How not to design your AI Apps
Are you experiencing poor performance, scalability issues, ethical concerns, inadequate user experience, or failures in your AI applications? These may be the underlying causes preventing you from achieving your desired outcomes.
Data & Training
Data Hoarding:
Collecting massive amounts of data “just in case,” without a clear purpose, leads to storage costs, privacy risks, and unmanageable datasets.
Example: A retail company stores terabytes of transaction logs “for future AI use” but doesn’t clean or label them. When they finally start training a recommendation system, 40% of the data is corrupted or duplicated, causing extra storage costs and unusable models.
Fix: Establish a data strategy – only collect and store what has a defined use case. Implement retention policies and data cataloging.
Garbage In, Garbage Out (GIGO)
Using low-quality, biased, or unrepresentative data without cleaning or validation.
Example: A bank trains a credit risk model on old customer data but doesn’t remove records of fraudulent or inactive accounts. The model rejects good customers and passes bad ones because it learned from flawed data.
Fix: Build a data quality pipeline – automated validation, deduplication, and bias checks before training.
Single Dataset Dependency
Relying solely on one dataset (often from one source) and assuming the model will generalize.
Example: A self-driving car company tests its AI only in sunny California. When deployed in New York during winter, the perception system struggles with snow and fog, causing near-misses.
Fix: Diversify data sources, use synthetic or augmented datasets, and cross-validate across different environments.
Overfitting by Design
Building models that perform exceptionally on training data but fail in real-world environments.
Example: A hedge fund creates a trading algorithm that perfectly predicts past market moves (98% accuracy) by overfitting historical data. When deployed live, it loses millions because real market patterns differ.
Fix: Use cross-validation, regularization (dropout/L1/L2), and test on unseen holdout sets to ensure generalization.
Feature Leakage
Accidentally including future or target-related information during training, making the model unrealistically accurate.
Example: A hospital AI predicts patient mortality using lab data, accidentally including variables like “time of discharge,” which only appear after a patient outcome is known. The model looks perfect but is useless in live settings.
Fix: Strictly separate training vs. prediction-time features; validate with feature importance and leakage detection tools.
Model Development
Model Worship
Over-prioritizing state-of-the-art or large models (GPTs, Transformers) without considering simpler, cheaper, and equally effective solutions.
Example: A startup uses GPT-4 to classify customer emails (a trivial task) instead of a small logistic regression model. Their cloud bill spikes to $50k/month for something solvable with a $50/month solution.
Fix: Start with simple baselines (logistic regression, decision trees). Only move to advanced models if they outperform and justify costs.
Black Box Dependency
Deploying opaque models without interpretability tools or explainability checks, especially in regulated industries.
Example: A bank deploys a deep neural network for loan approvals. When regulators ask why a customer was rejected, the bank can’t explain, facing fines and forced model shutdown.
Fix: Use explainability tools (SHAP, LIME) and ensure transparent reporting for regulated industries.
Ignore the Baseline
Skipping simple benchmarks (rule-based or linear models) and jumping straight to complex AI.
Example: A logistics firm spends months building a complex route optimization AI. Later, a simple rule-based script outperforms it because most deliveries follow predictable, fixed routes.
Fix: Always create a benchmark solution (manual rules or heuristics) as a baseline to validate the value of complex AI.
Over-Optimization for Metrics
Chasing accuracy, F1, or AUC while ignoring real business impact (latency, cost, customer experience).
Example: An e-commerce company tunes its AI for 99% accuracy in detecting fraudulent orders, but latency grows to 5 seconds per transaction, frustrating customers and reducing sales.
Fix: Align model optimization with business KPIs (e.g., revenue, churn reduction), not just accuracy or F1.
Static Models in Dynamic Environments
Failing to retrain or adapt models as data drifts (concept drift).
Example: A social media platform’s recommendation model isn’t retrained for 6 months. User behavior changes, engagement drops 30%, and new trends aren’t surfaced.
Fix: Implement model monitoring & automated retraining triggered by drift detection (data or concept drift).
Deployment & Operations
Glue Code Hell
Stitching AI components together with ad-hoc scripts instead of proper pipelines (MLOps).
Example: A team connects data pipelines, training scripts, and APIs with dozens of cron jobs and Python scripts. When one script fails, the entire ML system breaks, and no one knows where.
Fix: Use MLOps frameworks (MLflow, Kubeflow, Airflow) for standardized, maintainable pipelines.
Manual Retraining
Retraining models manually every few months rather than automating retraining and monitoring.
Example: A fraud detection system is manually retrained every quarter. By the time it updates, fraudsters have changed tactics, causing financial losses.
Fix: Automate retraining using CI/CD for ML with scheduled jobs, validation tests, and canary deployments.
“Train and Forget”
No monitoring for accuracy decay, drift, or bias post-deployment.
Example: A speech-to-text model is deployed for call centers but not monitored. Over time, accuracy drops because new slang and accents emerge, and customers start complaining.
Fix: Implement model monitoring dashboards for drift, accuracy, and latency, with alerts for retraining.
Ignoring Infrastructure Costs
Deploying huge models without considering inference costs, scaling, or latency constraints.
Example: A company uses a 175B parameter LLM for chat support without optimizing. The inference cost hits $300k/month, exceeding the value the bot delivers.
Fix: Optimize inference via quantization, pruning, caching, or smaller models; estimate ROI before deploying large models.
Shadow AI
Allowing teams to use AI models without proper governance, versioning, or security review.
Example: A marketing team uses ChatGPT plugins with customer data without IT oversight. Sensitive customer details get leaked into external logs, violating GDPR.
Fix: Create a central AI governance policy – approval workflows, versioning, and security audits for all AI tools.
Organizational & Process
AI for AI’s Sake
Building AI because it’s “cool” rather than because it solves a validated business problem.
Example: A restaurant chain builds an AI-driven menu recommendation engine, but customers already order the same 5 dishes. The project costs $2M and delivers no measurable ROI.
Fix: Validate projects with clear business cases (ROI, KPIs) before investing in development.
POC Trap
Getting stuck in endless proof-of-concepts without productionizing.
Example: A telecom company runs 12 AI POCs (churn prediction, chatbot, network optimization) but never integrates them into live systems due to lack of a production strategy.
Fix: Establish a roadmap for productionization – include deployment and scaling plans from day one.
Lack of Cross-Disciplinary Collaboration
Isolating data scientists from engineers, domain experts, or end-users.
Example: Data scientists design a medical diagnosis AI without involving doctors. The output is clinically irrelevant, and hospitals refuse to adopt it.
Fix: Use agile, cross-functional teams (data scientists, engineers, domain experts) for all AI projects.
Ignoring Regulatory/Ethical Risks
Not accounting for privacy (GDPR), bias, or explainability requirements.
Example: A recruitment AI filters candidates but is later found biased against women due to historical hiring data. The company faces lawsuits and PR damage.
Fix: Build compliance checks (bias testing, explainability, GDPR/CCPA reviews) into the AI lifecycle.
Underestimating Change Management
Rolling out AI without preparing teams, processes, or customers for adoption.
Example: A factory deploys an AI scheduling system but doesn’t train supervisors. Workers override the system manually, nullifying its benefits.
Fix: Include training and adoption plans for end-users, with feedback loops and gradual rollout.
Generative AI Specific
Prompt Engineering Obsession
Spending excessive time tweaking prompts rather than structuring proper retrieval-augmented pipelines or fine-tuning.
Example: A support team spends weeks crafting complex prompts to get GPT-4 to answer FAQs, instead of building a retrieval-augmented system that uses their knowledge base.
Fix: Use retrieval-augmented generation (RAG) or fine-tuning to reduce manual prompt tweaking.
Hallucination Blindness
Not validating AI outputs, assuming LLM responses are always factual.
Example: A law firm uses an LLM to draft legal briefs without human review. The AI fabricates case citations, leading to court sanctions.
Fix: Add grounding (knowledge bases, APIs) and human-in-the-loop validation for critical outputs.
Overuse of LLMs
Using large language models for tasks that could be done with deterministic systems or small models.
Example: A logistics startup uses GPT-4 to parse shipment data, costing thousands per month. A basic regex-based parser would have done the job for free.
Fix: Use fit-for-purpose models – small, open-source models or traditional systems for structured tasks.
No Guardrails
Failing to use moderation, grounding, or safety layers, leading to reputational and compliance risks.
Example: A bank launches a customer-facing chatbot powered by GPT-4. Without moderation, the bot gives financial advice that violates regulations, leading to fines.
Fix: Implement content filters, guardrails, and policy-based moderation before user-facing deployment.
AI is the buzzword. Everyone wants in. But in the rush to launch the next “smart” app, too many teams skip the thinking part—and build digital disasters instead.
From bots that don’t understand basic questions to AI features no one asked for, the tech world is full of apps that confuse, mislead, or outright break trust. The problem? It’s rarely the AI model—it’s the design, the assumptions, and the lack of human-centered thinking.
By learning from real-world mistakes—and committing to better design principles—we can build AI systems that are not only intelligent, but ethical, useful, and trustworthy.