As we can see that AI is the future of every small or large business. On one hand, we know AI is the future of business. That is why there is a huge difference between knowing the future of AI and how we implement AI within the business.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. Todays 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 a 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% 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. 5G will certainly help with areas like computing at the edge, but the overall infrastructure requirements for training and inference include significant investment in computer and storage systems that most companies lack in their on-premises data centers. Public cloud will be a fast start option as expected the big public cloud companies to offer embedded AI services sooner due to their existing scale and capabilities.
Data issues. Running AI without data is like trying to run a car without gas. Yet, a recent report showed that just 18% 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 silos, 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 is 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 the type of quality AI talent you may need to create a bang in 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 decisionmaking, not all leaders are ready to embrace machine-led decision making. Reports show just 26% of senior leaders show a commitment to AI initiatives, and just 17% 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 has seen in digital transformation, when a leader isn't on board, its incredibly hard to get adoption to take effect.
Its 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%of the company's revenue. Its 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. This is where it is critical to learn about the ways to engage and get started, even if on a small scale. Much like big data, AI
A discouraging learning curve. As 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. Its no wonder some companies have gotten discouraged by peeling the onion of AI development. Still, there is hope.