Humanize AI Pro vs WriteHuman

# Generative AI Will Rethink Knowledge Work, Not Replace It

The discussion of generative AI and knowledge work seems to divide into two camps: people who think it will displace millions of analysts, writers, and programmers, and people who think it is just another overhyped autocomplete. Neither camp sees the more compelling picture: the way the technology will revolutionize knowledge work by commodifying routine cognition, and increasing the premium on judgment, taste, and responsibility.

Think about the strengths of the tools. They draft emails, they summarize documents, they generate code snippets and other boilerplate, they produce decent first drafts of almost any kind of writing. Each of these things used to consume enormous amount of a worker's time. According to a study conducted at MIT, employees using ChatGPT on writing tasks became 40 percent more productive with high quality rates. Also, according to GitHub reports, the developers working with Copilot became much faster compared to those working without it. Once the cost of the first draft gets close to zero, the draft itself stops having any value.

The value starts lying in the processes surrounding the draft. Someone has to pick the right problem to solve, someone has to check if the result is correct, and someone has to figure out when the wrong answer sounds correct. AI models make up references, they interpret the context incorrectly, they please the user. When the lawyer submits an AI-generated case citation, as it happened in the famous *Mata v. Avianca*, this lawyer is not replaced by the machine, she is caught for the lack of professional judgment. Validation and accountability are still humans' responsibility, and they become more important with the increase of the generated material.

There is a danger in this change though, especially for entry-level workers. Knowledge work has always been an apprenticeship, where junior analysts write memos and junior developers fix bugs. Once the machine takes these tasks, the company will have to invent some alternative way of developing expertise, otherwise, there will be no senior workers who could oversee the machines. Companies that use AI only to reduce costs will end up with reduced talent pipeline.

It is argued, however, that the capabilities are growing too fast, and eventually, all knowledge work will be automated. The possibility should be taken seriously. Historically, though, automation tends to redefining occupations, not destroying them. The spreadsheets did not kill the accountants; they killed the arithmetic that the accountants had to do and expanded what the accountants had to analyze.

The proper reaction is neither panic nor complacency. Workers should improve their skills in framing problems, in critical analysis, and in domain expertise. Companies should rethink jobs and training to take into account this automation. Generative AI is an excellent leverage, and as any leverage, its effect depends on its user and his/her understanding of the object.

# 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.