According to a recent survey from SADA Systems of IT professionals at large companies, artificial intelligence (AI) and internet of things (IoT) are the primary areas of focus for enterprise investments in new tech in 2018. Of the 500 IT professionals surveyed, 38% claimed that AI was the primary focus of emerging tech projects, with IoT and blockchain coming in at 31% and 10%, respectively.
IoT connected devices often generate the dizzying amounts of data necessary to train machine learning models. Of companies surveyed, more have IoT workflows already in production than AI. This is because a stable IoT and edge computing foundation are often prerequisites for enterprises to break ground on a machine learning model in the first place, though recent cloud PaaS rollouts of pre-built, adaptable ML models are shaking up the landscape for development.
A study by Vanson Bourne (via Forbes) details that enterprises are primarily investing in AI to improve customer experiences and drive revenue through product innovation. But roadblocks to successful implementation are still aplenty.
Breaking Down Barriers To AI Investment
The question that opens an enterprise’s wallet for AI investment is not just, “How can we implement a machine learning model?” Instead, it’s, “How can we implement a machine learning model that adds value to our bottom line?” Failing to answer this question can lead to wasted dollars and dead-end projects.
Although AI leads the way in new tech investment, there are still plenty of pitfalls for enterprise implementation. In a CIO article, Chris Curran notes that issues with leadership, alignment with business goals and the lack AI-skilled engineers can cause projects to stop dead in their tracks. Companies need to make sure that leadership understands the business case for investment and that a specific department leader spearheads AI development rather than letting small, fractured AI projects flare out across departments.