Getting to Broad-Scale AI in the Enterprise

Artificial intelligence is one of the most ubiquitous tech terms in use today – and, some would argue, overused. But don’t be turned off by the hype. AI is already having very real and significant benefits in the enterprise, and its impact is poised to grow even stronger. 

Organizations that have adopted AI expect it to transform their businesses and provide a multitude of competitive advantages: improved efficiencies, agility and responsiveness, and predictive capabilities.

PepsiCo, for example, has shifted from relying upon hard drives and individual cloud locations when sharing learnings  leading to missed opportunities and lost insights  to a AI-infused platform. As a result, they've unlocked the ability to make higher-quality business decisions as a much more rapid pace, the company tells CGT.  

Read on for more exclusive insights from PepsiCo and Kellogg's — including how Kellogg's is helping break down data silos within its organization in order to more successfully scale AI across the business — and dive into the steps needed for CG companies to implement AI in the enterprise.

#1 Create Cross-Functional Buy-In

Adopters who see AI transforming their business within three years grew to 75% in 2020, while the portion believing it will transform their industry within three years rose even more steeply to 61%, according to Deloitte

The potential across the business is vast, with consumer goods companies exploring its capabilities across a range of a sectors. For example, both Mars and Mondelez are among those experimenting with AI within their marketing efforts. Mars is developing new technology that incorporates AI to recognize the facial expressions of people watching their advertisements. The data of their reactions is aggregated, and the company uses this to guage the impact of their marketing campaigns. The company ultimately hopes the technology will unlock real-time feedback via automated assessments. 

Mondelez, meanwhile, recently launched a campaign that married AI with social media to amplify its Kinh Do mooncake brand during a timely festival in Vietnam. 

For companies that are just starting their journey, it’s important to first and foremost identify what the business drivers are and clearly define where AI can add value. Teams should comprise people with both tech and business skills. Each AI initiative should also have its own executive sponsor with processes in place for reporting on progress and results.

There also needs to be a high level of commitment from leaders. Among the characteristics of “AI high performers” are senior executives who are aligned and committed to AI, have a roadmap that prioritizes AI initiatives linked to business value, adopt standard execution processes to scale AI, and develop or customize their AI capabilities internally, according to McKinsey’s State of AI in 2020 report.

Some of the biggest gaps between AI high performers and others aren’t only in technical areas, such as using complex AI-modeling techniques, but also in the human aspects of AI, such as the alignment of senior executives around AI strategy and adoption of standard execution processes to scale AI across an organization,” notes Bryce Hall, an associate partner at McKinsey.

#2 Get All Your Data in One Place

There’s little doubt today that brands need greater insights about their customers, as well as faster time to market.

Automating workflows for improved efficiencies is essential at global healthcare company GSK. “Many teams rely upon the data that our group is distributing, and there can often be dozens of documents being circulated in one week," Jonathan Germain, manager, sales operations and planning, tells CGT. “So it’s of the utmost importance that we develop systems that will take the human error segment out of the workflow.”

Every moment of every day, someone is buying brands off of a shelf. But how much visibility do CGs have into their inventory? Not much, according to Dan Cook, Walmart food team lead, savory and tea, at Unilever.

“Do I know if those brands are out-of-stock? Do I know time of day? Do I know what inventory I need to supplement at a store level to ensure that an outcome is driven so that we keep the shelf full and grow market share,’’ says Cook. “That's what you're going to see AI solve,’’ and it’s already happening, along with its ML subset.

People are sharing more data than they ever have with CGs, mainly through online channels, and they want brands to do more with that data for better personalization and customer experiences, Cook noted.

At Rich Products, a family-owned food manufacturer, unifying its data in one place was critical to creating a more agile supply chain during the pandemic. The company, which sells to restaurants, bakeries, and foodservice companies, deployed a cloud-based planning platform to centralize various critical demand and supply data, and it expects the move to help it optimize its operations and make more informed investment decisions. 

#3 Move Beyond Pilots: Invest in People and Processes

When companies underinvest in people and processes, they quickly lose momentum with AI implementations, according to Boston Consulting Group, because it is deceptively easy to launch successful AI at scale pilots. Without change management, it’s nearly impossible to move beyond pilots and scale AI across the business.

At Kellogg’s, most of the AI and machine learning initiatives are linked to a hard business case or specific marketplace problem officials are trying to solve, says Lesley Salmon, senior vice president and CIO of Kellogg Co. “That ensures we are focusing our resources on something that will matter to our actual business performance.”

Every function and business unit at Kellogg's prioritizes use cases based on factors ranging from their business impact to their ability to advance the company’s technology, to their ripple effect from the standpoint of change management, she says. 

Consequently, Kellogg’s officials “embrace a test and learn culture,” Salmon says.

Apparel maker Levi’s is seeing results from its investments in AI to improve customer experience and demand forecasting. Now, the company is scaling its AI implementation on the heels of a successful pilot that demonstrated improved accuracy in AI-driven demand forecasting.

“Scaling it should enable more precise inventory investment, lead to less markdowns and clearance, prevent waste, and enhance sustainability, all of which will improve our margins,” explained Chip Bergh, Levi’s president and CEO. “This will be powerful in combination with the ongoing work AI has been contributing to pricing and promotion.”

#4 Tie AI Benefits to Change Management

Of course, people are protective of their data, and getting them to share information may be easier said than done. But siloed data also makes it hard for leaders to get a holistic view of company data.

“Anyone who has ever worked for a large organization knows that information silos are a challenging fact of doing business,’’ Salmon says. “The left hand doesn’t always know what the right hand is doing, and employees who are supposed to be working in concert are out of sync.”

To address that, Salmon has partnered with Kellogg’s global chief growth officer to host internal data summits to educate employees on how IT is using advanced data and analytics across the company. “As a result, AI is helping us connect dots and break down silos by transforming the ways our teams communicate, collaborate and coordinate their workstreams.”

To ensure AI investments pay off, organizations must tie AI deployments to change management. Employees must learn to trust AI tools and it is incumbent upon employers to assuage their fear of losing control over their work. Otherwise, those investments may be underutilized, underfunded, and ultimately unsuccessful. 

PepsiCo heavily invests every year on information that helps in the development and building of its brands and to identify future trends and foresights.  

The food and beverage giant used to rely on people to store and share learning, which lived on hard drives or individual cloud locations. This resulted in a significant amount of data and insights being lost and it became a key barrier to scaling and democratizing knowledge, according to Sioned Winfield, global insights digitization director of PepsiCo's AI-powered Ada platform.  

So officials recently deployed the "Ask Ada” AI platform to better leverage consumer-centric content. The goal is to elevate “the quality, speed, and scale of consumer-centric and commercial business decisions, and by doing so, generate more smiles for our consumers and colleagues,’’ Winfield says. 

Early results have been positive, and the platform enables PepsiCo “to better capitalize on existing knowledge, tools and capabilities to drive better data informed decision making,” she says.

Echoing Salmon, Winfield notes the biggest deployment challenge was internal data sharing and “educating individuals and teams to evolve away from c-drive squirreling to cloud-based sharing sites.”  

To counter this, officials set expectations of the platform’s mission and has held one-on-one sessions to get people comfortable with the technology and how it works.

AI is a Game-Changer

AI is increasingly being integrated into everyday business for everything from managing and automating IT infrastructure, to gleaning new customer insights, identifying and responding to cyber threats, and even improving the hiring process. 

AI enhances the overall consumer experience, improving personalization and customer service, noted Winfield. In designing a data- and AI-driven organization, smart leaders will think of the implications across the enterprise. That means if AI is applied in the supply chain, it also impacts commercial divisions.

“The integration of AI doesn’t happen in a silo, so all areas of business need to be considered in order for them to derive the most benefit from the transformation process,’’ Winfield says.

CG companies should be focused on building the workforce of the future, creating an agile workplace learning environment so that people can be continuously upskilled. Speeding up the implementation and scaling of AI gives any company the opportunity to rethink and evolve its strategy, she adds. The results can be game-changing.

See Related Content