How AI will make your job more satisfying and productive

While some jobs may be lost in the short term, history tells us that technological advances that led to increased productivity, as Generative AI is expected to do, eventually led to an increase in jobs, and more importantly, to better jobs. While AI might streamline and, in some cases, even fully replace mundane tasks, there are many reasons why the essence of problem-solving, decision-making, and engaging human interactions — the very aspects that make jobs fulfilling — will remain firmly within human hands.

OpenAI unveiled ChatGPT, a transformative leap beyond previous language models used in tools such as Alexa, Siri, and Google Assistant. This leap was made possible by richer training data and enhanced computing power, enabling ChatGPT to better comprehend the context in which each word appears, predict sentence flow and generate coherent responses. 

But Generative AI tools are currently still limited. For example, earlier this year I asked ChatGPT to answer a business problem I typically give to my MBA students at Columbia Business School. I shared basic facts about a struggling company, where sales had fallen to $8 million in 2020 from $10 million in 2019, while call center complaints had increased to 150,000 from 100,000.

My MBA students are asked to identify the problem and its possible causes and explain what to do next. ChatGPT’s answer would have received a very low grade in my class: the chatbot adeptly summarized data but fell short of providing the deeper insights and judgments required to synthesize the information into the possible causes of the problem and what the company could do about them.

There’s good news in that failure: Human judgment remains essential in data-driven decision-making. Combining data analytics with human intuition — Quantitative Intuition — is the key ingredient of effective decision-making. AI’s shortcomings highlight the irreplaceable role humans play in exercising judgment, particularly in nuanced problem-solving scenarios.

AI can take over the mundane, while people can embrace the satisfaction that stems from problem-solving, decision-making, and meaningful human interactions in their professional lives. The aspiration is not to be bogged down with creating tables but to invest time in interpreting them, pondering the “so what,” and crafting the “now what.”

There are many concrete examples of how this can work. One important field is medicine. Human interaction is crucial for most diagnosis and treatment tasks, which hinges on comprehending the patient’s condition and concerns.

But in today’s complex healthcare environment, doctors face challenges in finding time for patient interaction. An American Medical Association study revealed that doctors spent just 27% of their time in direct contact with patients, while 49.2% was given to inputting and updating electronic health records (EHRs) and desk work. Even during patient consultations, doctors spent 37% of that time on desk-related tasks — often typing into devices while sitting with patients.

AI offers an avenue for improvement, allowing doctors to spend more time with patients, offloading to chatbots transcriptions of conversations into records, execute doctor-provided verbal instructions and lessen administrative burdens. AI could also fully replace doctors’ interactions or allow nurse practitioners assisted by AI to handle more mundane tasks like routine visits of chronic disease patients, alleviating the workload on doctors. AI already holds the potential to handle such tasks. A 2023 study in JAMA Internal Medicine, comparing physician and chatbot responses to patient questions on Reddit’s r/AskDocs, found that chatbot responses were preferred in 78.6% of cases. 

Next, AI can reshape customer service calls. In real-time call recordings, chatbots assist call center employees by providing prompts and decisions. This support extends to chat interactions, enhancing agent efficiency. We are all familiar with the annoying stage of “talking” to the interactive voice response (IVR) and being forced to go through an endless menu of choices before we get to truly converse with a human. This may raise an aversion to the automation of call center interactions.

However, the recent advances in AI now provide the opportunity to skip the IVR all together and start a real conversation with a machine that can offer human-like solutions for routine issues, while seamlessly transitioning to human interventions for more complex queries. Chatbots play a crucial role in resolving simple problems, preventing them from escalating into unnecessary human interactions, ensuring faster routing of intricate concerns that demand higher-level analysis.

This transformation also benefits customer service agents, rendering them more available and engaged in creative problem-solving, rather than redundant information repetition. Consequently, call center personnel will need to upskill, evolving to assess involved matters instead of merely processing transactions — a call center employee for an insurance company may need to be able to navigate coverage complexities instead of just processing bill payments.

A third area is computer programming. Generative AI, known as co-pilot, has become a vital tool in coding — the equivalent of having a colleague who has read almost every code on the internet, offering real-time guidance as coders write. This technology liberates programmers from the tedium of repetitive coding tasks so they can allocate their time to bigger problems.

By embracing AI, programmers gain the capacity to think expansively and immerse themselves in addressing larger complexities. The efficiency surge translates to increased code output, exemplified by a recent Microsoft study revealing that GitHub co-pilot users completed tasks 55.8% faster than their unassisted counterparts.

Additionally, AI proves invaluable in debugging code — a parallel to proofreading in writing — an often-dreaded task among programmers. Programmers often describe their experience with AI much more as a colleague that sits on your side of the screen than a software tool on the other side. Can this increase in productivity lead to demand for fewer programmers? Possibly, but then again in order to develop more AI tools like ChatGPT, and considering its rapid growth, we will likely need many more programmers.

We can gain some insights from past technological advances. With assistive technology, some people will be able to upskill. That’s a reasonable and expected outcome: When banks introduced ATMs in the 1980s, the industry added jobs. The number of bank tellers has expanded from around 250,000 in 1970 to 500,000 today, with a substantial addition of 100,000 since the year 2000. Tellers are assuming more prominent sales roles, actively introducing customers to financial products including credit cards, loans and investments, which led not only to higher profitability for the banks but also to increased employee satisfaction. 

The future of AI in the workplace holds the promise of allowing us to spend our time at work on more meaningful and enjoyable tasks. While concerns about job security linger, it is crucial to recognize that the true threat lies not in AI itself but rather in the reluctance of those who resist learning its intricacies. Indeed, people will not be replaced in their job by AI, but by a human who knows how to take advantage of it. The evolving landscape of work demands a proactive stance, with the understanding that AI’s potential can be maximized by those who engage with it actively. 

Oded Netzer is the Arthur J. Samberg Professor of Business at Columbia Business School and co-author of the book “Decisions over Decimals: Striking the Balance Between Intuition and Information. (Wiley, 2022).

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