Challenges in Scaling AI Innovations Across Teams

Scaling AI is less about model performance; it's about the infrastructure discipline and data maturity underneath it. One unexpected bottleneck companies often hit while trying to scale AI in production is “data lineage and quality debt.” Why it’s unexpected: Many organizations assume that once a model is trained and performs well in testing, scaling it into production is mostly an engineering and compute problem. But in reality, the biggest bottleneck often emerges from inconsistent, incomplete, or undocumented data pipelines—especially when legacy systems or siloed departments are involved. What’s the impact: Without robust data lineage (i.e., visibility into where data comes from, how it’s transformed, and who’s using it), models in production can silently drift or degrade due to upstream changes in data structure, format, or meaning. This creates instability, compliance risks, and loss of trust in AI outcomes in the regulated companies like Banking, Healthcare, Retail, etc. What’s the Solution: • Establish strong data governance frameworks early on, with a focus on data ownership, lineage tracking, and quality monitoring. • Invest in metadata management tools that provide visibility into data flow and dependencies across the enterprise. • Build cross-functional teams (Data + ML + Ops + Business) that own the end-to-end AI lifecycle, including the boring but critical parts of the data stack. • Implement continuous data validation and alerting in production pipelines to catch and respond to changes before they impact models. Summary: Scaling AI is less about model performance and more about the infrastructure discipline and data maturity underneath it.

I’ve worked with a lot of finserv engineering leaders trying to figure out where AI fits in their SDLC. Almost every conversation starts the same way: “Our devs are experimenting with Copilot and ChatGPT.” “We want to adopt AI, but we don’t know where to start.” Neither did we at first. AI is everywhere. Exec teams are pushing for it. The potential upside is huge. But here’s the problem… AI doesn’t work the same way with existing software as it does vibe coding a new app. And what works for an individual doesn’t work for teams of engineers. More than that, there’s no organization-wide adoption, so there are no speed gains. But here’s what I’ve learned: the blocker isn’t the tools or even the team members (which it can often feel like). It’s the lack of a system. The lack of standardization across the SDLC. The lack of process rigor. AI is being used the way Excel macros once were…individually, inconsistently, and invisibly. The result? Here’s what I keep seeing behind the scenes: ✅ Teams lack organizational playbooks – Engineers using AI in isolation, PMs trying to vibe prompts, no shared standards - many on the team aren’t using it at all. ✅ Tooling and process mismatch - Fragmented tools, different use cases, and inconsistent results. ✅ Context debt - No structured documentation on the system, no vector stores, resulting in low-quality AI outputs across codebases. ✅ Lack of AI trust - Concerns around AI missing critical business logic, introducing bugs, or failing quality checks stunt broader adoption. ✅ Time wasted - Without a clear roadmap to org-wide adoption, individual AI training just diverts resources from the roadmap with little to show for it. ✅ Leadership pressure - Leaders see the potential, but don’t have a way to scale wins beyond individual contributors. So, how do you change? The orgs that are doing this right are following a clear path: 📌 Stage 1: Experimental Engineers try Copilot or GPT on side projects. It’s fun, but isolated. No measurement. No reusability. 📌 Stage 2: Standardization Prompt libraries emerge. The org starts agreeing on how AI supports code, tests, PRs. Manual usage becomes repeatable. 📌 Stage 3: Systemization Individual agents are embedded in workflows. e.g. a pull request bot that uses your standardized AI prompt. Systems talk to systems. Humans supervise. 📌 Stage 4: Autonomous Coordination Agents hand off to each other. One agent’s output is another’s input. Humans handle exceptions and the parts of the SDLC that AI can’t automate. The hard part? Getting from Stage 2 to Stage 3. It takes enforced consistency. One process, defined org-wide. Without that, there are limited efficiency gains. AI can’t move from tool to teammate. I’ve come to believe this: If your org hasn’t defined how agents participate in your SDLC, you haven’t adopted AI. You’ve adopted experimentation. Thoughts? What's working or not?👇 #AI #AgenticEngineering #DevEx #SoftwareDevelopment #EngineeringLeadership

12 critical questions before you scale AI across your enterprise. Answer wrong and join the 95% failure rate. You're not alone if this sounds familiar. 95% of companies hit this exact wall. MIT's latest research shows a brutal truth: Most organizations can run successful AI pilots. But they completely fail when they try to scale across the enterprise. The gap between "proof of concept" and "business transformation" is where careers get stuck. Where companies get stuck. The problem isn't your technology. It's your strategy. Scaling AI isn't just "do more pilots." It requires answering fundamentally different questions: → Authority and accountability at scale → Infrastructure that can handle enterprise workloads → Change management beyond early adopters → Governance that prevents AI chaos These 12 questions separate the winners from the losers: WHO ↳ WHO will have authority to override departmental resistance? ↳ WHO will be accountable when AI decisions create consequences? WHAT: ↳ WHAT data infrastructure must be rebuilt for enterprise workloads? ↳ WHAT governance framework will prevent AI sprawl? WHERE: ↳ WHERE will legacy systems create integration bottlenecks? ↳ WHERE will you establish AI centers of excellence? WHEN: ↳ WHEN will you pull back if pilot metrics don't translate? ↳ WHEN is the optimal sequence for rolling out AI? WHY: ↳ WHY are successful pilots failing to replicate results? ↳ WHY will your approach create defendable competitive moats? HOW: ↳ HOW will you maintain AI performance as complexity increases? ↳ HOW will you transform culture from "AI as tool" to "AI as capability"? The companies that answer these questions first will dominate 2025. The ones that don't will spend another year in pilot purgatory. Save this for your next strategy session. Your competitive advantage depends on it. ♻️ Repost to help leaders avoid costly AI scaling mistakes ➕ Follow Gabriel Millien for AI strategy that works Infographic style inspiration: @Prem Natarajan

AI is a force multiplier, not a fix-it fairy. “How do we scale AI across the enterprise?” Wrong question. Start with this one instead: “Are we automating dysfunction?” I’ve seen this play out often: A company spends millions on a GenAI project to reduce support tickets. But their documentation was outdated and the product UI broken. No AI can fix that. Another company deploys agents to accelerate sales ops. But their reps don’t trust the data, and the CRM is a mess. The AI becomes a very expensive intern, ignored by everyone. Here’s the pattern: 1. Leaders think AI will save them. 2. They pour money into tools and pilots. 3. They ignore the root problem: broken process, bad data, or unclear accountability. Then they blame the tech. AI is a force multiplier and it scales what’s already there; good or bad. Before you scale AI, ask: - Are our people, data, and incentives aligned? - Will AI augment excellence, or automate failure?