6 Reasons Why We Haven’t Seen Full AI Adoption

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On one hand, we know AI is the future of business. After all, manpower simply isn’t fast enough to keep up with the pace of consumer demand. That said, there’s a big difference between knowing AI is the future and actually implementing AI within your business successfully. That latter part—AI adoption—is where many companies are finding themselves stuck.

No one said digital transformation would be easy—but you’re not alone if you assumed AI adoption would be a cakewalk. Today’s AI is a miracle worker. If it can translate languages, process invoices, and change marketing messages in real time, it must be a magic bullet. Right? Except when it comes to implementation. Yes, AI is meant to make your business life easy. But real-life conditions don’t always cooperate. If your company has been less than successful in its AI efforts, you are not alone. The following are a few reasons that I see AI adoption failing to reach full-penetration in businesses around the globe.

  • A lack of infrastructure. Just as many companies realized legacy systems were holding them back from full digital transformation, so it is that a lack of infrastructure may be holding them back from AI adoption, as well. A recent study shows just 15 percent of companies have the right technological infrastructure to support AI. What does that mean? They don’t have systems that work fast enough … that can process data quickly enough … that can hold the multitude of data required for AI to work at its optimal level. Technology will always perform to its lowest common denominator. A lack of infrastructure—or a lack of the type of advanced infrastructure needed for optimal AI adoption—will always keep you from enjoying the full benefits of AI.
  • Data issues. Running AI without data is like trying to run a car without gas. Yet, a recent report showed that just 18 percent of companies have a strategy in place for accessing and maintaining the types of data necessary for AI to function effectively. Some may not have enough—others may have tons stuck in siloes, making it difficult to access from other parts of the enterprise. Either way, you won’t be operating at 100 percent if your data isn’t clean, relevant, organized and accessible. Long story short, until companies invest in quality data management and procurement, their AI will not be effective.
  • A lack of talent. It’s one thing to launch AI. Most companies can do that in some form via an as-a-Service provider that incorporates AI into their marketing or sales software, for instance. But what about attracting (and affording) the type of quality AI talent you may need to create a bangin’ AI strategy throughout your enterprise? For large companies, this may not be an issue. But what about the small and medium-sized businesses that have limited IT teams and budgets? What about companies located outside primary geographic markets that are having difficulty recruiting the most capable technicians to their cities? Those companies may be at a definite disadvantage in successful AI adoption.
  • A lack of vision at the top. Just as not all leaders are ready to embrace data-led decision-making, not all leaders are ready to embrace machine-led decision-making. Reports show just 26 percent of senior leaders show a commitment to AI initiatives, and just 17 percent of respondents said their companies had mapped out AI opportunities throughout the company. It could be fear. It could be a lack of understanding. But the fact remains not all leaders are ready to bite when it comes to AI. And as we’ve seen in digital transformation, when a leader isn’t on board, it’s incredibly hard to get adoption to take effect.
  • It’s expensive. You have to spend money to make money—at least that’s what they say in business. Big companies can afford to do that. Take Amazon, for instance. AI also plays a huge role in Amazon’s recommendation engine, which generates 35 percent of the company’s revenue. It’s estimated that its Alexa speakers could add an additional $10 billion in sales to Amazon by 2020, according to RBC Capital. Clearly, they can afford to spend millions in AI investment. Unfortunately, we’re not all Amazon. Most of us have much smaller margins and much less wiggle room for technological ROI.
  • A discouraging learning curve. As I’ve said many times over, change is never easy, least of all in digital transformation. AI adoption itself has a particularly challenging learning curve. Because of the above issues, many have found that what they thought would be a supercharged way to increase efficiencies has been like undertaking a home renovation; every wall they tear down leads to a whole new problem they didn’t realize was there in the first place. It’s no wonder some companies have gotten discouraged by peeling the onion of AI development. Still, there is hope. A recent study showed 78 percent of businesses felt they experienced significant or moderate value in their AI investment. There is a potential pay off for those willing to work out the kinks in their AI experiences.

Over the next few decades, AI is predicted to be the biggest commercial opportunity in the world—for companies and nations both. AI could advance the global GDP by 14 percent by 2030—$14 to $15 trillion. That’s no chump change—which is why despite the glitches of AI adoption, we need to keep moving ahead if we want in on the action. How do we do it? Start with the basics. Make sure you are fully digitized so that you can pull and utilize data across departments. Make sure your AI projects are scalable so they can grow and spread throughout the company. And lastly, make sure that you have a cohesive AI strategy in place. At this point in digital transformation, you simply can’t advance without one.

The original version of this article was first published on Forbes.

Futurum Research provides industry research and analysis. These columns are for educational purposes only and should not be considered in any way investment advice. 

Author Information

Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.

From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.

A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.

An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.


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