AI Adoption Playbook

Credal
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  • 两周一更
AI Adoption Playbook

Welcome to The AI Adoption Playbook—where we explore real-world AI implementations at leading enterprises. Join host Ravin Thambapillai, CEO of Credal.ai, as he unpacks the technical challenges, architectural decisions, and deployment strategies shaping successful AI adoption. Each episode dives deep into concrete use cases with the engineers and ML platform teams making enterprise AI work at scale. Whether you’re building internal AI tools or leading GenAI initiatives, you’ll find actionable insights for moving from proof-of-concept to production.

单集

  1. Lattice's Allen Jeter on Building Practical AI Assistants That Transform Enterprise Operations

    2天前

    Lattice's Allen Jeter on Building Practical AI Assistants That Transform Enterprise Operations

    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. Topics discussed: Implementing AI assistants for People Operations that provide 24/7 support for employee questions about benefits and company policies. Building a security bot that helps sales teams respond to customer security questionnaires faster, reducing bottlenecks and accelerating sales cycles. Evaluating the crowded AI vendor landscape with specific requirements rather than getting caught up in marketing hype. The importance of integrating AI tools into existing workflows like Slack channels to maximize adoption without changing user behavior. Creating effective prompt engineering strategies to help teams customize AI responses and maintain accuracy across different domains. Implementing proper governance and permissions structures that respect existing data access controls to ensure compliance. Measuring success through concrete metrics like reduction in manual work hours and decreased time-to-answer across departments. Using AI to enrich support ticket metadata automatically, enabling better insights without manual categorization work. Balancing experimentation with security guardrails to enable innovation while protecting sensitive company and customer data. Resources Mentioned: Credal’s blog post, “The Enterprise Adoption Curve: Lessons Learned So Far”

    50 分钟
  2. The Simple Framework That Got 100% Employee AI Adoption

    2月14日

    The Simple Framework That Got 100% Employee AI Adoption

    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. Topics discussed: A proven "top-down, bottom-up" implementation framework that combines executive buy-in with identifying and empowering internal AI champions who can drive adoption through monthly AI Council meetings and team challenges Detailed ROI calculation methodology for AI initiatives, illustrated through a case study showing how 10% productivity gains on a $5M payroll can translate to $3M in additional business value at 6x EBITDA multiple Specific approach to AI governance using three core documents - policies for internal data usage, client data handling, and vendor data management - that must be established before any employee training begins Concrete example of high-ROI automation: a $20K investment to eliminate 7 days of manual accounting work monthly, improving employee satisfaction while enabling team to focus on higher-value activities Strategic methodology for creating "aha moments" by having employees first experiment with AI in their personal domain expertise before applying it to work processes, making adoption more intuitive Practical framework for quick wins: identifying 90-minute process improvements through Loom video analysis of employee pain points, then rapidly implementing targeted AI solutions

    44 分钟
  3. Ramp's Ian Macomber on Building Model-Agnostic Systems

    1月21日

    Ramp's Ian Macomber on Building Model-Agnostic Systems

    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.   Topics discussed: How Ramp's decentralized approach to AI procurement enabled rapid experimentation while maintaining standards and leading to the discovery that most point-solution AI tools show surprisingly weak retention rates. Their data architecture strategy: managing complex databases with 133,000 columns by implementing semantic layers and guardrails that make enterprise-scale text-to-SQL accessible to non-specialists. The small team philosophy they implemented: breaking larger teams into units of 12 or fewer people, fostering rapid iteration while maintaining the velocity of a startup despite growing to over 1,000 employees. Their approach to foundation model selection: treating AI providers like ride-sharing services, constantly evaluating performance and cost metrics to switch between models based on specific use cases. Their strategy for building defensible AI products: focus on sophisticated integrations that combine organizational data, spending policies, and real-time market information in ways that point solutions can't replicate. Listen to more episodes:  Apple  Spotify  YouTube Website   Episode 2.

    41 分钟
  4. How Checkr's Head of ML/AI Drove Enterprise-Wide AI Adoption Through "Boring" Use Cases

    1月7日

    How Checkr's Head of ML/AI Drove Enterprise-Wide AI Adoption Through "Boring" Use Cases

    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. How Checkr's week-long company-wide AI hackathon yielded an unexpected outcome: non-technical teams (legal, marketing) outpaced engineers in creating practical AI solutions by focusing on their daily pain points rather than technical capabilities Their data processing architecture: handling billions of searches across thousands of data sources by implementing AI-powered verification workflows with human-in-the-loop fallbacks for edge cases The "T-shaped team" structure they implemented: instead of hiring separate specialists (data scientists, ML engineers, AI researchers), they built teams with broad skills across disciplines who deeply understood business problems Their approach to high-stakes AI applications: enforcing a strict checklist process that includes PII masking, data retention policies, and mandatory security/legal/ethics reviews for any customer-facing AI feature Their 90-day shipping philosophy: breaking down large AI initiatives into quarter-sized chunks with clear KPIs, allowing rapid iteration while maintaining compliance with emerging AI regulations Learn more about Checkr's approach to AI implementation on their engineering blog or read about their compliance framework in their trust center.

    48 分钟
  • 4 集

关于

Welcome to The AI Adoption Playbook—where we explore real-world AI implementations at leading enterprises. Join host Ravin Thambapillai, CEO of Credal.ai, as he unpacks the technical challenges, architectural decisions, and deployment strategies shaping successful AI adoption. Each episode dives deep into concrete use cases with the engineers and ML platform teams making enterprise AI work at scale. Whether you’re building internal AI tools or leading GenAI initiatives, you’ll find actionable insights for moving from proof-of-concept to production.

信息

  • 创作者
    Credal
  • 活跃年份
    2025年
  • 单集
    4
  • 分级
    儿童适宜
  • 版权
    © Copyright 2025 All rights reserved.
  • 节目网站
    AI Adoption Playbook

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