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# Generative AI and Knowledge Work

Typing a description of generative AI into a search engine yields two major camps of thought. The first camp believes the widespread adoption of generative AI will result in large scale automation of analysts, writers and programmers. The second camp believes these technologies are little more than glorified autocomplete systems and therefore will have little impact on the workforce.

The reality will be more nuanced. Routine cognition will be commoditized and the value of ethical judgment and accountability will increase.

Generative AI is effective at a myriad of cognitive tasks. These systems can now perform many activities that comprise a knowledge worker’s job. For example, Email, document and code drafting. First iteration generative systems have shown their capabilities and dramatically reduced the time it takes to perform writing and coding tasks. Researchers at MIT and the famed EIC (Economic Intelligence Unit) at Forbes found that ChatGPT increased the quality of writing and reduced the time required to write a document by 40%. Likewise, developers using Copilot dramatically increased their productivity and reduced the time it takes to perform software development tasks.

What gets valuable are the things adjacent to the draft. This includes deciding what problem to solve, assessing the fidelity of the output, understanding when a seemingly correct answer is actually false, and recognizing flaws in others’ thinking. There have been major concerns about the quality of Large Language Models and the confidence with which they make assertions, and these issues are becoming more amplified. These models may make users of the technology feel as though they’ve been replaced, but in truth, these users have often simply failed to adequately evaluate the models’ outputs and exercised poor professional judgment. Verifying the integrity of content and holding parties accountable are activities that will become increasingly important and that, for the foreseeable future, will require human interaction.

While technologies to assist knowledge work are not new, automation at this scale to perform tasks that require critical judgment and professional skills, such as drafting memos and analyzing data or legal frameworks, would dramatically reshape the practice of knowledge work and potentially displace human professionals. However, there is an opportunity for companies to redesign how they integrate and deploy this technology to address the needs of their business while preserving professional personnel and mitigating risks associated with technology displacing human professionals.

Critics believe that automation will eventually impact even the most judgment-based jobs. While it’s true that virtually no job will be completely immune to automation, it’s important to recognize the net positive impacts that automation has historically had on the labor force. The introduction of spreadsheet technology, for example, ultimately enabled accountants to spend less time on mundane, repetitive tasks and led to a greater focus on higher-level analysis.

Automation and the changing nature of work present both challenges and opportunities to the workforce and to the employer. The nature of jobs should be redesigned by employers to incorporate relevant oversight by a human operator and to give employees opportunities to leverage and deepen their critical analysis and domain-based skills. The job of generating ideas and determining what to do with the new ideas brought on by Generative AI will be a greater challenge for the workforce than figuring out how to incorporate this technology in the workplace.

The Future of knowledge work will be transformed by Generative AI but not replaced.

Discussions about the impact of generative AI on knowledge work usually fall into two categories: one that anticipates radical change and claims that analysts, writers and programmers are going to be displaced like crazy; and the other that declares it to be nothing more than an overhyped extension of “autofill.” Both overlook the more interesting reality. The future of generative AI will deeply impact the way that knowledge work is transformed, giving rise to a world where routine cognition becomes more and more commoditized, but judgment, taste, and accountability take on greater significance.

Look at these systems and think about what they do well. They compose emails, summarize documents, write boilerplate code, and create decent first-drafts of almost any written material. Once these tasks would take a lot of time with a pro. But according to a study from researchers at the Massachusetts Institute of Technology, employees who rely on ChatGPT to write experience a boost in speed and quality, completing tasks some 40% faster and with better ratings. Likewise, Copilot helped developers finish tasks much faster than those who didn't use it, according to GitHub. As the effort to produce the first draft approaches being completely free, it is the creation of the first draft that loses its value.

It is the "drafts" themselves that become valuable, the surrounding things. Who will determine what sort of problem is worth solving, who will accept or reject his or her output as correct, and who will be able to tell when an answer that sounds so good can't be right. Generative models concoct citations, misread context and indulge the users' assumptions. The inauthentic lawyer mentioned, as occurred in a widely reported circumstance – Mata v. Avianca – employed an AI-generated case citation rather than failing to be a lawyer, she failed to be a professional one. Verification and accountability is a human responsibility, and it becomes even more crucial to ensure the accuracy of generated content as it increases.

There's a serious risk, though, in this change and this is particularly for the newer folks. As is typical for apprenticeships, entry-level knowledge work has been a place where people learn by doing small tasks: junior analysts learn by writing memos, junior developers learn by fixing small bugs. Until AI takes over these tasks, organisations will have to look at alternative strategies to develop their expertise, otherwise they may end up with not enough seniors supervising AI. Firms that over-rely on AI as a tool just for reducing expenses can end up depleting their talent pool.

Respondents believe that with the fast pace at which capabilities are developing, even judgment-driven work can be automated in the future. Such a possibility cannot be ruled out. History indicates, however, that automation is more likely to change occupations than eliminate them. Accountants were not exiled by spreadsheets, rather hours of adding and subtracting were cut short and what was once considered within an accountant's realm to analyze expanded.

Panic or complacency are both wrong answers. Building worker skills to frame problems and perform critical evaluation and domain expertise. Employers should create new human positions and trainings. Like a lever, Generative AI can be a force to be reckoned with – depending on who is wielding it, and how well they understand what they are pushing.

The battle lines in the discussion around generative AI and knowledge work are drawn between the proponents who see the coming of the age where analysts, writers, and programmers will be replaced en masse and the skeptics who see the technology as a glorified autocomplete. Both are correct in broad strokes but miss the nuance that generative AI will disrupt the world of knowledge work not by replacing specific professions but by commoditizing the means of production. In other words, the basic cognizance tasks will become cheaper and easier, while the more nebulous skills will be in higher demand.

To begin with, it is worth speculating on what exactly generative AI can do. It can write emails, summarize documents, write boilerplate code, and generate copy for most products. In short, it can do most of the tasks that a person would do in an entry-level position. According to the researchers at MIT, people who used the AI to complete their writing tasks were able to finish them around 40% faster and of a better quality. Similarly, GitHub’s statistics show that developers using Copilot write code much faster than those who do not. Therefore, there is no doubt that using generative AI makes the process of completing a task much quicker and cheaper.

With that said, the commoditization of the means of production will affect the value of the activities performed around it. It implies that the first draft is no longer the end of the process but its beginning. Someone will always have to take the paper that the AI has generated, decide whether it is accurate enough, and edit it into quality content. Additionally, someone will have to make the rules that the AI will follow and judge whether the output is suitable for the purpose. Currently, it is not a problem because most of that work is usually done by junior specialists or those in unrelated fields. If generative AI becomes ubiquitous at the workplace, there will be a scramble for senior staff to do the work of entry-level employees

A lot of people argue that the spread of such technologies will lead to a point where even the most nebulous skills will be taken over by AI. One has to admit that the argument is not without its basis.

As the technology advances, there is a realistic scenario where it can become sophisticated enough to handle tasks that require judgment and discretion. However, it is also logical to presume that the introduction of generative AI into the workplace will affect the nature of work itself. The most likely outcome is that the means of production will be shifted to the individual, similar to how spreadsheets affected accountants. There is no question that spreadsheets did not replace accountants but changed the nature of their work, which in turn increased the demand for people who could operate spreadsheets.

Therefore, the best course of action for both employees and employers is to prepare for the change. For workers, that most likely means learning to develop and maintain judgment and discretion. For employers, it indicates a need to change the structure of the workplace and the employee training programs. All in all, generative AI will be a lever that can either destroy the workplace or improve it at an unprecedented scale.