Great AI Cannot Be Built on Data the Business Does Not Trust

Most businesses do not have an AI problem.

They have a truth problem.

The information exists somewhere—in the CRM, an email, an accounting platform, a spreadsheet or the memory of an experienced employee. The problem is that each source may contain a different version of what is accurate, current and authoritative.

In Episode 05 of the Create Your Own Gravity AI Mastermind Aftershow, Jeff Valin and Chuck Boyce examine the data-quality layer beneath successful AI implementation.

Chuck approaches the problem from the business outward: define the workflow, establish the rules and determine which source owns the truth. Jeff approaches it from the technology inward: once those boundaries exist, AI can help clean, enrich, associate and synchronize the information faster than a human team.

If AI can move faster than any employee, what happens when the information beneath it is incomplete, inconsistent or wrong?

Meet the Guests

Jeff Valin

Jeff Valin is a marketer, automator and entrepreneur with more than 30 years of experience connecting proven marketing strategy with technology. He coaches GHL Agencies to sclae. Through AutoMagic247, Jeff helps businesses use automation, AI and connected systems to improve customer communication, follow-up and revenue-generating workflows.

Chuck Boyce

Chuck Boyce is the founder of Cibi Creative, an AI consulting and education company helping owner-led businesses identify worthwhile AI opportunities and implement them responsibly. His systems-first approach focuses on business readiness, data quality, process ownership and ensuring technology solves a defined problem before money is spent.

Together, Jeff and Chuck expose the tension that separates good AI from great AI.

Jeff shows what becomes possible when AI is allowed to accelerate the work. Chuck reveals what must already be understood before that acceleration can be trusted.

The model may create the output. The quality of the business beneath it determines whether that output can be trusted.