AGASI · Pulse for FinTech

A GenAI capability baseline for FinTech teams

See whether your teams use GenAI safely with regulated and customer data. Measure capability across prompting, verification, data handling, and responsible use, reported at the team level.

Also available for: Enterprise AI transformation · Consulting

The challenge

In a regulated environment, unseen GenAI use is a control gap

FinTech teams adopt GenAI quickly, but in regulated workflows the exposure isn’t just productivity; it’s data, accuracy, and defensibility.

Regulated and customer data is the first-order risk

The exposure isn’t only accuracy; it’s PII and financial data going into GenAI tools. Usage dashboards won’t surface unsafe data-handling behavior.

Unverified outputs carry compliance weight

In regulated workflows a hallucinated figure or misstated policy isn’t a typo; it’s a control failure. You need evidence that outputs are being checked.

Enablement has to satisfy risk and compliance

Rolling out GenAI without a defensible baseline makes it hard to show second-line and regulators that adoption is controlled and staff are competent.

Sample output

What you receive

A GenAI capability baseline across four reporting areas, plus an enablement action plan to guide training assignment and workflow reinforcement.

Capability by Role

GenAI capability scores by primary role, showing whether gaps are function-specific or organization-wide and where to focus first.

GenAI capability by role — Mean SJT score (0–100)

Enablement Profile

Error patterns by role mapped to three enablement streams: Data Handling, Verification, and Prompting & Task Framing, so you can tailor interventions by function.

Enablement profile by role
% of respondents with each error type
Error Type
RoleOracle TrusterTangentialPassive PrompterData LeakerNo Error
Customer Success (n=17)47%12%18%12%12%
Finance (n=11)9%27%36%18%9%
HR / People (n=42)31%14%24%10%21%
Marketing (n=19)37%16%21%26%
Operations (n=8)75%13%13%
Product / Design (n=6)17%50%33%
Sales (n=7)57%14%14%14%
Strategy / PMO (n=10)50%10%20%20%
NoneLow (1–25%)Moderate (25–50%)High (50–75%)Critical (75%+)
Source: AGASI GenAI Capability Pulse
Note: Error type segmentation based on n=120 respondents (excluding outliers/speeders). Cross-organization patterns by role; directional only.

Confidence vs. Competence

A confidence-competence matrix that reveals who is overconfident about their GenAI skills, the group most likely to miss errors and least likely to seek help.

Confidence vs. Competence matrix — Median split on SJT, mean split on confidence (N=153)
Confidence
HIGH CONFIDENCE

23.5%

Overconfident

n=36

High self-confidence, low actual capability. Highest risk — most Verification and Data Handling errors.

33.3%

Capable

n=51

Confident and competent. Lowest error rates. The benchmark group.

25.5%

Emerging

n=39

Low confidence, low capability. Needs foundational enablement.

17.6%

Underconfident

n=27

Low confidence, but reasonable capability. May underuse GenAI.

LOW CONFIDENCE
LOW COMPETENCEHIGH COMPETENCE
Competence (SJT Score)

Risk by Profile

Verification and data handling error rates by confidence profile, showing where overconfidence translates directly into operational risk.

Verification and Data Handling errors by Confidence–Competence quadrant — Mean errors per respondent (max 3 per type)

Skill Gap Priority

The most common skill gap across all respondents ranked by dimension, so you know which capability to prioritize in enablement design.

Share of respondents whose weakest SJT dimension is — % by skill area

What we measure

Five capability dimensions

The assessment covers the skills that matter most for safe, effective GenAI use in daily work.

Prompting & Task Framing

How well teams structure requests, provide context, and define constraints for GenAI tools.

Verification & Validation

Whether outputs are checked for accuracy, hallucinations, and completeness before use.

Data Handling

Awareness and habits around sensitive data handling when using GenAI tools.

Ethical & Responsible Use

Understanding of bias, attribution, and organizational policies for GenAI.

Workflow & Audience

How well GenAI outputs are adapted to the intended audience and integrated into real workflows.

How it works

Administer → Analyze → Act

From assessment to action plan in 2 weeks.

1

Administer

15–20 min online

Team members complete a short, confidential online assessment covering all five capability dimensions.

2

Analyze

Cohort patterns

We aggregate results at the team level to identify patterns, strengths, and reliability hotspots.

3

Act

Action plan readout

Leaders receive a prioritized action plan with clear next steps for enablement and workflow improvement.

Common questions

What leaders typically ask before running a Pulse.

No. The Pulse reports at the team and cohort level only. Individual responses are confidential and never shared with managers or HR.
All data is collected securely and processed in aggregate. We follow enterprise-grade data handling practices and can work within your organization's compliance requirements.
No. The Pulse is completely tool-agnostic. It measures capability and behaviors, not familiarity with any particular GenAI product.
The online assessment takes 15–20 minutes per person. The full cycle from launch to action plan readout is typically 2 weeks.
A capability snapshot across four reporting areas — drawing on all five assessed dimensions — plus a prioritized action plan with recommended next steps for enablement.
Yes. The assessment measures responsible-use awareness and data-handling behavior, giving risk and compliance an evidence-based baseline instead of self-reported assurance.
Reporting is aggregate and evidence-based, so it can support second-line, model-risk, and audit discussions about how controlled your GenAI adoption actually is.

Contact us

Get a GenAI capability baseline + enablement action plan for your fintech teams.

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