{ "story_id": "8daade02a227a2fb0c35369420648642", "desk": "drm3", "revision": 1, "published_at": "2026-09-04T02:47:14.000Z", "content_hash": "1391848998d76d9b8474f4bc658f645f3352ca85118460e2e3a054853417e435", "hash_basis": "sha256 over `headline\\ndek\\nprose`, plus `\\n` + the canonical citations JSON when any source is placed, plus `\\n#blog` for blogs", "basis": { "headline": "Agentic AI transforms Global Business Services in Colombia", "dek": "McKinsey expert says agentic AI will shift GBS from efficiency centers to value creation engines.", "prose": "McKinsey & Company's Ignacio Gorupicz, a leader of the Service Operations practice in Latin America, stated on 2026-09-03 that Agentic AI forces Global Business Services (GBS) to evolve beyond traditional capabilities of scale, standardization, and cost reduction. [^1]\n\nGartner estimates that businesses will allocate $2.59 trillion to AI spending in 2026. [^2]\n\nJerry Haywood, CEO of boost.ai, stated that businesses are not seeing AI ROI because they have not dismantled their processes and remade their organizational workflows to harness AI's power. [^3]\n\nMcKinsey & Company calls this the 70-20-10 rule: 70% of the business value from an AI program comes from people, culture and process change; 20% comes from data infrastructure; and a mere 10% stems from the actual AI algorithms. [^4]\n\nSeveral high-profile studies, including a widely discussed MIT study, found that most organizations are not seeing a return on investment from their AI spending. [^5]\n\nAccording to INEGI data, micro, small, and medium economic units concentrated 70.7% of employed personnel and generated 56.5% of the income of economic units. [^6]\n\nDue to high operational costs, large banks often prioritize high-volume segments like consumer or corporate loans, leaving micro, small, and medium enterprises (SMEs) as a lower priority. [^7]\n\nMcKinsey & Company reports that performing operations via application costs 0.03 USD, which is 95% cheaper than the 0.65 USD cost of branch transactions. [^8]\n\nTraditional banks historically rely on physical networks, which incur significant operational costs including rent, cash handling, maintenance, and on-site staff. [^9]\n\nAccording to the analysis, Agentic AI adoption could reduce the size of Global Business Services teams by 20% to 30% while simultaneously making them significantly more productive. [^10]\n\nThe article asserts that while traditional automation executed predictable tasks, Agentic AI can incorporate judgment, synthesize information from different contexts, and participate in decisions previously requiring human intervention. [^11]\n\nThe author argues that as AI assumes more transactional work, traditionally administrative functions must evolve toward advisory, strategic, and decision-making roles. 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