If your team is using AI but you can't point to a single business result it's created, this episode is for you. Bhuva Shakti, Chief Ethics and Culture Officer at Women in AI and creator of the AI Revenue Engine framework, joins host Swati to unpack why so many businesses deploy AI tools and still see nothing change.
Bhuva walks through the real reasons AI adoption stalls: starting with the tool instead of the problem, ignoring data quality, and automating broken workflows instead of fixing them first. Whether you run a two-person team or a global enterprise, this conversation gives you a clearer, more human way to think about where AI actually belongs in your business.
About the Guest:
Bhuva Shakti has spent nearly three decades on Wall Street across technology, operations, risk, and regulatory change. She currently serves as Chief AI Ethics and Global Culture Officer at Women in AI and as CEO of Wallet Max. She created the AI Revenue Engine (AIRE™), a framework helping organizations move from AI experimentation to measurable business results.
Key Takeaways:
- Start with the business problem, not the AI tool. Ask what you're actually trying to fix before picking any technology.
- Check your data before you automate anything. Poor quality or incomplete data will sink even the best AI deployment.
- Don't automate a broken process. Review whether a workflow still makes sense before layering AI on top of it.
- Small businesses often adapt faster because they have fewer approval layers, but should still pick one real pain point rather than spreading thin.
- When something goes wrong, check three things: was the goal realistic, was there human accountability, and was the underlying data or workflow the actual problem.
- Notice the time AI frees up and use it intentionally. Saving three hours only matters if you do something meaningful with them.
Connect With the Guest:
Episode Chapters:
[00:00] The Business Owner's AI Dilemma — a familiar scene of staring at a tool and wondering what to actually do with it
[03:10] (approx.) The Biggest Misconception — why starting with the technology instead of the problem sets businesses up to fail
[08:45] (approx.) The Data Problem Nobody Talks About — why messy or incomplete data quietly wrecks AI deployments
[13:20] (approx.) Small Business vs. Big Business — fewer layers, faster decisions, but fewer resources to recover from mistakes
[18:00] (approx.) Where AI Is Already in Your Life — the invisible ways AI already shapes how you work and shop
[22:30] (approx.) One Practical Thing to Try This Week — a simple, low-cost way to test AI on a real problem
[27:15] (approx.) When AI Gets It Wrong — how to evaluate what actually broke instead of writing off AI entirely
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