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Research Proves the Benefits of Getting Ahead of the AI Curve

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“Artificial intelligence” is at peak buzzword status. As marketers, we see it everywhere: Affixed to the claims of the latest marketing tech, alongside warnings that it will eradicate jobs like content production, and peppered into predictions of coming trends.

Many marketers are used to being bombarded with hype about “the next big thing.” In this case, they either become numb to mention of AI because it’s usually not actionable or approachable, or they’re disillusioned because it’s just a catchy term for tools that aren’t really AI.

Not a strong case for this new wave of technology, even though experts, like Sundar Pichai, have likened its impact on society to that of fire or electricity.

At the Marketing Artificial Intelligence Institute, we’ve talked for a long time about the importance of AI, and more recently, about the massive impact of early adoption on an organization’s ability to get ahead. Early AI adopters, or AI “Pioneers,” are actively “establishing positions in both customer and labor markets that may make it hard for others to draft off of their hard work. The many advantages reported by Pioneers suggest that early AI movers may be especially hard to catch.”

That caliber of competitive advantage is hard to ignore. And research is continually proving the benefits of getting ahead of the AI curve. McKinsey & Company released a report last year about the early stages of AI adoption across certain industries. Their findings? AI can deliver true value to the serious adopters—vast improvements in day-to-day business functions, higher profit margins, and a widening performance gap. 

Executives Often Feel Uncertain About AI, But Should Invest Anyway

For the robust report, researchers surveyed over 3,000 AI-aware C-level executives across 14 industries and 10 countries. Their findings show that marketers aren’t alone in feeling uncertain about AI use cases, AI’s potential ROI, and why they should care about AI at all.

Few companies researchers spoke to, outside of the tech sector, have actually deployed AI at scale: Only 20% said they use any AI-related technology at scale or in a core part of their business. And, upon reviewing more than 160 use cases, researchers found AI was only deployed commercially about 12% of the time.

That said, the companies that moved past the uncertainty and seriously invested in AI saw real results. Those who combined an already strong digital foundation with proactive strategies saw massive increases in profit margin, and the performance gap between them and the AI laggards will continue to grow.

Challenges to AI Implementation Exist, But Shouldn’t be Blockades

As the report points out, there are benefits to participating in the first wave of AI implementation—but there are also substantial obstacles to realizing those benefits.

In the macro sense, the report shows that some of AI’s challenges transcend individual firms to loop in developers, governments, and the larger workforce.

First, the workforce must be retrained “to exploit AI rather than compete with it.”

Second, the cities and countries that decide to become global AI hubs must compete to attract the appropriate AI talent and investment.

Finally, there must be progress on ethical, legal and regulatory implications for using AI—and the huge amounts of data that power it.

On a company-by-company basis, the hard truth of AI is there are no shortcuts to training the technology. AI’s success depends on a strong digital foundation, meaning companies are often faced with the challenge of training the machines on a vast amount of unique data.

Even if smaller companies are ready to face that obstacle, the report points out an unfortunate truth: Larger companies are usually better-poised to harness AI thanks to more and better structured data, as well as more technically skilled employees. Conversely, the report found that some smaller companies had an advantage over larger ones because they faced fewer roadblocks with legacy IT systems and their organizations were more agile. 

Marketers: Here’s What it All Means for You

Between the clear benefits and daunting challenges lie a few key takeaways for marketers on the cusp of ignoring or embracing AI.

1. AI adoption requires C-suite buy-in. To generate the momentum for organization-wide implementation, the executive suite must be fully on board with adopting at scale and in a core part of their business. According to the report, “Respondents from firms that have successfully deployed an AI technology at scale tended to rate C-suite support nearly twice as high as those from companies that had not adopted any AI technology.”

2. AI adoption outside of the tech sector is at an early, experimental stage. Because of the uncertainties we discussed earlier, many companies are hesitant to jump in with both feet.

3. Early adopters are already creating competitive advantages and widening the gap between themselves and the AI laggards. Not only are they set up for success as AI continues to take off, they aren’t implementing to save on business expenses. Instead, early AI adopters focused on increasing revenue and market share—the cost reduction potential comes secondarily.

Many organizations are taking a “wait and see” approach, but the need to act is urgent. Going from a theoretical understanding of what's possible with AI to understanding the actual use cases for your business is difficult—but essential.

AI is Here. What Will Marketers Do About It?

We understand that AI implementation is daunting. And for some, the challenges may seem to outweigh the benefits. But you and your organization have the opportunity now to be proactive in advancing knowledge and capabilities before your competitors beat you to it.

The Marketing Artificial Intelligence Conference (MAICON) helps you get started with, and scale, artificial intelligence in your marketing.

MAICON is designed to help marketing leaders truly understand AI, educate their teams, garner executive support, pilot priority AI uses cases, and develop a near-term strategy for successfully scaling AI. You can learn more about it below.

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