The sponsors of an AI adoption initiative are its driving force in the modern workplace. Typically, senior managers define the vision that shapes the changes to business processes and sponsor new training for employees.

Executives were more interested in improving products or internal operations, helping in decision-making and freeing workers to focus on creative tasks.

“If you’re worried about the impact of AI on jobs, it should be good news that humans in many different roles are required to build, deploy, operate, and sustain these systems.”

Historically, people in business and IT roles have not understood each other’s work. Business roles cover activities like human resources, marketing, finance and management. IT roles cover the creation or configuration of the IT systems people use, as well as data-driven roles like data scientists, analytics specialists, AI/machine-learning engineers and data engineers. There is now, arguably, an additional knowledge gap between AI teams and other IT professionals.

Cross-functional roles bridge the gap between IT and business knowledge. Sometimes this bridging involves high-level coordination, like the product manager for AI systems and services at Shopee, an e-commerce platform in Southeast Asia, who ensures alignment across the organization and finalizes multi-stakeholder decisions. Sometimes it means multidisciplinary teams more specifically focused on governance, compliance or ethics. 

The professional knowledge of frontline workers is essential in order to effectively integrate AI into the work they do. They are often called upon to evaluate AI suggestions or output, making their professional judgement more important than ever. For this reason, training employees who are not already very experienced in their role to work with AI could represent a challenge.

“Going forward, system design efforts, deployment efforts, and ongoing post-deployment operational support efforts will continue to work out better with participatory inputs and strong support from the frontline workers.”

In some industries the trend is to hire fewer entry-level workers. 

“The very same technology… can both reduce opportunities for entry-level workers through productivity increases and expand entry-level work opportunities through enhanced levels of embedded training, guidance, and performance support.”

Human judgement remains an essential component in most work augmented by machine learning:

• AI can’t understand context
• Complex systems also represent a stumbling block for AI – 
• AI is still unable to understand emotional situations
• AI is not self-reliant 

For all these reasons, humans still need to have the final say when it comes to AI-generated recommendations.

“One of the great advantages of having humans and smart machines working alongside each other is that humans can confirm that an automated decision is ‘sensible.”

It’s important that humans working with machines understand the business processes the AI is trained to facilitate, so that they grasp the reasons for AI decisions and are able to determine their adequacy.  Similarly, AI systems need functionality to provide explanations for the decisions that they make to human workers, so that humans have the information they need to evaluate those decisions.  Explaining AI decisions also encourages workers to buy into AI integration in their roles.