The Wright Way to AI: Seeing the Bigger Picture

Visual Formalization Matters

The Wright brothers weren’t the first to dream of powered flight. What set them apart wasn’t just their technical skills or perseverance—it was their ability to see the airplane as a system. Instead of tinkering aimlessly, they used detailed sketches, models, and visualizations to understand how every part, including the pilot, worked together as a cohesive whole.

That mindset, the ability to see the big picture, is what we need in designing AI application today. It’s not just about clever prompts, sleek interfaces, or advanced APIs—it’s about how everything connects. The real value comes from the system as a whole, and without understanding those connections, you risk building something that doesn’t fly.

I’ve seen this firsthand. Building software that serves millions has taught me how small decisions can snowball into major consequences. My turning point came when I discovered David Harel’s statecharts. Originally designed for embedded systems, they turned chaos into order and revealed the power of visual formalization—the use of diagrams, models, and sketches to transform complexity into clarity.

Visual formalization strengthens every stage of development—requirements, design, testing, and beyond. Most importantly, it illuminates the bigger picture, enabling smarter decisions, better communication, and stronger systems. Consider an AI-powered customer support assistant:

1. Input Preprocessing

The user submits a query, which goes through text normalization, language detection, or tokenization to ensure it’s ready for the next step.

2. Model Inference

The cleaned input is sent to a language model, such as a fine-tuned GPT or BERT, which processes the query. Context like user history or a knowledge base might also be included to refine responses.

3. Post-Processing

The model’s output may require adjustments—extracting key details, formatting the tone, or preparing for further actions.

4. Orchestration with External Systems

The response could trigger downstream tasks: querying a database, processing refunds, or updating CRM records.

5. Monitoring and Feedback

Metrics like response time and user satisfaction are tracked, feeding into retraining, tweaking, and improving the system.

Each step in this pipeline is interconnected. Miss one, and the entire system falters. A visual map of this workflow transforms it from a black box into something you can optimize, debug, and trust.

Just as the Wright brothers’ blueprints allowed them to fly, visual formalization empowers us to build AI systems that soar.

Simplicity Wins

The best systems are both functional and understandable. That’s especially important in AI applications, where LLMs can feel like magic while obscuring the rest of the system. The problem is, what you can’t see, you can’t improve.

Visual formalization fixes this. By mapping out how your application connects to the LLM—before, during, and after—you’re not just documenting; you’re designing.

• Pipeline Diagrams show how user inputs flow through the system.

• Dependency Maps highlight potential bottlenecks—services or datasets every other component relies on.

Once you visualize these connections, finding ways to optimize them becomes intuitive, and improvements happen faster.

Helping Others See What You See

Explaining an AI application to non-technical stakeholders can feel like an uphill battle. “Just trust me” rarely inspires confidence. Visual diagrams, on the other hand, turn confusion into collaboration.

• Cross-Functional Clarity
  A well-designed diagram communicates your system’s flow in a way that everyone—from business stakeholders to developers—can understand.

• Stakeholder Alignment
  Shared visual models speed up decision-making and reduce misunderstandings by giving all parties the same mental picture.

In AI, where black-box models are common, diagrams open the box just enough to foster a common understanding that drives team alignment.

Finding Hidden Risks

All AI applications carry risks—biased datasets, brittle integrations, unreliable APIs. The challenge is that these vulnerabilities rarely wave red flags in plain sight.

Visual formalization helps reveal them:

• Pinpoint Vulnerabilities
  Diagrams can highlight where user inputs interact with external APIs or where sensitive data is processed.

• Prioritize Threats
  Visualizing dependencies makes it easier to see which components are truly critical and need extra protection or redundancy.

A clear system map is crucial to spotting risks before they escalate into critical failures.

Innovation Comes from Connections

Breakthroughs often stem from reimagining existing ideas. Visualizing your AI system can uncover hidden opportunities. For example, combining preprocessing steps can save time and resources. Caching certain outputs may significantly improve performance.

These aren’t revolutionary ideas, but they can have a big impact—and they’re easier to spot when you see the entire system laid out.

Principles for Effective Visual Formalization

In today’s world of increasingly complex AI applications, the ability to see the full picture isn’t just an advantage—it’s a necessity. Whether you’re mitigating risks, fostering collaboration, or identifying opportunities, visual formalization turns complexity into clarity. By mapping out pipelines, dependencies, and feedback loops, you enable teams to innovate confidently, adapt quickly, and deliver meaningful results. Just as the Wright brothers achieved flight by viewing the entire machine, you can build transformative AI applications by visualizing every connection.

Keep It Simple

The key to effective diagramming is designing visuals that emphasize simplicity, semantic breadth, scalability, and clarity. You don’t need fancy or hyper-detailed notation. In fact, complexity often chases people away, as happened with UML (Unified Modeling Language), which became so intricate that many teams simply gave up.

In contrast, effective diagrams use:

• Minimalist Shapes & Symbols
  Boxes, arrows, diamonds, and a handful of icons for storage (database icon), external systems (cloud), or processes (gears).

• Focus on Key Relationships
  Show how data flows, where dependencies exist, and the high-level system operation without drowning in details.

• Modular Design
  Keep diagrams flexible and easy to adjust, so you can swap or expand components without overhauling everything.

• Optimized for Collaboration
  A diagram isn’t just for the engineering team; it’s for everyone involved—business executives, product managers, and more.

Practical Diagram Types

1. Pipeline Overview
   A simple flowchart that shows how data moves from preprocessing, to model inference, to post-processing.

2. Dependency Map
   A diagram of key components, revealing bottlenecks and single points of failure.

3. Feedback Loops
   Visualizing how user feedback or performance metrics get integrated into model retraining and system improvements.

4. System Integrations
   Illustrating how the AI system connects to APIs, databases, and other external dependencies.

These diagrams don’t need complex tools or excessive detail. The simpler they are, the more inviting they become to stakeholders, and the easier they are to evolve with your system.

Conclusion

The beauty of simple diagrams is their universality. They’re approachable, adaptable, and resistant to obsolescence as your AI system changes. By keeping visualizations straightforward, you establish a shared language that facilitates innovation, avoids confusion, and keeps your team focused on building meaningful, effective AI applications. In the same way the Wright brothers saw the airplane as an integrated whole, seeing your AI systems as interconnected ecosystems is key to achieving true lift.