Building an AI-first platform strategy

AI has fundamentally raised the stakes for developer platforms. In the pre-AI era, platforms were largely about developer productivity, streamlining builds, deployments and environments. Today, that’s table stakes. What’s new and urgent is that platforms must now:

• Govern the use of generative models and proprietary data in real time, balancing speed with entirely new categories of risk such as; bias, leakage, hallucinations and IP exposure.

• Bake compliance, telemetry and trust into every interaction — not as audits after the fact, but as adaptive, always-on guardrails.

• Enable composability and large-scale experimentation, where teams stitch together models, services and data products at a pace and scale that was unthinkable even two years ago.

This isn’t an incremental shift; it’s a paradigmatic one. AI forces platforms to handle risks and opportunities that didn’t exist before, at an intensity and speed legacy strategies can’t keep up with. And they must do all of this without reverting to uninformed and overreaching control measures or paralyzing governance. This is why what I like to call the Goldilocks principle becomes essential: not bloated or brittle, but balanced and building just enough capability, just in time, for the teams that need it.

The Goldilocks approach: Keeping your platform strategy lean

Too often, platform strategies swing to extremes. At one end, we see platforms that are inflexible and bloated — sprawling portals full of tools nobody asked for, rigid processes that slow delivery, or rigid standards that teams quietly work around. At the other end, we see under-invested and chaotic environments where every team is rolling out their own pipelines, security checks are ad hoc, and effort is duplicated — leading to increased operational risk.

Both extremes come from the same root cause: trying to predict too little or too much upfront. Either the platform team guesses what everyone might need years in advance, or they stay hands-off until fragmentation and silos force a crisis.

This doesn’t mean cutting corners. It means teams need to constantly tune their platform to fit the current moment, while also paving the way for what’s next.

What’s needed?

We’ve helped clients get back on track by encouraging them to shift their mindset and adopt a number of useful techniques.

• Lean scaffolding to empower, not constrain. For example, building shared pipelines with built-in security and compliance that are still flexible enough for teams to extend.

• Lean enablement that ensures the platform evolves with teams. That might mean spinning up GPU environments only when experimentation hits a certain scale.

• Lean investments based on real usage, not theoretical needs — like scaling observability once multiple teams are hitting production, rather than chasing a “perfect” solution upfront.

The four pillars of an AI-first platform strategy

One way of developing a robust AI-first platform strategy is to think in terms of four key pillars. 

• Production strategy

• Consumption strategy

• Management strategy

• Workbench strategy

Each one has a distinct focus and purpose — let’s take a look at them in more detail now.

Production strategy

This is about being able to answer the question “How do we build AI-enabled products safely and reliably?"

Your production strategy is about making AI operational. Think model delivery pipelines, real-time observability, policy-as-code and continuous validation. This is where safety, performance and reliability live.

In an AI-first world, production platforms need to support:

• Continuous retraining and evaluation.

• Secure and governed data access.

• Multi-modal model deployment.

• Feedback loops from production to experimentation.

When done effectively, this layer turns experimentation into impact without compromising trust.

Consumption strategy

This is where you ask — and try and answer — "How do teams access and apply AI capabilities effectively?"

A great platform makes it easy to consume AI capabilities. This means abstracting complexity while keeping teams focused to value-add work where applicable.

Examples include:

• API-first access to foundational models and embeddings.

• Fine-tuning services and experimentation workbenches.

• Data discovery and lineage via self-service interfaces.

• Clear SLAs, quotas and usage visibility.

If your teams are building brittle scripts and calling shadow APIs, it’s a sign your consumption strategy needs work. These hacks don’t happen because engineers want shortcuts, they happen when the platform doesn’t provide what they need.

In the AI era, this often looks like unmanaged calls to external LLM APIs, hacked-together GPU provisioning scripts, or shadow model endpoints running without monitoring or compliance. At the moment, these feel like productivity boosts. Over time, they create fragility, security risk and damage trust in the platform.

A strong consumption strategy flips this. The right way becomes the easy way, with supported APIs, self-service environments and reusable patterns that let teams move fast without breaking the enterprise.

Management strategy

This is where we try to solve the problem of "How do we govern, evolve, and scale AI responsibly?"

This is the layer that often gets ignored until it’s too late.

Management strategy is about adaptive governance: embedding compliance, auditability and security as part of the developer experience. It shouldn’t be something that’s just bolted on.

The key elements include:

• Just-in-time policy injection based on context.
  Policies aren’t static. They need to adapt dynamically to the situation. For example, a data scientist fine-tuning a model on sensitive data might automatically trigger stricter logging and access rules, while a team experimenting with public datasets works under lighter guardrails.
  
  

• Explainability and risk scoring built into pipelines.
  Every model promotion runs through automated checks that produce an explainability report and a risk score (bias, drift, compliance exposure). Teams check whether their model passed compliance standards and if they did, also learn why. Governance teams can also receive real-time signals instead of audit surprises months later.
  
  

• Platform telemetry feeding proactive governance.
  Usage data, API calls, model inferences and data access patterns flow into dashboards that flag anomalies (like sudden spikes in token usage or access to restricted datasets, for example). Governance shifts from reactive policing to proactive, continuous risk detection.
  
  

• Automated, federated governance.
  Instead of relying on a centralized committee that slows everything down, governance is federated across domains and automated through the platform. Each product team owns the compliance and quality of its data products, with embedded governance expertise (legal, security, ethics) and automated enforcement built into their pipelines.

AI expands your risk surface, whether that’s in the form of potential data leakage, model bias or untracked API calls. This is precisely why your platform must respond dynamically and contextually, and enforce policies in real time while letting teams move fast.

Workbench strategy

Here we answer "How do we empower builders to experiment and learn?"

The workbench is where ideas turn into value. It’s the integrated, user-centric environment that unifies service catalogs, APIs, SDKs and interactive tools into a single, intuitive interface for developers and data scientists. A strong workbench strategy streamlines access to AI capabilities, simplifies complex workflows and accelerates experimentation and deployment, while still keeping the human at the center.

A well-designed workbench enables:

• Pre-configured environments for data science and model prototyping.

• Synthetic data generation and scenario simulation for safe, rapid exploration.

• Shared spaces that encourage cross-functional collaboration and knowledge sharing.

• Metrics and telemetry to track experimentation outcomes and platform ROI.

• Adoption support with seamless onboarding that helps teams realize value from day one.

• Monetization alignment, ensuring value capture matches how users perceive meaningful outcomes.

• Agile fluency with workflows that adapt to teams at different levels of maturity.

• Human-centric AI design embedding trust, agency and usability into the tools themselves.

Think of it as an AI playground that is hardwired into the backbone of the enterprise. It's an environment where advanced capabilities are made accessible, complexity is abstracted away, and innovation compounds across teams.

Platform assets: Your impact multiplier

One of the most powerful levers in an AI-first platform are the platform assets. These assets are reusable models, datasets, fine-tuning recipes, prompt templates, monitoring modules, to name just a few. But as with everything in the AI-first era, it’s about balance: just enough, just in time, for the teams that need it.

When curated and shared with the right balance, these assets create network effects: faster time to value, better consistency across teams, lower compliance burden, and stronger cultural adoption of AI. Each strategy informs the others — consumption shapes production, production is guided by governance, and both are enabled through the workbench — creating a virtuous cycle of value.

This doesn’t happen by accident. Strategy, stewardship, and community, continuously tuned to the organization’s AI maturity, are what make platform assets multiply their impact without overwhelming teams.

Take the first step toward an AI-first platform

This isn’t about a moonshot. It’s about getting smarter and more strategic with what you already have. Your teams are ready to build with AI. Is your platform ready to support them?