By Jason Palleschi and Annalisa Camarillo 

Why AI Readiness Became the Industry’s Central Theme in 2025

2025 has become a pivotal year for AI in financial crime. Across industry events, including Transform Finance, ACAMS chapter meetings, 1LoD gatherings, the FRC Leaders Convention, and Money20/20, financial crime risk leaders, regulators, and analysts all echoed the same message. AI is moving from exploratory to operational.

With that shift comes a more fundamental question. Are institutions ready for AI?

This post summarizes the strongest insights shared across these events and outlines what AI readiness really means for leaders preparing for 2026. It covers:

  • The good news that AI adoption is delivering real value to operational efficiency and risk identification, supported by updated 2025 guidance from regulators and analysts.
  • The core challenge that the primary barrier is no longer technology, but organizational readiness.
  • Five AI concepts that every FIU must understand, based on themes shared consistently by practitioners, experts, and regulators.
  • A 2026 AI Readiness Framework that highlights the competencies investigators, supervisors, and QA teams now need.
  • Where technology fits, including how Quantifind supports explainability, governance, and investigator enablement.
  • What FIUs can do next, including readiness planning and participation in transformation workshops.

If 2024 was the year AI entered industry discussions, 2025 is the year the industry began defining what it takes to operationalize AI safely and effectively. Institutions that prepare now will be better positioned to lead in 2026.

The Good News: AI Adoption and Its Benefits Are Accelerating in 2025

Across every major financial crime event this year, one encouraging trend stood out. AI adoption in financial crime is gaining real momentum.

Industry analysts reinforced this shift across multiple reports:

  • McKinsey (2025) noted that “risk and compliance teams are accelerating deployment of domain-specific AI models, supported by clearer regulatory expectations.”
  • Deloitte’s 2025 Financial Crime Trends report highlighted that more institutions have “moved beyond experimentation into structured AI-enabled workflows.”
  • Forrester (2025) identified explainable AI as “a top priority for financial crime platforms in 2025.”
  • Gartner (2025) emphasized that demand for “AI with transparent, traceable logic” is driving procurement decisions in AML.

Regulators contributed to this momentum. In early 2025:

2025 marks a turning point. AI is no longer a future concept; it is becoming core infrastructure for modern FIUs.

AI Adoption Is Rising, Yet Readiness Is the Real Barrier

Across every event Quantifind participated in this year, one theme consistently rose to the top. The largest barrier to AI adoption is not the technology. It is organizational readiness. Not readiness in the abstract, but readiness in the practical areas that determine success, including:

  • People and skill sets
  • Data quality and consistency
  • Evidence lineage and documentation
  • Governance and model oversight
  • Workflow alignment
  • User competency and judgment

A panelist at a recent event described the challenge simply: “AI governance starts with user governance.”

Another speaker at a different session added. “You cannot operationalize what you do not understand.”

Investigators are no longer finders of information. They are interpreters, validators, and explainers of AI-generated intelligence. Many institutions have not equipped teams for this shift, and the resulting readiness gap now defines the pace of adoption.

Why the AI Readiness Gap Matters More Than Ever

As institutions adopt more automated intelligence, the investigator’s role is changing in fundamental ways.

  • From collector of facts to interpreter of intelligence
  • From searcher to validator
  • From gatherer to explainer
  • From manual reviewer to judgment-driven analyst

Yet few FIUs have clearly defined the competencies required to support this evolution. This readiness gap was reinforced repeatedly during the 2025 conference circuit and aligned directly with updated regulatory expectations, including:

  • FATF’s 2025 guidance emphasizing documented human oversight.
  • The OCC’s 2025 supervisory priorities highlighting user training and override documentation.
  • The FCA’s 2025 AI review focusing on user competency.
  • The Basel Committee and BIS guidance emphasizing transparency and human review as prerequisites for responsible deployment.

The industry now agrees. Institutions cannot scale AI without scaling user understanding and governance.

5 Things Every FIU Should Know About AI as We Head into 2026

As institutions move toward AI-enabled financial crime risk management, we’re seeing a pattern: teams aren’t struggling with whether to use AI, they’re grappling with which AI to use, how to use it responsibly and effectively, and in a way that strengthens existing workflows. Here are five essential insights, supported by 2025 industry dialogue:

  1. AI Does Not Only Mean ChatGPT
    We noticed some people are conflating AI exclusively with generative AI due to its popularity, so let’s unpack it.
     “ChatGPT cannot run your investigations. Purpose-built models can.”

    What to understand:
    – Generative AI is not the right application for most compliance decisions
    – LLMs lack auditability unless wrapped in strict guardrails
    – Purpose-built models designed for sanctions, network detection, and investigative workflows are more appropriate

    This distinction alone removes 50 percent of the fear around AI

  2. AI Must Be Explainable To Be Safe To Deploy
    Regulators did not oppose AI. They opposed black box AI.
    “If you cannot show how you got the answer with AI, you will not pass an exam.”

    What to understand:
    – Explainable models allow investigators to trace decisions
    – Evidence lineage matters more than model sophistication
    – Transparency is more valuable than prediction complexity

    This distinction alone removes 50 percent of the fear around AI

  3. Legacy Systems Won’t Cut It
    Multiple speakers acknowledged a hard truth: Legacy AML systems were not designed for modern intelligence.
    “Most banks do not have an AI problem. They have a plumbing problem.”

    What to understand:
    – Data fragmentation/quality is the biggest blocker to AI success
    – AI requires unified, structured, cross-silo intelligence
    – You cannot layer modern intelligence on top of broken inputs without fixing the foundation

    This is where enrichment platforms like Quantifind serve as the “intelligence layer” that bypasses legacy limitations.

  4. Model Risk Management Now Applies to Users
    The strongest point from the entire event: Model governance is no longer only about the model. It is also about the people using it.
    “Human oversight is part of the system. Train the human, not just the model.”

    What to understand:
    – Investigators need training on how to interpret model outputs
    – QA needs visibility into why a model made a recommendation
    – Supervisors need the ability to override and document rationale

    This is the essence of AI literacy.

  5. AI Literacy Drives Competitive Advantage
    Not because it replaces investigators, but because it strengthens them.
    “The institutions that understand AI will outpace those that simply deploy it.”

    What to understand:
    – AI supports strategic thinking, not clerical work
    – Investigators who understand AI produce better decisions
    – Teams with AI fluency scale faster and reduce operational drag

    By 2026, AI literacy will differentiate top FIUs from those stuck in reactive mode.

What Readiness Looks Like: The AI Fluency Framework for FIUs (2026-Ready)

This expanded framework reflects what institutions will need to modernize safely and effectively.

Analytical InterpretationModel InteractionDecision Governance
Skills Required- Understand enriched intelligence across sanctions, PEPs, adverse media, corporate data, and network context

- Evaluate entity resolution confidence

- Interpret risk scores and how they were constructed

- Trace evidence lineage from input to outcome

- Identify missing or contradictory signals		- Validate and challenge AI-generated outputs

- Identify incomplete or inconsistent model inputs

- Provide structured feedback

- Recognize anomalies or drift

- Override automation responsibly
- Clear rationale for decisions

- Documentation suitable for QA, audit, and regulators

- Consistency across investigators and regions

- Accountability for human judgment at every step
PurposeThe foundation of modern investigations.What regulators increasingly expect to be documented.Skills support safe and defensible AI operations.

Where Quantifind Helps FIUs Accelerate Readiness

Quantifind addresses readiness gaps through the following capabilities.

  1. Clear evidence of lineage: Every enrichment is linked back to the source article, sanction list entry, registry record, or network node.
  2. Transparent scoring: Analysts can drill down to see why an entity was flagged, which risk factors contributed, and how the model reached its conclusion.
  3. Structured dossiers: All intelligence is consolidated into a single, easily understandable view that analysts can quickly validate.
  4. Decision clarity: Quantifind highlights relationships, patterns, and typologies so investigators can interpret meaning rather than gather data.
  5. Built-in regulatory alignment: Quantifind’s platform includes features designed to meet 2025 expectations for explainability, transparency, and accountable human oversight.

Quantifind does not replace investigators. It enables investigators to perform their role with greater clarity, speed, and defensibility.

2026 Will Reward The AI Ready

Across the 2025 event landscape, including Transform Finance, ACAMS chapter events, 1LoD, the FRC Leaders Convention, SIBOS, and Money20/20, the same conclusions were emphasized:

  • Regulators expect institutions to understand their AI.
  • Boards expect efficiency and modernization.
  • Investigators expect better tools.
  • The gap between AI-ready and AI-hesitant institutions is increasing.

AI readiness is now a competitive advantage, a compliance requirement, and a performance multiplier.

A Call to Action

If your institution wants to accelerate its readiness:

Join Quantifind’s 2026 AI Transformation Workshops. These workshops will help FIUs build foundations for safe and effective AI deployment. Stay tuned for more information. 

Or schedule a strategy discussion today. We can help assess your current readiness and define the steps needed to move forward.