How Will Generative AI Transform the Future of Work?

The short answer is that nobody yet knows, but here’s a summary of recent articles by folks trying to figure out how generative artificial intelligence (AI) might shake up specific jobs and the workplace as a whole in coming years.

  • “Implementing AI during a worldwide talent shortage” is (kind of) about the Catch-22 in which companies lack enough human talent to implement AI systems so they can make do with less human talent. That is, as much as many companies want to harness the power of AI (presumably to automate or at least augment certain jobs), they can’t find the talented people necessary to get those systems into place. Oh, the irony.

New research by SambaNova Systems has shown that, globally, only 18% of organizations [in the UK] are rolling out AI as a large-scale, enterprise-scale initiative. Similarly, 59% of IT managers in the UK report that they have the budget to hire additional resources for their AI teams, but 82% said that actually hiring into these teams is a challenge….[O]nly one in eight IT leaders have fully resourced teams with enough skilled workers to deliver on what the C-suite is asking. A further one in three are struggling to meet the demands placed on them. The rest (over half) are unable to deliver on the C-suite’s vision with the people they have.”

So, what should companies do? One idea is to upskill workers so that they can implement these new and powerful large language models (LLMs) (like the latest Generative Pre-trained Transformer (GPT) models everyone is gushing about). Another idea is to outsource such projects. So, the future of AI will first require many companies to upskill their human workforces.

  • “ChatGPT Is a Stunning AI, but Human Jobs Are Safe (for Now)” is about whether the amazing and increasingly famous ChatGPT large language model will soon be able to replace a large portion of the workforce. Some believe the answer is yes. An article in The Guardian, for example, recently stated “professors, programmers and journalists could all be out of a job in just a few years.” 

But Jackson Ryan, writing in CNET, says it’s not that simple. “[ChatGPT] doesn’t know how to separate fact from fiction. It can’t be trained to do so. It’s a word organizer, an AI programmed in such a way that it can write coherent sentences…It definitely can’t do the job of a journalist. “

Instead, Ryan sees its future use as more a tool than a kind of automation: “ChatGPT and its underlying model will likely complement what journalists, professors and programmers do. It’s a tool, not a replacement.”

  • In CDO Trends, Paul Mah writes about “How AI Will Change Work.” He tends to agree that generative AI will be used more as a tool than a replacement. He writes, “Just like how graphic designers took to using computers, programmers, designers, and writers will soon need to start using AI tools to stay relevant. If you have yet to try your hand at writing the prompts used to generate AI art, let me assure you that it is harder than it looks. Part programming and part writing, it takes a fair amount of effort to get text-to-image systems to generate the artwork that you want.” In short, using these tools effectively requires skills and expertise in itself.

He too agrees that, at least for now, “generative AI won’t kick every creative worker out of their jobs, but it will change how they go about them, and where their time and energy will be focused.” So, how will generative AI jumpstart productivity? Toker suggests three ways: 1) Faster writing as the AI creates notes and rough drafts that writers can build on and revise, 2) Improved customer service as employees “get a transcript of any conversation and get generative AI to produce an analytical summary of what was said,” and 3) Faster mock-up creation “by building out the basic scaffolding at the early stages…[giving] workers more time for creative exploration with customers.”

  • In “7 AI predictions for 2023 from IT leaders,” various AI and information technology experts weigh in on what’s going to happen with AI over the coming year. For example, David Talby, CTO at John Snow Labs, notes, “Dozens of companies offer you products that will draft essays, ad copy, or love letters. Instead of searching through stock photography, you can type a query and get a newly generated image. And this is just the beginning – we’re only scratching the surface of generative voice and video applications, so it will be interesting to see innovations and use cases come forth in the coming year.”
  • In “5 Ways to Use ChatGPT in Your Workflow,” Hillel Fuld discusses using ChatGPT to boost content marketing by using it to come up with ideas on any given topic, helping to create a first draft, suggesting titles, helping with research and shortening text with character limits.
  • In “How to Save Your Job from ChatGPT,” Trung Phan says the new tech can create documents on a range of issues, including legal, financial analysis, sales pitches and even corporate strategies. [Note: Given the bot’s propensity for fiction and exaggeration, however, firms had better be extremely careful for using it for these purposes.] Ethan Mollick, an innovation professor at The Wharton School of the University of Pennsylvania, is quoted as saying, ““I think people are underestimating what we are seeing from ChatGPT…If you are a white-collar worker, this is transformative for productivity.” Phan goes on to cite other possible uses:

Lawyers will probably write legal briefs this way, and administrative assistants will use this technique to draft memos and emails. Marketers will have an idea for a campaign, generate copy en masse and provide finishing touches. Consultants will generate whole Powerpoint decks with coherent narratives based on a short vision and then provide the details. Financial analysts will ask for a type of financial model and have an Excel template with data sources autofilled.

  • In Harvard Business Review‘s “ChatGPT and How AI Disrupts Industries,” the authors writes, “AI presents opportunities as well and will create new jobs and different kinds of organizations. The question isn’t whether AI will be good enough to take on more cognitive tasks but rather how we’ll adapt.”

The authors disagree with the conventional wisdom that generative AI will simply improve the speed with which writers write, programmers code or artists create. They note that GPS-plus-AI-maps did not just make taxi drivers better at their jobs; they made it possible for hundreds of thousands of Uber and Lyft drivers to compete with taxi drivers. In other words, it changed the whole paradigm. The authors are not sure what these new work paradigms will look like, but they expect they will be transformative.

  • In “Beyond ChatGPT: The Future Of AI at Work,” Karl Moore points out some of the most critical flaws associated with ChatGPT. In particular, the bot does not actually read sources and cite works. Therefore, it can’t be trusted to get things right and can’t help the reader determine the validity of an analysis by drilling down into source materials.

One possible way around this in the future is by coupling generative AI with semantic search, which “seeks to improve search accuracy by understanding the searcher’s intent and the contextual meaning of terms as they appear in the searchable dataspace.” He states, “When generative AI and semantic search are used together, you can ensure that generative AI chatbot responding to a customer query is providing correct company information, and that responses being provided to answer a critical client question is based on the most up-to-date competitor information.”

  • In “Stumbling with their words, some people let AI do the talking,” the authors discuss the case of the dyslexic business owner who had ChatGPT hooked up to his email account by tech consultant Danny Richman. Now when he writes a message to his clients, “the AI instantly reworks the grammar, deploys all the right niceties and transforms it into a response that is unfailingly professional and polite.” After Richmond wrote about the success of the application on Twitter, many people reached out to him: “They said they always worried about their own writing: Is my tone appropriate? Am I too terse? Not empathetic enough? Could something like this be used to help with that?” One person even told him, “If only I’d had this years ago, my career would look very different by now.”
  • In “Four Paths to the Revelation,” Ethan Mollick formulates “exercises to help you understand the potential impact of AI.” First, treat it like “a magic intern with a tendency to lie, but a huge desire to make you happy.” Give it proper oversight, using your own expertise to guide it, and you’ll become far more productive. Second, give it a scenario and ask it to give you multiple choices about what happens next. You can use this to do anything from create choose-your-own-adventure games to business scenario planning. Third, ask it for lots of ideas. For example, “Give me 25 ideas about how to make money with a medical degree in an industry other than medicine.” After it lists out the ideas, ask it to elaborate on the ideas that seem most interesting and feasible to you. Fourth, use it to engage in a joint hallucination. The example he uses is “Taylor Swift starts a supergroup with the Beatles who have time-travelled from 1969.” He then asks for ideas related to scenarios such as a list of songs they’d perform together (including songs they’d write jointly), a Stephen Colbert monologue after their concerts, and a play about how the Beatles and Swift first met.

But you don’t need to stop there, says Mollick. There are many other options, from asking it to write some working computer code (yeah, it can do that, though the code may not be perfect) to seeing how many of your current duties you might automate.

We’re Just Getting Started

So that’s my synthesis of some of the articles out there on how generative AI will change the future of work. These are just the tip of the proverbial iceberg. There will be many more and better articles as we all become more familiar with these technologies and as thousands of new applications are built around them.

Here’s hoping we can keep up.

Featured image from Mirko Tobias Schäfer, https://commons.wikimedia.org/wiki/File:KUKA_Industrial_Robot_Writer.jpg