Easy but hard to implement, no talent but alleviates talent deficit

When we hear experts and vendors talking about the state of artificial intelligence these days, we can be forgiven for wondering what it takes to bring AI to the table in a practical way. Is it a complex undertaking that requires careful planning, or is it inherent in nearly every solution available today? Is it too hard to find talent to create AI, or is AI is filling the talent gap? Is AI driving digital transformation, or is digital transformation driving AI adoption?

There is no doubt that spending on artificial intelligence continues to grow. For example, research from ROBO Global predicts spending on AI and machine learning will exceed $375 billion by 2025. Lisa Chai, Partner and Senior Research Analyst at ROBO Global, said: “These are all good indicators of adoption and acceleration.”

Still, not all AI initiatives are at the forefront and center of their business plans. “Sometimes it feels like a stealth mode approach,” says Diego Tartara, Globant’s chief technology officer. AI can pose some risks, but “companies are realizing that the risks of not including AI in the equation are greater.”

But do the risks of not embracing AI outweigh the risks of moving the technology forward? The picture is mixed, especially when it comes to implementation, talent and digital transformation.

Easy assembly is expected, but it also adds complexity. Many executives expect AI to solve every business problem and be easy to deploy. “Using AI to implement innovative processes requires time, a team of AI engineers, and deep industry knowledge to manage the deployment. There are currently over 10,000 AI companies in the United States alone. , most of these companies have little commercial validation and track record.”

Additionally, the AI ​​is not plugged in, so it cannot give immediate results. Instead, it should be part of a longer journey that could reshape business decisions over the coming months or years. “AI looks easy at first glance, as if you could just connect a few lines of code or low-code boxes or plug into a platform and get results,” he says. “Implementing AI is harder than that. Producing good and meaningful results means doing a lot behind the scenes.”

Paradoxically, while business leaders may think AI is easier than it really is, other business leaders think AI is harder than it really is. SirionLabs CEO and Founder Ajay Agrawal said: “They stay away because they believe that adopting and deploying such innovative technology must necessarily be a complex and cumbersome process.”

What could help ease adoption is the “rapid increase in the number of AI products offered as SaaS,” continues Agrawal. “Businesses can get started quickly and get value in days without worrying about time-consuming configurations, redesigns, or lift-and-shift replacements.”

Nowhere is there enough talent to build AI, but maybe AI can help. In addition to creating the business case, there is the issue of finding or training the people to put it all together. “The biggest problem currently hindering AI adoption is the lack of AI talent, as the job market for skilled workers remains tight.” he says Mr Chai. “Too many organizations are trying to tackle projects they have no experience with, such as AI, instead of venturing and integrating with the right partner who can bring outside expertise. Not only as a person, but as a joint partner in running the core business.AI is more than just hiring a few experts, there are operational methods and disruptive needs that may not be suitable for in-house resources. .”

At the same time, one of AI’s most pressing business cases is to augment or fill talent shortages. AI as a way to fill new roles emerging across the enterprise. “AI, like any advanced technology, frees people from repetitive tasks and enables them to develop new and advanced skills,” he points out. “In addition to automating mundane tasks, AI-based solutions can enhance and scale more complex tasks. , can improve the way people work.”

Digital transformation is fueling AI. While many use cases for AI have been formulated, one of the most compelling reasons is to support digital transformation initiatives. Conversely, efforts to support digital transformation also pave the way for AI. “Where there was tougher resistance, adoption was through digital reinvention,” he says. “No matter how traditional or analog business perceives it, once it goes digital, it means that it is effectively competing in the tech space. All companies are technology companies. Even in traditional, old-fashioned industries, AI is gaining more ground, first as operational support and then by driving business reinvention.”

So the whole question is whether AI solves more problems than it creates. The jury is out yet, but so far there’s a lot of promise.