All areas of IT must brace for AI impact
In other words, just because artificial intelligence tools can provide answers doesn’t mean you should use them. If good old business intelligence tools do the job just fine, stick with what you know. But AI is a great way to uncover information hidden within vast amounts of data — as long as you’re willing to use the information that surprises you, according to Jana Eggers, CEO of Nara Logics, a synaptic intelligence company based in Cambridge, Mass.
“If you aren’t willing to learn, don’t do an AI project. Do a regular analytics project,” Eggers said during her presentation at the TDWI Accelerate conference in Boston earlier this year.
That’s sound advice in a time when all we hear about is the power and promise of AI technologies like cognitive computing, natural language processing and machine learning. Using AI judiciously can save companies a whole lot of time and money on a tech that’s exciting but may not be appropriate for the job. It’s also important to carefully consider where and when to use AI because artificial intelligence affects nearly all areas of IT, along with the people, processes and corporate culture underlying the business.
AI technologies require vast amounts of data, collected from sensors, applications or the mobile devices in users’ hands. Collecting all that data to feed AI systems requires a tremendous amount of storage — so much so that companies are moving their data warehouses to the cloud. Companies also need staff members with data science skills to make sense of the data, developers who know how to work with AI — the list goes on.
The articles in this guide provide deep insight into the dos and don’ts of AI, how AI affects the data center and IT staffing, tips for figuring out the ROI of AI, and more.
While cognitive computing tools have come far in utility, they still have some important gaps. Understanding these hitches may be key to getting value from the platforms.
Experienced users of machine learning tools share how their organizations are using the technology to solve a variety of analytics problems in their businesses and for customers.
Technology has progressed to the point where even non-technical people can do complex jobs. Artificial intelligence can learn to code. Where does that leave software development?
For all the intense interest in AI investments, the ROI for AI is not well-understood. Rather than make bad guesses, CIOs should treat AI projects like venture capital investments.
For AI applications, the future is now. But implementing artificial intelligence in an enterprise data center presents obstacles for network, storage and compute infrastructures.
End users are in the crosshairs of business data privacy issues, especially when it comes to information gleaned from artificial intelligence technologies.
As IT pros experiment with AI, many will do so on the public cloud. But choosing from an ever-growing list of AI services, from AWS, Azure and others, is no easy task.