“Good Enough to Start, Governed Enough to Scale,” Says Rehan Shah, AWS

February 26, 2026

Rehan Shah, General Manager and Head of Channel and Partner Sales for US Greenfield at AWS, explains how the right mix of AI tools, trustworthy data, and strong controls turns early AI trials into real business results. He shows how AWS provides access to top models, better value, responsible AI practices, and secure ways to connect your systems. Examples include instant insights from manufacturing data and Breakthru Beverage moving hundreds of servers, plus quick AI helpers like a Sales Coach and a Legal Assistant. He also shares how to keep costs in check and set up a company-wide AI program with clear budgets and accountability.

• What does it take to move from quick wins with Amazon Q to custom agents on Bedrock that scale across the enterprise?
• When is “good enough” data enough to start, and how can AI assistants surface gaps while improving data quality over time?
• Which operating model and risk-based guardrails help leaders control cost and compliance while accelerating adoption?

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(The interview was shortened and edited using ChatGPT)

Mark Kohout: Welcome to the Adastra podcast. I am Mark Kohout, and I lead the governance practice at Adastra North America. We are in Las Vegas at AWS re:Invent 2025. My guest is Rehan Shah, General Manager and Head of Channel and Partner Sales for US Greenfield at AWS. Rehan has more than 20 years of experience helping Global 2000 organizations modernize with cloud and AI, building strong partner ecosystems, and leading inclusive, high-performance teams. Rehan, thanks for joining us.

Rehan Shah: Thank you. Great to be here.

Mark Kohout: Let’s start with your background. What drew you to data and AI?

Rehan Shah: I have spent my career at the intersection of growth, change, and technology, with roles at AT&T, Lumen, NTT, Windstream, and now AWS. My focus has been building strong teams, improving sales performance, and partnering with C-suites across finance, healthcare, manufacturing, and tech. The big lessons are to focus on business outcomes, surround customers with a strong partner ecosystem, and scale by developing leaders. At AWS, I help customers and partners adopt a GenAI-first mindset, put trusted data and governance in place, and apply AI assistants to automate workflows, improve decision-making, and deliver faster insights.

Mark Kohout: How do you describe AWS’s positioning in this space?

Rehan Shah: Our goal is simple. AWS is the best place to build and run real-world AI assistants. We innovate quickly and stay grounded in what enterprises value most, which is security, reliability, and operational excellence. We are proud to be the first major cloud provider certified to ISO 42001 for responsible AI. We meet customers where they are, whether that is ready-to-use assistants like Amazon QuickSight or building and scaling custom assistants on Amazon Bedrock and Agent Core. We are model-agnostic through Bedrock, giving access to leading models such as Amazon Titan, Anthropic, Meta, Mistral, and Cohere, so customers can choose the best model per use case.

Two recent engagements with Adastra stand out. First, Torn Attack, a manufacturing company serving frontline operations, needed a faster, lower-risk way to get answers from ERP and document repositories. Teams waited hours for routine data pulls, were burdened by ad hoc reporting, and knowledge was fragmented across systems. Adastra, with AWS, led discovery, governance, and change management, and built a secure, read-only, Bedrock-powered assistant that turned natural language questions into instant ERP insights across inventory, work orders, invoices, production, and pricing. Answers that took hours now take seconds, workloads shrank, response times improved, and the solution laid the groundwork for retrieval-augmented intelligence over 20 years of sales and service data.

Second, Beverages Breakthrough Group needed to move off legacy data centers that limited scalability, raised costs, and complicated disaster recovery and compliance. Adastra and AWS migrated more than 600 servers to a secure, scalable AWS environment and modernized mission-critical applications, improving security and compliance. BBG is deploying an AI sales coach and a legal assistant, with projected annual savings of 100,000 dollars and 56,000 dollars, plus about 190 hours saved and an 80 percent reduction in manual contract replication.

Mark Kohout: What sets AWS apart? What is the secret sauce?

Rehan Shah: We offer a complete stack for AI agents, from infrastructure and data to models and applications, and we support open standards like MCP and HOA so tools interoperate with strong security. Our purpose-built infrastructure includes custom silicon such as Trainium, which delivers up to 30 percent better price-performance compared to GPU alternatives. We pair cutting-edge technology with proven security, governance, scale, and reliability, and we provide industry solutions for healthcare, retail, manufacturing, and more. For remote and mission-critical operations, Amazon is extending low Earth orbit connectivity through Amazon LEO to keep sites reliable and connected to the cloud.

Mark Kohout: From your perspective, what is driving customer investment in data and AI right now?

Rehan Shah: Productivity, workflow automation, and innovation. Inside Amazon, agents that handle Java code upgrades saved about 4,500 developer years and 260 million dollars annually, which shows the scale of impact. Customers embedding agents into core processes have seen 40 percent faster query resolution and 65 percent better personalization. Intelligent troubleshooting agents can reduce issue resolution time by 86 percent. Combine that with Gartner’s projection that at least 15 percent of work decisions will be made by autonomous AI agents by 2028, and it is clear why leaders are targeting repeatable, high-value workflows with measurable ROI.

Mark Kohout: What are the biggest challenges you see in deploying AI?

Rehan Shah: Fragmented data across systems, inconsistent quality, and weak governance create brittle solutions and higher risk. Leaders must balance innovation with security, reliability, and operational excellence, and ensure agents operate within security, ethical, and regulatory frameworks. Clarity of value is also essential. Gartner predicts more than 40 percent of AI projects will be canceled by 2027 due to unclear value. The answer is to start with a trusted data foundation, align governance from day one, pick use cases with clear, measurable outcomes, and drive adoption and change management, including executive alignment, ownership, guardrails, communication, and training. The right partner helps pilots become production programs so value compounds over time.

Mark Kohout: Cost is always a concern. How can enterprises control costs and still innovate with AI?

Rehan Shah: Use cost-efficient compute like Trainium 2, with up to 30 percent better price-performance. Lower storage costs with Amazon S3 Vector, with up to 90 percent savings for training and retrieval. Use autoscaling, including scaling to zero, and build on a unified, governed data foundation for predictable spend and faster time to value. At the operating model level, a federated AI Center of Excellence provides guardrails, token budgets per team, standardized use case prioritization, practical change management, and transparent usage tracking and chargeback. Align to responsible AI and enterprise risk frameworks so innovation accelerates without compromising safety, privacy, or compliance.

Mark Kohout: For decision makers implementing autonomous systems, where should they focus to deliver lasting results?

Rehan Shah: Start with high-quality data optimized for AI workloads so assistants have accurate, timely context. Integrate data across lakes, warehouses, and applications to keep analytics and AI consistent across the business. Put enterprise-grade governance in place to protect sensitive information and maintain compliance. Show quick wins with ready-to-deploy assistants like Amazon Q, then expand to custom agents on Bedrock. This sequence builds confidence and momentum while managing risk.

Mark Kohout: How does AWS qualify and work with partners like Adastra, and what does an ideal AWS partner look like?

Rehan Shah: We qualify and empower partners through AWS Competencies and Service Delivery validations, prescriptive training, experience-based accelerators, and co-selling and funding pathways so customers can move from pilot to production with confidence. Ideal partners combine domain expertise with strong technical capability, prioritize use cases, co-create roadmaps, and establish the right data foundation and governance. Adastra exemplifies this with hands-on acceleration sessions, repeatable blueprints on Bedrock and Agent Core, leveraging Amazon Q and QuickSight, staying current on enablement and funding programs, partnering with field sellers, and pairing it all with robust change management and executive alignment so outcomes stick and time to value accelerates.

Mark Kohout: To close, agentic AI is moving from demos to real outcomes, like BBG’s AI sales coach and legal assistant delivering measurable savings, and Torn Attack’s self-serve ERP answers going from hours to seconds. The playbook is to start with trusted data and governance, show quick wins with Amazon Q, expand with Bedrock for custom agents, keep costs predictable with the right building blocks and FinOps, and work with partners like Adastra to translate capabilities into business value. Rehan, thank you, and thanks to our audience. If you want a low-risk first step, reach out to Adastra, and please like and subscribe. So long for now.

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