A pronounced “AI velocity gap” exists between rapid developer AI adoption and slower, centralized standardization
Developers are already moving fast with AI. Lots of them are using tools like ChatGPT to write code, debug, or streamline their workflows. They’re not waiting around for formal policies or committees. This isn’t a trend limited to a few tech companies, it’s happening in nearly every forward-leaning organization. The problem isn’t speed. The problem is the disconnect between that speed and the pace of decision-making at the leadership level.
Phil Fersht coined this term “AI velocity gap” to describe the widening gap between how quickly developers are adopting AI tools and how slowly enterprise leadership is establishing structure around them. Right now, dev teams are moving on their own. Without guidance or guardrails, they’re often paying personally for third-party APIs, sidestepping your compliance processes, and exposing your enterprise data to unnecessary risks.
If you think this sounds familiar, it is. This is “shadow IT” all over again. But this time, it’s infused with far more complexity and risk. We’re talking about third-party LLMs (large language models) processing proprietary datasets, potentially customer data, financial information, codebases. Without proper oversight, that’s a direct path to data leaks, compliance violations, and blind spots in core systems.
This is about control that doesn’t slow people down. You don’t want to suppress innovation by trying to manage it with traditional approval workflows. That won’t work here. Executives should focus on building systems that scale with developer initiative instead of restricting it. Your job isn’t to pick winners from the top down, it’s to reduce organizational friction before developers route around you completely.
Monolithic enterprise AI platforms are ineffective in a fast-changing AI landscape
There’s a common reaction to developer-led adoption, lock it down. Create one official enterprise-grade AI platform, pick a “gold standard” model, and require everyone to go through it. Enterprise platform teams often lean in this direction. They pause everything, form long-term roadmaps, negotiate with vendors, compare performance metrics, and aim to launch an end-to-end platform that includes approved models, workflows, and policies. They aim for maximum control.
This strategy fails, not because the intent is wrong, but because the AI landscape moves too fast. These one-platform plans take 12 to 18 months to build. Meanwhile, your chosen model could be obsolete in 3 months. Developers don’t wait that long. They go around it, using newer, better models, often paid for with personal company cards. That quickly turns into a pile of unmonitored systems, with poor security, rising costs, and no visibility.
Even if you manage to ship the platform, you’re still behind. You’ll have standardized on a single model that works well for one task and poorly for others. The LLM that’s great at summarizing legal contracts is ineffective at developing new software features. The one that can help with Python isn’t suitable for financial forecasting. Needs vary, across departments, use cases, and business units. A centralized, fixed system doesn’t fit that environment.
Executives need to accept a new reality: the idea of locking down AI inside a fixed platform will slow you down. It won’t secure innovation; it will fracture it. The risk isn’t model misalignment, it’s that your AI plans will miss the wave entirely while developers create their own fragmented ecosystems. The best path forward isn’t tighter control. It’s more intelligent direction, clear protocols, flexible interfaces, and a tight feedback loop with your stakeholders. That’s how you stay current and useful.
Shifting from prescribed, inflexible platforms to flexible, composable products is essential
Most platform teams are still thinking in terms of static suites, one platform to serve every use case. That approach doesn’t reflect how developers work today. Developers don’t ask for the full stack. They ask for specific capabilities: a clean NLP model here, a fast embedding engine there, maybe a reliable API to chain tools together. What they need are building blocks, not command centers.
Bryan Ross has talked about this shift extensively. He pushes for what he calls “golden paths”, a platform model that focuses on modular, composable products rather than centralized workflows. These are flexible services exposed as APIs that developers can plug into. You’re not telling teams how to solve a problem. You’re giving them the resources to solve it faster, securely, and in ways that scale.
When your platform functions like a product, where the team treats their internal services with the same design principles as customer-facing software, you get buy-in. Developers choose to use it, not because they are forced to, but because it reduces surface area, simplifies integration, and gets them results. Initiatives move faster. You also avoid the bottleneck of long approval processes or mass rewrites every time a better model comes online.
From a leadership standpoint, the objective is not standardization, it’s adoption. Your platform team needs to operate more like a product team with a clear roadmap, user feedback loops, and versioned capabilities that evolve rapidly. Leaders must understand that composable architecture is what allows AI to scale across diverse teams without falling apart. Flexibility creates adoption. Adoption creates alignment. Alignment creates control.
Standardized API gateways are critical for managing AI usage without hindering innovation
Developers are already using a range of different AI tools, models, and frameworks. Without enforcing a common contract, every team ends up building their own integrations, handling different types of outputs, and managing security in inconsistent ways. You can’t scale from that.
What works is enforcing a baseline without overreaching. The industry is already settling on some informal standards, like the OpenAI-compatible API format. Multiple backends, such as vLLM, now support that structure. It doesn’t mean you lock into one model provider. It means all model access follows the same contract, so switching one out doesn’t create downstream breakage.
Placing that API behind a gateway allows you to enforce corporate standards, like output schemas, runtime validation, rate limits, structured logging, and cost caps. If you want stability, you mandate JSON-constrained outputs. That’s what separates experiments from product-ready systems. And once the API gateway becomes your access point, it also becomes your control point, for everything from observability to budgeting.
For CIOs and CTOs, a common interface isn’t just a technical preference, it’s a strategic move. Without it, your teams burn time building unreliable pipelines and reconciling schema mismatches. With it, you get durable integrations, faster onboarding, and consistent metrics. Use the gateway to enforce structure without limiting exploration. That’s where you find the balance between freedom and control.
Robust data access governance is essential for secure AI deployment
If you’re deploying AI without strict access controls, you’re creating future problems, security problems, compliance problems, trust problems. The right approach is to embed AI capabilities inside the security architecture you’ve already hardened. This means centralized identity, access control, and secrets management should not be bypassed, even for exploratory use cases.
The way forward is runtime credential retrieval, developers or services don’t store credentials statically, and access is granted dynamically through verified identity systems your enterprise already uses. Authorization should connect directly to your existing IAM (identity and access management) infrastructure. This makes enforcement easier, auditing cleaner, and attack surfaces smaller. Personal access tokens, hardcoded API keys, and unmanaged endpoints are unnecessary risks that responsible leadership shouldn’t ignore.
The technical rules here are simple, and C-suite leaders need to back them fully: no embedded keys, no unmanaged identities, no third-party access without enterprise oversight. That’s not slowing down AI adoption, that’s ensuring the adoption doesn’t turn into exposure. You can enable safe experimentation, but the foundation has to be secure.
Security is still the control layer that makes everything durable. And in the AI era, governance needs to operate in real time, across both internal and third-party models. Executives who treat this as an IT back-office issue are missing the shift, security is now directly tied to agility. Poor governance slows down integration, but good governance, automated through trusted identity frameworks, enables faster execution with far less risk.
Embracing controlled flexibility through “golden paths” encourages innovation while maintaining oversight
You don’t need to force every team to comply with a fixed standard. But you do need a clear, supported path that gets most people moving quickly, and safely. This is the essence of a golden path. It’s the recommended approach, one that balances security, observability, cost management, and developer usability. But it must also allow for exceptions, on clear terms.
When a team wants to go off-path, they should be able to. But doing so should trigger checks: structured logging, security reviews, tighter cost monitoring, and justification. This encourages accountability without restricting initiative. Platform teams can build this logic into the tooling, a flag, a form, a tracked log, and leadership should review the exceptions weekly. Not to block change, but to adapt the golden path based on real usage and needs.
This creates an internal system that evolves. You’re not relying on assumptions. You’re using data from actual developer behavior to tune your platform and governance. Teams get the freedom to innovate. Leadership gets confidence that the innovation is structured, observable, and aligned.
C-suite executives often struggle with how much control to enforce. The answer is: enough to reduce chaos, not enough to slow momentum. The override mechanism is crucial. You don’t want to fight users, you want to learn from them. If multiple teams are bypassing the path for similar reasons, your governance isn’t wrong, it’s outdated. Use that data to iterate, refine, and stay current without surrendering control.
Implementing guardrails instead of rigid gates empowers scalable and agile AI experimentation
Trying to predict and control every AI use case through a committee won’t work. The space evolves too quickly. New models, new interfaces, and new business requirements surface faster than centralized teams can keep up. Companies that delay rollouts for exhaustive planning lose momentum, and the result is predictable, teams sidestep the system and build their own tools in silos.
The better move is to create adaptive guardrails, guidelines that allow developers to move fast while ensuring you maintain visibility, consistency, and operational safety. These guardrails give platform teams the authority to structure decisions, on cost, performance, compliance, without dictating how every problem gets solved. They allow your organization to scale AI use without increasing friction.
What enterprises need today is a structured environment where the rules support action. That means performance limits set in telemetry tools, cost ceilings enforced through usage caps, and standardized audit logs that capture who’s doing what, with which models, and why. None of that stops innovation. It just makes the outcomes traceable and governed.
Executives need to stop thinking about standardization as a gate that holds progress until every box is checked. AI success at scale comes from decentralization, with aligned incentives and base-level controls. Guardrails give leadership enough data to steer without needing to approve every step. It’s a shift from prediction to responsiveness, enabled by systems that watch, measure, and adapt continuously. If you want AI to be an enterprise capability, not just a series of pilots, this mindset shift is non-negotiable.
Reducing developer decision fatigue is critical to productive AI integration
AI introduces a range of new technical decisions: choosing the right model, structuring prompts properly, handling retrieval methods, tracking token usage, and so on. All of that creates cognitive load. And when developers face too many undifferentiated choices, you lose speed and consistency. They get distracted solving infrastructure problems instead of building actual features.
The role of the platform team is to carry that load. Not by removing capability, but by abstracting complexity. Provide smart defaults. Offer templates that follow best practices. Create unified interfaces that handle messy integration behind the scenes. When well executed, developers only deal with what’s necessary. They don’t wrestle with prompt formats or hunt down model docs, they focus on outcomes.
This isn’t about dumbing things down. It’s about enabling deep work without layers of confusion. High-leverage teams always operate within strong abstraction layers. The same must be true for AI platforms. Reduce the noise and people move faster, make fewer mistakes, and create more scalable systems.
For the C-suite, the critical realization is that decision fatigue doesn’t just slow teams, it fragments strategy. If every developer is making isolated decisions about models, formats, and observability, you don’t have one platform; you have hundreds. Leaders must invest in internal tools that reduce friction at scale. That cohesion is what turns experiments into competitive advantage. Otherwise, you’ll end up with a mess of isolated progress that can’t deliver at enterprise scale.
Concluding thoughts
AI isn’t waiting. Your developers aren’t either. They’ve already integrated it into workflows using whatever tools let them move fast. The real decision is whether leadership keeps up, or gets bypassed. The choice isn’t between control or chaos. It’s between outdated control and intelligent direction.
You don’t need to slow things down to stay compliant. You need infrastructure that scales with your top performers and safeguards the rest. Guardrails, composable services, and centralized observability give you that. This isn’t theoretical, it’s operational. Done right, it turns scattered AI experiments into a cohesive, secure, and cost-aware system.
Build systems that let people move fast with clarity. Reduce friction, not choice. Optimize for decision velocity. AI is already reshaping how companies operate, it’s your move to shape how your company leads.
