A virtually unlimited customer service workforce, contracts that manage themselves and automating everything from hotel bookings to factory floor operations -- all at a lower price tag and more efficient outputs.

Artificial intelligence (AI) is promising results like these across industries, so it is no wonder that 46 percent of CIOs report considering adopting AI as an enterprise solution.

While the benefits that AI-driven technologies can bring to business might seem clear-cut, the actual process of adopting and deploying systems is anything but. Indeed, only 4 percent of businesses report fully adopting an AI solution.

So what’s stopping them?

The answer to the disparity between the reported desire to deploy AI and the actual adoption rates comes down to three key issues that CIOs and other tech decision makers will need to address if they are considering bringing AI into the fold in their business. In doing so, they’ll create a much clearer path to AI enablement -- with far greater odds of success.

(Mis)understanding what AI really means for business

Automating menial tasks and slashing costs is the kind of promise that make leadership’s ears perk up. But even as enthusiasm toward exploring AI is common in modern enterprises, few take the time to truly understand what the technology is, and what it can mean for business.

For one, AI systems aren’t something most businesses can build themselves. The hardware, raw processing power and expertise to build such a system far exceed almost any business. Luckily, firms such as Amazon, Google and IBM have invested in building AI platforms, increasingly opening their AI tools and features up for businesses to build solutions upon.

Another common error businesses make is not clearly defining what problem needs to be solved. AI isn’t the answer to everything, but rather, to a specific business goal or set of goals. Leaders that clearly define the parameters of what AI should solve and then plainly lay out what outputs and results should be will find themselves much more likely to be successful.

Finding the people to power AI

Unfortunately, informed and capable talent able to help build and deploy AI solutions simply don’t exist in the masses. While the idea of AI is not new, the skills and training required to master it have only relatively recently been introduced in curricula and formal IT training programs.

The AI talent pool that does exist is largely flocking toward the tech giants for the moment. But if history is any indicator, the AI revolution will encourage more young talent to enter the field.

Just as Apple’s introduction of the App Store essentially created a flood of new jobs in developing apps that did not exist before, AI will create a brand new market of opportunity. Additionally, as AI continues to be offered as a commodity by the Google and Amazons of the world, the required baseline knowledge of AI won’t be as high for companies looking to hire talent that can create solutions.

Quelling fear and building trust in AI

One of the largest barriers to truly embracing artificial intelligence is the most human: fear. When CIOs talk of automated processes and potential cost savings from AI, it’s natural for frontline employees to interpret this as a threat to their jobs.

In order to be successful with any AI solution, it’s the responsibility of leadership to show how AI is actually making employees’ lives easier, taking repetitive and often time-consuming tasks off their plates so they can focus on what they actually love to do.

For recruiters, this might mean a chatbot automatically handles candidate interview scheduling so employees can focus on the more strategic and interpersonal aspects of talent acquisition. For nurses, AI could translate to less time filling out easily automated paperwork and more time with patients.

Regardless of industry, leadership needs to focus as much time making a human case for AI as it does making a business one. When the fear subsides, a final step is making sure the business can establish trust with its partners and customers in a new solution. Mistakes can be costly to a business -- whether it’s a human or AI that makes it -- and organizations need to be confident their solution is secure and compliant with all regulations and standards that govern their existing data.

AI is a vital tool for businesses to meet client’s rising expectations for speed and accuracy in the future. While adoption has proven to be slow thus far at the enterprise level, learning from where others have failed is key, and businesses that are able to overcome pitfalls and proactively identify roadblocks set themselves up for far greater chances of success.

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Antonis Papatsaras

Antonis Papatsaras

Antonis Papatsaras is chief technology officer at SpringCM.