An intelligent, data-driven and automated wave of technology is rolling into enterprises, steering business leaders into waters that require new tools, new skills and new expertise.
At the 10th annual GPU Technology Conference, in San Jose, NVIDIA invited business experts from leading companies to share their thoughts on the possibilities and practicalities of AI adoption in enterprises.
Here are a few key takeaways from a dozen “AI for Business” panels and presentations delivered by industry executives at GTC.
Foster a Digital Mindset
It all starts with the mindset: moving beyond the current position of a company to where it envisions to be based on customer needs, what it wants to be and how it aspires to operate.
“For me, it was being able to approach it from a different mindset because otherwise, we can’t get anywhere,” Debra King, chief information officer of Agriculture at DowDuPont, said during a fireside chat on “Driving AI Innovation During Business Transformation.”
The right mindset also involves reflecting on which operations within business units can tap into corresponding “superpowers” of digital technology, such as AI and the cloud.
Cultivate a Data-Driven Culture
Data is the oxygen for modern innovations, and GPUs filter it for the highest level of quality. A diverse set of GPU-accelerated data can be used to extract more sophisticated analysis, identify patterns and intricacies within them, and run more complex machine learning.
“Spend time on good data,” said John Elliott, managing director at Accenture Digital, who was part of a panel on “Driving Operational Efficiencies with AI.”
At the same time, businesses need to be mindful of data privacy concerns and factor in security applications that ensure customer privacy.
Build a Value Case
Few businesses can do it all at once, so figure out priorities and determine what can make an impact.
Focus on friction points that are associated with the core of your business’s operational efficiency. Whether it’s managed costs or time to market, justify a case for it.
Initially, it helps to work with problems for which most of the solutions have been figured out but spend more resources on improved outcomes.
“For problems that require a significant level of research and development, it’s important to pick problems that get you outcomes that you cannot get today,” said Arun Subramaniyan, vice president of Data Science and Analytics at BHGE Digital and a co-panelist with Elliott. “If we just get the outcomes faster, then that doesn’t really justify production.”
Attract Top Talent
It’s not always the pedigree. It’s also about purpose, potential and passion.
Getting the right talent to your location can sometimes be a challenge, but businesses can create opportunities for which people don’t hesitate to move.
At the same time, enterprises should be looking for prospective employees who demonstrate enthusiasm and a willingness to work toward the overall mission of the company.
“I look for someone who is just so excited about the opportunity,” King said. “It’s really hard, it’s going to take a lot out of you, but we’re building something great for the company, which will have an impact on the world and you have to be passionate about it.”
People with backgrounds in machine learning, data science and domain expertise contribute to some of the strongest teams.
Not everyone is already steeped in these areas — and the fields themselves are evolving quickly — so a willingness to learn is also important. NVIDIA’s Deep Learning Institute offers hands-on, self-paced, online training in AI and accelerated computing to solve real-world problems.
Recordings of every GTC session will be online 30 days from today.