As 2026 approaches, generative AI (GenAI) is at a critical turning point: organizations are moving beyond pilots, but most still struggle to achieve scalable, measurable impact. While nearly 80% of companies have experimented with GenAI, only a small fraction report tangible business value. This highlights the urgent need to bridge the gap between innovation and execution.
Regulatory frameworks, particularly the AI Act and Digital Operational Resilience Act (DORA) in Europe, are now actively shaping the industry, making compliance, risk management, and transparency non-negotiable for high-risk AI applications. Success in 2026 will not only require technical advancement, but also process harmonization, robust governance, and targeted upskilling of teams to manage AI responsibly and at scale.
Three primary trends are defining the landscape:
- Agentic AI is evolving from conversational chatbots to autonomous agents capable of executing complex, multistep tasks—transforming workflows and decision-making across sectors.
- Domain-specific and small language models (DSLMs and SLMs) are gaining traction, enabling privacy-preserving, tailored solutions that meet stringent regulatory demands and deliver scalable intelligence.
- Advanced anti-fraud defenses: There is a critical need for defenses against deepfakes and synthetic documents amid the escalation of AI-driven fraud in onboarding, Know Your Customer (KYC), Anti-Money Laundering (AML), and collection processes.
CRIF is uniquely positioned to govern and guide these trends. It’s not just about adopting more powerful models but also unlocking the value of existing information assets and integrating them into secure, auditable, and compliant decision engines powered by AI.
For the financial sector, this means turning risk into a competitive advantage. Organizations that successfully operationalize responsible AI and standardize their decision-making processes will unlock both operational efficiency and lasting strategic advantages.
The rise of agentic commerce has driven the emergence of new transaction protocols and standards, presenting both opportunities and risks for financial services, telcos, and utilities. Organizations must adapt to AI-driven transactions, embedding trust, identity, and compliance at the protocol level.
