When it comes to developing artificial intelligence (AI) for mobile apps, many businesses assume that the process is similar to traditional software development. However, this assumption couldn't be further from the truth. In reality, AI projects require a unique approach, workflow, and success criteria that set them apart from traditional software development.

The Fundamental Differences

One of the primary differences between AI and traditional software development is the iterative nature of the process. AI projects follow an iterative framework, emphasizing discovery and adaptation, whereas traditional software development follows a linear execution path. This difference in approach has significant implications for how businesses approach AI development.

Iterative Exploration vs. Linear Execution

AI projects use an iterative workflow to develop models that can learn from data and improve over time. This process involves problem definition, data preparation, model development, evaluation, deployment, monitoring, and retraining. In contrast, traditional software development follows a linear workflow, where the focus is on building a single, standalone application.

Key Differences Between AI and Traditional Software Development

When it comes to developing AI for mobile apps, businesses need to understand that AI projects are more complex and require a different approach than traditional software development. Some of the key differences include:

  • Development Lifecycle: AI projects follow an iterative framework, emphasizing discovery and adaptation, whereas traditional software development follows a linear execution path.
  • Problem Definition: AI projects require a clear definition of the problem to be solved, including business objectives and key performance indicators (KPIs).
  • Data Preparation: AI projects require high-quality data that is relevant, diverse, and representative of the target audience.
  • Model Development: AI projects involve developing models that can learn from data and improve over time. This process requires expertise in machine learning, deep learning, and natural language processing.
  • Evaluation: AI projects require rigorous evaluation to ensure that the model performs well on unseen data and meets business objectives.
  • Deployment: AI projects require careful deployment to ensure that the model is used correctly and provides value to end-users.

Why AI Projects Demand Iteration

AI projects demand iteration because they involve complex problems, uncertain data, exploratory model development, and probabilistic outcomes. To succeed with AI projects, businesses need to prioritize data strategy over immediate model building, build cross-functional teams, embrace phased development, plan for model decay and drift, and continuously monitor performance.

Recommendations for Success

To ensure success with AI projects in mobile apps, businesses should:

  1. Prioritize Data Strategy: Focus on building robust data infrastructure and conducting pre-project audits to identify potential issues.
  2. Build Cross-Functional Teams: Assemble teams that include data engineers, data scientists/AI engineers, and domain experts to provide feedback and guidance.
  3. Embrace Phased Development: Start with a minimum viable product (MVP) and iterate incrementally based on feedback and performance metrics.
  4. Plan for Model Decay and Drift: Monitor model performance continuously and plan for retraining and updates to ensure that the model remains effective over time.

By understanding the key differences between AI and traditional software development, businesses can unlock the potential of AI in mobile apps and create innovative solutions that drive growth and revenue.