Decoding Artificial Intelligence for Businesses

A great TAG session at Cox Enterprises! The Artificial Intelligence experts in the Metro Atlanta area successfully deciphered AI for the un-initiated and the worried. 

Here are some takeaways for those rearing to go commercial with AI:

Collection of customer data over the last 10-15 years, GPU-accelerated computing, and mathematics advances have led to the current hype around Artificial Intelligence. AI is an amalgam of pattern recognition, object recognition, and Natural Language Processing. There are hundreds of AI pilots underway, but most are in guinea pig state. Fears that if enterprises do not leap into AI, they will become dinosaurs are exaggerated. 

Crucially enterprises should determine the following criteria when deciding on AI.

Which function/customer need are they aiming to replace?

Does AI show real precedents in the industry/domain? What is the state-of-the-art technology in AI which is relevant to your industry?

Do you have the requisite raw data? Are you prepared to buy it for Al's purposes?

Which process will AI scale-up, optimize or make more productive?

This research is crucial to ROI as the adoption curve in AI is a minimum of three years. 

Sectors getting VC attention are finance and healthcare. 

Machine Learning and AI will be crucial to data security in finance. But currently, data is overwhelming AI. AI processes are far from state-of-the-art. Still, an eventual AI revolution is inevitable as AI can chase scenarios, speed up reactions and throw up multiple possibilities of causation and correlations. 

Most enterprises will jump in at the early and late majority stages. 

Examples where we already encounter AI are Amazon and Netflix recommendations. In the credit score industry, mortgage AI will soar, but later. 

Conclusion: Strategic potential of AI is immense. Enterprises need to strategize. Budgets should cater to implementation and validation and the cost of acquiring talent. Businesses should train stakeholders to think about breaking down functions to optimize eventual AI adaption.