How digital natives are winning the AI race

Why are born-digital companies pulling ahead in the race to use artificial intelligence to spur impressive growth? Because they take a decidedly different approach to AI than their born-traditional counterparts do. Any company falling behind in this contest can learn from the exemplars' experience.

This article is part of our Born-Digital study, where AlixPartners set out to research born-digital companies' unique blend of strengths and challenges and identify the most pressing needs and areas of focus needed to sustain their success.

Artificial intelligence (AI) is the hottest tech topic in the world right now. And born-digital companies are clearly ahead in the race to get maximum business value from AI. That’s not surprising. What is surprising is the size of the gap between the front-runners in this race and the laggards: born-traditional companies. In our recent study, we not only sized the divide; we also defined what, precisely, sets the two groups apart and how the laggards can catch up.

AI takes center stage

From nanotechnology cancer diagnoses to package delivery-time estimates, movie recommendations, and video games, AI plays a central role in the products and services that most born-digital companies offer. AI doesn’t even have to be visible to customers to do its job. In fact, in the business-to-consumer world, consumers usually are not consciously aware that a product or service is AI enabled. They only know that they like their new Snapchat filter or virtual-reality-enabled home design app. And they may not realize the extent to which the messages they see about offerings and companies in their social media are being driven by AI aimed at winning over new customers.

When it comes to improving the customer experience to grow market share—and revenues—many companies turn to AI to craft smart pricing, promotion, and advertising strategies. Take financial services giant JPMorgan Chase.

The company uses AI to tailor marketing messages such as headlines in e-mails to targeted segments of customers, such as prospective borrowers. In an experiment, an AI generated headline delivered nearly twice the number of weekly applications for home equity lines of credit than the human-generated headline delivered.¹

But customer acquisition isn’t the only thing companies are using AI for. They’re also deploying it in back-office functions—to automate processes like accounts payable, to reduce the risk of human error in business processes, to inform decision-making, and to achieve cost-saving efficiencies in their operations. AI is becoming increasingly pervasive at born-digital companies across an array of industries.

Why? Findings from our study suggest five reasons.

Reason #1: AI is essential to born-digital companies' growth strategies

For many digital natives, AI is not a choice; it’s central to their business model, product, service, or operations. And it plays a vital role in those companies’ growth strategies. AI is woven into the very fabric of those organizations from their inception, which gives them a major head start. By contrast, their born-traditional counterparts must take time to launch AI powered transformations in order to achieve the same benefits.

Our study found significant differences between born-digital companies and born-traditional companies in such areas as their views of AI as a growth driver, management of the customer journey, and use of customer data.

Born-digital companies in our study cited enhanced customer experience and AI/analytics as their top two drivers for growth.

What's driving growth at born-digital companies:

• 83% of born-digital companies say that adopting new technologies will drive growth in their companies
• 80% of born-digital companies have fully digitalized their customer journey
• 20% of born-traditional companies have fully digitized their customer journey

Reason #2: Born-digital businesses use AI to complement people, not replace them

Born-digital companies don’t think of AI as just another tool in their company’s technology tool kit. They know that AI can perform routine work which enables people to bring more insight and creativity to their work. They know, given the right training data, AI can even learn on its own about vital topics such as customer desires, margin drivers, and equipment failures. And it can even generate valuable, new knowledge that can boost a company’s performance on multiple fronts.

For instance, an AI inspector can vastly reduce the number of items a human inspector has to look at by automatically passing products that are clearly in spec based on pictures or video. To foster such collaboration, companies apply computer algorithms and model outputs in a give-and-take fashion. That is, employees continue to help the algorithm learn, and they modify business processes based on outputs from the algorithm. As algorithms become more advanced in accuracy, they can take on more work in such forms as volumes of data handled and the processes the data get applied to.

To illustrate, AI is increasingly being used to prioritize and steer messages coming into contact centers. In mortgage lending, for instance, the message “I want to make an offer on a house today” gets prioritized for loan origination one way, while “I have a problem and need to speak to a manager” gets prioritized another way.

Reason #3: Born-digital companies understand the AI model development process

Born-digital companies recognize that they need the right digital platform to use AI at scale—that is, throughout their enterprise versus simply to improve a few business processes here and there. Of course, a digital native can still end up making technology gambles that don’t pay off.

But born-digital enterprises understand the fundamental importance of the AI platform. What's more, most of them have the ability to build that platform and then adapt and retool it when technology takes a meaningful leap forward. Born-digital businesses also know that an overall lack of well-structured and labeled data from which algorithms can be trained presents a major roadblock to AI right now. Training data is needed to help an AI algorithm identify relationships between different types of data, to help the algorithm understand cause-and-effect connections, and to help the algorithm make decisions. As those activities improve, AI models become more accurate and useful. To that end, born-digital companies recognize the immense value of data. Indeed, they cite data architecture and data management as highest-priority investments required to drive growth.

Reason #4: Born-digital businesses manage AI talent effectively

Born-digital companies manage AI talent in ways that support their growth agenda. For instance, they tend to have a greater proportion of the right AI talent in their workforces than their born-traditional counterparts do—whether by recruiting such talent from outside or developing it internally. With internal development of AI skills, when the company clarifies how AI is being used to fuel growth, people understand which skills are needed to make this work. They can start building those naturally through on-the-job learning or through participation in training programs.

Employees with AI skills excel at such activities as computer science and statistics but also display creativity and perseverance in applying domain knowledge to model complex business problems. Having enough of the right AI talent enables born-digital enterprises to apply AI holistically across their businesses versus relegating it to a specialty function that can devote time to only a few projects.

Of course there's a shortage of people in the labor force available to recruit who are already fully trained in what AI is and how to use it in business. That makes internal talent development a top priority, and some born-digital leaders have already taken steps to meet that imperative.

Reason #5: Born-digital enterprises foster an experimentation culture

Using AI strategically to meet business goals such as growth requires a culture characterized by experimentation. And that, in turn, hinges on generous doses of creativity and perseverance. For example, when building an AI model aimed at improving customer-facing business processes, a data scientist typically tries several different algorithms and adjusts the parameters within them to yield the best results. Companies characterized by rigid, risk-averse organizational cultures—such as many born-traditional businesses—have difficulty incorporating AI into how they do business on a daily basis.

For many born-digital companies, the openness and ability to engage in experimentation are inherent in their organizational cultures. That’s because, from their inception, such companies have been pioneering new approaches in some or all aspects of their businesses.

AI breakthroughs come from trying new ways of using the technology. And the more a company experiments with it, the more results the AI models generate. These results can be corrected and fed back into the algorithm, thereby making the model increasingly accurate and therefore more useful.

AI laggards can still catch up. Here's how:

Any company—whether born-digital or born-traditional—that finds itself falling behind in the AI race can start catching up. How? Craft and execute a focused strategy for monetizing AI.

Companies can monetize AI by:

• Improving internal business execution
• Wrapping AI around company offerings
• Selling insights

These moves vary in potential for value capture and degree of difficulty of implementation. But a comprehensive AI monetization strategy will show how best to draw from all three tactics.²

Across both born-digital companies and born-traditional companies, the majority of value being captured—and a great place to start—comes from putting AI into the hands of the employees who make the decisions. Wrapping AI around products and services is a more advanced application and can be revenue generating on its own, or it can simply enhance the customer journey and therefore stickiness with customers. Selling insights produced or enabled by AI is the third way companies monetize the technology. Insight selling is the most difficult way to monetize AI, but it can be successful for niche players. For companies that have fallen behind, it’s never too late to start or improve AI capabilities.

Pulling ahead—and staying ahead—in the AI race isn’t easy for any company—whether it’s born-digital or born-traditional. Thankfully, though, companies that realize they’re lagging behind can take action to correct their position. Adopting some of the attitudes and practices demonstrated by born-digital exemplars can constitute an excellent first step.

Leaders' takeaways

1. Identify ways AI could help grow your business, ways of making AI a tool for human/machine collaboration in your workforce, and ways your IT platforms could be improved so that AI becomes useful throughout your organization.
2. Brainstorm ways to beef up the proportion of AI talent in your workforce—whether through new hiring strategies or leading-edge training—and encourage people throughout the organization to experiment with using AI to improve the business.
3. Consider the benefits—and costs—of the various AI-monetization strategies available. Study examples of companies in different industries that have used each strategy successfully. Determine which monetization strategy would best suit your organization’s needs and unique characteristics.