Easy but difficult to implement, no talent but alleviating the talent shortage
Listen to experts and vendors discuss the state of AI these days, and one may feel confused about what it takes to realistically bring AI to the table. Is it a complex task that requires deep planning or something that is becoming inherent in almost all solutions available now? Is it too hard to find talent to create AI, or is AI filling the talent gap? Is AI driving digital transformation or is digital transformation fueling AI adoption?
There is no doubt that spending on artificial intelligence continues to rise. ROBO Global research, for example, projects that spending on artificial intelligence and machine learning will surpass $375 billion by 2025. It seems this is more than just throwing money at the latest shiny objects. “Most of the companies we talk to are not just evaluating AI implementations, but they are often preparing with ROI and the results they are trying to achieve,” he says. lisa chaipartner and senior research analyst at Global Theft. “These are all good indicators of adoption and acceleration.”
Still, not all AI initiatives are front and center in business plans. “In some cases, it can still feel like a stealth mode approach,” he says. Diego Tartarachief technology officer they gloat. AI can bring some risks, but “companies have realized that the biggest risk is not including AI in the equation.”
But do the risks of not incorporating AI outweigh those of going ahead with the technology? The picture is mixed, especially when it comes to implementations, talent, and digital transformation:
Expectations of easy assembly, but also more complexity. Many executives hope that “AI will solve all business problems and be easy adoption,” Chai says. “Implementing a transformation process using AI will take time, a team of AI engineers, and deep industry knowledge to manage the implementation. Currently, there are over 10,000 AI companies in the US alone, and most of these companies have very little commercial validation and track record.”
Also, the AI just isn’t wired to start generating results right away. Instead, it should be part of a longer journey that has the potential to reshape business decisions for months and years to come. “The AI seems deceptively easy, as if all you need to do is plug in 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 more difficult than that. Being good and producing meaningful results involves doing a lot of 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 technology, still relatively new; some companies see that and are a little intimidated,” he says. Ajay AgrawalCEO and founder of Sirion Laboratories. “They assume that adopting and implementing such transformative technology must necessarily be a complex and cumbersome process, so they stay away.”
What may help facilitate adoption is “a rapidly growing number of AI products delivered as SaaS,” Agrawal continues. “Businesses can get started quickly, without having to worry about lengthy setups, redesigns, or lift-and-shift replacements, and start realizing value in days.”
There is not enough talent to build AI, but AI can come to the rescue. Along with making business cases is the question of finding or training the people who will put it all together. “The biggest issues holding back AI adoption today are the shortage of AI talent, as it remains a tight job market for skilled technical workers,” Chai says. “Many organizations try to take on projects they don’t have expertise in, like AI, rather than venture out and integrate with a suitable partner who can bring in outside expertise. Not just as a supplier for some well-defined positions, but as a joint partner in running your core business. AI is more than just hiring a couple of experts, there is a way to operate and a need for disruption that might not be a good fit for in-house talent.”
At the same time, one of the most pressing business cases for AI is increasing or filling the talent shortage. AI as a way to fulfill the new roles that will emerge in companies. “AI, like other advanced technologies, frees people from repetitive work and allows them to develop new, higher-level skills,” he says. “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 companies with better data and enabling them to drive better business results.”
Digital transformation stimulates AI. While many use cases are being formulated for AI, the most compelling reason is to support digital transformation initiatives. Conversely, efforts to support digital transformation also pave the way for AI. “In cases where there is more severe resistance, adoption has happened through digital reinvention,” says Tartara. “No matter how traditional or analog a company may perceive itself to be, once digitization kicks in, it means they are effectively competing in a technology space. Every company is a technology company. Even in very traditional and old-fashioned industries, AI is gaining more ground, first supporting operations and then driving the reinvention of the business.”
The question in all of this is, then, does AI solve more problems than it creates? The jury is still out, but so far, it’s very promising.