Yesterday I had the pleasure of attending the Academia Meets Practice (AMP) conference at the Muma College of Business in Tampa. This annual conference is organized by the Executive Doctor of Business (DBA) program and alumni. I spent the day listening to some very smart people talk about Artificial Intelligence (AI). They talked about how it is currently being used in the workplace, and how it will become the next important tool that all of us will use. A theme I picked up on is how AI will become a collaborator and not a replacement for people. But this will require adaptation as AI is the latest disruptive technology. This has me reflecting on my experience with a past disruptive technology–word processing.
Life in the Typewriter Age
The course I took in high school that had the biggest impact on my professional life was typing. Scheduled between classes on calculus and physics, each day I learned to type on a mechanical typewriter. When I entered the workforce I had secretaries working for me who would take dictation and do typing. I would use a tape recorder to dictate correspondence, and the secretary would type up letters and mail them for me. When I became a faculty member, I was able to type first drafts of my papers rather than hand writing them to save time. The secretary would take care of revisions that had to be retyped from scratch. When I produced the final copy with edits written by hand in ink, I could hand it to her (they were all women) and she would type a final copy, compose a cover letter, make copies, and submit to a journal via physical mail. Life was good.
Then along came word processing. One day the department secretaries came to work to find an IBM PC on their desks. At first the impact was to make them more efficient. They only had to type first drafts from scratch. Subsequent drafts could be copy edited on the screen. Then faculty got computers and soon they were expected to type their own papers and handle their own revisions on the screen. Email came along and the physical letter became obsolete. Faculty handled their own email correspondence.
There were two major disruptions that occurred. First, faculty became their own secretaries. This made some aspects of the faculty job more efficient, but it added workload. In 1985 a secretary handled journal submissions for me. Today I have to go online and do it myself–a process that can be cumbersome and frustrating. Second, secretaries transitioned to administrative assistants. They weren’t fired, but rather their jobs evolved to handle administrative functions that for a state university have expanded tremendously over time. Their jobs today are a lot more complex and mentally demanding than merely typing.
AI Is the Latest Disruptive Technology
One of the points made yesterday was that AI disruption will be more than the deniers expect and less than the technophobes fear. It is going to change how much of our content is created. This will have most impact on those whose jobs are more on the production side–people who sit at their computer all day drawing images and writing routine content. Thomas Pavey explained how AI was affecting his business. About 70% of the content creation he outsourced he was now able to do in house. Not only was this a cost savings, but it is quicker to ask DALL-E to create some images than it is to locate, enter into contract, and then wait for a human illustrator.
As production of content becomes quicker and easier, there will be less demand for those skills. This doesn’t mean that individuals who are working for companies doing those jobs will become unemployed. More likely they will transition to doing other things. Some might wind up as experts in using AI tools to create content and do other tasks. AI requires human intervention–we have to tell it what to do and correct it when it does it incorrectly. We have to fact check it and copy edit its writing. We cannot just turn a task over to AI and be done with it.
All of us who create content as part of our jobs, will be using these tools as labor saving devices. Just as word processing has made it easier to produce written content because every draft does not have to be retyped from scratch, AI can produce much of the routine or “boilerplate” material. Jason Cherubini talked yesterday about how he uses ChatGPT to write first drafts of routine correspondence. Jill Schiefelbein demonstrated how it can be used to personalize video messages through the use of highly realistic avatars.
If we look back at prior disruptive technologies, we can get some clues about what to expect. New technology disrupts the nature of jobs rather than the existence of jobs. A century ago a calculator was a job title and not a device. Today we have fewer people who spend the day doing routine calculations and more people who are data analysts who conduct and interpret data analyzed by machine. Tomorrow we might have fewer people writing content and more people deciding what content needs writing, and using that content in new and impactful ways. Some people argue that AI is a qualitatively different disruptive technology that will have a bigger impact than anything we have seen in the past, but I have my doubts. To me it is a very useful tool that will produce a period of adaptation as it changes our working and nonworking lives as has the automobile, telephone, computer, and a host of other disruptive technologies that came before it.
Image created by DALL-E 4.0
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Dear Paul,
I admire both your work and your blog. To me, they enhance the academic experience by offering a broader perspective.
In your recent post, I was particularly drawn to your analogy of the typewriter. To me, AI—in both research and practice—increasingly resembles Schrödinger’s cat. We find ourselves both underestimating and overestimating AI at the same time—it is both dead and alive. By focusing only on the spectacular feats—such as OpenAI’s “Sora” generating videos—we may overlook the substantial benefits already available.
Reflecting on the recent advancements, I’d like to highlight the specific impacts of AI on my research experience:
Transcription of Interviews: Transcribing in qualitative research is often seen as a chore, regardless of one’s passion for the data and topic. Even with trained student assistants, transcription takes roughly 6 minutes for every minute of interview. With AI, this is reduced to just 1-1.5 minutes per interview minute.
Images for Teaching and Presentation: While copyright is crucial for artists’ livelihoods, it is cumbersome to source free images or secure usage rights for non-commercial purposes. Even with academic use exceptions in my country, often the images do not precisely correspond with the intended message. AI-generated images have saved me approximately 60% of the time usually spent on this task.
Note-Taking: During meetings and interviews, manual note-taking is essential for me to foster better discussions and to avoid interrupting my interlocutors excessively. I’ve begun dictating these notes to Word or using an AI tool and then sorting the transcribed content with ChatGPT to organize the themes. This practice has halved the time spent on documentation.
So, what’s my point? Your typewriter analogy is spot-on: AI is the new typewriter. It enables us to be more professional and to meet the growing demands of our academic and practical work. As a commentary on your blog post, I believe that many benefits are already within reach. Let’s embrace them to minimize routine tasks and excel at the core of our work.
Sincerely,
Kim
* This post was written by me, but corrected by ChatGPT-4-turbo.
Great points, Kim.