How to find high-impact AI use cases in Fintech, Banking, Telco and Insurance

Implement AI in your business strategically

We’re kicking off our AI Strategically webinar series with a deep dive into building the right foundation and identifying high-impact AI use cases for your business. Throughout the series, we’ll explore AI-driven solutions tailored for:

• Banking, Telco, Insurance and Finance industries
• Product, Innovation and Customer Success leaders
• Anyone who is looking for a way to implement AI in their company

What you’ll learn

How to get started – Pinpoint the right use case and master the implementation process.

Costs & ROI – Understand investment requirements and how to measure returns.

Real-life examples –   Learn from successful AI implementations and gain practical insights.

Key success factors – Avoid common mistakes and set your organization up for AI success.

Drive AI in your company & skip common challenges

Unclear expectations – Many companies don’t know what to expect from AI, or their expectations are too big. Know where is the sweet spot, what’s possible and what isn’t.

Initial steps confusion – You know what you want to impement but don’t know where to start? We’ll show you how to be clear on what to do and plan your whole timeline.

Compliance AI comes with regulatory challenges – especially in data-sensitive industries. We teach you how to navigate the maze of compliance to ensure your implementation is both secure and lawful.

Costs and ROI concerns – In any corporate project, there must be a clear path to positive ROI. If you don’t know how to plan and calculate it, the project might not even get started. Don’t let unclear ROI expectations get in the way of the implementation. Learn how to predict it.

Implementation challenges  – Translating AI strategies into actionable plans can be overwhelming. The road to implementation is often unclear. We tech you how to ensure your AI initiatives move seamlessly from concept to reality.

Post-implementation – Any AI project requires regular monitoring, fine-tuning, and updates to remain effective and relevant. Models can degrade over time as data and business conditions change. Learn to continually manage and update the software to get even better results.