6. Is our AI maturation keeping up with data growth?
To stay ahead of the curve, businesses should set a target to match their decision-making speed to that of anticipated growth in data volumes over the next year, advises Euan Davis of the Cognizant Center for the Future of Work. For instance, IT leaders who expect 30 percent growth in data should set a goal of increasing their organization’s speed of insight by 30 percent. “Anything less will impact the speed of doing business in this fast-changing world,” Davis says.
[ Public data sets can help with training. Read also: 6 misconceptions about AIOps, explained. ]
7. How can we find next-gen AI talent?
AI is not just about technology; it is also about people. “Critical to leveraging the possibilities of AI is hiring talent that can understand the technology and business needs and create solutions, not just build models,” says Ben Pring of the Cognizant Center for the Future of Work.
“Organizations should deeply focus on HR plans (hiring and retention) that prioritize securing the next generation of talent; without it, it will be virtually impossible to keep pace in markets that are being disrupted at light speed,” Pring says.
Having the right people in place, including data engineers and data scientists, is imperative to success “as they will be able to identify and correct small issues before they potentially become big problems,” says Josh Perkins, field CTO at digital platform company AHEAD.
Of course, external hiring alone won't get you there; it also pays to train up AI talent. Read also: Artificial Intelligence (AI): 4 novel ways to build talent in-house.
8. How will humans and machines interact in our environment?
This will guide plans for everything from enhancing the user experience of internal bots and platforms to upskilling and retraining employees. You’re contemplating how, where, and when to deploy AI to complement employee capabilities, says Ringman of Telus International.
9. Should we create an AI center of excellence?
While it’s beneficial to start small and build momentum, there is often value in developing an AI center of excellence. “In the formative stages of AI adoption, it is good to set up an AI center of excellence where subject matter experts either report directly or through the dotted line,” says Babic of Hyland. “This center of excellence provides focus and dedication to the topic and allows a centralized approach to patterns and practices derived through learning.”
10. How can we better democratize AI?
“Some leaders are surprised to learn that democratizing AI involves more than the process itself,” says David Tareen, director of AI and analytics at SAS. “Often, culture tweaks or an entire cultural change must accompany the process. Leaders can practice transparency and good communication in their democratization initiatives to address concerns, adjust the pace of change, and result in a successful completion of embedding AI and analytics for everyone.”
Nolan of IDA Ireland recommends educating the broader team on the art of the possible. “AI shouldn’t just be the preserve of the IT or software engineering team,” says Nolan, who urges workforce education and awareness building.
[ Get exercises and approaches that make disparate teams stronger. Read the digital transformation ebook: Transformation Takes Practice. ]
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