If you have found this page, you may be a researcher, a journalist, a programme manager, an award jury member, or a fellow traveller curious about the work behind the work. Welcome. The page below is a record. The record is in progress.
This page is a record of a decade-long inquiry into one question, written first as a doctorate in 2020, then as peer-reviewed papers published from 2017 to 2019, and continuing as working papers, frameworks, and quiet practice. The question keeps widening. The work, slowly, keeps following.
In 2016, almost nobody in Indian marketing or Indian academic management studies was writing seriously about artificial intelligence. The phrase felt premature, slightly silly, the kind of thing you said at conferences to get a polite laugh. I had begun a doctorate the previous summer and decided, against the prevailing advice, that AI was where the field was going to bend hardest in the next decade. The thesis I would eventually defend in 2020 examined how Indian consumers were already beginning to behave when machine-learning recommendation systems became the invisible architects of their attention. The question kept widening as I worked. By the time the viva was done, the doctorate had begun to feel less like a credential and more like the first floor of a longer building.
The work since has stayed loyal to the same long question, which is now the through-line of everything else on this site: what makes humans worth more, not less, as the machines get better? The research is the part of the answer that has to be written carefully, with footnotes and supervisors and the patience of academic publication. The practice, fifteen years of running E21 Designs, of advising founders and public figures, of writing the essays, of holding counsel, is the part of the answer that has to be lived.
Both halves shape each other. The page below is the published half. The rest of the site is the lived half. Together they are one project.
An In-Depth Study on the Video Consumption Preferences of Indian Consumers Using Machine Learning Techniques. Doctorate in Management Studies, viva voce defended November 2020. Supervisor: Dr. Jayam R.
The thesis set out to do something simple in description and difficult in execution: build the first empirical map of how Indian consumers were actually choosing, watching, abandoning, and returning to video content in the early years of algorithmic recommendation. The question mattered because, by 2017, every major piece of video Indians watched had begun to be selected, ordered, and timed by machine learning systems most people did not know they were interacting with. The descriptive statistics available at the time could not see the patterns underneath the behaviour. Machine learning could.
What the work surfaced, by the end, was a finding I have spent the years since elaborating in other forms: the recommendation systems were not just changing what Indians watched. They were quietly re-shaping what Indians found themselves wanting to watch in the first place. The interior life of taste, of patience, of attention itself, had started to belong to the algorithm. That single observation became the spine of every essay and every framework on this site since.
, from the closing chapter of the thesis · 2020
The thesis is held by the institution that awarded it. Selected scholars and serious peers may request access for academic purposes via avinaash.me@gmail.com.
Three peer-reviewed papers were co-authored with my doctoral supervisor and published as the doctorate was being written. Author name: Avinaash M. Each paper was a small attempt to put on the academic record an argument that was not yet a consensus position in Indian marketing or management studies. With a few years of hindsight, the arguments are now mainstream. At the time they were written, they were considered slightly ahead of their season.
An early framing of marketing as a discipline that would, within a decade, increasingly need to speak fluently to algorithms rather than only to humans. Many of the ideas in this paper later widened into what is now called AI visibility and Generative Engine Optimisation. Written in 2019. Read it again with that timestamp in mind.
A mid-decade synthesis of the structural changes AI was already beginning to make in the marketing function. The paper read, at the time, as a forecast. Re-read now, it reads as a record of the inflection point itself.
The earliest of the three papers. Argued that AI would become a default substrate of business transformation, written in a year when this was a fringe position in Indian academic and industry conversation. Awarded Excellent Paper at the National Conference on Transformation in Management. The award is mentioned here only because the position the paper took was unfashionable at the time of writing.
Beyond peer-reviewed publication, original frameworks are released here as working papers under Creative Commons. Each one is dated, versioned, and open for adaptation. The list below is the current writing rhythm. New papers are added every quarter.
A 12-page argument for what AI cannot replace, why the gap matters, and how leaders and brands can build for it. Will carry a Zenodo DOI for citation. The flagship paper of the framework that the rest of this site is built around.
Status: drafting · target release Q3 2026
The case for kindness as the load-bearing layer beneath culture, brand belief, and sustainable growth. Eight named operational practices. Three case studies. Companion executive summary for board briefings.
Status: outlining · target release Q4 2026
Eight named patterns observed across fifteen years of brand work in India, updated for the AI age. Each pattern with a definition, a behavioural prediction, and a marketing implication. The continuation of the doctoral thesis question into the post-2024 landscape.
Status: research phase · target release Q1 2027
A research-grounded framework for the nine human capacities that compound in value as AI capabilities increase. Used quietly in workshops with founders and CXO teams; being formalised as a working paper for publication.
Status: drafting · target release Q4 2026
For peer-reviewed publications, please use the standard journal citation under the author name Avinaash M.
For working papers and frameworks published on this site, the suggested citation format is:
Avinaash M (2026). [Paper Title], Version [n.n]. Dr. Avinaash, dravinaash.com/research. Creative Commons Attribution 4.0.
Citations of any work originating here are warmly welcomed. If you cite a framework in your own published work, please write to avinaash.me@gmail.com with the citation. Cited papers are tracked here, and significant adaptations are featured in the Dispatch.
If you arrived here looking for the credentials, you have them. The doctorate, the supervisor, the journals, the years, the award, the registration. If you arrived here looking for the work behind the credentials, the rest of the site is built for you. The credentials are the floor. The site is the building. The building is still going up. Slowly.
Slowly,
Dr. Avinaash.
Chennai · written by hand · narrated by voice · 2026
The research practice, written down: how original knowledge is actually made when summaries are free.