The traction over the last few years in the artificial intelligence (AI) and machine learning (ML) space is remarkable, and I’m not just talking about consumer-based products like self-driving cars, or virtual assistants like Google Assistant, Alexa, or Siri. While those products get the headlines, AI/ML is rapidly spreading across the enterprise IT space. I feel like I can’t go a day without a company mentioning AI or ML as part of their product or forward-looking strategy. It’s not just for crazy, sci-fi predictive analytics projects in a bunker somewhere. While that definitely still happens, AI and ML (and deep learning too) are being used across all aspects of IT: big data, cloud, IoT, security, infrastructure, systems management, etc.While AI/ML is a top priority for businesses that expect it to have a significant (positive) business impact as they continue to digitally transform, investments remain modest because of its sheer impact on all aspects of the infrastructure. Challenges associated with infrastructure cost, lack of in-house expertise, and insufficient data quality are just the start.
AI/ML relies on many aspects of the technology stack—from physical hardware like storage, compute, and networking to software that handles governance, security, and compliance. For an effective, efficient, and reliable AI/ML implementation, all aspects of the stack should be in sync, but in most organizations, they simply are not. In fact, when asked which aspect of the stack was the weakest link in terms of being able to deliver an AI/ML environment, governance and security/compliance are currently viewed as the weakest. Big data platforms are also viewed as weak links, in big part due to the number of tools required to satisfy requirements. ESG research shows that a majority of organizations leverage at least three different tools to train, develop, test, tune, deploy, run, and/or manage machine learning models.
Of those that have adopted these technologies, who on the inside is pushing organizations to adopt them, and who is farthest along in using AI/ML? What are the common use cases for current and planned AI/ML deployments? Our research shows that senior IT executives are most responsible for defining AI/ML initiatives and strategies, while IT is far and away the farthest along in terms of a line of business currently using or planning to use the technologies. That aligns well to the most common AI/ML use case today: improving operational efficiency, with additional use cases such as risk reduction, improving security, customer insights, and prediction rounding out the top five.
I compare the adoption of AI and ML in enterprise IT to what flash storage has done to the storage market, but on a much larger scale. If you aren’t aboard the AI/ML rocket ship, figure out a way to get on it — both as a customer relying on it and as a vendor providing technology to enable it. It will change the way we think about IT, and it will change the way IT operates.