The seven levers of technology: Different industries, same levers of change.

Ever felt lost in the jargon of tech? Overwhelmed by all the buzzwords and new tools? You are not alone!
A data scientist at a bank builds fraud models. A cloud architect in healthcare designs scalable systems. A retailer like Marks & Spencer uses AI to optimize supply chains. A manufacturer like SKF applies predictive analytics to reduce machine downtime.

Consider these real industry use cases:

• Retail (Marks & Spencer, Walgreens, Walmart on Azure & Databricks): AI demand forecasting, supply chain optimization, customer personalization.

• Manufacturing (SKF, Siemens, Rolls Royce, Stanley Black & Decker): Predictive maintenance, IoT telemetry analysis, sustainability improvements.

• Banking (HSBC, TD, NatWest): Fraud detection, risk reduction, compliance automation.

• Healthcare (Novartis, NHS, Walgreens): Drug discovery acceleration, patient outcome prediction, scalable secure systems.

At first glance, these stories feel unrelated. But beneath the surface, all tech work reduces to a handful of universal levers: sense, explain, predict, enhance, mitigate, invent, and scale.

“Technology is anything that wasn’t around when you were born.” — Alan Kay

Understanding these levers gives you two advantages:

1. You build skills with clarity: you know exactly which lever you’re strengthening.

2. You frame problems sharply: before reaching for a tool, you know which lever to pull.

Here is the common language of tech work.

The 7 Levers of Technology

• Sense what is happening

  You cannot improve what you cannot see. Every industry begins by collecting reliable signals.

  • Goal: Observe and Measure - build reliable signals.

  • Skills involved: instrumentation, telemetry, monitoring, data engineering, real-time data streaming, cloud data warehouse/lakehouse, experimentation, generative AI, visualization, observability, etc.

  • Examples:

    • In logistics, DHL and FedEx track millions of parcels in real time using IoT sensors.

    • In healthcare, wearables like Apple Watch and continuous glucose monitors generate patient data streams.

    • Shell uses Databricks to ingest IoT data from over 3,000 oil wells, forming a real-time operational view across fields.

• Explain why it is happening

  Once data is collected, the next lever is explanation. Why did something happen? What correlates with what?

  • Goal: Analyze and Understand - find causes and drivers.

  • Skills involved: statistics, causal inference, visualization, critical thinking, domain knowledge, etc.

  • Examples:

    • Netflix uses A/B testing and deep analytics to understand user engagement drivers.

    • Airlines use delay attribution analysis to separate weather, crew, and mechanical causes.

    • Pharma companies run clinical trial analysis to detect causation versus correlation in drug outcomes.

• Predict what will happen next

  With historical and present data, we can anticipate future states. This allows better planning and proactive decisions.

  • Goal: Forecast and Anticipate - plan from likely futures.

  • Skills involved: machine learning, deep learning, probabilistic modeling, scenario simulation, MLOps, model explainability, etc.

  • Examples:

    • Walmart predicts demand at the SKU level to optimize inventory across thousands of stores.

    • PepsiCo uses Microsoft Azure AI to forecast demand and streamline its global supply chain.

    • Uber predicts surge demand patterns to match drivers with riders.

• Enhance the system for better outcomes

  Optimization is about making systems faster, cheaper, or more effective. Improvement is about increasing the value delivered - raise efficiency and outcomes.

  • Goal: Optimize and Improve - raise efficiency and outcomes.

  • Skills involved: performance tuning, A/B testing, design thinking, automation, systems thinking, solution architecture, etc.

  • Examples:

    • Cloud providers constantly optimize infrastructure cost per unit of compute.

    • Shopify merchants use automated pricing engines to maximize revenue per visitor.

    • Hospitals improve patient throughput by streamlining discharge processes.

• Mitigate risks and prevent harm

  Here the focus is not on adding value, but on reducing harm, waste, or risk.

  • Goal: Reduce and Prevent - limit downside and fragility

  • Skills involved: reliability engineering, risk modeling, monitoring, security, compliance, machine learning, data security, threat modelling, etc.

  • Examples:

    • Banks reduce fraud risk with anomaly detection on credit card transactions.

    • Cybersecurity firms like CrowdStrike prevent breaches through continuous monitoring.

    • Airlines reduce fuel burn by optimizing flight paths in real time

• Invent new solutions

  Not every lever is about tweaking existing systems. Sometimes the challenge is to build something that never existed before.

  • Goal: Create and Innovate - build what does not yet exist

  • Skills involved: research, rapid prototyping, creativity, design, generative AI, market research, etc.

  • Examples:

    • OpenAI, Anthropic, and Google DeepMind invent new frontier AI models.

    • Moderna created an entirely new class of mRNA vaccines during the COVID-19 crisis.

    • SpaceX designed reusable rocket boosters that transformed space economics.

• Multiply impact by scaling sustainably

  Great solutions that cannot scale will collapse under demand. Sustainability and resilience matter as much as invention.

  • Goal: Scale and Sustain - make it robust and repeatable.

  • Skills involved: distributed systems, DevOps, governance, process design, systems thinking, operational excellence, platform engineering, etc.

  • Examples:

    • Netflix built a global content delivery network to stream reliably to 200 million subscribers.

    • Stripe scaled its payment infrastructure to handle trillions in annual transaction volume.

    • Governments deploy digital public infrastructure like India’s UPI, which scaled to billions of monthly transactions.

    • Epiroc (manufacturing) implemented “AI Factory” on Azure + Databricks to standardize operations across 150+ countries, cutting waste and boosting quality by ~30%

Why this matters

If you are building skills, ask yourself which lever you are getting stronger at. Are you sharpening your ability to sense and explain? Are you leaning into invention? Are you learning to scale systems?

If you are framing a problem, step back and ask: which lever am I trying to pull? Am I here to optimize, to predict, to reduce risk, or to invent? Clarity at this level saves wasted effort downstream.

Ask: “Would this still matter if the current platform/tool/vendor disappeared tomorrow?” If yes, you’re working on a lever. If no, you’re on borrowed relevance

The tools will always evolve. Today it is cloud, AI, and edge. Tomorrow it will be something else. But the levers remain the same. They are the universal grammar of technology work.

“Every time you face a problem, ask yourself: which lever am I pulling? That clarity is the starting point of great work.”