Key Quote

The model (LLM) is only as powerful as the data you feed it. To generate real business value, you need huge volumes of high-quality, customer-specific data.

Session Description

AI products are only as powerful as the data behind them, and that data needs to be customer-specific. In this session, we’ll explore why the emergence of AI has made integrations so critical, how data access directly increases product value, and how product leaders should think about their integration strategy in today's world.

Notes

Why AI needs Integration data

Everyone is building AI features - not just AI companies, but any company adding an AI feature or capability to their product

And it’s happening across industries: financial services, HR tech, revenue management, procurement

The next wave of software will be defined by agentic AI.

By 2028, 1 out of 3 enterprise companies will build agentic AI into their software applications. This was at 1% last year.
AI agent market is expected to grow by 44.7% by 2034 and be a $144 billion market by then
By 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from 0% in 2024

You need huge volumes of high-quality data to power AI

The model (LLM) is only as powerful as the data you feed it. To generate real business value, you need huge volumes of high-quality, customer-specific data.

That data is scattered across CRMs, HR systems, ticketing platforms, file storage, and accounting tools.

Integrations are the pipelines that connect all these different systems into something your AI can actually use.

Without integrations, your AI is basically blind—it can only give you broad, surface-level answers.

With integrations, you can provide real customer value