Sunitha Ray, Field Operations CTO at Shopify, says there's a big difference between enterprise AI and generative AI and business leaders need to know the use cases and potential returns on investment for each category.
Ray, a Constellation Research BT150 member, was VP of IT at Shark Ninja and has a unique view of AI since she has been on both the sell side and buy side of enterprise spending.
In our chat, we covered the difference between generative AI and enterprise AI, how to think about returns and the need for reskilling.
Here are the takeaways from my chat with Ray.
Differentiating between enterprise AI and generative AI. Before taking on the role at Shopify, Ray was the VP of IT at Shark Ninja and led the artificial intelligence team and genAI projects.
"I differentiate between genAI and enterprise AI at this point. Enterprise AI is about optimizations and figuring out solutions to problems," explained Ray. "We did a project where we designed the supply chain network, plotted optimal manufacturing plant and distribution center locations based on customer service levels we wanted to meet. That's enterprise AI."
GenAI is more about getting access to all types of data and then creating something new, said Ray. "While genAI has a lot of use cases, but for corporate use cases it has a long way to go for ROI," said Ray. "Enterprise AI can still be leveraged more effectively."
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Where generative AI works well. Ray said genAI has great use cases, but they tend to be in marketing and personalization. Images and text can be generated on the fly to personalize goods and offers for consumers. At Shopify, the company is leveraging genAI to give merchants imagery, product catalog and personalization options inside the platform.
Overspending? "We are big believers in genAI, but I feel like the amount being invested may be disproportionate to the returns that companies will see in the next year or two," said Ray.
Ray said that unless enterprises see clear returns from generative AI, they will pull back on investments. "There will be a huge wave of benefits coming in, but if companies are not seeing returns early, they may pull back on funding," said Ray. "I don't want to be negative, but there has been a lot of investment already and ultimately the C-suite will be looking at the bottom line."
Enterprises should also be honest about their AI readiness. One big reason genAI projects have stumbled is data strategy, said Ray, who noted investing in data strategy first will ensure better AI results.
The difference in leadership on the vendor side vs. the buy side. Ray said she's excited to be on the sell side with Shopify, which is a leading platform with a lot of AI.
Ray said:
"The big difference between the buy side and sell side is buy side is always about managing constraints and managing resources. Sometimes you may not always make the best decisions. You might compromise because you don't have the budget, people on board and the right resources."
"On the sell side, you don't have those constraints because companies are always trying to make their product superior and provide better total cost of ownership to customers."
DIY vs. buy decisions. Ray said DIY is predominate in AI projects today because consulting companies are still building out practices and enterprises are also honing skills. "When everything is changing so rapidly, companies are scrambling to reskill and start frameworks to generate use cases, have workshops and implement," said Ray.
Bridging genAI skill gaps to improve genAI projects. "What I would do differently if starting off with genAI today is to have a readiness workshop instead of jumping in," said Ray. "Are we ready as an organization to invest and create value from AI? Most companies would probably say no, but that doesn't mean you don't start. I would have parallel tracks for data strategy and AI."
Ray said enterprises should also start with baselines to track progress and then prioritize use cases. "One of my favorite ways of prioritization is the effort vs. impact metrics. How much effort do you put in and how much impact can you get with minimal effort? Take those use cases to senior management," she explained.
Final word. "I'm very excited about generative AI. I just want to make sure companies have the necessary guardrails to make sure projects don't fail. I see AI being a total game changer for most organizations," she said.
Ray added that enterprises should also lean into employee reskilling over the next two to three years. "The transformation is going to happen in the next two to three years and it's going to be exciting to see how it changes corporate structure and industries overall," she said.
