When AI Becomes the Platform: How Product Leaders Should Decide Between Feature, Product or Platform

AI is no longer a novelty to bolt onto a product. The technology is asking organisations an uncomfortable strategic question: should AI be a feature, a standalone product, or the platform that powers everything else? The answer matters — for investment, for engineering, and for long‑term defensibility. Get it wrong and you’ll waste capital; get it right and you may have created the bedrock of future differentiation.

Three strategic modes: feature, product, platform

Not every AI need warrants a platform. Think in three modes:

• AI as a feature — add incremental value (autocomplete, recommendation, summarisation). Low engineering lift, fast user benefit.
• AI as a product — AI is the primary UX and value proposition (an AI tutor, a conversational agent sold as a subscription).
• AI as a platform — a shared infrastructure, data fabric and set of services that multiple product teams consume (vector search, fine‑tuning pipelines, orchestration and observability).

These are not mutually exclusive, but they require different organisational models, metrics and governance. Product leaders often slide between them without making the choice explicit — and that’s where problems start.

Make the choice through value, control and absorptability

When deciding, evaluate three practical dimensions:

• Value concentration: Is the majority of user benefit concentrated in one experience, or is it spread across many products? If value lives in a single UX, treat AI as a product; if it’s horizontal, a platform makes sense.
• Data and control: Do you own unique data and the means to label and curate it? Exclusive, high‑quality data often justifies building a platform and an AI moat.
• Absorptability risk: If a useful AI feature is easy to copy and a larger platform could add it as a feature, you face platform displacement — a real strategic risk.

A short, sharp rule I use: build a platform only when you can demonstrate persistent cross‑product value and when competitors can’t easily replicate your data + workflows.

Lessons from the field: what Physics Wallah and platform leaders teach us

Look at recent edtech projects to see how this plays out. Physics Wallah’s Alakh AI / Gyan Guru shows a pragmatic hybrid approach. The company indexed its proprietary content into vector stores and deployed a RAG architecture to power personalised tutoring and doubt‑resolution. The experience is a product for students, but it rests on a platform of content, indexing and orchestration.

This pattern — product front end, platform back end — is common among leaders. McKinsey’s recent coverage of AI high performers highlights the same dynamic: organisations that win often combine product experiences with platform capabilities and strong leadership sponsorship (see McKinsey).

One more cautionary observation: once a product feature proves valuable, larger platforms with distribution can absorb it. The strategic implications are well articulated in this piece on platform displacement (FourWeekMBA). If your feature is easily portable and depends on commodity models, you risk commoditisation unless you lock in differentiated data or superb integration.

Practical playbook for product leaders

If you’re deciding which route to take, follow these pragmatic steps:

• Start with a hypothesis and measure outcomes: define the user outcome your AI investment must move and instrument it. If it moves a narrow KPI for many products, platform thinking might be justified.
• Prototype fast, then test portability: build a product‑grade prototype (a minimum RAG pipeline, for example), then ask how tightly it couples to your data and UX. If portability is low, you’ve found moat potential.
• Design governance and cost models: platforms require chargebacks, SLOs, and observability. Don’t treat the platform as an internal free good — give it product management and a roadmap.
• Protect early experiments: protect small bets from corporate bureaucracy. Create an API layer and contract boundaries early so teams can iterate independently without rebuilding core services.
• Observe the RAG tipping point: Retrieval‑Augmented Generation (RAG) transforms generic models into contextually accurate assistants. If RAG improves your outcomes significantly, you have a strong argument for a shared retrieval layer and vector stores — that’s platform material. See the state of RAG discussions here: Squirro on RAG.

Organisational implications: how teams must evolve

Deciding platform vs product is as much organisational as technical. If you choose a platform you need:

• Cross‑functional engineering teams responsible for observability and cost control.
• A product team that treats APIs and developer experience as first‑class users.
• Clear SLAs and billing mechanisms so product teams internalise costs and avoid runaway experiments.

Without these, the platform becomes either a black box or a ghost — both fatal to long‑term value creation.

Where to start today

If you’re a CPO or CTO unsure which path to follow, pick one measurable customer outcome, prototype a lightweight RAG flow, and then test the three dimensions I mentioned: value concentration, data control and absorptability. That evidence will tell you whether to ship an AI feature rapidly, to productise the capability, or to invest in a platform that others will rely upon.

Decisions about AI are strategic, not tactical. Choose deliberately, instrument ruthlessly and protect the parts that create durable advantage. Do that and your AI investments will stop being a string of expensive experiments and start becoming an engine for predictable product advantage.