Currently, every type and size of organizations are implementing artificial intelligence. According to the Gartner report, “70% of organizations will integrate Artificial Intelligence technology to assist employees’ productivity by 2021.”

AI provides more utility than merely automating processes. Speaking about digital marketing, AI helps brands to achieve personalization and engagement to drive revenue and loyalty in the current consumer environment.

From product recommendations to customized content, chatbots, dynamic pricing, AI has changed marketers’ game. According to the McKinsey study, 44% of companies reduced operational costs and increased business revenue by using AI in marketing.

Now, the question is –  how can you put AI technology to work on your project? Well, there are no shortcuts for AI success. So, we’ve jotted down a list of a few essential tips for setting the AI project for success.


Here’re some tips to Set Up AI project up for success

  1.  Formulate an executive strategy

Before thinking about AI, a few questions must be answered by a variety of crucial company executives:

  • Do they want to disrupt their respective marketplaces by developing a different type of strategy or value proposition?
  • Do they seek to be the best among their competitors? If the aim is to stay level on a competitive market or even catch up to the current leader.

Before your first AI project is set in motion, it is mandatory to answer all the above questions. If it is not followed, then the operational teams will be left out aimlessly to dig through data and look for a story to tell. The data is fun and interesting. It serves no purpose on its own. Starting with clear goals creates only solutions in search of a problem.


  1. Identifying your AI Use Cases

The best way to link AI strategy to business strategy is to ensure AI delivers maximum value for the business. This is an initial step that involves looking at what business is trying to achieve and what unique challenges your business is facing; all you need to do is recognize potential solutions through AI technology.

Any leading AI development company can help your first project with this process. Don’t limit yourself to a limited number of use cases at this stage since you will only be narrowing down your options in the second step. The goal should be to explore many ways in which AI could help your organization to achieve its key strategic objectives.


  1. Working out AI priorities

After identifying a few important AI use cases in the previous phase, trying to embark on too many projects at once can be a spell disaster. That’s why you need to rank your use cases to their strategic importance to the business. The goals should be:

  • The AI use cases represent your business’s most significant opportunities or help solve the most primary business challenges. If you are a small business owner, you may only want to focus on one key AI priority at a time.
  • Smaller AI projects are relatively quick, easy, and inexpensive to implement. To identify “quick wins” can help you demonstrate the value of AI, win people over, and show the seeds for more significant AI projects.


  1. Recognize the Internal Capacity Gap

 There’s a fine line between what you need to accomplish and what you have the organizational ability to actually do within a given time frame.


A business should know what it’s capable of and what it’s not from a tech and business process perspective before launching into a full-blown AI implementation.”


~ Tang, General Manager of TechCode’s Global AI + Accelerator program.


He also added, “Sometimes this can take a long time to do”. Addressing the internal capability gap means identifying what you need to acquire and processes that need to be integrally evolved before getting going.

There must be some existing projects or teams that can help do this organically for specific business units.


  1. Start Small at a Time

All you need to do is apply AI to a small sample of your data rather than taking too much too soon. Start with short and straightforward, use AI incrementally to prove value, collect feedback, and then expand accordingly.


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