The 4S AI Framework

Most engineering leaders can’t answer a simple question: “What specific business problem does our AI solve?” The result? AI projects that never reach production, burned budgets, and skeptical executives.

The 4S Framework stops this waste. It separates AI projects that create real business value from expensive tech demos.

The Core Principle

Before building anything with AI, identify which single dimension drives competitive advantage for your use case:

• Speed — Sub-second response requirements
• Scale — Concurrent operations for millions of users
• Scope — Multiple AI capabilities in one platform
• Sophistication — Complex decision-making humans can’t replicate

Pick one. Multiple dimensions mean scattered focus and budget overruns.

Speed: Why Milliseconds Mean Millions

Money-saving rule: If faster response doesn’t increase revenue or reduce costs, don’t optimize for speed.

Chatbots answering in 50ms vs 500ms? Users don’t care. Your budget shouldn’t either.

Speed matters when latency directly impacts conversion rates, user retention, or operational throughput. Real-time fraud detection, high-frequency trading signals, autonomous vehicle decisions — these are legitimate speed investments.

Ask: “If our AI responds 10x faster, does our business metric improve?” If not, speed isn’t your dimension.

Scale: When Millions Use Your System

Netflix’s recommendation engine handles 200 million users simultaneously. It was built for scale from day one. But standard systems collapse under AI workloads.

Only invest in scale when user volume justifies infrastructure costs. A recommendation engine for 1,000 users doesn’t need distributed inference. A recommendation engine for 10 million does.

The trap: building scale infrastructure “in case we grow.” Scale is expensive. Build for current volume plus a reasonable growth multiplier. Not for hypothetical millions.

Scope: One Platform, Multiple AI Jobs

The problem with scope: finding engineers with both ML expertise and domain knowledge is hard. The solution: shared AI infrastructure that serves multiple use cases.

Shopify’s AI platform handles recommendations, fraud detection, and inventory optimization with shared infrastructure. The key insight: build platforms when you need multiple AI capabilities. Buy point solutions for single use cases.

Scope makes sense when:

• You have 3+ AI use cases with shared data patterns
• The cost of separate solutions exceeds platform maintenance
• Your team can maintain a shared ML infrastructure

If you only need one AI capability, a platform is over-engineering.

Sophistication: When Complex Models Win

Engineers want simple, explainable AI. Business often demands complex models that outperform simple ones.

Credit scoring accuracy improved significantly with complex models. The trade-off: less explainability, more monitoring overhead.

The rule: build robust monitoring around sophisticated AI rather than demanding simple AI for complex problems. If the business value of accuracy outweighs the cost of monitoring, invest in sophistication.

Red Flags That Waste Money

Stop immediately if you see:

• No clear bottleneck in speed, scale, scope, or sophistication
• Optimizing metrics that users don’t care about
• Building AI solutions and then looking for problems to solve
• Pursuing multiple dimensions simultaneously without explicit prioritization

Each of these patterns leads to projects that demo well but never reach production.

The 5-Minute Project Filter

Before approving any AI project, run it through these five questions:

1. Identify the bottleneck: Which dimension matters most?
2. Calculate the cost: What happens if we don’t solve this?
3. Measure success: How do we know it worked?
4. Pick one focus: Speed, OR Scale, OR Scope, OR Sophistication
5. Plan the minimum: What’s the smallest version that proves value?

Projects that can’t clearly answer all five questions aren’t ready for investment.

How to Apply This

Run your current AI projects through the 4S filter. For each project, identify:

• Which single dimension drives its value
• Whether that dimension is genuinely a bottleneck today (not hypothetically)
• What the minimum viable implementation looks like

Projects that pass this filter have a dramatically higher chance of reaching production and delivering measurable business value.

Challenge: How many of your current AI projects can clearly identify their single dimension? The ones that can’t are your highest-risk investments.