Korea Leads in AI Adoption, Yet Few Succeed: Five Conditions for Real Transformation

AI Operations Major Transformation: 5 Conditions That Turn Adoption Into Results

AI adoption sits at 28% among domestic companies-the highest globally. Yet true digital transformation through AI remains rare. The gap: overestimating what AI can do on its own and underestimating the work of redesigning processes, org structures, and roles.

As Kang Ji-hoon of BCG Korea notes, real outcomes come when AI is embedded end-to-end, not bolted onto legacy workflows. If you run operations, treat AI like a new operating system: it demands new rules, new teams, and new metrics.

The Five Conditions for an AI-Driven Operations Overhaul

1) Executive ownership with clear value targets

Set a top-down mandate with quantified value pools by function (cost, cycle time, quality, service levels). Tie each AI use case to specific KPIs and a P&L owner. Fund a 12-18 month program, not a set of pilots.

• KPIs: unit cost, first-pass yield, on-time-in-full (OTIF), backlog, SLA adherence
• Cadence: monthly value reviews; quarterly reallocation of budget to winners

2) Process redesign with human-in-the-loop

Redraw SOPs so AI and people share decisions and tasks. Define handoffs, guardrails, and exception paths. Document what changes at the edge (who approves, who monitors, who escalates) before scaling any model.

• Deliverables: updated SOPs, RACI, control limits, exception playbooks
• Outcome: fewer handoffs, shorter queues, higher decision velocity

3) Industrial-grade data and MLOps foundation

Move from ad hoc scripts to a managed stack: cleaned data pipelines, feature stores, monitoring, and rollback. Standardize APIs so models plug into ERP, MES, CRM, and workflow tools without manual stitching.

• Requirements: data quality SLAs, lineage, access controls, audit trails
• Ops: CI/CD for models, drift monitoring, incident response within set SLAs

4) A product operating model and redefined roles

Stand up cross-functional squads (Ops + Data + Engineering + Risk) that own outcomes, not tasks. Create new roles: AI product owner, model ops lead, prompt/library steward, and AI QA. Align incentives to value delivered, not model accuracy.

• Structure: 6-10 person squads per value stream with embedded SMEs
• Governance: weekly demos, monthly kill-or-scale decisions

5) Governance, risk, and change at scale

Codify how you approve, monitor, and retire models. Train frontline teams, update performance reviews, and make adoption visible. If it's not in the SOP, the audit, and the bonus plan, it will not stick.

• Controls: model registers, bias tests, explainability standards, access logs
• Change: role-based training, floor support, adoption targets by site/team

What This Looks Like in Practice

Stop asking "Where can we use AI?" and ask "Where does waiting, rework, or variance hurt margins the most?" Start with three use cases that remove bottlenecks: demand forecasting to stabilize planning, dynamic scheduling to reduce idle time, and AI copilots to cut ticket handling time.

Each use case must ship with a new workflow, new controls, and measurable impact within one quarter. Anything that cannot show value fast gets paused and re-scoped.

90-Day Starter Plan

• Weeks 1-2: Pick 3 value pools. Set baseline metrics and owners. Map current workflows and decision points.
• Weeks 3-6: Build thin-slice solutions with shadow "human-in-the-loop" steps. Draft new SOPs and RACIs. Stand up data pipelines and monitoring.
• Weeks 7-10: Run controlled trials in one site or team. Track impact daily. Fix failure modes and tighten guardrails.
• Weeks 11-12: Lock SOPs, train users, and move to limited production. Prepare scale plan for next 3 sites/teams.

Metrics That Matter

• Cycle time: -20-40% in targeted workflows
• Manual touches: -30-60% on defined steps
• Quality: +2-5 pts in first-pass yield or SLA hit rate
• Throughput: +10-25% without extra headcount
• Time-to-decision: -50-80% for repeatable decisions

Common Traps (And How to Avoid Them)

• Tool-first thinking: Always start with value and process, not models.
• Pilots that never scale: Require integration into core systems before a pilot starts.
• Accuracy worship: Measure business outcomes, not just model metrics.
• IT bottlenecks: Use platform teams and standard APIs to speed delivery.
• Change fatigue: Train by role, support on the floor, and reward adoption.

If You Need a Reference Model

For a deep dive on building AI at scale across operations, see this overview from BCG on enterprise AI programs: AI at Scale.

To upskill teams by job function and accelerate adoption, explore curated programs here: Complete AI Training - Courses by Job.

Bottom Line for Operations Leaders

AI will not fix broken processes. Redesign the work, set ownership, and build the foundation that lets models run safely at speed. Do that, and the 28% adoption figure starts to translate into throughput, quality, and cost advantages you can see on the P&L.