT Site. Compare the de-skilling risk in Designing AI Products That Don't De-Skill Users and Cognitive Offloading. Pairs with Higher-Order Thinking as the individual-learning frame for the "increasing in relevance" side of SCI's repricing (Justin Sung).