The Brutal Truth About Enterprise AI Adoption

In this episode, Amir speaks with Ameya Brid, Global Director of Data & Analytics at Invista, about the maturation of GenAI conversations in the enterprise. They dive into the shift from hype to implementation, real-world challenges like data quality and change management, and how composable architecture is helping organizations adapt to rapid innovation cycles.

🔑 Key Takeaways

From Hype to Value: GenAI conversations are moving beyond experimentation into outcome-driven initiatives—but most companies still struggle to define measurable KPIs.

Top Barriers to Scale: Poor data quality, fragmented systems, unclear use cases, and skills gaps continue to stall enterprise GenAI efforts.

Composable > Monolith: Modular, API-driven architectures provide agility to swap components as the tech rapidly evolves.

Change Management Rebooted: Adoption now means embedding insights directly into workflows—not just “viewing reports.”

Upskilling is Social: Peer-driven learning and internal documentation are outperforming formal training in the GenAI era.

🕒 Timestamped Highlights

00:00 – Introduction to Ameya and Invista’s work in manufacturing and chemicals

01:58 – How GenAI conversations have evolved over the past 18 months

03:52 – Marrying business outcomes with AI capabilities

06:04 – The five biggest barriers to GenAI implementation: use case clarity, data quality, skills gap, governance, and change management

11:53 – Managing constant tech evolution with composable architectures

15:02 – Data quality’s outsized impact on GenAI success

17:46 – Why CFOs must now invest in data quality

20:41 – Change management: From “read the dashboard” to “integrate AI into your workflow”

24:03 – Upskilling through shared learning and internal knowledge loops

💬 Quote of the Episode

"The cost of bad data today is far higher than it was 10 or 20 years ago—not just in decision-making, but in the process itself." – Ameya Brid