Anand Bhardwaj

A small statement of principle:

AI augmentation should not be seen as a path to excellence. Excellence, rather, should be a pre-condition for AI augmentation.

In knowledge intensive work, this is how we preserve rigor and reflexivity.

Thank for coming to my TED talk.

ASAC 2026 Call for Papers May 22 to 25, 2026  | Calgary, AlbertaIndigenous Colloquium: Thursday, May 21Bissett School of Business, Mount Royal University. Submission deadline: Sunday, January 25, 2026 at midnight easte…

asac.ca/callforpaper...

As you plan for the 2026 conference circuit, know that Calgary is a) gorgeous in May, and b) a short drive from Banff. Hint hint.

Academics on BlueSky, contribute to Blue Sky Thinking at the Administrative Sciences Association of Canada conference.

Qual/Tech folks: the TIM division beckons

Reflecting on recent experience with public sector bureaucrats and my latest field study:

Discretion is anathema to automation, even the fancy “AI” version. In other words, situated judgement is very costly to encode.

Safe: People who can productively bend the rules, and be recognized for it.

Why is it that the most fun part of the PhD (writing the monograph) happens at the same time as the least fun part (applying for a job)? One of life's little ironies.

Based on my preliminary understanding of internet culture, there’s basically no way to say two words and not refer to Benedict Cumberbatch. Pneumatic Cauliflower.

RIP, prince of darkness.

I had similar thoughts when social media was emerging.

Here is a doing that is being done at a massive social scale and there’s an essential quality check component that is missing.

This has implications for both the technical correctness and the aesthetic qualities of what is generated.

Many (most?) genAI users cannot effectively evaluate what they have generated.

It depends on the user and the task. But a good chunk of all genAl tasks are sufficiently specific to require expert or esoteric knowledge to evaluate.

“Did it do a good job?”

The chatGPT (or equivalent) user who can answer that question, can do the job without chatGPT.

A cautionary tale about genAI sycophancy. Treat it like a hotshot analyst. Especially one that produces exactly what you want, in record time. Impressive, no doubt, but requiring considerable oversight and real quality checks.

The AOM 2025 program is out! Happy browsing to those who celebrate.

aom2025.eventscribe.net

I'm curious to see how the annual meeting this year in Copenhagen will unfold.

Will Americans show up? Will international scholars in America (who risk not being able to re-enter) do so as well? What about scholars in European universites?

For some, Canada (e.g. ASAC) might offer an alternative.

Respectfully disagree. I like negative spaces in storytelling (see Andor S2 for a well-executed example). Not everything needs to be explained "in canon".

I’m not ashamed to admit that I log out, then look.

Next year, ASAC is going to be held in Calgary, May 22nd-25th 2026.

Alberta is gorgeous this time of the year, and Banff is like an hour and a half drive away from Calgary. Hint hint.

Just got an R1 with minor revisions on a qual field study manuscript.

I’m sure there are things that feel better than this but I can’t for the life of me imagine what.

Maybe it always was and I never noticed. ASAC gave me major EGOS vibes, and I mean that as huge compliment.

A professor on my floor introduced me to her colleague as an “ethnographer of technology” yesterday.

Still riding that high.

So I sure picked an *interesting* year to go on the job market.

I'm principally interested in agency, and who has agentic cut here.

Trivially, theres difference in how much the scholar can "look in the black box" by doing the programming themselves, but in this era of libraries/packages I'm not sure even the r/python mavens really know whats under the hood.

On my mind these days:

Is there a meaningful distinction between:

Computationally entangled interpretive research (e.g. using an LLM to interpret data)

vs.

Interpretive data science (e.g implementing and interpreting an LDA topic model).

#researchmethods

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Editing combines my loves of academic reading and organizing. In the midst of assigning reviewers for ASAC'25.

Best day ever.

#mgmtsky #academicsky

Reading papers by members of the division is always a great place to start, but you're probably already doing that!! You may enjoy listening to a podcast hosted by Jan Recker and division chair Nick Berente: www.janrecker.com/this-is-rese...

The CTO division has the best symposia and PDWs, hope to run into you there! (Disclosure: I'm a former division exec, totally biased opinion)

Similar bg here.
1. Attended conferences.
2. Ran a bunch of topic models of WoS and SCOPUS exports of abstracts.
3. Read review papers. AoM annals is great.
4. Wrote abductive memos that mapped my empirics to theoretical concepts. If you dont hate it a month later you may be on to something.

We define AI broadly, from process-specialized “enterprise” applications of machine learning to generative AI and LLMs. We welcome qualitative and conceptual studies that illuminate the lived experiences of experts as they navigate the integration of AI into their work practices.
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We seek to understand the entanglement of everyday routines, practices, and relationships of domain and technical experts, and the processes of AI development, implementation, and adoption.
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Consider submitting to the mini-track "Working with AI: How AI shapes and is being shaped by routines, practices, and expertise of domain and technical experts", part of the Special Interest Group on Organizational Systems Research and Applications (SIG-OSRA). By @mayurjoshi.bsky.social & me
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