When technical systems fail at companies like Netflix or Etsy, every minute of downtime can cost millions. That's why incident.io is building AI systems that can automatically investigate and diagnose technical problems faster than human engineers.

In this episode of The AI Adoption Playbook, Lawrence Jones, Product Engineer at incident.io, tells Ravin how they're creating an automated incident investigator that can analyze logs, traces, and metrics to determine what went wrong during an outage. He shares their methodical approach to AI development, focusing on measurable progress through evaluation metrics and scorecards rather than intuitive "vibe-based" changes.

Lawrence also discusses the evolution of their AI teams and roles, including their newly launched AI Engineer position designed specifically for the unique challenges of AI development, and how they use LLMs themselves to evaluate AI system performance.

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What happens when AI capabilities outpace organizational readiness? At Shopify, this tension has pushed them to develop a practical implementation approach that balances rapid experimentation with sustainable value creation. 

Spencer Lawrence, Director of Data Science & Engineering, shares how they've evolved from simple text expansion experiments to sophisticated AI assistants like Help Center and Sidekick that are transforming both customer support and merchant operations.

At the heart of their strategy is a barbell approach enabling self-service for small AI use cases while making targeted investments in transformative projects. Spencer also explains how their one-week sprint cycles, sophisticated evaluation frameworks, and cross-functional collaboration have helped them overcome the common challenges that prevent organizations from realizing AI's full potential.

Successful AI implementation requires more than just technical solutions — it demands new organizational structures, evaluation methods, and a willingness to constantly reevaluate what knowledge work means in an AI-augmented world.

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Forget theoretical planning — MongoDB dove headfirst into AI adoption and let real-world usage guide their strategy. David Vainchenker, Sr. Director of Enterprise Initiatives & Tools at MongoDB, joins Ravin on this episode of The AI Adoption Playbook to share this practical approach and unpack their evolution from simple chatbots to sophisticated agent-based systems. 

David shares their practical challenges with measuring AI's business impact, explaining why time savings metrics alone weren't convincing to leadership without translating to actual dollar savings or increased capacity. He also offers candid insights about security concerns, copyright issues with AI-generated code, and the delicate balance between innovation and governance.

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In this episode of The AI Adoption Playbook, Allen Jeter, Director of IT at Lattice, describes how his team transformed internal operations by strategically implementing AI assistants across multiple departments.

Starting with a clear focus on reducing manual work and response times, Allen walks Ravin through how Lattice built their first AI solutions, from an experimental chatbot using Okta Workflows and Pinecone to production-grade systems serving their People Operations and security teams.

What sets Lattice's approach apart is their pragmatic focus on solving real business problems rather than chasing AI for its own sake. By identifying specific pain points, implementing security guardrails from the beginning, and deploying AI directly within existing workflows like Slack, they've achieved impressive adoption across the organization.

Allen also shares invaluable advice for IT leaders looking to implement AI, emphasizing early experimentation, stakeholder involvement, and the importance of understanding your business problems before attempting AI solutions.

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What's the secret to successful AI adoption? According to Robert Mitchell, Chief AI Officer at WSI, it's not just about choosing the right tools, but it's about mastering a delicate balance between executive vision and hands-on experimentation. After helping countless mid-sized businesses implement AI, Robert explains why these organizations need fundamentally different approaches than enterprises, focusing on quick wins and easy implementation over maximum capability.

His conversation with Ravin on this episode of The AI Adoption Playbook explores WSI's unique dual-track implementation approach, combining executive planning with grassroots experimentation. Robert shares practical insights on building effective AI councils with representation across all business functions, ensuring that AI initiatives benefit from diverse perspectives and real-world operational knowledge.

Robert also walks through WSI’s proven framework for balancing top-down strategy with bottom-up experimentation, why SMBs require different solutions than enterprises, and how to build truly cross-functional AI governance.

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What happens when you enable SQL engineers to build sophisticated AI systems with just 26 lines of code? At Ramp, it sparked a transformation in how they approach AI implementation: instead of relying on ML specialists, they're democratizing AI development across their organization.

But that's just one part of Ramp's unconventional story. As Head of Analytics Engineering & Data Science, Ian Macomber explains how processing financial data for 30,000 companies led them to reimagine enterprise AI architecture. By building model-agnostic systems that can switch seamlessly between foundation models, they're creating sustainable competitive advantages while maintaining cost efficiency.

In this episode of The AI Adoption Playbook, Ravin sits down with Ian to unpack how Ramp evolved from basic receipt matching to sophisticated cross-functional AI systems that are reshaping enterprise financial operations.

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Episode 2.

What happens when you pause all company operations for a week to teach everyone — from marketers to legal teams — how to build AI co-pilots? At Checkr, it sparked an innovation wave from an unexpected source: their non-technical teams started outpacing engineers in creating practical AI solutions.

But that's just one part of Checkr's unconventional story. As Head of ML/AI, Muskan Kukreja explains how the rise of the gig economy forced them to reimagine background checks for a world where workers change jobs daily. By using AI to drive costs down to $1 per check, they're expanding trust-building tools beyond enterprise clients to serve entirely new markets.

In our first episode of The AI Adoption Playbook, Ravin Thambapillai, CEO of Credal.AI sits down with Muskan to unpack how Checkr is using AI to transform background checks from a weeks-long process into a same-day service.

Learn more about Checkr's approach to AI implementation on their engineering blog or read about their compliance framework in their trust center.