Dos and Don’ts for Managing AI Software Development

The popularity of artificial intelligence is skyrocketing, as is the demand for data scientists. According to a LinkedIn research from August 2018, there was a scarcity of over 150,000 workers with data science capabilities in the United States alone, and this demand is expected to expand by about 30% by 2020, according to IBM.

It's not unexpected that organisations looking to implement AI software development

often enlist the expertise of external agencies who specialise in this type of service to reduce risk. In this article, we'll look at the problems of assembling a data science team and how to overcome them so that outsourced AI initiatives succeed.

Preparation Phase

The roots of AI software development project success are often laid before the start of the project, during the planning phase. At this point, it's critical to prepare for AI adoption, properly manage the process, and eliminate as many potential hazards as possible.

Small Initiatives: Begin with the smallest budget feasible. It takes time, effort, and money to implement a company-wide AI strategy. It's pointless to construct a sophisticated product from the ground up without first verifying the models' efficacy. Instead, pick one business segment to test AI software development in and begin with a proof-of-concept to validate the notion.

Determine goals: Determine what you want to accomplish with AI, as well as your success and failure criteria. A solid outsourced AI software development company should be able to help you design these goals, both the major aim and the small victories, and track their progress during the development process.

Development Phase 

It's time to start developing after everything has been planned, the strategy has been developed, the data has been supplied, and the team is ready to go. It's now that your outsourced AI software development team is working on a proof-of-concept that will assist you to validate all of your initial assumptions about incorporating AI into your processes.

Share and Learn 

Make every effort to respond to any questions that may arise. Share everything that you know and they need about how you currently tackle the problem they're attempting to solve with AI software development, as well as what they should keep in mind at certain phases.

Do you have any doubts about whether or not the project is on track? Share your worries with the team. 

Iterate: Aside from the ability to check assumptions as quickly and cheaply as possible, one of the reasons behind the "start small" strategy is that you can easily iterate. Working in Scrum allows you to examine the development results at the end of each sprint. Furthermore, working on a solution may generate other fresh ideas. Attend the demos, keep an eye on the progress, and talk about the ideas that will emerge.

Finishing Phase 

It's normally time to discuss the results and consider the future steps at the end of the development period. At this point, you should receive some recommendations for future models and implementation of AI software development in your firm.

Keep Patience: When compared to constructing a big product, starting small and validating your assumptions in a tiny portion of your organisation with a proof-of-concept can yield quick results. Even if you decide to optimise just a small component of your process, the models will take some time to analyse the data, learn, and start producing the intended outcomes.

The Bigger Picture: It is at this point that you must consider how to scale it in the most efficient manner. Should you provide the models with extra information about more items or users from different locations? You can test additional assumptions after you know what models of AI software development can truly achieve.

So, are you ready for the implementation of AI software development?