How to accelerate Artificial Intelligence (AI): 9 tips
Artificial Intelligence (AI) has moved from “when will we do it?” to “how will we speed it up?” in many organizations.
AI passed some important tests during the pandemic, says David Tareen, director of AI and analytics at SAS. “The pandemic put AI and chatbots in place to answer a flood of pandemic-related questions. Computer vision supported social distancing efforts. Machine learning models have become indispensable for modeling the effects of the reopening process.”
But the future upside of AI is still considerable. “Artificial intelligence is designed to reveal what you can’t see due to the sheer volume of data that is available,” says Josh Perkins, field CTO at digital platform company AHEAD. “If there’s one reason IT leaders should accelerate the broader adoption of AI, it’s the ability to uncover opportunities that generate real business value through insights and efficiencies where perhaps there were none.”
That puts pressure on IT teams to deliver and work harder to overcome the challenges that exist in scaling the implementation and adoption of AI in the enterprise.
We asked AI experts for tips on actions IT leaders can take to accelerate AI use and maturity in their organizations.
“Often, leaders do not know where to begin or bite off more than they can chew,” says Peter A. High, author of Getting to Nimble: How to Transform Your Company into a Digital Leader and president of the technology and business advisory firm Metis Strategy.
“AI and machine learning efforts are best directed at specific use cases, and it may require engaging a broader ecosystem to bring it to life, especially if you have a paucity of AI and ML talent.” Finding great use cases, partnering with business leaders to bring them to life, and engaging with a broader ecosystem for insight, talent, and technology helps, High says.
“One overlooked challenge with AI initiatives is the time commitment required before tangible results can be delivered,” says Ravi Rajan, head of data science at cyber insurance company Cowbell Cyber. “Without clear goals and planned milestones to show progress, AI projects can rapidly turn into discovery.”
What can you train your team to do internally? Where can you hire new talent that will help on this journey? What external partners will be key to transformation? “Answers to these questions will help develop a more sustainable plan,” High says.
Every business now needs big data specialists, process automation experts, security analysts, human-machine interaction designers, robotics engineers, and machine learning experts. None of them are easy to find. Businesses that want to accelerate AI results need to kick off what Ben Pring and Euan Davis of the research-oriented think tank Cognizant Center for the Future of Work call a “skills renaissance.”
“In addition to having sophisticated hiring and retention plans, organizations need to work harder to leverage the talent they already have,” Pring says.
