Kyndryl’s Martin Summers says that businesses need to focus on upskilling and ensuring that their IT infrastructure ready for tech advancements.
Artificial intelligence has already cemented itself as the tool every business needs in its arsenal to be successful. In the few short years since the tech burst onto the mainstream, billions have already gone into adopting and utilising the technology.
Although the need for AI skills have been highlighted at least for the last few years, its speedy adoption is hindered by a workforce left untrained, trying to keep up with the technological advancements.
According to a recent Kyndryl Readiness Report, which surveyed 3,200 business leaders and decision makers worldwide, while confidence in AI adoption is growing, only 29pc leaders feel adequately prepared for AI-related challenges.
But the numbers aren’t much higher for other tech necessities as well, with 61pc leaders admitting that they don’t feel completely ready to manage future risks with IT infrastructure.
Martin Summers, Kyndryl’s director of practice leader for applications, data and AI says that a lack of preparedness when it comes to AI literacy will result in inefficient implementations, causing complications and slowing down the digital transformation of a business.
This will also introduce security concerns, Summers adds: “Employees who lack AI literacy may unknowingly expose sensitive data or misconfigure AI models, leading to vulnerabilities.”
Moreover, without proper training in AI ethics and bias mitigation, organisations “risk deploying AI systems that produce biased or inaccurate results,” he explains.
It is not a hard prediction to make that the demand for AI skills will grow, however, Summers, who has spent more than 20 years in various leadership positions, says that the businesses that fail to prioritise AI upskilling “risk falling behind”.
“AI will become an integral part of decision-making across industries,” he says, one which would require all employees to brush up their skills.
Training the workforce
Bridging the AI literacy gap requires a proactive approach, explains Summers. This would include internal training programmes, partnering with educational platforms and offering structured learning opportunities such as boot camps and certifications.
“In-house training initiatives should range from foundational AI literacy courses to specialised technical training in machine learning and AI applications,” he adds.
According to the Kyndryl lead, successful AI upskilling would need a “structured and scalable” approach to teaching. Self-paced courses delivered through online platforms will allow employees the time to learn while partnerships with institutes and third-level organisations can enhance their quality by tailoring it to the industry’s needs.
In 2023, Udemy, the online skill-development provider, found a major uptick in the demand for semiconductor and generative AI skills. While in its latest report, the platform reported a staggering 859pc increase in the consumption of generative AI learning on Udemy Business.
“Embedding AI literacy into performance evaluations can reinforce the importance of upskilling across the organisation,” Summers advises, explaining that the nature of this evolving technology would require companies to adopt a “culture of lifelong learning”.
According to the Kyndryl lead, this culture of learning should also allow experienced employees to take up mentorship positions to help accelerate knowledge transfer.
However, becoming AI ready and building resilience requires more than just training the workforce. According to the Kyndryl Readiness Report, nearly 44pc of mission-critical IT infrastructure is at or near its end-of-life, creating significant barriers to AI adoption.
In addition to upskilling employees, businesses need to prioritise modernising their IT environments, Summers says. This can be achieved by investing in scalable cloud solutions, upgrading data management systems and integrating AI-compatible infrastructure.
“Ensuring robust cybersecurity measures are in place is also essential to protecting AI-driven processes and sensitive data.”
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