AI perspectives for business leaders.
Research, analysis, and practical guidance on adopting AI effectively in your organization.
AI Readiness 2026: Why almost every company uses AI, and almost none are ready for it.
88% of companies already use AI — but only 1% call their AI strategy fully mature. We unpack the six building blocks of AI readiness, why 80% of AI projects fail to deliver, and what boards should ask before investing further.
V-TEKI Research
Enterprise AI Risk 2026: Companies are moving faster than they can prove it’s safe.
88% of companies now use AI, yet leading AI companies’ own transparency scores dropped from 58 to 40 out of 100. We map the ten types of AI risk — from hallucination to Shadow AI — and the board-level checklist for keeping it in control.
V-TEKI Research
AI Governance 2026: Keeping AI safe, accountable, and under control.
Only ~33% of companies say they’re genuinely good at managing AI risk — including AI that acts on its own. We break down the eight building blocks of AI governance and the questions every board should be asking before scaling AI further.
Why most AI transformations stall — and how to restart them.
Most organisations adopt AI but remain stuck in "pilot purgatory" — with over 80% of AI projects failing to reach production. We examine the structural and strategic root causes, from weak data foundations to misaligned operating models, and outline how to restart stalled programmes.
Document intelligence in banking: From weeks to minutes.
Document processing in lending can take 18–24 hours per file. We examine how AI Document Intelligence pipelines — combining automated classification, OCR, and cross-document validation — compress that to under 2 minutes and eliminate manual validation errors at scale.
Is our organisation structurally ready for autonomous decision-making?
Agentic AI systems that plan, act, and decide with minimal human intervention are moving from pilots to production. We outline the governance, technical, and cultural readiness dimensions organisations must address before autonomous decision-making becomes core infrastructure.
Responsible AI governance: A framework for financial services in Southeast Asia.
As regulators across ASEAN tighten AI oversight, we outline a practical five-pillar governance framework for financial institutions navigating compliance.
Building a data culture before you build AI: Lessons from the field.
Around 85% of AI projects fail due to poor data quality, governance, and literacy — not weak algorithms. The organisations that succeed with AI are those that first built robust data foundations.