With all the hype around AI, one would think that just about every business would have adopted some form of it by now. The fact is that enterprise-scale AI is still largely out of reach for a majority of companies, especially small and medium-sized ones. This inaccessibility can be attributed to four major factors.

Barriers to Using AI

The first factor: misinformation. More often than not, what comes to mind when people hear the term AI are robots, flying cars, and large-scale job loss. Oh, and did we mention robots? While the latter aren’t necessarily incorrect, they drive the AI narrative in the wrong direction. Thanks to modern media’s thirst for anything and everything clickbaity, filmmakers, television writers and even journalists are jumping on the Elon Musk bandwagon, eager to warn the public about the dangerous AI-takeover threatening our humanity.

The second is AI-hoarding. With the advancements AI has made in just the last decade, along with the latest buzz in the business world, one would think that just about any business—big or small—would be adopting some form of this technology. The fact is that it continues to be far too expensive to be accessible to everyone, and giants like Google, Amazon, Facebook, and Alibaba are to blame.

When small and medium-sized companies develop new AI capabilities, oftentimes these cross-industry giants are quick to buy these companies out, adopting the technology to improve their own business workflows, not to release it to the public. It’s by doing this they are able to reach almost complete market-domination, making it virtually impossible for competitors to keep up.

The third factor at play is a serious lack of AI talent (and not to mention diversity). There are simply not enough data scientists, researchers, and AI engineers in the job market. This will undoubtedly change significantly within the next decade, but for now, competition to acquire this talent is fierce. And if scarcity wasn’t enough, the giants who are hoarding technology are also hoarding and poaching talent, offering them conditions and salaries far beyond what other companies could.

And the final obstacle to AI accessibility: AI enterprise solutions are largely bespoke. Simply put, we have not reached the stage where AI solutions can come out-of-box, ready to install and integrate with any business’ IT infrastructure and workflows. AI model creation and deployment is a very costly and laborious process, tailor-made to each enterprises’ needs.

What Can Smaller Companies Do?

For more businesses to have a shot at integrating AI solutions, three things are necessary: education, time, and the democratization of AI technologies.

As mentioned before, the low numbers of AI talent available results in a lack of resources to build and deploy solutions, and also makes these experts extremely costly to recruit. As the field of data science becomes more popular and available as a course of study in universities and tertiary institutions around the world, we will begin to see a much larger output of talent coming into the workforce. While that won’t stop companies like Baidu and Amazon from recruiting top candidates and buying out smaller companies for their technology, it does provide more chances for small and medium-sized players to acquire their own talent and solution-building capabilities. This educational revolution is in its beginning phases, however, meaning that industry will need a bit more time to reap its rewards.

When it comes to smaller companies benefitting from AI, we’ve already mentioned that for a majority of them these types of technologies are out of reach. AI solution-providers need to create solutions that can be integrated and utilized by more than one end benefactor. In fact, there are already AI vendors out there doing just this. Nexus FrontierTech has developed Podder.ai, ¹ an operating system allowing businesses to integrate AI models straight into their IT environment, regardless of being on-premises or in the cloud, allowing for easier deployment and scalability. Businesses using Podder.ai will no longer have to depend on sourcing all their AI models from one vendor, or depending on various vendors to adjust each and every model to fit their current workflows.

Enterprise-scale AI adoption is not impossible, just costly and out-of-reach for most businesses for the time being. But the tech landscape is changing, and it’s changing fast. With a ramp-up in data and tech-focused curriculums in educational institutions, and the development of solutions that make AI more accessible and easier to integrate, what seems like an unattainable fantasy for most small and medium-sized businesses may very well be their reality in the years to come.

About the Authors

Terence Tse is a professor at ESCP Europe Business School and a cofounder and excutive director of Nexus FrontierTech, an AI company. He has worked with more than thirty corporate clients and intergovernmental organizations in advisory and training capacities. In addition to being a sought after global speaker., he has written over 110 published articles and three other books including the latest Amazon best seller, The AI Republic: Building the Nexus Between Humans and Intelligent Automation. Follow him on Twitter: @terencecmtse            

Mark Esposito is a socio- economic strategist and bestselling author, researching the Fourth Industrial Revolution and Global Shifts. He works at the interface between Business, Technology and Government and co-founded Nexus FrontierTech, an Artificial Intelligence company. He holds appointments as Professor of Business and Economics at Hult International Business School and at Thunderbird Global School of Management at Arizona State University. He is equally a faculty member at Harvard University since 2011. He has authored/co-authored over 150 peer reviewed publications and 11 books, among which 2 Amazon bestsellers: Understanding how the Future Unfolds (2017) and The AI Republic (2019). Follow him on Twitter: @Exp_Mark)