Wendy Zhang: What Companies Get Wrong About AI

Wendy Zhang, Director of Governance and Data Strategy at Sallie
Mae, discusses why companies must put the right culture, value and
quality into their AI initiatives Since Gartner’s famous
proclamation that 85% of AI projects end in failure, the maturity
of enterprise AI functions has improved dramatically. But the high
number of projects that continue to end in failure suggests that
many companies are still getting the basics of AI development
wrong. In this week’s Business of Data podcast, Wendy Zhang,
Director of Governance and Data Strategy at banking company Sallie
Mae, shares her views on why so many AI projects don’t deliver
results. For Zhang, common issues like poor data quality, trouble
identifying valuable applications for AI and lack of buy-in for
investing in or adopting AI technologies are symptoms of a more
basic problem. “There are a lot of different reasons [AI projects
fail],” she says. “But it all starts with a lack of fundamental
understanding of AI, what it is and what it can or cannot do.” AI
Success Starts with Asking the Right Questions Zhang warns against
doing AI for the sake of AI. She argues that companies must start
with the business challenges they need to solve before considering
what value AI might bring to these initiatives. “The next [thing]
you have to really assess is, is this something that AI can
actually do?” she continues. “Is this appropriate for the business
problem you’re going to solve?” Once an AI-focused executive has
identified projects that could benefit from AI-driven technologies,
they must consider what they need to deliver these projects
successfully. This includes assessing what resources, funding,
datasets and support they’ll need for each project. “It’s really
got to become the company’s DNA,” Zhang adds. “It requires people
to really look at a lot of your business processes and to think
about different possibilities, and that requires mindset change.”
“It’s not so much working and doing the same things over and over
and just automating a few things and having AI on the side,” she
concludes. “If you really want to get a massive benefit, you have
to be able to experiment and fail and also incorporate that into
your core business.” Simpler is Often Better for AI Beginners When
companies are new to AI, they typically don’t have fully formed
strategies for adopting these technologies. It’s more common for
enterprises to begin experimenting through trial and error to
discover the types of AI systems that are relevant to them. When
starting out on this journey, it’s good practice to start with
projects that can be delivered using data the company already has.
The sooner they are implemented and delivering value, the better.
Similarly, Zhang notes that simpler AI models can be easier for
fledgling AI teams to deliver. Even for more advanced AI functions,
she warns against overcomplicating AI systems unnecessarily. “I
think of simple models as, in plain terms, you get more bang for
your buck,” she quips. “The more complicated the models are, the
harder it is to have a higher interpretability.” “The other
component is having the right people,” she adds. “It’s important to
build AI capabilities in-house. However, when you first start out
with a pilot project, it might be beneficial to get external help,
just so that you can get the ball rolling and gain some momentum.”
“You really have to go through a lot of trial and error,” she
concludes. “Start with pilot projects to score some small wins to
get some buy-in and build your credibility to get faith for your
team.” Key Takeaways • Start with the right questions. Only embark
on AI projects when they’re the best answer to a pressing business
question • Find use cases you can deliver with what you have.
Identify what data, resources and support you’ll need before you
begin • Simpler is often better. As any engineer will tell you, the
more parts something has, the more bits there are that can break