AI is being used in other ways in the enterprise as well, to do things like improve the efficiency of the supply chain, facilitate customer interactions, and help employees perform office tasks. Albemarle has been using AI as a virtual assistant since the recent pandemic lockdowns. “We were a little ahead of the game, mainly out of necessity,” says Thompson. “The pandemic forced us to find ways of self-servicing 7,000 employees at home.”
The self-service chatbot developed at Albemarle evolved into a tool to help with other corporate functions, which then developed into a virtual personal assistant that manages federated workflows, making it easier for employees to work with several systems at once without having to log into all of them. An employee, for instance, can participate in workflows and make inquiries by just communicating with the bot using natural language, and the bot interfaces with the enterprise business systems.
Patrick Thompson, former CIO and digital transformation officer at Albemarle
Albemarle
But in a few short months, generative AI is beginning to take traditional AI to another level for applications like predictive maintenance. “Interactions become more conversational so you can ask questions and get different insights about the state of equipment,” says Thompson. “It can be used to curate internal and external industry data that’s then used to train traditional algorithms to deliver agile results.”
Moreover, generative AI offers an entry point for companies in sectors yet to use traditional AI. Sectors, such as finance, where most companies began developing data platforms years ago to use with analytical tools, are now experimenting with the newest AI technology using the same platforms.
“Generative AI can also be used to parse publicly available data on markets and companies to help make investment decisions,” says Chris Herringshaw, global CIO of Janus Henderson, the British-American global asset management group. “Rather than spend a lot of time manually researching all of that information, we want to use generative AI to summarize what’s out there, tell us where the signal in the noise is, and suggest areas for us to look into.”
The challenges and rewards of early adoption
Aside from a lack of maturity of the underlying technology, several other obstacles need to be overcome before enterprises further embrace generative AI. The first challenge is the lack of skills both in-house and among vendors that sell traditional applications.
