Accelerating Data Contracts with GenAI (aka AI Agents): A Practical Approach

In today’s data-driven world, building and maintaining data contracts is critical for ensuring data quality, consistency, and compliance—especially in industries like asset servicing, where data flows between multiple systems, stakeholders, and regulatory environments.

But here’s the challenge: Manual processes are slow, error-prone, and can’t keep up with the pace of business.

That’s where Generative AI (GenAI)—or what I like to call AI agents—comes in. These AI-powered tools can automate, accelerate, and even enhance how we create, manage, and govern data contracts.

Drawing from my experience in working with and establishing first principles for complex, distributed data organizations at HSBC and Janus Henderson, here’s a practical guide on how GenAI can transform data contract workflows. While I’m using asset servicing as an example, the same principles apply to any complex, distributed data ecosystem

First, What Are Data Contracts?

At their core, data contracts are agreements between data producers and consumers that define:

• What the data looks like (structure & schema)
• How it should behave (quality rules & validations)
• What it means (semantic context)
• Who’s responsible for what (ownership & accountability)

In asset servicing, data contracts govern critical processes like trade settlements, corporate actions, fund accounting, and regulatory reporting. A single data error here can cause millions in financial exposure, regulatory breaches, or reputational damage.

So, How Can GenAI Help?

I know it’s a cliché, but it’s true: "AI won’t replace your data engineers, but data engineers who use AI will replace those who don’t."

Think of AI agents as specialized, intelligent assistants—automating repetitive tasks, reducing manual errors, and freeing up your teams to focus on higher-value work.

Summary of AI Agents Required & Their Purpose

These AI agents work together to streamline the entire data contract lifecycle, ensuring speed, accuracy, and compliance at every stage.

10 Practical Ways to Use GenAI for Data Contracts

?1. Automating Data Contract Drafting

The Problem: Drafting data contracts manually takes days (sometimes weeks), with endless back-and-forth between data engineers, business analysts, and compliance teams.

GenAI in Action (Drafting Agent):

• Analyses existing database schemas, business rules, and regulatory guidelines
• Automatically generates draft data contracts with predefined fields, data types, constraints, and descriptions

Impact:

• Reduces drafting time from days to minutes
• Ensures consistency across contracts
• Minimizes human error in data definitions

?2. Semantic Understanding & Data Mapping

GenAI in Action (Semantic Mapping Agent):

• Detects similar fields with different names (e.g., dividend_amount vs. div_amt)
• Standardizes field names and creates transformation rules

Impact:

• Faster data integration
• Fewer errors in data transformations

3. Generating Data Quality Rules

GenAI in Action (Data Quality Agent):

• Analyses historical data patterns and identifies common issues
• Generates data quality rules automatically based on anomalies and trends

Impact:

• Proactive identification of data quality issues
• Reduced manual effort for data stewards

?4. Converting Natural Language to Data Contracts

GenAI in Action (Natural Language Agent):

• Reads natural language requirements
• Converts them into structured data contract rules

Impact:

• Bridges the gap between business and technical teams
• Speeds up the requirements-to-implementation cycle

?5. Proactive Data Contract Maintenance

GenAI in Action (Maintenance Agent):

• Monitors data pipelines and schemas in real-time
• Suggests automated contract updates when changes are detected

Impact:

• Always up-to-date data contracts
• Reduced technical debt

?6. Automating Regulatory Compliance

GenAI in Action (Regulatory Compliance Agent):

• Analyses regulatory texts
• Generates data contract rules to enforce compliance

Impact:

• Faster compliance with regulatory standards
• Reduced legal risks

7. Data Contract Testing & Validation

GenAI in Action (Testing & Validation Agent):

• Generates test cases based on contract rules
• Automatically runs validations against sample datasets

Impact:

• Faster, more comprehensive testing
• Fewer bugs in production

8. Collaborative Data Contract Development

GenAI in Action (Collaboration Agent):

• Summarizes stakeholder discussions
• Highlights key points, conflicts, and potential compromises

Impact:

• Faster decision-making
• Better alignment between teams

?9. Automated Documentation

GenAI in Action (Documentation Agent):

• Automatically generates comprehensive documentation for data contracts

Impact:

• Improved knowledge sharing
• Easier onboarding for new team members

10. Predictive Data Contracting

GenAI in Action (Predictive Agent):

• Analyses trends in business data and external factors
• Predicts emerging data requirements

Impact:

• Future-proof data ecosystems
• Proactive governance

What’s Next?

1. Start Small: Identify one high impact use case where an AI agent can help.
2. Build Fast: In just a couple of weeks, you can validate if GenAI adds real value.
3. Iterate & Scale: Learn from the first success, refine the approach, and expand across your data ecosystem.

If you’re curious about how AI agents can transform your data contract processes, let’s connect. The future of data governance is already here—let’s build it together.

#DataContracts #GenAI #AIforData #AssetServicing #DataGovernance #AITransformation