The Ultimate AI Strategy Guide

Due to the growing interest in AI among forward-looking companies, many CIOs are currently racking their brains to build an appropriate AI strategy plan in order to move closer towards their business objectives. Although there are plenty of solutions offered by AI vendors, enterprises are still missing information about what is coming.  The following guide, based on a report from Gartner, will help you follow the best path in the field of Artificial Intelligence.

Artificial Intelligence is not just meant for digital companies, but also for enterprises from any industry, including retail and consumer goods, manufacturing, banking and financial services or healthcare. There are quite a few reasons for companies to think about adopting an AI strategy now. These include, among many others, moving into new business opportunities and staying one step ahead of their competition. Nevertheless, a lack of qualified staff and data management tools are slowing enterprises down and forcing them to seek third-party solutions. To this respect, companies must keep an eye on the following recommendations:


  • Ensure data ownership. Owning your training data allows you to create, edit, modify, share and restrict access to the data. In some cases, companies are forced to renounce rights over their training data and processes granting these privileges to third-parties. Therefore, it is essential to be well-informed and to have total control over your data, as mentioned in our previous post The Power of Owning Your Training Data.

 

  • Leverage both human and machine intelligence. Human-in-the-loop, a branch of Artificial Intelligence, makes the most of humans and machines to create learning models. This yields a significant improvement in the training quality of dataset processes by bringing together the speed of a machine and the decision-making capacity of humans. In Bitext, we truly believe that the human component is essential for AI. That´s why our concept is primarily based on a combination of linguistic knowledge and computer science.

 

  • Rely on middleware tools. One of the main hurdles appears when it comes to separate between the two main pillars of AI: training and learning processes. A middleware tool, as its name implies, stays in the “middle” of both processes to make it easier for developers to focus on a specific application. In this respect, it's important to have platform-independent training data at disposal since it will make it easier for you to harmonize diverse AI projects, to exchange data with partners, and to have freedom of choice when seeking for alternatives. Check this post to know more about the so-called Platform Independence.

In Gartner’s report Clarify Strategy and Tactics for AI by Separating Training and Machine Learning, Bitext was also recognized as ‘the Future of NLP’ for being an emerging AI middleware provider for chatbot systems offering state-of-the-art NLP tools to make AI understand humans. Do you have the AI bug but you’re not sure how to proceed? Contact us without commitment, we are happy to assist you! Don’t forget that Artificial Intelligence is a window to the future. Are you ready to look through?

 

Bitext, NLP, AI

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