How The Fortune 500 Are Using AI (And You Can Too)

With all the recent buzz around AI, you might assume this amazing technology has suddenly burst onto the scene. In fact, it’s decades-old and has recently taken a quantum leap forward.

What’s changed is that AI, once the preserve of tinkerers and early adopters, has gone mainstream, and is being spearheaded by a host of Fortune 500 companies at the forefront of the AI revolution. Some of the most transformative uses of AI are taking place in plain sight at some of the world’s largest companies.

Just take Coca-Cola, who partnered with Bain & Company to invest in the latest OpenAI technologies, such as Dall-E and ChatGPT 4. Artists were invited to use the brand’s iconic assets to create their own art, and AI helped drive responsive, personalized brand experiences consumers felt they were a part of. 

The results? 10-30X faster concept iteration, and 38% higher messaging resonance.

Time-to-value like this contradicts the notion that large, long-established brands have been slow to adopt (particularly in the digital age). After all, leaner startups have had the advantage of agility these past few years. 

But when it comes to AI, these models are absolutely dependent on data. And data is something larger corporations have in spades. Which begs the question: can smaller companies really compete against the behemoths? Actually, yes. 

Any company can gain the “Fortune 500 advantage” by focusing on the quality of their data and harnessing it to train and integrate AI into their workflows. We’ll look at the precedent these larger corporations have set, and discuss how to apply these strategies at any scale. 

AI adoption in Fortune 500 companies

As witnessed across recent earnings calls, the Fortune 500’s obsession with artificial intelligence has shown no sign of slowing.

The cynics might say this is hyperbole designed to placate analysts. But sectors of the economy long considered staid are proving fertile ground for AI solutions that yield substantial, real-world benefits.

Let’s look at some tangible examples of household name businesses using AI to drive outsized performance.

Walmart

Walmart has used Natural Language Processing (NLP) technology for years to refine website search results, and recently added voice recognition capabilities to its ordering system. Customers can speak to their voice assistant of choice (e.g. Alexa) to ask for orange juice, bread, or toilet paper. AI refines the request and uses past purchasing behavior to determine the most likely brand and size to place in the cart.

The company uses a similar voice recognition solution for in-store associations. The “Ask Sam” app uses voice recognition and AI chatbot tech to equip employees with internal knowledge, such as their upcoming work schedule or a product’s location. 

Amazon

Amazon’s use of AI ranges from the predictable to the truly futuristic. Most consumers are likely familiar with their most used AI feature – summarized product reviews – which highlight the most common pros and cons synthesized from customer feedback. 

However, some lesser known and advanced use cases include the palm-based payment system Amazon One. This new kind of biometric identification uses GenAI images of the hand’s surface and veins to identify shoppers with the tap of a palm – eliminating the need for a digital wallet.  

Apple

Apple is considered a pioneer in AI and is well known for its Siri voice assistant. However, its move into GenAI has been more measured. This is set to change with its recently announced plans to deeply integrate AI capabilities across its ecosystem.

In partnership with OpenAI, Apple introduced "Apple Intelligence", a suite of AI-powered features designed to enhance user experience across their devices. This includes a major upgrade to Siri, making it more context-aware and capable of handling complex queries with improved natural language processing​.

How to gain a Fortune-500 advantage

We've seen companies of all sizes uplevel their AI capabilities using the customer data they’re already collecting. 

Through this work, we’ve pinpointed three areas that drive the biggest impact.

Data quality

First and foremost, AI runs on data. Or more specifically, good data. If the data fed into AI models is old, inaccurate, or biased, the results will also be outdated, inaccurate, or biased. Remember, input determines output. 

Take healthcare. Artificial intelligence has the potential to detect and diagnose patients at a rapid rate for earlier, more proactive treatment. But it also comes with the significant risk of getting it wrong. MIT Technology Review found multiple examples of AI reaching the wrong diagnosis due to mislabeled data or data they couldn’t trace to a known source.

The tricky thing is there are a lot of culprits behind “bad data," like silos in your tech stack or delays in data processing. While we could discuss the qualities for good data for hours, for now, we’ll stick to the fundamentals that lay the foundation for advancements in AI. 

Good data is consolidated, consistent across systems, unique (i.e., no duplicate entries), compliant with privacy regulations, and updated in real time. This requires a few key capabilities in your tech stack, like being able to easily integrate new tools, leverage real-time event pipelines, automate quality assurance checks, and ensure data pipeline observability. 

With this foundation, businesses can better train their AI and machine learning models. For instance, using the historical data stored in a warehouse, businesses can make predictions about customer behavior and preferences. When you pair that prediction with real-time event data, you can then engage customers based on their likelihood they’ll perform a specific action in that moment.

Data privacy

People are much warier of how their data is being collected and used. They don’t want their personal information to be at risk, or for their data to be sold by unknown third-parties for sketchy marketing ploys. 

Data privacy laws and regulations vary depending where you are in the world, but a global precedent was set with the GDPR in Europe. The California Consumer Privacy Act followed suit, and multiple other states in the U.S. have data privacy laws going into effect shortly. 

The point being: you have to have a forward-thinking approach about privacy. Regulations are coming, and also, it’s an ethical stance that will help win customers’ trust. 

Of course, AI has made this  more complicated. From AI models being trained on copyrighted material, to proprietary information being inputted into third-party genAI chatbots, there’s concern over AI risks and regulations. (Unsurprisingly, people’s trust in AI has dropped significantly over the past year.) 

Already, the EU is leading the charge with the first AI Act, which I suspect will once again set a global benchmark. 

So, how do you get ready for the regulations to come? It goes back to the foundation you’ve already set when it comes to data protection and privacy. Your policy on AI should be a natural extension of the data governance policy you’ve already created for safely handling customer data. For instance, if you’re in the EU or do business in the EU, you should already be operating in alignment with data residency laws. And across the board, you should already have an up-to-date data inventory that classifies information by its risk level. (You can learn more about essential features like this by checking out our Privacy Portal.) 

With these fundamentals in place, you can then start to integrate AI and ML into your pre-existing privacy features to scale compliance (e.g., better identifying security risks, ensuring GenAI prompts don’t pull in highly sensitive customer data, and so on). 

Organizational readiness

As we speculate about the future of AI, one thing we know with almost absolute certainty is that AI will become a “silent partner” in operations. From co-pilots to genAI-led customer engagement, every function will be thinking of how they can use AI to be more efficient. 

For organizations, the goal today should be to understand which processes are most likely to be automated in the coming months. And while short-term roles may emerge, like prompt engineers, there’s also the likelihood that eventually everyone will be well-versed in how to use AI for optimal results. 

As with any form of rapid change, organizations need to have a framework in place to act as a guardrail. This means thinking through how AI will impact your company-specific operations and industry, how you’ll be upskilling employees, the rules of engagement around AI usage (which is particularly important in highly regulated industries), and how AI will translate to greater efficiency and cost-effectiveness. 

Why AI matters for brands of all sizes

It isn’t a question of if you should be leveraging AI, but a matter of how you plan to do so. While larger companies have the advantage of more data to train their models on, AI can (when used appropriately and with equitable access) act as an equalizing force. It can help accelerate production and unlock advanced insights that would otherwise be unachievable (or take months of manual resources). 

What does act as a moat around AI, however, is the quality of your data. By focusing on data hygiene, privacy compliance, and interoperability, you can tap into the innovative potential of artificial intelligence – on any scale.