Governance, Context, Orchestration: How To Turn Raw Data Into Real Dollars

Many companies struggling to use AI to generate automation and intelligence that actually drives revenue face a similar roadblock: bad data.

If businesses can’t trust their data, they’ll miss new monetization opportunities and jeopardize existing operations. AI doesn’t magically make bad data better. What the technology will do is make bad decisions with low-quality or inaccurate data much faster than a human would.

“Every level in the enterprise is hard-pressed to figure out where to go on AI,” said Avinash (Avi) Deshpande, field CTO at Workato. “Success starts with really knowing and trusting the data.”

According to Avi, there are three key pillars to any AI-ready data strategy:

• Governance: Beyond controlling access, businesses need to be able to audit all the subsequent agent-to-system and agent-to-agent interactions. For example, routing them through secure proxies can enhance security and trust without impacting agility.

• Context: With so much data available, AI systems need to know the relevant information for every prompt. Enterprises must make sure data is enriched, routed, and consumed according to their unique operations.

• Orchestration: Ultimately, the AI agents need to be able to reach all the data they need to execute on prompts. It’s how businesses move from AI at the edge of operations, to embedding AI agents into end-to-end workflows.

Ultimately, building an AI-ready data estate is more than a one-off project; it’s a life-long transformation. And the goal is about more than just using the latest chatbot. As technology progresses at a rapid clip, it’s about making sure enterprises are ready to take advantage when new innovations emerge that can help streamline operations and grow market share.