AI Impact on the Workforce: Reinventing Roles from Customer Service & Beyond

AI is impacting many traditional job roles, even those usually not at the whim of rapidly changing technologies. It’s also creating jobs and providing an opportunity for certain low-skilled jobs to be upskilled into AI oversight roles or AI-augmented roles. 

“AI isn’t going to steal your job. Someone who knows how to use AI is going to steal your job.”

Tracy Mendolia-Moore

What this means is that employers must carefully analyze, rethink, and reinvent their employee’s job roles as well as consider the impact on the wider business. It’s not an easy task, and we’ve summarized some expert insights on how to reinvent roles impacted by AI. 

Accenture, in a report shared by the World Economic Forum (WEF), estimates that 40% of all working hours could be impacted by generative AI or large language models (LLMs) such as ChatGPT. This is because 62% of employee work time consists of “language” tasks, and now ChatGPT and other LLMs have “cracked the code on language complexity.” But it doesn’t mean AI will replace humans. Accenture believes that 65% of the time spent on language tasks can be transformed into more productive activity through “augmentation and automation.”

Familiar Jobs Will Change Forever

As we’ve already seen, AI is creating new jobs, but it is forever changing some traditional roles like that of the customer service assistant, secretary, or administrator. The impact of AI on these roles must lead organizations to reinvent these positions entirely because, quite simply, they can’t have staff sitting around, only needing to perform half of their usual role. And, of course, any business seeks to maximize AI’s potential to save labor hours and increase productivity. 

Accenture says that reskilling people effectively is the key to using AI and that employers will need to ensure staff develop the new skills that are required. 

“Success with generative AI requires an equal attention on people and training as it does on technolog. This means both building talent in technical competencies like AI engineering and enterprise architecture, and training people across the organization to work effectively with AI-infused processes.”

We’ve already discussed some of this in the sixth of our 2024 AI Predictions Mini-Series: AI + HITL – Enhanced Understanding of Human in the Loop. Human-in-the-loop has two key facets. First, humans are essential for training, supervising, and testing AI output. Second, humans will continually work side-by-side with AI to maximize the outputs of this still-emerging technology. Both include the elements of oversight essential to mitigate the risks of AI and ensure quality results. 

Breaking Down Existing Roles in Order to Upskill or Recruit

Accenture recommends that companies break down existing roles into “underlying bundles of tasks” to assess where AI’s impact will be. After this process, upskilling can begin, and companies may need to recruit for new roles that could include “linguistics experts, AI quality controllers, AI editors, and prompt engineers.”

Reinventing the Customer Service Role

The following graphic is Accenture’s breakdown of a customer service job into 13 constituent tasks in its report “A new era of generative AI for everyone.” 

Source: WEF/Accenture 

The breakdown roughly fits Accenture’s estimation of AI’s impact. We know that’s greater for customer service roles because AI chatbots can serve customer’s basic needs and inquiries instead of humans, and that’s just the start. 

How to Reinvent Any Role Impacted by AI

Bhaskar Ghosh, H. James Wilson, and Tomas Castagnino of Accenture writing “GenAI Will Change How We Design Jobs. Here’s How.” for HBR, say leaders should first analyze the impact of AI in their own domain before working with their technology leaders on “reinventing jobs, reconstituting processes, and refocusing the organization’s talent and reskilling strategies to realize the full potential of generative AI.” 

This process, per the Accenture authors, begins with the following:

  1. Break down the job into tasks
  1. Determine whether each task involves the intensive use of language (natural, computational, or mathematical)
  1. Assess how knowledge is used to perform the task (consider problem ambiguity and the need for collaboration or subject matter expert validation) 
  1. Analyze how generative AI may affect each task

“As a rule of thumb, tasks that entail recurring processes are candidates for full automation with generative AI, while tasks that require creative reasoning, collaboration, and judgment are candidates for augmentation with AI.”

From here, tasks with low potential for transformation by AI become human tasks. Those with a high potential for transformation may become AI-automated or augmented tasks. Moreover, new “high-value” human tasks could be identified as a result of the job “recomposition” or reinvention. 

These high-value human tasks may include the oversight roles necessary for AI outputs to mitigate risks. 

The Accenture team says the three types of tasks will be the building blocks to redesign jobs and unlock the value of AI. These redesigned jobs will then re-integrate with other redesigned jobs in an organization and “radically change how work gets done.”

Three types of tasks to reinvent jobs:

  • Automated
  • Augmented
  • Human 

The authors further recommend a company-wide strategy for adopting AI and any associated technologies and investing as much in “evolving operations and training people” as in technology. They also note that:

  • Leaders must ensure AI is “responsible and compliant” 
  • AI should not create unacceptable risks for the business
  • Output is carefully reviewed and overseen by humans
  • Output is accurate, unbiased, and free from intellectual property issues

Incidentally, Ghosh et al. for HBR say we’re at the stage where most organizations are experimenting with AI by using “off-the-shelf foundation models.” but that:

“The biggest value for many will come when they customize or fine-tune models using their own data to address their unique needs. In organizations that have moved beyond off-the-shelf generative AI to models the company continually customizes for its operations and business, the opportunity for enterprise reinvention is particularly large.”

For more on the future of AI, read our 2024 Prediction Series Wrap-Up: Our Top 7 AI Predictions for 2024. 

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