Leveraging AI For Product and Company Growth

How Does AI Fit into a Business Model?


What Are AI Project Components?

  • Type of company: If you’re an enterprise-level, customer-facing business, your AI model might look a lot different from a smaller business using AI automation to facilitate in-house teams. Solutions can vary depending on your company’s B2B or B2C purpose, but be assured, AI models can adapt to just about any situation.
  • Stage of company: Startups and enterprises will have different plans of action and various resources for implementation. Companies with resources for large data science teams will operate differently than organizations without them. Also, the company challenge varies depending on whether you’re in seed series, series C, or something later.
  • Users: A critical part of implementation is knowing who will use your product. Small in-house user bases need a different model than larger, customer-facing solutions. This could determine how best to create, deploy, and store your AI solution. Think not only processing power, but whether you’ll use on-premises or cloud solutions for deployment. Consider who your users are, how AI can solve their problems, and how you’ll know it’s working.
  • Data: The data available to you will determine a lot of your AI as well. Those of you with large amounts of unstructured data for processing could find deep learning solutions more applicable. If you’ve got a considerable need for interaction with customers on the human level, that could also determine the types of AI solutions you’re building.
  • Models and techniques: Finally, the most effective techniques will involve answering questions about the previous and finding techniques that best address those issues. Your data science team can’t just build the coolest thing. They must understand the business needs and apply those techniques to specific KPIs.

How Do Effective AI Projects Work?



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