Stop AI Failures Before They Start: Mastering the Process Breakdown
Most AI projects fail. Not because the technology isn't ready, or because the team lacks expertise, but because they skip the most critical step: understanding what humans actually do.
After consulting on dozens of AI implementations, we've seen businesses where brilliant engineers have spent months building sophisticated models that produce unusable outputs. Meanwhile, the projects that succeed follow a surprisingly simple principle that is often overlooked: they break down human processes before automating them.
This isn't just another "best practices" guide. It's part of the systematic approach we use at Artanis to build AI that actually works in the real world. By the end of this article, you'll understand why process breakdown is the foundation of successful AI implementation, and you'll have a step-by-step method to apply it to your own projects.
Why AI Projects Fail Without a Process Breakdown
The appeal of AI is obvious: feed data in, get intelligent output. This "one-shot" approach feels intuitive to a lot of people. You want to automate invoice processing? Bung invoices into ChatGPT. Need to qualify sales leads? Build an AI that reads email threads and outputs "qualified" or "not qualified."
This thinking leads to spectacular failures.
The problem isn't technical capability (not always, anyway). It's that we're asking AI to solve nuanced problems in a single step. Real-world tasks involve dozens of micro-decisions and contextual judgements that humans make automatically. When we compress all of this into a single AI prompt, we create an accuracy problem that's very tricky to solve.
Consider this: if your AI model needs to get 10,000 decisions right (like generating a 100x100 pixel image), and it's 99.9% accurate on each decision, the chance of getting everything right is 0.999^10,000 = 0.004%. Even with near-perfect individual accuracy, the overall result will be wrong 99.996% of the time.
This mathematical reality explains why so many AI projects produce outputs that are "almost right" but completely unusable. The issue compounds when AI engineers and domain experts work in silos. Engineers focus on model performance metrics, whilst domain experts can't articulate what makes a good output without seeing examples.
The solution here isn't better AI, it's better problem breakdown and a close partnership between the AI engineer and the domain expert.
The Critical Role of Process Breakdown in AI Success
Process breakdown is the systematic analysis of how humans complete a task, step by step, with the goal of identifying which steps truly require AI and which can be automated with traditional software.
The methodology works like this:
First, map the human workflow. Sit with domain experts and watch them work. Don't just ask them to describe what they do, observe the actual process, including the small decisions they make unconsciously.
Second, categorise each step. Some steps involve pattern recognition, subjective judgement, or handling unstructured data - these are good candidates for AI. Others follow clear rules or manipulate structured data, and these are perfect for deterministic software.
Third, don't overuse AI. The goal isn't to use AI everywhere possible, but to use it only where necessary. Each AI component should solve the smallest possible problem, making it easier to achieve high accuracy.
This approach transforms impossible problems into manageable ones. Instead of asking AI to complete an entire complex task, you're asking it to make specific, focused decisions within a larger system of reliable components.
The benefits extend beyond accuracy. When AI handles smaller tasks, the outputs are easier for humans to review and correct. The system becomes modular, so you can improve individual components without rebuilding everything. Most importantly, you can measure and optimise each step independently.
Process breakdown isn't a technical exercise, it's an operational one. It's a collaboration between domain experts and engineers that ensures the final system actually solves real-world problems.
A Common but Flawed Approach to AI Projects
AI Architect: The Vision
Let me share a concrete example that illustrates why the "one-shot" approach fails so consistently.
A startup approached us after struggling for 12 months to build an AI floor plan generator. Their vision was compelling: users would input a list of rooms, and the AI would generate a realistic architectural floor plan. The concept had clear market appeal, and the team was talented.
The problem was their approach. They were training an image generation model to go directly from a room list to a complete floor plan with no intermediate steps, and no process breakdown.
After a year of development, their results were consistently unusable. The AI would generate images with smudged lines, curved walls where there should be straight ones, and rooms that didn't match the input requirements. The outputs looked vaguely architectural, but no architect would ever use them.
AI Architect: The Reality
When we examined their approach, the mathematical problem became clear. Their floor plans were 100x100 pixel images, meaning the AI needed to get roughly 10,000 pixels correct simultaneously. Even with 99.9% per-pixel accuracy, the probability of generating a completely correct image was essentially zero.
The engineering team had fallen into a classic trap: they assumed that feeding more data into a more sophisticated model would eventually solve the problem. They were technically correct; with enough training data and computational resources, the approach could theoretically work. But the scale required was orders of magnitude beyond what they had access to.
More fundamentally, they were trying to solve the wrong problem. They wanted to build AI that generates floor plans, when what they needed was AI that helps with specific aspects of floor plan generation.
This is the pattern we see repeatedly: teams with strong technical skills but limited domain knowledge attempt to automate entire workflows with AI, then spend months trying to improve accuracy on an inherently impossible task.
The Right Approach to AI Implementation
The breakthrough came when we applied process breakdown to understand how architects actually create floor plans.
We spoke with an experienced architect and asked her to walk us through her process. The insight was immediate: she never goes directly from a room list to a visual floor plan. Instead, she follows a two-step process.
First, she creates what's called an adjacency matrix. This is a simple grid showing which rooms should be next to each other. For a 10-room floor plan, this is a 10x10 matrix with 1s where rooms are adjacent and 0s where they're not. This step involves spatial reasoning and design principles, but the output is structured data, not an image.
Second, she translates this matrix into a visual floor plan by drawing rectangles to represent rooms and connecting them according to the adjacency relationships. This step is primarily mechanical. Once you know which rooms go where, the visual representation follows established architectural conventions.
How to break down the AI Architect problem
This process breakdown revealed a much better AI architecture. Instead of training one model to generate entire floor plans, we could:
Train an AI to generate adjacency matrices from room lists (a much smaller problem involving 100 decisions instead of 10,000)
Use deterministic software to convert matrices into floor plans - a reliable process that never produces smudged lines or impossible geometries
The results were transformative. The AI-generated adjacency matrices were consistently accurate because the problem was appropriately sized for the model. The floor plans were always visually clean because they were generated by reliable software rather than unpredictable AI.
Most importantly, the entire system was controllable. When users wanted to adjust room layouts, we could modify the adjacency matrix and regenerate the visual plan instantly. When the AI made mistakes, they were contained to the matrix generation step and easy to identify and correct.
This project took two zoom calls to redesign and a few weeks to implement, compared to the 12 months the team had already spent on the one-shot approach. The key insight wasn't technical; it was understanding that AI should complement deterministic software, not replace it entirely.
Step-by-Step Guide to Process Breakdown for AI
Here's the systematic approach we use to break down processes for AI implementation:
Step 1: Understand the Real-World Context
Start by spending time with domain experts who currently perform the task you want to automate. Don't just interview them, but watch them work. Schedule multiple sessions to observe the process under different conditions.
Ask specific questions about their decision-making: "When you decide this invoice needs manual review, what are you looking for?" or "How do you know this email indicates a qualified lead?" Often, experts make decisions based on patterns they haven't consciously articulated.
Document not just the main steps, but the micro-decisions and edge cases. These seemingly minor details often determine whether your AI system will work in practice.
Step 2: Categorise Each Step
Once you understand the complete workflow, categorise each step into three types:
Deterministic tasks can be solved with traditional software. These involve clear rules, structured data manipulation, or calculations. Examples include formatting outputs, validating data against known criteria, or performing mathematical operations.
AI-suitable tasks involve pattern recognition, handling unstructured data, or subjective judgements that require contextual understanding. Examples include extracting information from documents, classifying text sentiment, or identifying anomalies in data patterns.
Human-required tasks involve high-stakes decisions, creative judgement, or complex reasoning that current AI cannot reliably handle. These should remain with humans, at least initially.
The goal is to minimise the scope of AI tasks whilst maximising the overall system capability.
Step 3: Prioritise Smaller AI Tasks
For each AI-suitable task, ask whether it can be broken down further. Instead of "analyse this contract," consider "extract key dates, identify unusual clauses, flag potential risks." Each smaller task is easier for AI to handle accurately.
Design AI components to produce outputs that humans can easily review and correct. A list of extracted dates is easier to verify than a complete contract analysis.
Step 4: Stakeholder Alignment
Keep domain experts involved throughout the technical design process. They need to understand what the AI will and won't do, and engineers need to understand what constitutes good output.
Create feedback loops where domain experts can review AI outputs and refine the requirements. This iterative process is essential for building systems that work in practice.
Step 5: Test in Stages
Build and test each component separately before integrating them. This makes it easier to identify accuracy problems and ensure each step works reliably.
Start with small datasets and simple versions of each component. Once the basic workflow is functioning, you can add complexity and scale up the data.
This staged approach prevents you from discovering fundamental problems late in the development process, when they're expensive to fix.
The Business Benefits of Process Breakdown
This methodical approach delivers significant advantages that extend well beyond technical success:
Faster Development Cycles: When AI components solve smaller, well-defined problems, they're quicker to build and test. Teams can iterate rapidly on individual components rather than rebuilding entire systems.
Higher Accuracy: Focused AI tasks consistently achieve better performance than monolithic models trying to solve complex end-to-end problems. This translates directly into more usable outputs.
Better Control and Monitoring: Modular systems make it easy to identify where problems occur and measure performance at each step. When something goes wrong, you can pinpoint the issue quickly rather than debugging a black box.
Improved ROI: Systems that actually work deliver measurable business value. The process breakdown approach significantly increases the likelihood that your AI investment will generate positive returns rather than becoming a costly experiment.
Easier Scaling: As your needs grow, you can upgrade individual components without rebuilding the entire system. This makes AI implementations more sustainable and adaptable over time.
Perhaps most importantly, this approach builds confidence among stakeholders. When business users understand what AI is doing at each step, they're more likely to trust and adopt the system. When engineers can measure and control individual components, they can make realistic commitments about performance and timelines.
Frequently Asked Questions
How do I know if my process is too complex for AI?
If you can't clearly articulate the steps a human expert follows, or if those steps require extensive background knowledge that's difficult to encode, the process may be too complex. Start by automating smaller portions of the workflow rather than attempting end-to-end automation.
What if domain experts can't explain their decision-making process?
This is common with highly experienced professionals who make intuitive decisions. Spend more time observing rather than interviewing. Ask them to think aloud while working, and focus on recent, specific examples rather than general processes.
How small should AI tasks be?
Small enough that you can easily create test datasets and measure accuracy. A good rule of thumb: if you can't manually verify the AI's output in a few minutes, the task is probably too large.
When should I use deterministic software instead of AI?
Use deterministic software for any task that can be solved with clear rules, even if those rules are complex. AI should handle tasks that require pattern recognition or subjective judgement that's difficult to encode as rules.
How do I handle tasks that seem to require both AI and deterministic approaches?
Break them down further. Most complex tasks can be decomposed into smaller steps that clearly fall into one category or the other. The key is spending enough time understanding the human process.
Building AI That Actually Works
The technology industry loves to focus on the latest AI capabilities and breakthrough announcements. But the most successful AI implementations aren't built on cutting-edge models. They're built on careful process analysis and systematic problem breakdown.
After working with dozens of businesses struggling with AI implementation, the pattern is clear: the projects that succeed are those that take time to understand human workflows before attempting to automate them. They use AI selectively, applying it only where it adds genuine value, and they complement it with reliable software for everything else.
This approach requires more upfront thinking, but it leads to systems that actually work in production. More importantly, it creates AI implementations that business users trust and adopt, rather than impressive demos that never deliver real value.
The next time you consider an AI project, resist the temptation to jump straight to model selection and training. Start with process breakdown. Spend time with domain experts. Understand the real workflow before you attempt to automate it.
Your AI project's success depends not on the sophistication of your models, but on the quality of your problem analysis. Get this foundation right, and you'll build AI that doesn't just work in theory, it works in the real world.
Ready to apply process breakdown to your AI projects? Our team at Artanis specialises in helping businesses implement AI solutions that actually deliver results. We work with domain experts to break down complex processes and build reliable, scalable AI systems. Get in touch below to discover how process breakdown can transform your AI initiatives.
