In Episode 23 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is rejoined by Greg Freeman, CEO & Founder at Data & AI Literacy Academy, where they discuss why most organisations have mistaken tool training for AI literacy – and why a data-literate workforce, critical thinking and the willingness to rebuild processes from the ground up matter far more than knowing which buttons to press in Copilot.

They also explore why data quality and governance are finally having their day in the sun, how leaders must role-model the discipline to challenge AI-generated work, and why the businesses winning with AI focused on four or five core processes rather than spinning up 95 pilots.

They also discuss:

• Why the line between data literacy and AI literacy is blurry, and why Greg wants it to stay that way.
• Why a data-literate workforce is the enabler of an AI-ready workforce.
• Why data quality, governance and risk management have gone from "not that sexy" to the most important topics in the business.
• Why there isn't a Copilot buttonology programme in the world that can teach critical thinking.
• How AI slop is bleeding from LinkedIn into the work employees put in front of internal and external audiences.
• Why leaders must ask pointed questions of AI-enabled work to test whether the human in the loop has actually done their job.
• Why people treat LLMs like Google, and why that makes them less able to challenge the answers.
• What separates the asker, the conversationalist and the process redesigner, and why 98% of employees are still stuck at the first stage.
• Why leaders need the mindset to burn processes down and rebuild them with AI at the core, rather than layering it on top.
• Why enterprise learning conflates training with education, and why that is the root of the tool-centric problem.
• How hyperscalers and training partners are incentivised to teach the tool rather than transferable principles.
• Why a workforce using Copilot instead of Google is an expensive thing, not a useful thing.
• Why generative AI gives executives a hands-on "aha moment" that dashboards never did.
• Why nine out of ten AI conversations mean generative AI, and what that costs organisations in forecasting, decisioning and recommendation opportunities.
• Who should own the operating model conversation with the board, and why it depends on having the right kind of data and AI leader.
• Why cutting headcount and graduate intake is the wrong reason to do AI, and why AI-native graduates are the hires to make.
• What the US market's shift from 95 pilots to four or five core use cases teaches UK businesses.
• How democratising AI capability into local teams frees the central team to focus on the big wins.
• Why customer-facing AI use cases remain a minority, and what Lloyds Bank gets right.
• How to measure AI literacy by whether people see and solve business problems differently.