Getting Started with AI

While there are a lot of real time business cases for AI and its influence across industries, it is important to find the most important business case that is a differentiator for your organization.

Step1: Defining AI Objectives

Each company is at its own maturity in their AI adaptation. Depending on the current maturity and data science background, we need to determine an AI objective.

At this stage -

  • Determine if and how machine learning can help solve your specific business challenge.
  • Select the use case you’d like to analyze, describe the business problem in detail and define the desired results.
  • Get a broader idea by understanding how competing companies are implementing ML&AI
  • Explore how your data can be used to solve the business cases, assess opportunities and risks associated with the project

Step2: Specify the next step for Execution

Once the objectives are defined and clarity achieved, we need to determine the best approach for delivering it.

At this stage -

  • Understand the potential results the project would deliver. Develop baseline models and prepare an implementation timeline.
  • Determine what is more important – cost and time or the differentiation?
  • Verify technology stack and identify the tools and technologies that are right for you
  • If there is a skeleton of machine learning system already in place, understand the changes needed for customization

Step3: Prototype Development

It’s time to build a prototype of the model and deploy it in a test environment for a quantitative comparison with real time existing data.

The prototype will give more details about the technical approach and machine learning methods used in the solution. Even more, the prototype will help management experience machine learning in real time.

Step4: From Prototype to Live

Once verified of the models effectiveness, we then work towards a production version of the solution. The solution may be stand-alonish or may need integrating to an existing system or platform depending on the environment. The final solution is continuosly monitored for its effectiveness and accuracy in the changing environment.