Artificial intelligence – and related forms of automation and analytics – has become the tool of choice to help companies fill their talent gaps. The catch is that companies are struggling to find the skills needed to identify, build, and deploy the AI and automation needed to solve their skills shortages.
For example, Kshitij Dayal, senior vice president of Legion, lists AI capabilities on the HR side, such as workforce management and demand forecasting and, most importantly, a positive work environment in improving knowledge of employees’ wishes and needs. Automating tasks through AI, or augmenting the human workforce, results in greater productivity across the board. Acute skills shortages are better addressed, while employees and managers can focus on higher-level tasks.
All very well. But setting up AI capabilities takes skill, and that’s one of its biggest challenges.
AI/ML helped reduce headcount within their organization
Rackspace Technology’s latest survey of 1,420 IT managers confirms this. In many cases, AI/ML is replacing work once done by humans. 62% of respondents say that implementing AI/ML has reduced headcount in their organization. Additionally, 69% say AI helps improve the ability to hire and recruit new talent.
The biggest hurdle, according to executives interviewed, is the need for more AI and machine learning capabilities and the talent to manage data effectively.
The most common problem or obstacle encountered is the shortage of qualified talent, cited by 67% of them, followed by the failure of algorithms or models (61%) and the cost of implementation (57%). %).
“AI and machine learning are not ready to implement on their own” underline the authors of the report. “It’s hard to find skilled people who can work with technology and data to optimize results.”
To address these issues, 82% of respondents said they have made efforts to recruit employees with AI and machine learning skills in the past 12 months, while 86% have increased their AI and machine learning workforce. over the past 12 months.
Challenges to adopting AI and machine learning:
- A shortage of skilled talent: 67%
- Failure of algorithms/models: 61%
- The cost of implementation: 57%
- Lack of technological infrastructure: 54%
- Lack of internal skills/difficulty recruiting: 51%
- Difficulties related to the technical infrastructure: 49%
The other notable takeaway from the survey is the high degree of trust in AI results – and the fact that respondents are comfortable with the steps taken to ensure that trust. Despite concerns about data and internal resistance, confidence in the outcomes of AI projects remains high among IT decision makers surveyed, with 73% saying they have confidence in the answers provided by AI. 72% say sufficient checks and balances are in place to avoid negative consequences of using AI, while 80% of respondents do not think AI/ML responses require additional human interpretation .
High business confidence in AI decisions
Nearly three-quarters of executives, or 73%, say they always trust the analytics provided by AI and machine learning technologies. They say they have processes in place to ensure the AI is fair and unbiased. To that end, 72% say there are enough checks and balances in place to avoid negative consequences of using AI. Additionally, 77% say decisions about AI and machine learning are made by the “right people” within their organization, and 71% say there is sufficient governance to guard against misuse. misuse of AI.
virtual clouds (57%), Internet of things (51%), AI and machine learning (46%), blockchain (36%), robotics (34%) and 5G (31%) .
Technologies that are implemented within respondents’ organizations include virtual cloud networks (57%), Internet of Things (51%), AI and machine learning (46%), blockchain ( 36%), robotics (34%) and 5G (31%).
“Respondents consistently cited a lack of internal resources capable of understanding and refining the use of AI machine learning technologies,” the survey authors state. “Assess your current training processes and internal capabilities to determine if you should recruit externally or use the resources you already have.” To cultivate stronger AI and machine learning capabilities internally, you should consider increasing your company’s participation in conferences or events, and offering online training to your teams.”