By Jaewon Kang
With its power to analyze data and spot trends, artificial
intelligence is being used to develop new fashions, songs and TV
ads. Now it is making inroads into another creative area that is
possibly even more subjective: new flavors and foods.
McCormick & Co., Conagra Brands Inc. and PepsiCo Inc. are
among the food giants using AI to cook up new concepts such as
bourbon pork-tenderloin seasoning and pudding flavors meant to call
to mind unicorns.
AI can sift through huge numbers of ingredient combinations to
offer outside-the-box suggestions that human developers overlooked.
The technology can also scrutinize current taste trends to figure
out what customers crave now and predict what they'll want next.
And it can do it so much faster than a staff of food scientists and
food testers.
The push toward AI comes as packaged-food conglomerates face
intense pressure from a crowded marketplace. The companies have
long relied on research-and-development practices that took years
to yield new products -- but now consumer tastes are changing
faster than ever, as people seek new flavors and find a widening
array of specialty foods online. Brands can doom themselves by not
adjusting quickly to new trends or changing preferences, such as a
desire for low sugar or natural colors.
Of course, AI has its limits. Taste is personal and complex, and
while AI can speed up what are essentially trial-and-error
processes by suggesting new flavor combinations, food giants
continue to rely heavily on testing and feedback by human staffers
when it applies these recommendations.
Indeed, some food executives say the emerging question is how to
balance these technologies and the institutional knowledge that
made their brands popular. "The biggest challenge is being able to
reconcile what AI would tell us versus human intuition that has
historically run our business," says Michael Lindsey, chief
transformation and strategy officer for PepsiCo's Frito-Lay and
Quaker businesses in North America.
Visualizing flavors
One company that has seen a fruitful collaboration between AI
and human researchers is McCormick, the Baltimore-based spice
maker.
Earlier this year, the company joined with International
Business Machines Corp. to better crunch data on ingredients, trial
recipes and the reaction of taste testers to create new seasoning
combinations. The company, which started 130 years ago as a maker
of fruit syrups and flavoring extracts, today manufactures and
distributes spices, seasoning mixes and condiments in addition to
designing flavors for other food companies. The company is
consulting AI to test and manufacture new items ranging from pork
seasoning with cocoa and cinnamon to seasonings resembling
everything bagels that consist of clove powder and vanilla
extract.
McCormick's chief science officer Hamed Faridi says the
company's development practices are complicated because of the pure
volume of its components: some 10,000 ingredients, colorings and
preservatives across its product line, procured all over the world.
Developers typically consider some 500 ingredients before settling
on a recipe for a new seasoning mix that might contain at most two
dozen ingredients, Mr. Faridi says.
Given that complexity, McCormick is hoping that artificial
intelligence can cut its development process for new products --
which can stretch on for a year -- by up to 70%, Mr. Faridi
says.
Already, he says, machine learning is making the usual
development process shorter and simpler.
In the past, the scientists would simply concoct and test dozens
of recipes of their own -- sometimes as many as 150 -- while taking
some to focus groups for feedback.
Now, the developers check their own ideas against suggestions
from the AI, to look for useful concepts. The AI makes its
decisions based on a data set that captures everything from
formulations food scientists have tested in the past to ingredient
characteristics such as kosher and moisture levels. Developers can
also tell the AI how far outside the box of standard ingredient
combinations they want it to look. Developers are typically given a
brief that spells out things like how a product will be used and
what specific requirements it has, such as target price and a
mandate to include natural ingredients.
The partnership between McCormick and IBM also has resulted in a
range of new items. For example, McCormick's technicians might not
stray far from oregano, basil and other Italian herbs in designing
a new pizza seasoning, says Mr. Faridi. But artificial intelligence
suggested cumin as an addition because it had proved popular in
other relatively new seasoning formulas McCormick had created, even
though it is relatively uncommon in Italian dishes. The
nonintuitive choice of spice for the recipe nudged staffers to put
unexpected mixes into taste tests that wouldn't have otherwise made
it out of the lab, he says.
AI assistance also led to Tuscan chicken, bourbon pork
tenderloin and New Orleans sausage-seasoning mixes that incorporate
more ingredients and flavors than McCormick's developers would have
thought to try on their own, Mr. Faridi says. McCormick started
selling these three seasonings, but haven't rolled out
cumin-infused pizza seasoning.
"AI doesn't have that bias," says Mr. Faridi.
How to find a unicorn
Some AI systems aren't just hunting for flavor combinations:
They're scrutinizing data to see what concepts will resonate with
customers.
Conagra, the manufacturer of Hunt's ketchup and Slim Jim jerky,
uses AI to sift through data on everything from social media to
consumer consumption to spot trends and examine patterns in
consumer demand.
For instance, the AI noticed a growing amount of unicorn-related
images on the web and enthusiasm among young consumers for
unicorn-themed foods, makeup and accessories. Conagra decided to
leverage that interest. It turned to its AI-enabled development
system to identify the taste and look consumers associated with the
mythical creature -- which led to pastel-colored Snack Pack pudding
with cotton candy-like flavors and cartoon unicorns on the
packaging.
"AI is really good at highlighting and identifying constant
themes around images" that might make for good products, says
Thatcher Schulte, senior director of predictive sciences at
Conagra. "People can't process as much information as quickly as
machines."
The food maker also introduced gluten-free Healthy Choice bowls
and a nondairy version of Reddi Wip cream based on an algorithm's
insights. AI helps Conagra figure out which trends to focus on by
highlighting the ones that are gaining traction, says Corey
Berends, senior vice president of research and development at the
company. "The confidence is way higher," Mr. Berends says.
Frito-Lay, the PepsiCo subsidiary that is home to dozens of
product lines and hundreds of flavors, ramped up its use of AI
about four years ago. Frito-Lay used AI to create champagne
vinaigrette and coconut curry flavors for its Australian chip brand
Red Rock Deli, as well as the Off the Eaten Path line of healthier
chips made from unconventional snack ingredients such as peas. It
also recently used analytics to target consumers on the East Coast
with its spicy snack brand, Turbos Flamas.
It is now trying to figure out how to use AI to chemically dial
up the aroma of its snacks so that people get a strong whiff as
soon as they open a bag.
Christine Cioffe, PepsiCo's senior vice president of
sustainability and global snacks research and development, says AI
helps the company process data rapidly, while it is most lucrative
for the company to seize a trend.
"It looks at it the way a consumer does" by identifying patterns
in food consumption that are getting more popular among shoppers,
Ms. Cioffe says, adding that AI should help Frito-Lay cut its
development process to a third of its current length.
Frito-Lay also is using computer models and predictive analytics
to test that certain packaging materials won't expose snacks to too
much oxygen, making them stale, and to modify processing equipment
to make the texture of potato chips as appealing as possible.
"We want every potato chip that gets into a bag of Lays to not
have any defects," says Ms. Cioffe.
Ingredients supplier Ingredion Inc., meanwhile, has used AI in
the robot it uses to measure texture, which it calls T-Rex, for
about 10 years. Tony DeLio, senior vice president of corporate
strategy and chief innovation officer at the Stevia maker, says AI
has allowed the company to implement up to 15 times more
experiments and develop a more comprehensive understanding of the
properties of different ingredients. T-Rex can improve the texture
of products it helps food manufacturers make by testing different
combinations and samples more quickly. For instance, Ingredion
consulted T-Rex when it helped a food company make soup products
feel more creamy.
"The cost of a lot of technology has been prohibitively high.
It's coming down," says Mr. DeLio.
Many food makers say they plan to make greater use of artificial
intelligence. Approximately 38% of food executives have at least
partially implemented AI to monitor their warehouse operations,
according to a coming survey by Deloitte LLP. About 18% said they
are using AI to build technologies that give consumers access to
more product information and recommendations for new ones.
"The cycle of continuous improvement for customer engagement and
product will become the norm," says Barb Renner, vice chairman and
U.S. leader of the consumer-products group at Deloitte.
The limits of tech
Even as AI systems spread through the food industry, though,
many experts are careful to point out that the systems won't
eliminate the need for humans anytime soon.
Most important, algorithmic suggestions still need human
oversight. Last year, for instance, researchers at the
Massachusetts Institute of Technology had an AI aggregate hundreds
of pizza recipes and generate new ones. But they didn't just run
with the AI's suggestions, such as a pizza topped with Italian
sausage, jam and shrimp. Instead, MIT asked a chef to add final
touches to these combinations and make sure they tasted good. The
team, which has also tested AI-enabled perfume and graffiti,
believes collaborations between humans and algorithms generate the
most creative results.
There's another human element that might trip up AI, other
experts warn: subjectivity. "Tastes and what people believe to be
good for them can change at any point in time," says Daniel Neill,
associate professor of computer science and public service at New
York University.
Robin Lougee, an IBM researcher who works with McCormick, says
that, so far, food companies have invested less in artificial
intelligence than some other industries in part because taste is so
personal and complex.
"The science of flavor is not so well understood," she says.
Ms. Kang is a Wall Street Journal reporter in Chicago. She can
be reached at jaewon.kang@wsj.com.
(END) Dow Jones Newswires
October 09, 2019 19:21 ET (23:21 GMT)
Copyright (c) 2019 Dow Jones & Company, Inc.
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