executive interview series:

Scaling Craftsmanship with AI: A Conversation Between Matthew Kraus & Gunjan Doshi

In an era where digital transformation separates tomorrow’s leaders from yesterday’s champions, Gunjan Doshi—Founder & Chairman of InRhythm and Arula.AI—is a globally recognized authority on enterprise AI, platform engineering, and AI-driven software delivery. Across two decades, Gunjan has led teams that architected and scaled world-class platforms for major enterprises—powering billions of dollars in annual transactions and elevating security, reliability, and speed-to-market.

InRhythm has been a trailblazer for digital organizations in the enterprise, establishing modern product operating models, platform engineering practices, and design systems that turn legacy complexity into repeatable, high-trust execution. Arula.AI, his newest venture, brings those lessons to AI-native workflows—helping institutions like Goldman Sachs, Fidelity, and Morgan Stanley embed intelligence into mission-critical operations without sacrificing craftsmanship or control.

Matthew Kraus, CEO of Skyline Windows, sat down with Gunjan to explore how AI, digital tools, and smarter systems can help a fourth-generation, family-owned manufacturing and installation business scale—without losing its soul.

Matthew Kraus: Gunjan, Skyline Windows is over 100 years old. We’ve built our reputation on craftsmanship, precision, and personal service, not technology. For companies like ours, why does digital transformation matter now?

Gunjan Doshi: That’s the perfect starting point, Matthew. AI and digital transformation aren’t about turning Skyline into a tech company. They’re about augmenting your craftsmanship with intelligent systems that give your teams superpowers. When I walk into logistics-heavy, field-driven businesses, I see the same pattern we’ve optimized inside large enterprises: codify the best of your craft into platforms, then layer AI agents on top to make every job safer, faster, and more predictable.

Matthew: Training is a huge part of what we do. We have a multilingual workforce with a lot of institutional knowledge, but not all of it is documented. How can AI make a real difference there?

Gunjan: That’s one of the most powerful starting points. With AI learning assistants, training stops being static.
 — New installers get personalized, adaptive onboarding.
 — Veteran expertise becomes living SOPs and job aids generated in real time.
 — Voice interfaces keep crews hands-free, while real-time translation removes language barriers.
 — Usage data highlights where crews get stuck, triggering micro-lessons on demand.
 This is the same playbook we’ve used building enterprise platforms: capture the institutional knowledge, embed it into a scalable system, and let AI make it continuously smarter.

Matthew: Field operations are another pain point. We’re juggling traffic, weather, multiple crews, and complex installation sites. It’s hard to coordinate perfectly every time.

Gunjan: Exactly. In enterprises, we’ve replaced generic templates with context-aware orchestration. For Skyline, AI can generate project-specific work orders that account for crew skills, site constraints, weather, and traffic—and optimize routes and sequencing accordingly. Think of it as bringing the intelligence of a modern trading or payments platform to your field operations: same rigor, fit for craft work.

Matthew: Our clients are architects, property managers, and contractors—and they expect a sense of urgency. But most of our updates today are manual, which take time to produce. In what ways can AI help us there?

Gunjan: This is where Skyline can truly differentiate. A conversational interface built on your project data lets clients ask, “What’s the status of the 101 Park install?” and get instant, auditable answers. Behind the scenes, an AI agent assembles the truth from schedules, change orders, and site reports—no email ping-pong. In finance, that level of self-serve transparency won market share; in construction and manufacturing, it becomes a moat.

Matthew: We also want to grow beyond New York. How could AI help us plan for that kind of expansion?

Gunjan: We’ve helped enterprises shift from reactive planning to signal-driven capacity models. AI can read regional demand signals—permits, seasonal cycles, pipeline velocity—and recommend staffing, inventory, and partner mix. It’s the same approach that helps large platforms anticipate volume spikes worth billions and stay resilient.

Matthew: This sounds promising, but we’re a legacy company. Change can be tough when you’ve done something well for decades.

Gunjan: Completely agree. Durable transformation happens in confident increments. At InRhythm and Arula.AI, we start with a focused pilot—an AI field-service app or a digital training assistant. Teams feel the benefit immediately, and adoption spreads organically. Then we layer in platform capabilities—observability, governance, design systems, and developer experience—so the solution scales without chaos.

Matthew: If you had to summarize the opportunity for companies like ours in one line?

Gunjan: Build smarter systems that protect what makes you exceptional. You already have what others chase: trust and skill. AI-powered platforms let you operationalize that advantage at scale—so every install, every client interaction, and every decision gets measurably better.

Gunjan’s Enterprise Platform Playbook (What’s Worked at Scale)

●     Product Operating Model: Shift from project funding to platform & product funding; prioritize outcomes over tickets.

●     Platform Engineering: Standardize golden paths for build/test/deploy; enable speed with guardrails.

●     Design Systems & DX: Shared components, accessibility by default, and intuitive developer tooling to reduce cycle time.

●     AI-Driven SDLC: Requirements, code review, test generation, and incident triage augmented by domain-tuned AI copilots.

●     Observability & Risk: Real-time SLOs, policy-as-code, and automated controls that satisfy the toughest regulators.

●     Customer Transparency: Self-serve status, audit trails, and explainable decisions—the trust engine for high-value clients.

Track Record at a Glance

●     Platforms led by Gunjan’s teams have supported billions of dollars in annual transactions across financial services and other regulated industries.

●     InRhythm has stood up digital organizations inside enterprises—launching design systems, platform teams, and AI-enabled workflows that compress delivery cycles and raise quality bars.

●     Arula.AI brings those patterns to AI-native operations, turning complex, human-driven processes into predictable, intelligent systems.

About the Participants

About Gunjan Doshi (Executive Bio)

Gunjan Doshi is the Founder & Chairman of InRhythm and Arula.AI, and a globally recognized thought-leader in enterprise AI, platform engineering, and AI-driven software delivery. He has led cross-functional teams that designed and scaled mission-critical platforms moving billions of dollars in annual transactions—while meeting stringent requirements for security, reliability, and regulatory compliance. At InRhythm, he pioneered operating models and design systems that helped Fortune-scale companies modernize at pace. At Arula.AI, he is productizing those learnings into AI-native workflows that turn complex, human-centric processes into measurably better, safer, faster outcomes.