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Artificial Intelligence: How to Build A successful A.I. Strategy?

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Artificial Intelligence has gained its importance in almost all the industries over the past few years and most of the businesses have started using it. Despite the huge momentum it has gained, few businesses are yet to figure out how to use it in their business and implement properly devised strategies.

In this blog let’s take a look at some of the crucial points in building a successful AI strategy.

Determining how you want to leverage AI in your business

Before considering on implementing AI for your business you first need to determine the tools and technology you would want to consider and implement.

The options to choose ranges from platform providers like Google, AWS to niche AI technology providers.

Platform providers are now providing AI services for mass consumption and offer a wide range of AI factors like image and voice recognition, translation, and language analysis.

On the other end of the spectrum, there is also a rise in purpose-built technology providers that are focused on delivering AI-related technology for specific vertical or use-case.

It is important in determining and analysing as to which technology provider to choose to keep in mind the offering and strategies they provide that aligns with your desired AI application.

Identifying the areas where you can use AI

Companies might often consider implementing AI in their businesses but might be unsure of the areas to use.

The key to this issue is to weigh out options and priorities and frame your thinking on how could you best utilize them and the possible resources that might be needed in implementing them.

These ideas will become your primary factors in considering your AI-driven optimizations.

Look for opportunities that could streamline your existing workflows and work patterns, or better yet, eliminate them altogether.

Be clear and concise on the level of autonomy you want your AI solution to have

Fix a clear autonomy level for every AI deployment. A popular thinking in AI is that the more you give to AI, the more value you derive. However, this isn’t always the case.

Implementing AI is a balancing act of value versus risk management; where if your AI fails, it will undoubtedly cause friction in your workflow.

The more autonomy you give to AI, the more friction it would create when it makes a mistake. For the first time users, it is suggested to use AI on a smaller scale and then scale it up as it becomes successful and gains popularity among users.

Identifying and analysing the existing data and the data expected for your AI to be successful.

For any business or industry, data is the king. But in AI, data becomes a critical and strategic component to the outcome we desire. Like humans, AI to needs the ability to learn to become knowledgeable and provide value. For example, to teach an AI solution to discern cats and dogs, it has to be shown a lot of pictures labelled ‘cats’ and ‘dogs’ to be trained.

The quality of the data fed into these ai determines the quality of value it provides. So based on the AI initiatives you’ve identified for your business you need to validate that you have the data you can teach the AI to learn how to accomplish these tasks.

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