Grammarly vs WriteHuman

# Generative AI Will Reshape Knowledge Work, Not Replace It

Discussion about generative AI and knowledge work usually divides people into two groups: one that forecasts a large-scale replacement of analysts, writers, and programmers, and the other that regards the technology as nothing more than overhyped autocomplete. Both of these views overlook the more interesting truth. Generative AI will fundamentally reshape knowledge work by turning routine cognition into a commoditized service, at the same time increasing the value of judgment, taste, and accountability.

Think about the things that these systems do well; they can draft emails, summarize documents, generate standard code, and produce acceptable initial versions of almost any kind of text. Previously, such tasks took up huge parts of a professional's day. A study carried out by researchers at MIT showed that workers who used ChatGPT on writing tasks completed them about 40 per cent faster and received higher quality ratings. Likewise, GitHub found that developers who used Copilot finished their tasks considerably faster than those who did not use it. Once the cost of producing an initial draft reaches zero, the draft ceases to be the valuable part of the job.

What gains value is all that surrounds the draft. It is up to a person to decide which problem is worth solving, to assess whether the output is accurate, and to know when a confident-sounding answer is actually wrong. Generative models tend to make up citations, misinterpret the context, and cater to users' assumptions. In the widely publicised case of *Mata v. Avianca*, a lawyer who had submitted an AI-created case citation has not been replaced by technology; rather, she has been revealed by her failure to show professional judgment. The duties of verification and accountability stay the responsibility of humans, and they become all the more important as the amount of generated content increases.

There is, however, a very real risk, in particular for those who are new to the field. Traditionally, entry-level knowledge work has acted as an apprenticeship whereby junior analysts have learned by preparing memos and junior developers by correcting small bugs. If AI takes over these kinds of tasks, then organisations will have to find new methods of developing expertise or they will end up with a shortage of senior employees who can oversee the machines. Businesses that view AI merely as a way of cutting costs may end up weakening their own talent pipelines.

It has been argued that capabilities are developing so rapidly that even kinds of work which require a great deal of judgment will one day be automated. While this possibility must not be ruled out, history indicates that automation generally has a tendency to redefine jobs rather than eliminate them; just as spreadsheets did not get rid of accountants but instead abolished hours of arithmetic and increased the range of analyses that accountants were expected to carry out.

The right approach is not to panic nor to become complacent. Workers ought to develop their abilities in framing problems, carrying out critical evaluation, and gaining expertise in their field. Employers should redesign their jobs and training programmes with human oversight in mind. Generative AI is a powerful tool, and, like any tool, its impact depends on who is using it and on how carefully they understand what they are manipulating.

# 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.