Artificial intelligence and machine learning will automate many business and life tasks, from driving trucks to piloting ships to handling customer calls — and actually carrying on rudimentary chats with them. What’s not discussed often enough, however, is the actually impact on the jobs of AI creators and administrators themselves — developers, analysts, and data administrators and everyone else in the information technology orbit charged with building out these revolutionary systems. In essence, AI will play a role in helping to smooth out the rough spots of AI development.
IT and data professionals have much to gain from the AI revolution. I recently had the chance to explore some of the possibilities with leading industry observers, who see the roles of IT managers and professionals being elevated to greater business responsibilities as a result of being relieved much of the grunt work of AI.
The ability to manage the data going into enterprise decisions is the next great frontier for AI and machine-learning. Already, “self-managing infrastructure is relatively common. We see this in both infrastructure as a service and platform as a service,” says Jerry Overton, head of AI at DXC Technology. “Self-managing data stores are still in their infancy.”
At this point, “autonomous data management is still mostly research concepts and not fully mature,” he continues. Ultimately, “autonomous data management will evolve in a manner similar to natural language recognition. It is now common to see machines properly interpret spoken language based on context and usage. The same is true for databases. We will eventually begin to see databases provide the same interpretations of data. Still, that day is still somewhat far off.”
The use case for AI and machine learning is “anything that speeds up the time it takes to go from raw to redefined data,” says Overton. “This includes automatically masking data, automatically tagging and matching data, and automatically migrating and contextualizing data.”
As it progresses, AI and ML is first helping to untangle the low-level data problems that slow down the march toward data-driven decision-making. These technologies are helping “drive significant improvements to data quality in various ways, including: automated data cleansing, de-duplication, and data augmentation,” says Alan Jacobson, chief data and analytics officer at Alteryx, In the process, “routine and repetitive tasks will likely be replaced with automation, and IT professionals will be able to get to the higher value work of optimizing the servers to a higher degree, focusing on the human side of security and access management. They will no longer be a slave to the scheduled maintenance patterns that fill so much of the day.”
AI may be relegating a lot of grunt work to machines, but that doesn’t mean human professionals are cut out of the process. In fact, greater automation increases the need for collaboration across technology ranks, “Many tasks are being automated, plus we’re seeing more self-service by other data professionals like data scientists and developers who are doing their own provisioning,” says John Murphy, senior VP with EnterpriseDB. “The intent is to move more quickly while providing access to greater amounts of data. It used to be that you knew which data was interesting — now you don’t, so people want access to all the data. There is both a challenge and opportunity to be educated – the DBA and data analysts, scientists, developers need to know more about the other.”
This means IT professionals need to stay engaged — and we need more professionals to get involved with AI and machine learning. While technology can automate much of the grunt work, human oversight is still needed. “Even after the algorithm is implemented, it must be monitored and adjusted over time with expertise,” says Jacobson. “Significant risk can occur when the implementation is led by someone that does not have strong business or domain knowledge or doesn’t fully understand how AI and ML work.”
Even in the best of all worlds, “many solutions never make it to production, as they can’t fit into the existing process, or require changes that the business can’t make,” Jacobson says. Or, “poor models that are not doing what the creator intended can occur. These risks are no different than any other technology implementation and can be solved with the right people, experience and training in place.”
