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AI Use Cases

What are the best AI use cases for your organization?

From predictive analytics to generative AI, enterprises are finding practical ways to unlock value from data. Passerelle helps you identify, prioritize, and deploy AI and ML applications that drive measurable ROI. By starting with the right use cases, your business can achieve quick wins, demonstrate success, and create momentum for enterprise-wide adoption.

AI Use Cases

Which AI use cases will deliver the most impact for your business?

AI succeeds when it’s tied to clear business outcomes. Choosing the right projects avoids wasted investment and accelerates trust in the technology.

With use-case-driven adoption, you:

  • Deliver fast ROI and encourage adoption across the business.
  • Leverage existing data to solve immediate challenges.
  • Build a scalable framework for expanding AI applications.
  • Reduce risk by focusing on achievable, governed initiatives.

Identifying and implementing AI use cases requires a disciplined, business-first approach. Passerelle’s framework helps organizations target the right opportunities and deliver results quickly. Through our AI Use Case Framework, we’ll build your AI strategy through:

  • Discovery – Explore business priorities and data opportunities.
  • Prioritization – Select high-value, achievable initiatives.
  • Deployment – Implement AI models with best practices and integrations.
  • Validation – Measure ROI and refine based on business outcomes.

Testimonials

“We have a small team. We like to punch above our weight, and we look at who we’re competing against. We need something to help us out, and that’s why a strong toolset that includes Generative AI will help us produce things faster and get some of the monotonous reporting out of our hands. It will help us along our path of efficiency.” Josh Nash, SVP and Director of IT at Camden National Bank

Passerelle AI + ML Use Cases

AI + ML Readiness

Results from AI and ML applications can only be as trusted as the data they use. The fundamentals of data management, data preparation and data stewardship have never been more important. Passerelle helps organizations build a scalable data management practice focused on creating a single version of the truth in the Snowflake Data Cloud. With trusted data in place, you can bring AI and ML applications directly to your data, promoting security and making insights accessible to the right people at the right time.

Generative AI - Extract insights from unstructured data (documents, transcripts).

Generative AI can be used to mine insights from existing data sources that have been traditionally difficult to access such as PDFs, call center transcripts, contracts, and documentation, using Large Language Models (LLMs) to sift through volumes of data with interfaces that require little to no technical expertise. GenAI can have an immediate impact on customer satisfaction and operational efficiency use cases while eliminating time-consuming and error-prone manual entry and reporting on unstructured and semi-structured data.

Predictive ML - Forecast trends, reduce churn, optimize operations.

Predictive Machine Learning uses historic and third-party data to forecast future trends and behaviors, such as customer churn, product demand, and support for next-best product initiatives. Other potential use cases include mitigating losses by identifying potential fraud or financial risks and improving resource allocation with planned maintenance or optimized logistics.

Process Automation – Streamline repetitive workflows.

AI can take over manual, repetitive tasks such as data entry, application processing, or approvals, freeing teams to focus on higher-value work. By automating workflows across departments, organizations gain measurable productivity improvements while reducing human error and operational bottlenecks.

Fraud & Risk Detection – Identify anomalies to protect revenue and compliance

Machine learning models can analyze patterns in transactions, claims, or user activity to spot anomalies that signal fraud or compliance risks. With continuous monitoring, organizations can act proactively to reduce losses, protect customer trust, and meet regulatory requirements in real time.

Customer Experience – Personalize interactions and predict next-best actions

AI enables personalization at scale by analyzing customer behaviors, preferences, and historical data. From next-best product recommendations to sentiment analysis, these insights help organizations improve retention, boost loyalty, and deliver a seamless, individualized experience across channels.

Compliance & Regulatory – Use LLMs to strengthen governance and reduce audit overhead

Large Language Models (LLMs) can scan and validate policies, contracts, and regulatory documents against compliance frameworks. Automating this process reduces manual review cycles, strengthens governance, and provides auditable transparency—helping organizations stay ahead of evolving regulatory demands.

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Data
Rocket
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Guide
to Agile Data Governance
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Guide
to Modernization for Banks and Credit Unions

Frequently Asked Questions

Q: How much does a use case cost to implement?

A: Most use cases start between $25K–$50K, depending on complexity.

Q: How quickly will we see results?

A: Many organizations see impact in weeks, not months.

Q: Do we need a large data science team?

A: No. Smaller teams succeed with the right frameworks and governance.

Case Studies

We help businesses harness the power of their data.

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Camden
National Bank
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Dimagi
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Vermont
Gas
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Material
Bank
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