4 AI Trends that will Define the Future of Data Science

Prepare your AI ecosystem to match with the data challenges of the future

Companies across the world are increasingly adopting AI for their smooth business operations. The technology unleashed its constructive potential during the onset of COVID-19 in performing a wide range of tasks that are complex and cumbersome for humans, bolstering employee productivity. Right from managing tasks ranging from planning, envisaging, and predictive maintenance to customer service chatbots, aiding data analytics, and more, businesses are extracting the maximum out of this disruptive technology.  

Rapid Spike In AI Applications

AI is one of the most revolutionizing technologies of our time. The current surge in AI research and investment has resulted in an incredible rise in AI applications. These applications do not just promise to yield better business outcomes but enhance the human experience as a whole. The technology is currently being applied for a wide array of industries ranging from healthcare, retail, and banking, to logistics, and transportation. While these industries are using AI to automate their processes and sort out their analytics processes,  it is now time to think about the future possibilities with artificial intelligence.  

The Future Of AI Ecosystems

The rate at which technology is developing is beyond measure and the same is the case with how industries are taking advantage of it in terms of managing data. The road AI is heading towards features a vast AI ecosystem with several models and new dependencies. The tech world will witness new approaches to skills, governance, and machine learning engineering where data scientists and software engineers will collaborate to leverage machine learning. So, what should organizations expect in the future? After all, the success of an organization’s AI adoption will depend on how they master the complexity of altering their business processes to accommodate the new change. Here are the four AI trends organizations should bear in mind.   1. Upgrade first, create later. Instead of being in a hurry to create an AI model, optimize and update the existing models that are put in place. As every industry’s challenges and data requirements are different, AI models should be upgraded to suit the domain specifications and for that, data scientists with experience in the specific industry and scientific techniques should be on your radar.   2. Transfer learning will scale NLP Natural language processing will witness a massive growth in adoption along with increased potential due to transfer learning. Knowledge obtained after solving a problem will be stored and automatically applied to related problems, saving time for newer applications.   3. Governance will come crucial As newer predictive models will flood the markets, managing them all will become difficult. Only with proper governance, frameworks, and guidelines, organizations can govern the machine-generated data. Proper governance should follow all the ethical standards, which is why organizations should relook the roles and responsibilities of data scientists.   4. Polish Existing Talent As AI advances, organizations would want to look for greater AI literacy and awareness at all levels. As the business world is getting more data-driven, organizations will only be able to make the most of the technology if all the employees understand at least the basics of AI and data science. Hiring new talent altogether for this purpose will be tedious, hence organizations should train and polish the skills of the existing employees and prepare them with the fundamentals of what is essential, AI and data science. AI has already made tremendous strides when it comes to leveraging data science and automation. The algorithms will only become more complex and exceed human abilities in the foreseeable future. There to manage these advances, organizations should start preparing and strategizing now before it’s too late to catch up.