There is a significant disconnect between employers’ expectations for AI in the workplace and how employees are actually using it, according to a Slingshot/Dynata survey of 253 adults at U.S.-based companies.
The report found that while many employers have introduced AI to assist with initial research (62%), workflow management (58%) and data analysis (55%), nearly two-thirds of employees (63%) are primarily using AI to double-check their work.
Less than a quarter (23%) of employees surveyed said they feel fully trained on AI, despite 72% of employers believing their staff is adequately prepared.
This gap is particularly pronounced between genders, with 66% of men feeling adequately trained compared to just 44% of women.
Casey Ciniello, Reveal and Slingshot senior product manager, Infragistics, said behind the AI revolution is the stark reality that women are significantly underrepresented in the field of AI.
“As AI models become more advanced and Gen AI tools proliferate, women risk being left behind in the adoption of these technologies,” she said.
Ciniello said to bridge this gap, companies should implement a training curriculum that emphasizes continuous and structured learning rather than relying on occasional courses and workshops.
“This approach should be guided by a clear vision and designed to build on prior knowledge in a cohesive and systematic manner,” she explained.
By embedding these educational programs into their operations, organizations can ensure that women are equally equipped to succeed in an AI-driven workplace and fully leverage AI tools.
The report also underscores a disparity in perceived productivity gains: While 60% of employers believe AI is significantly boosting productivity, only 44% of employees report similar benefits, with 10% saying AI hasn’t improved their productivity at all.
Still, most employees are getting some benefit from AI, with 79% saying it saves them at least 1-2 hours a day, and 37% claiming they save 3-4 hours.
Ciniello pointed out the survey results indicated employees are using this extra time to reduce workload stress and prioritize tasks more effectively.
“AI-powered tools are revolutionizing collaboration, analysis and innovation, reshaping the very concept of productivity,” she added. “By automating mundane tasks and delivering insights through sophisticated analytics, AI can serve as a powerful catalyst for improving productivity.
She explained chatbots can facilitate smoother communication, virtual assistants boost internal collaboration and predictive analytics offer valuable insights, all of which contribute to creating a more streamlined and productive work environment.
“The integration of AI into the workplace must be guided by a strong commitment to ethical standards, ensuring that its benefits are achieved in a way that respects both individual rights and societal values,” she said.
The report also indicated AI implementation faces another hurdle, with 45% of employers reporting their company’s data isn’t ready to support AI, with siloed data across departments and platforms preventing AI from running smoothly.
Quality and Quantity Count
“In AI, both the quality and quantity of data are paramount,” Ciniello said.
Poor quality data can result in inaccurate predictions, while having too much data can lead to processing slowdowns and issues like overfitting.
Ciniello explained meticulous data preparation is essential for ensuring that AI systems perform accurately and efficiently.
Successful data preparation encompasses a series of processes, such as cleaning, transforming and structuring data to ensure it is ready for AI algorithms.
Ciniello explained with the right preparation, organizations can leverage AI and machine learning to drive higher sales and efficiency with relatively modest effort.
“Although this process can be time-consuming and resource-intensive, it is a crucial investment in the success of AI implementations,” she said.
