Artificial intelligence may seem like magic, which is fine to think if you’re a consumer asking ChatGPT for recipe ideas or movie reviews. But if you’re helping to lead an enterprise, it’s important to see AI for what it is — statistics.
“It’s not magic,” says Bruno Aziza. partner with CapitalG, Google’s venture capital offshoot. It’s probabilistic, so you’ve got to remember the way it works is by completing information or sentences based on a model that’s been trained. There’s a high level of probability and sometimes it’s not always correct.”
Aziza shared his views on Michael Krigmans’ CXO Talk series, urging business leaders to take a cold, hard look at generative AI, while recognizing the substantial innovation it can spur. (Aziza is also a contributor here at Forbes.)
Key to the success of AI in enterprises is the X factor, if you will, that has influenced the acceptance of every data-intensive technology that has come before it — trust. “If you don’t start with a foundation of trust in this business, using gen-AI is the equivalent of having found something that is really good at getting you the wrong answer very quickly,” says Aziza.
Think back to previous technologies, from business intelligence tools to data lakes. “There is something new about this technology, but there’s also something old about it,” he explains. “The fundamentals of having trusted platforms that have data that people can rely on is tremendously important because that’s what’s feeding your model, and that’s what is providing information at scale to your employees.”
If anything, generative AI could greatly amplify potential flaws in data. “GenAI will expose to more people the poor quality of your data,” says Aziza. “That’s why it’s really important to think about data quality as a fundamental block.”
- Step up data quality initiatives. To preserve or build trust, look to the quality of data feeding AI models first. Data should be reviewed and tested internally, as frequently as possible. Aziza reports seeing customers who review gen-AI chatbots or other applications to their data management teams before opening it up to consumers or customers. “It can effectively help with labeling at scale, identifying issues with the data or the data is empty or there is data inequality. Really starting with this concept of data quality is probably the first step of adopting genAI.”
- Step up governance. Governance is the other piece of building trust in AI output, other industry thought leaders point out. “Effective, enterprise-wide model governance is not something that can be dismissed until negative consequences emerge,” according to an analysis by Beena Ammanath, executive director of the Deloitte AI Institute. “Nor is it sufficient to take a wait-and-see approach as government rulemaking on generative AI evolves.”
- Step up everyone’s involvement. Don’t wait for the IT or data management department to do something about it. Given the potential consequences of untrustworthy or errant AI, “businesses face a need to account for generative AI risks today and those yet to emerge as the technology matures,” Ammanath states. “With the workforce, the duty to identify and manage risk is shared throughout the organization among both technical and nontechnical stakeholders. Within an AI governance initiative, employees, executives, and partners “need a clear sense of roles and responsibilities, as well as workforce training opportunities to enhance their AI literacy and skills,” she adds.
- Step up teamwork. “The enterprise may also create new roles and groups within the business, such as an AI ethics advisory board charged with overseeing and guiding the trustworthy use of Generative AI. As a part of this, businesses can look to building diverse teams that help shape and govern AI with a multitude of perspectives and lived experiences.” Support for teams was a theme echoed by Aziza as well. Along with building trust, business leaders “also have to think about how they orchestrate their teams around the genAI opportunity.”
- Step up sense of purpose. As AI gets underway, there are key questions that need to be asked, Aziza points out: “How can I get to my outcome faster? How can I provide a more compelling customer experience? How can I help maybe junior folks on my team accelerate their learning?”
AI may be a fascinating journey, not a magic carpet ride. The real magic isn’t the technology, it’s the people who work together to make things happen.
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