Scott Turner Enterprise organizations deploying AI often fail because they haven't solved the underlying coordination problem: ensuring decision-makers have timely, accurate information. The author learned through managing Fortune 500 clients that the solution isn't adding more tools, but treating data as a product by mapping each organizational decision to its required data inputs and eliminating information that doesn't support actual decisions. This foundation of clean decision flows is essential before attempting to scale AI systems.
Before I spent my days thinking about AI strategy, I spent a decade managing enterprise complexity at scale. Not the startup kind of scale, the kind where legacy systems are load-bearing, customers have seven-figure contracts, and “move fast and break things” means someone’s production line goes down.
The lessons I learned there are more relevant to AI deployment than most people realize. Because the fundamental challenge isn’t the technology. It’s the decision system that surrounds it.
The Initial Constraint
I joined a growing technology services company managing a diverse portfolio of enterprise clients, including Fortune 500 organizations, with a mandate to scale. The team was talented. The customer base was expanding. And the systems we relied on to coordinate work were buckling under the weight.
We were running Oracle databases, SharePoint collaboration platforms, and a patchwork of custom tools that had grown organically over years. Each system held a piece of the truth. None of them held the whole picture. The result was what every scaling organization eventually confronts: decisions were being made on incomplete information, and the people making them didn’t know what they didn’t know.
The Mistake Most Teams Make
When growth creates friction, the instinct is to add features. A better dashboard. A new reporting tool. An integration that pipes data from System A to System B. Each addition solves a local problem while making the global problem worse: more tools, more context-switching, more places where information gets stale.
The real problem was never features. It was decision flow. Who needs what information, when do they need it, and how confident can they be that it’s current? These are coordination problems, not technology problems. And coordination problems don’t get better by adding more tools. They get better by treating data as a product.
The Pivot: Treating Data as a Product
We made a deliberate shift. Instead of asking “What tool do we need?” we started asking “What decision does each role need to make, and what data supports that decision?”
This reframing changed everything. It meant:
Identifying What Data Actually Mattered
Not all data is equally valuable. We audited every recurring decision across the organization, from project prioritization to resource allocation to client health assessments, and mapped each one to the specific data inputs it required. The result was surprising: about 80% of our data infrastructure supported decisions that were made monthly, while the decisions that needed daily data were running on spreadsheets and tribal knowledge.
We flipped the investment. Daily decisions got real-time dashboards. Monthly decisions got automated reports. And a significant amount of data that no one was actually using stopped being maintained.
Connecting the Right People to the Right Information
We built a Tableau analytics layer that sat on top of our existing systems (Oracle, SharePoint, and our custom project management tools) and provided role-based views. Project managers saw project health. Account managers saw client portfolio status. Leadership saw portfolio-level trends. Same underlying data, different decision contexts.
The key wasn’t the technology. It was the discipline of maintaining a single source of truth. When the Oracle-to-IRIS database migration happened, the analytics layer made the transition nearly invisible to end users because the data contracts were well-defined. The infrastructure changed. The decision support didn’t.
Building APIs as Coordination Infrastructure
As the client portfolio grew more complex, including IoT integrations with platforms like Honeywell Forge. We needed systems to talk to each other without human intermediaries. We developed internal APIs that treated integration as a first-class product concern, not an afterthought.
This API-first approach meant that when new capabilities needed to plug into existing workflows, the integration cost was hours instead of weeks. It also meant that data quality issues surfaced immediately, at the API boundary, instead of hiding in spreadsheets until someone noticed the quarterly numbers didn’t add up.
The Results
Over the course of several years, the results compounded:
- Customer growth exceeded 500%, with the organization scaling from a regional player to managing a Fortune 500 client portfolio.
- Gross margins held between 40-60% throughout the growth period, a testament to operational discipline, not just revenue growth.
- The team grew to 15 people managing 50+ concurrent projects, with a coordination overhead that didn’t scale linearly with headcount.
- Decision latency dropped measurably. Project health reviews that used to require two days of data gathering became same-day conversations grounded in real numbers.
These aren’t vanity metrics. They’re the result of a systematic approach to a question every growing organization faces: how do you maintain decision quality as complexity increases?
Why This Matters for AI
Every lesson from that scaling journey maps directly to the challenges organizations face when deploying AI.
Data as a product. AI models are only as good as the data they’re trained on and the data they’re fed in production. Organizations that haven’t solved their data coordination problem will not solve it by adding AI. They’ll just generate confident-sounding outputs from inconsistent inputs.
Decision flow over features. The most common AI deployment failure isn’t a bad model. It’s a good model whose outputs don’t reach the right decision-maker at the right time in the right format. The same coordination discipline that made our Tableau dashboards effective is what makes AI copilots effective, or useless.
API-first architecture. AI capabilities need to integrate with existing workflows, not replace them. The organizations that built clean integration layers before AI are the ones deploying AI successfully now. The ones that didn’t are discovering that their “AI strategy” is actually a “data infrastructure remediation” project in disguise.
Operational discipline at scale. Growing from a small team to managing enterprise complexity taught me that the hardest part of scaling isn’t the technology. It’s maintaining clarity about what matters as everything gets louder. AI amplifies everything, including confusion. Without operational discipline, AI deployment at scale becomes a force multiplier for organizational dysfunction.
The Bridge
AI doesn’t replace the discipline of managing complexity. It amplifies it, in both directions. Organizations with strong decision systems, clean data architecture, and clear accountability will use AI to accelerate what they’re already doing well. Organizations without those foundations will use AI to accelerate their existing problems.
The question isn’t “Are you ready for AI?” It’s “Is your decision infrastructure ready for a force multiplier?”
That’s the question I help organizations answer, not with theoretical frameworks, but with the operational experience of having built, scaled, and maintained the systems that make intelligent decisions possible.
