AhnLab sets 2026 agenda: AI-first execution under "AXELERATE AhnLab"
AhnLab kicked off 2026 at its Pangyo headquarters, unveiling a management policy titled "AXELERATE AhnLab." The message is clear: move faster, execute better, and focus the business around AI to secure the next stage of growth. CEO Kang Seok-gyun outlined the direction during the New Year ceremony, emphasizing speed and accountability across the organization.
The core: AI-first transformation
The company named "AI-First Transformation" as its primary task for the year. That means product and service innovation centered on AI and a rework of internal operating methods to upgrade enterprise-wide execution. The goal is simple: build an organization that ships meaningful AI-driven outcomes, not just pilots.
- Reorganize work methods and processes to embed AI across functions.
- Strengthen competitiveness of products and services with AI at the center.
- Pursue balanced growth across business lines.
- Expand external synergies to build a mid- to long-term growth foundation.
Leaders from each division shared 2026 strategies and goals at the ceremony. The company also recognized teams and individuals with year-end awards.
Leadership message
"In the era of the AI paradigm shift, accelerating change and execution, along with product and service innovation centered on AI, are critical," said CEO Kang Seok-gyun. "In 2026, let's pool our capabilities in our respective roles to create a new leap forward for AhnLab."
Executive appointments
AhnLab announced the following promotions as part of its 2026 plan:
- Executive Vice President: Han Chan-seok (Business Division Head); Han Chang-gyu (Research Institute Director)
- Senior Vice President: Koo Hyeong-mo (Service Business Division Head); Kim Deok-hwan (Head of Financial Planning Office); Kim Hong-hyeon (Head of Network Development Office); Han Tae-su (Head of Convergence Development Office)
What this means for executives and strategy leaders
AI-first isn't a slogan; it's an operating model choice. The signal here: product pipelines, workflows, and partnerships will be re-scoped to favor AI-enabled outcomes with measurable returns and clear guardrails.
- Translate "AI-first" into selection rules: resources move to initiatives with clear data access, model fit, ROI, and risk thresholds.
- Resolve execution bottlenecks early: data quality, model deployment paths, security, and compliance.
- Balance growth by segment: protect core margins while funding AI bets with staged milestones.
- Use external synergies to compress time-to-value: partnerships, integrations, and co-selling where it reduces build time.
- Upskill leaders closest to the work so decisions don't stall. A practical starting point: align training to roles and use cases.
If you're building similar capability in your organization, see role-based learning paths for AI adoption here: AI upskilling by job function.
Your membership also unlocks:
