Listen to the experts and vendors discuss the state of artificial intelligence these days, and one can be forgiven for feeling confused about what it takes to bring AI to the table in a realistic way. Is it a complex undertaking that requires profound planning, or something that is becoming inherent in just about every solution now available? Is it too hard to find talent to create AI, or is AI filling talent gaps? Is AI driving digital transformation, or does digital transformation spur AI adoption?
There’s no question that spending on artificial intelligence keeps rising. ROBO Global research, for one, projects that AI and machine learning spending will top $375 billion by 2025. It appears this is more than simply throwing money at the latest shiny objects. “A majority of the enterprises that we spoke to are not just evaluating AI implementations but often prepared with ROIs and outcomes that they are trying to achieve,” says Lisa Chai, partner and senior research analyst at ROBO Global. “These are all good indicators of adoption and acceleration.”
Still, not every AI initiative is front and center of business plans. “In some cases it can still feel like a stealth mode approach,” says Diego Tartara, chief technology officer at Globant. AI may bring some risks, but “businesses have realized the greater risk is not including AI into the equation.”
But do the risks of not incorporating AI outweigh those of moving ahead with the technology? The picture is mixed, especially when it comes to implementations, talent, and digital transformation:
Expectations of easy assembly, but more complexity, too. Many executives expect that “AI will solve all business problems, and it will be an easy adoption,” Chai says. “Implementing a transformative process using AI will take time, a team of AI engineers, and deep industry knowledge to manage the deployment. Currently, there are over 10,000 AI companies out there just in the US alone and the majority of these companies have very little commercial validation and track record.”
In addition, AI simply isn’t plugged in to start immediately delivering results. Instead, it needs to be part of a longer journey that has the potential to reshape business decisions over the months and years to come. “AI seems deceptively easy, as if all one needs to do is connect a couple of lines of code or boxes in low-code, or plug into a platform, and you get results,” says Tartara. “Implementing AI is harder than that. Being good and producing meaningful results implies doing many things under the surface.”
Paradoxically, while business leaders may see AI as easier than it really is, others see it as more difficult than it really is. “AI is a bold, still relatively new technology — some companies see that and get a little intimidated,” says Ajay Agrawal, CEO and founder of SirionLabs. “They assume that adopting and deploying such transformative tech must necessarily be a complex and cumbersome process, so they stay away.”
What may help ease adoption is “a rapidly growing number of AI products delivered as SaaS,” Agrawal continues. “Businesses can quickly get started – without having to worry about lengthy configurations, re-architecting or lift-and-shift replacements – and begin getting value in days.”
Nowhere enough talent to build AI, but AI may come to the rescue. Along with making business cases, there is the matter of finding or training the people that will put it all together. “The biggest issues holding back AI adoption today are the shortage of AI talent as it is still a tight job market for technical skilled workers,” Chai says. “Too many organizations try to take on projects they don’t have experience in — such as AI — instead of venturing and integrating with a suitable partner that can bring external expertise. Not just as a provider for some well-defined positions, but as a joint partner on the operation of their core business. There is more to AI than hiring a couple of experts, there is a way of operating and a necessity for disruption that might not be suited for in-house talent.”
At the same time, one of the most pressing business cases of AI is to augment or fill in for talent shortages. AI as a way to fill new roles that will emerge across enterprises. “AI, like other advanced technologies, frees people from repetitive work and allows them to develop new, higher-level skills,” he points out. “In addition to automating mundane tasks, AI-based solutions can enhance and augment those that are more complex. AI can improve the way people work while providing enterprises with better data and allowing them to generate better business outcomes.”
Digital transformation spurs AI. While there are many use cases being formulated for AI, the single most compelling reason is in support of digital transformation initiatives. Conversely, efforts to support digital transformation blazes the path to AI as well. “In cases where there is more stern resistance, the adoption happened through digital reinvention,” says Tartara. “No matter how traditional or analog a business may perceive it is, once digitalization kicks in, it means that they are effectively competing in a technological space. Every company is a tech company. Even in very traditional, old-fashioned industries, AI is gaining more ground, first as operations support and then driving the reinvention of the business.”
The question out of all this is, then, does AI solve more problems than it creates? The jury is still out, but so far, it holds a lot of promise.