Two trend lenses are reshaping what Indian MBA committees are willing to approve this year: generative AI moving from a curiosity into a line item on corporate budgets, and sustainability reporting moving from voluntary to mandatory for India’s largest listed companies. Below are topic directions built specifically around those two lenses, each one distinct from a generic MBA topic list — the trend is the differentiator, not the specialisation.
Why These Two Trends, Specifically
A trend-lens topic is not simply “AI” or “sustainability” bolted onto an existing MBA topic — it has to be genuinely time-bound, answerable now in a way it could not have been answered three years ago. Two developments make 2026 a real window for both lenses in India. First, the Securities and Exchange Board of India’s Business Responsibility and Sustainability Reporting (BRSR) framework is mandatory for the top 1,000 listed companies by market capitalisation, which means a genuinely new, comparable dataset of sustainability disclosures now exists across Indian firms — a dataset that simply did not exist in its current comparable form before the mandate phased in. Second, generative AI tools have moved from pilot projects to operational use across Indian marketing, HR and customer-service functions quickly enough that the academic literature on their specific effects in Indian firms is still thin — which is exactly the gap a Master’s-level dissertation can credibly fill, at least descriptively.
AI-Lens Topic Directions
| Specialisation | Topic direction | Research question |
|---|---|---|
| Marketing | Generative AI adoption in content and campaign creation | How are marketing teams in [sector] adopting generative AI tools for content creation, and what factors predict adoption intention? |
| HR | AI-assisted recruitment and candidate screening | What is the perceived fairness of AI-assisted screening tools among job applicants and HR professionals in [sector]? |
| Operations | AI-based demand forecasting adoption barriers | What barriers do supply-chain managers in [sector] report to adopting AI-based demand forecasting tools? |
| Finance | Generative AI use in financial analysis and reporting workflows | How do finance professionals in [sector] describe changes to their analysis workflow after adopting generative AI tools? |
| General Management | Employee attitudes toward AI-augmented decision-making | What is the level of trust employees report in AI-augmented managerial decisions in [sector], and what predicts it? |
Sustainability-Lens Topic Directions
| Specialisation | Topic direction | Research question |
|---|---|---|
| Finance | BRSR disclosure quality across listed firms | How does the quality and completeness of BRSR disclosure vary across firms in [sector], measured by a stated scoring framework? |
| Marketing | Green claims and consumer scepticism | How does exposure to a sustainability claim affect purchase intention for [category], and does perceived greenwashing risk moderate the effect? |
| Operations/Supply Chain | Circular-economy practice adoption among MSMEs | What circular-economy practices (reuse, recycling, take-back) do MSME owners in [cluster] report adopting, and what barriers remain? |
| HR | Employee perception of employer ESG commitments | How does perceived employer commitment to ESG goals relate to employee engagement among [sector] staff? |
| Finance | Market reaction to BRSR-linked sustainability announcements | Is there an abnormal stock-price reaction around a firm’s sustainability-linked announcement in [sector] over [period]? |
Two More Directions Worth Naming
| Specialisation | Topic direction | Research question |
|---|---|---|
| General Management / Strategy | Digital-transformation maturity self-description in annual reports | How has the language firms in [sector] use to describe their digital-transformation initiatives changed across annual reports over [period]? |
| HR / Governance | ESG-linked executive compensation disclosure | What proportion of [sector] firms disclose an ESG-linked component in executive compensation, and how is it structured? |
Both directions are grade A: annual reports and remuneration disclosures are public filings, and a content-analysis design (coding language or disclosure elements across a defined set of reports and years) needs no company cooperation beyond what is already published. A coding scheme for this kind of content analysis is built the same way as any questionnaire — with an item-writing pass, an inter-coder check, and a documented decision rule for ambiguous cases.
Building the Literature-Review Gap Statement
A trend-lens topic lives or dies on its gap statement, because the whole justification for choosing a 2026-specific angle is that the literature has not caught up yet. A worked, illustrative example for the generative-AI-in-marketing direction above:
[Illustrative excerpt] “Existing studies on technology adoption in Indian marketing functions predate the widespread operational use of generative AI tools and largely examine earlier marketing-automation or CRM adoption. [Cite a specific pre-2024 Indian marketing-technology-adoption study you have actually read here] establishes that perceived usefulness and organisational readiness predict adoption of marketing technology broadly, but does not address the specific concerns — content originality, brand-voice consistency, disclosure to clients — that generative AI tools raise. This study extends that adoption framework to the generative-AI context specifically, in [sector], where no Indian study to date has examined it.”
What makes it pass: it names a real prior study (yours, once you have read one), states precisely what that study did not cover, and explains why the gap matters now rather than simply asserting novelty. Naming the adoption theory behind that gap is the same step covered, in full, in our guide to building a conceptual framework for an MBA dissertation.
How These Differ From the Site’s General MBA Topics List
Our broader guide to 40 MBA dissertation topics for 2026, graded by data access covers the full range of specialisations without a trend filter — marketing, finance, HR, operations and systems topics chosen for feasibility across the board. Every topic on this page, by contrast, is chosen specifically because it sits inside one of the two 2026 trend windows above; a topic that happens to mention AI or sustainability but does not depend on something genuinely new this year belongs on the general list instead, not here. If a topic direction above does not feel time-bound to you — if it could equally have been written in 2019 — it is not really a trend-lens topic, and you are better served picking from the broader list.
Grading These Topics the Same Way
The same data-access discipline that applies to any MBA topic applies here, and if anything matters more, because a trend topic tempts scholars into assuming a dataset exists before checking. BRSR-based finance topics are grade A — the disclosures are public, filed with stock exchanges, and do not need company cooperation to access. AI-adoption and consumer-perception topics are almost always grade B, needing a survey of a reachable population (employees across firms, consumers, MSME owners) rather than one company’s internal data. Any topic that implicitly assumes access to one named firm’s internal AI rollout data or its usage logs is grade C, and should be re-scoped to a survey of a broader population’s perceptions and experiences rather than one organisation’s numbers, unless that organisation’s cooperation is already confirmed in writing. Working out exactly which columns such a study needs — objective, hypothesis, variable, indicator, data source, analysis tool — follows the same shape as our guide to an MBA dissertation operationalisation table.
What to Watch For When Writing an AI or Sustainability-Lens Chapter
- Do not invent an adoption statistic. “70% of Indian firms have adopted generative AI” is exactly the kind of round, uncited figure that gets a literature review sent back — if you cite an adoption rate, name the survey, its publisher and its year, and open the source yourself before you write the sentence.
- BRSR is a disclosure framework, not a performance guarantee. A firm’s BRSR score measures what it disclosed and how completely, not necessarily its actual environmental or social performance — keep this distinction explicit in your literature review, since conflating disclosure quality with performance is a common and examiner-visible error.
- Generative AI tool names change fast. If your dissertation names a specific product, note the version or date you tested it, since the tool landscape shifts within the time it takes to write and defend a dissertation.
- A trend topic still needs a named theory. The Technology Acceptance Model, the Theory of Planned Behaviour or a diffusion-of-innovation framework anchors an AI-adoption study the same way it would anchor any other technology-adoption topic — the trend is the context, not a substitute for theoretical grounding.
Where This Fits in the Wider MBA Research Landscape
Indian business schools have been steadily widening what counts as an acceptable dissertation topic beyond the traditional finance-ratio or HR-satisfaction studies, and a well-scoped trend-lens topic sits comfortably inside that widening, provided it is treated with the same methodological seriousness as any other topic. A guide reviewing a trend-lens proposal is typically checking for two things beyond the usual objectives-hypotheses-methodology chain: that the trend context is real and dated (not an assumed or exaggerated premise), and that the study does not collapse into a survey of opinions about a trend with no theoretical anchor. Both checks are satisfiable with the same discipline this guide has walked through above — a named theory, a bounded population, and a literature-review gap statement that names what has not yet been studied.
One Feasibility Check Before You Commit
Before finalising an AI or sustainability-lens topic, confirm you can access the specific data source your research question implies: BRSR filings are on stock exchange and company websites and need no permission to read; a survey of employees or consumers needs your institution’s ethics clearance and a reachable sample, not a named company’s internal cooperation. If your topic direction genuinely requires one firm’s internal AI-rollout metrics or unpublished ESG data, either secure that access in writing before your synopsis is due, or re-scope toward the public-disclosure or survey-based version of the same question.
Frequently Asked Questions
Is an AI or sustainability-lens topic considered more “trendy” but less rigorous by examiners?
Not inherently — rigour comes from a bounded question, a named theory, a defensible sample and a real data source, exactly as with any MBA topic. A trend-lens topic loses rigour only if the trend substitutes for those fundamentals rather than sitting on top of them.
Do I need a special ethics clearance for an AI-related survey?
The same clearance any primary-data MBA study needs — informed consent, no deception, appropriate handling of any identifying data. AI as a subject matter does not itself trigger a different ethics process at most Indian business schools.
Can I study BRSR disclosures for companies before the mandate applied to them?
Yes, provided you clearly state the comparison period and note that voluntary pre-mandate disclosure differs in completeness and comparability from mandatory post-mandate disclosure — treating the two periods as equivalent without flagging this is a common analytical error.
What is the biggest risk in choosing a fast-moving trend topic?
That the specific tool, platform or regulatory detail you anchor the dissertation to changes meaningfully between your proposal and your defence. Anchor your research question to the underlying phenomenon (adoption, trust, disclosure quality) rather than to one product name or one year’s regulation text where possible.
Should I link this topic to the site’s general MBA topics list in my own literature review?
It is good practice to acknowledge where your trend-lens topic sits relative to the broader specialisation literature — for a marketing AI-adoption topic, for instance, situate it within the broader marketing technology-adoption literature, not only within AI-specific sources.
Will this topic still feel current by the time I defend, a year or more from now?
Anchor the research question to the underlying behaviour or disclosure practice (adoption, trust, reporting completeness) rather than to a single tool’s current feature set or one year’s exact regulatory wording, and the topic ages far better across the year or more between synopsis approval and defence. Revisit your introduction’s framing closer to submission to confirm the trend context you described at proposal stage still holds.
Is it acceptable to use generative AI tools to help research a generative-AI-adoption dissertation?
The irony is not lost on most guides, and most Indian institutions’ emerging AI-use policies apply the same way regardless of your topic — draft with assistance if your policy permits it, disclose if disclosure is required, and keep the research design, data collection and analytical judgement your own. The declaration format most Indian ordinances expect is worked through in our guide to citing ChatGPT and declaring AI use in an Indian thesis.
Tesify helps you check that a trend-lens topic is genuinely time-bound and properly sourced before you commit to it — the theory, the data-access grade and the literature-review gap, built from your own reading rather than an invented statistic.