In this episode of Inside Assemble AI, hosts Mac and Sam sit down with Priya Reddy, an expert in AI strategy and data literacy, to explore why the real bottleneck in AI transformation isn't technology, it's understanding!

Priya shares her insights on the critical importance of AI literacy across organizations, emphasizing that successful AI adoption requires bridging the gap between technical teams and business leaders. The conversation delves into why organizations must invest in people rather than just tools, how translation failures between data scientists and executives derail AI initiatives, and what it takes to build a culture of data-driven decision-making.

From real-world examples of AI adoption failures to practical frameworks for effective data literacy programs, this episode offers actionable guidance for leaders navigating the rapidly evolving AI landscape.

Key Topics Discussed:

• Why AI literacy—encompassing data, tech, and business literacy—is the foundation of successful AI transformation
• Real-world examples of how miscommunication between technical and business teams derails AI initiatives
• The difference between data quality issues and AI model problems, and why it matters
• How executives can ask the right questions about data and AI to drive better outcomes
• Building role-based, practical data literacy programs that stick
• The role of "bilinguals"—professionals who can translate between technical and business contexts
• Why data-literate leaders will define the next era of competitive advantage

About Our Guest:

Connect with Priya on LinkedIn: https://www.linkedin.com/in/priyareddy-dataleader/

Checkout her consultancy services: https://www.linkedin.com/company/priya-reddy-consulting/

In this episode of Inside Assemble AI, hosts Sam Dey and Mac Goswami speak with Sam Bhagwat, Co-Founder & CEO of Maastra, about the rise of AI agents and the tools needed to build them for production.

Sam shares his journey from Gatsby to Maastra, the developer pain points that inspired the pivot, and why context engineering is critical for effective AI agents. The conversation explores Maastra’s core primitives, the impact of the Model Context Protocol (MCP), and the challenges of moving from prototype to production. The episode concludes with a forward-looking vision of AI development—smaller models, broader access, and a future with a billion developers by 2030.

🔗 Guest Links

• LinkedIn: https://www.linkedin.com/in/sambhagwat/

• Mastra: https://mastra.ai/

• Book(s): https://www.amazon.com/Principles-Building-Agents-Sam-Bhagwat/dp/B0DYH5GHDD

This episode tackles the ultimate leadership challenge: achieving next-level performance across profit, people, AND purpose without trading one off against the others. How do you turn AI vision into daily execution? What's the minimum viable AI roadmap that delivers value in 90 days? And how do you coach teams through transformation while maintaining speed and accountability?

Guest:

Greg Mester, co-author of "The Change, Vol. 20" and upcoming author of "Get to the Next Level" (2025/26). As an experienced Agile Coach and Program Manager, Greg helps companies break through blockers and accelerate performance using proven PMP, Scrum, and Agile frameworks across IT, manufacturing, quality, supply chain, and finance.

Key Takeaways:

• The minimum viable AI roadmap that produces value in 90 days

• Leadership behaviors that compound profit, people, and purpose

• Proven frameworks for stakeholder alignment and team coaching

• Weekly leading indicators that actually predict AI success

• Governance guardrails that enable speed without compromising safety

• Growth metrics that drive action and resist gaming

• SMB-specific strategies for rapid AI adoption and margin improvement

Perfect for:

Executive leaders driving AI transformation, mid-market and SMB owners, product leaders, marketers, operations professionals, and anyone ready to move from AI hype to measurable results.

Season 2 delivers battle-tested strategies from leaders getting real results. Subscribe for practical, implementation-focused frameworks you can apply immediately.