2018 predictions for artificial Intelligence – Part 2

A remarkably complex technology, Artificial intelligence is advancing quickly. Surpassing the predictions that were made a decade ago, it’s reaching new heights in various domains. Although it is impossible to give a precise vision of how the future of AI will unfold, let’s take a look at some of the specific predictions about AI trends for 2018 and key implications.

AI will help answer the big question about data

Organizations are now able to take advantage of new tools and technical advancements, including easier methods for mining less-structured data, including natural language processing for text indexing and classification, enterprise application suites that incorporate more AI, emerging data lake-as-a-service platforms, public clouds that can take advantage of different kinds of data, automated machine learning and data management. With AI and other technologies offering value propositions that require it, that’s how many organizations will ultimately reform data architecture and governance. The enterprises that have already addressed data governance for one application will be on their way to developing best practices for effectively leveraging their data resources and working across organizational boundaries. Vendors are increasingly taking public sources of data, organizing it into data lakes, and preparing it for AI to use.

Functional specialists, not techies, will decide the AI talent race

AI will require knowledge and skill sets that data scientists and AI specialists usually lack as it spreads into more specific areas. As AI leaves the computer lab and enters everyday work processes, the functional specialists like retail analysts, engineers, accountants, and many other domain experts who know how to prepare and contextualize data, will be even more important than computer scientists. Enterprises that intend to take full advantage of AI should move to provide functional specialists with AI literacy. Companies must find ways to assess the skills of high-potential learners and put them on individual learning paths, to make them learn the basics of data science and how to think like an AI application.