Dear Valued Clients and Friends
We are living through one of the most consequential shifts in the history of finance. Artificial intelligence is no longer a distant prospect debated in boardrooms and technology conferences. It is here, it is active, and it is reshaping the way capital is allocated, analysed, and managed across the world.
India, in particular, stands at a remarkable inflection point. Our equity markets have shown resilience in the face of global uncertainty. Domestic flows remain strong. And our technology ecosystem is maturing at a pace that would have seemed improbable just a decade ago. Against this backdrop, the questions our clients are asking us have become more sophisticated: What role should AI play in my portfolio? Are the tools now available good enough to replace the advisor I trust? Where does human judgment still matter?
These are precisely the right questions to be asking, and they deserve honest, clear-headed answers rather than either breathless enthusiasm or defensive dismissal.
This month’s newsletter takes a careful look at the promise and the limits of AI-driven investing in the Indian context. Our view is not that technology is the enemy of good advice. It is that technology, used well, makes good advice better. The firms and advisors who understand that distinction will serve their clients far more effectively in the decade ahead.
As always, we are grateful for the trust you place in us, and we remain committed to thinking rigorously on your behalf.
Founder & Principal Advisor
AuRREnt Wealth
Artificial intelligence has moved from the pages of technology journals into your investment portfolio faster than most people expected. In 2024 alone, global corporate AI investment reached $252.3 billion, a figure more than thirteen times what it was a decade ago (Stanford AI Index 2025). Meanwhile, robo-advisory platforms are multiplying, fees are falling, and every fintech startup seems to carry the word “intelligent” in its pitch deck
So the question before you as an informed investor is a fair one: do you still need a human advisor, or has
technology quietly made that relationship redundant?
Our view, in short, is that AI is a genuinely powerful tool that changes how good advice is delivered. It does not yet change what good advice requires.
To be fair to the technology, robo-advisors do several things exceptionally well.
For an emerging affluent professional in their early thirties investing ₹25,000 per month across diversified mutual funds, a well-designed robo-platform is, frankly, a sensible starting point.
The AI story is not merely about financial technology. AI as a corporate and economic force is reshaping equity markets, and that has direct consequences for how you should think about portfolio construction.
Private AI investment reached $252.3 billion globally in 2024, up 44.5% from 2023 (Stanford AI Index 2025). Generative AI alone attracted $33.9 billion in private investment last year, more than eight times its 2022 level. This concentration of capital means that listed companies with significant AI exposure, primarily large US technology firms, have benefited from earnings upgrades and valuation re-ratings that are difficult to replicate across a broader market basket.
What this means for you: If your equity portfolio has had no exposure to global technology or AI-linked equities over the past two to three years, you have likely left meaningful returns on the table. For HNI portfolios, deliberate allocation to global AI themes, via feeder funds, fund of funds, or direct overseas investment under the Liberalised Remittance Scheme, deserves a structured review.
According to the NASSCOM-EY AI Adoption Index 2024, India’s AI adoption maturity score stood at 2.47 on a 4- point scale, a marginal rise from 2.45 in 2022. Notably, 87% of surveyed Indian companies are at the Enthusiast or Expert stages of adoption, and the number of companies in the advanced Expert stage has doubled since 2022. The Indian AI market is expected to grow at a 25–35% CAGR through 2027 (NASSCOM 2024).
What this means for you: Indian listed companies, particularly in BFSI, IT services, and manufacturing, that are successfully integrating AI into their operations may represent a structural multi-year opportunity. AI productivity gains are beginning to flow through to earnings in certain sectors.
Global venture capital invested in generative AI reached $49.2 billion in the first half of 2025 alone, exceeding the total for the entirety of 2024 (EY 2025). India received approximately $1.4 billion in private AI investment in 2023, ranking tenth globally (UN Trade & Development 2024), a modest figure that is rising.
| Metric | Metric Figure |
|---|---|
| Global corporate AI investment (2024) | $252.3 billion |
| GenAI private investment (2024) | $33.9 billion |
| India AI market CAGR (to 2027) | 25–35% |
| India private AI investment (2023) | ~$1.4 billion |
| India robo-advisory AUM (2025 est.) | $23 billion |
Sources: Stanford AI Index 2025; NASSCOM-EY 2024; Statista 2025; UN Trade & Development 2024.
Beyond the macro data, a fair question is: do funds that use AI or quantitative models actually deliver better returns for Indian investors? The answer, as with most things in investing, is nuanced
India now has a distinct category of “quant funds”, which are mutual funds that use algorithmic models, data analytics, and in some cases machine learning to select stocks and manage portfolios, with minimal or no human stock-picking judgement. The table below shows how three well-known quant funds have performed against the Nifty 50 over a three-year period.
| Fund | Strategy | 3-Year CAGR (approx.) |
|---|---|---|
| Quant Small Cap Fund (Quant AMC) | VLRT quantitative model | ~30%+ (5-yr: 33.3%) |
| Nippon India Quant Fund | Rules-based quant stock selection | ~20.83% |
| Tata Quant Fund | AI/quant model (Direct Plan) | ~14.03% |
| Nifty 50 (benchmark) | Passive index | ~13.36% |
The Nippon India Quant Fund delivered a solid 3-year CAGR of around 20.83%, comfortably ahead of the Nifty 50. The Tata Quant Fund, by contrast, only marginally beat the index, and notably, Tata Asset Management chose to merge the fund into its Tata Flexi Cap Fund in April 2025, effectively acknowledging that a pure AI/quant mandate was not commercially or strategically viable as a standalone offering.
Quant AMC’s impressive numbers are largely attributable to their small-cap strategy and concentrated positioning, which introduces significantly higher volatility alongside the returns. During 2025, the fund lost nearly 8% year-to-date while some peers remained relatively flat (Groww, December 2025). That is a risk investors must be prepared to absorb.
Most quant funds do not fully disclose the logic behind their models, making it difficult for investors or advisors to assess whether the strategy is genuinely durable or a result of overfitting to historical data. The takeaway is not that AI-driven funds are superior or inferior to conventional active management. It is that they add a meaningful third option in the Indian market, one that deserves a structured allocation decision, not a reflexive one.
For the emerging affluent and HNI client, the limitations of a purely automated approach become significant in ways that are easy to underestimate.
1. Behavioural Coaching
When Nifty fell sharply in October 2024 amidst FII outflows and a stronger dollar, many firsttime robo-platform users redeemed units at precisely the wrong moment. No algorithm sent them a call. No one walked them through the data and told them, calmly, that their goal had not changed. Behavioural coaching during drawdowns, arguably the single most valuable service a good advisor provides, remains a deeply human function.
2. Tax Optimisation
India’s tax code, as it applies to capital gains, inheritance, and trust structures, is nuanced and regularly revised. The interplay between short-term and long-term capital gains tax on equities and debt mutual funds, the grandfathering rules following the 2018 equity LTCG reintroduction, and the taxation of offshore investments under the Liberalised Remittance Scheme require judgement that automated systems are not equipped to exercise.
3. Concentrated Holdings and Unlisted Equity
Many HNI clients in India hold substantial wealth in a family business, promoter stock, or ESOPs. These positions have asymmetric tax, liquidity, and succession implications that no current robo-platform is designed to handle. The risk of a large undiversified position is not captured in a standard questionnaire.
4. Family Governance and Estate Planning
Questions around wills, nomination structures, trusts, Hindu Undivided Family accounts, and cross-border inheritance for NRI families are among the most consequential financial decisions a person will make. They require legal, fiduciary, and emotional intelligence in equal measure.
5. PMS and AIF Suitability
For portfolios above ₹50 lakh, the breadth of structured products, including unlisted bonds, real estate investment trusts, infrastructure investment trusts, and category II AIFs, requires individual suitability assessment, relationship with fund managers, and ongoing monitoring that automated platforms do not currently offer.
The most honest framing of the debate is not “robo versus human” but “how do we use the right tool for each
task?” Here are three concrete examples where the combination creates measurable value:
An AI-driven monitoring layer reviews a client’s entire multi-asset portfolio in real time, flags allocation drifts beyond set thresholds, and flags tax-loss harvesting opportunities before the financial year closes. This frees the advisor to focus the client conversation on strategy rather than administration.
Outcome: fewer portfolio errors, faster response to market moves, and a richer advisory conversation.
A client approaching their fifties wants to stress-test their retirement corpus against three scenarios: an early retirement at 55, a medical emergency, and a child’s overseas education. AI tools can generate probabilistic projections across hundreds of market environments in seconds. The advisor interprets the output in the context of the client’s risk temperament, family obligations, and business cashflows, the dimensions that the model does not know.
Outcome: decisions that are both numerically grounded and personally relevant.
An advisor manages a concentrated equity PMS portfolio. AI tools trawl earnings call transcripts, regulatory filings, and management commentary across fifty companies to surface early warning signals or emerging themes, in an afternoon rather than over several days. The advisor applies qualitative judgement on corporate governance, management quality, and sector thesis.
Outcome: a deeper research process without a proportional increase in cost.
Here is a clear framework for deciding where you stand.
Robo-Platform – Below ₹25–30 lakh
if you are building your first portfolio, investing below ₹25– 30 lakh in simple diversified equity or hybrid funds, have a long investment horizon, and a straightforward financial situation with no tax complexity, concentrated holdings, or estate planning needs. Use it as your starting layer, not your whole strategy
Hybrid Approach – ₹30 lakh to ₹5 crore
for the emerging affluent investor with ₹30 lakh to ₹5 crore in investible assets. Here, AI-driven monitoring and goaltracking tools working alongside a qualified advisor gives you cost-efficiency on the operational side and human judgement where it genuinely matters: allocation calls, behavioural guardrails, and tax planning.
Human Advisor – Above ₹50 lakh ticket size
when your situation involves a business, concentrated stock positions, inter-generational wealth, real estate, trusts, NRI family members, or any product above ₹50 lakh in minimum ticket size. The financial stakes of a mistake in these areas are too significant to leave to an algorithm that cannot ask the right questions, let alone answer them.
The most immediate and measurable advantage of AI-driven platforms is cost. Lower expense ratios and the absence of relationship management overhead mean more of your return stays with you over time. For a straightforward, diversified portfolio, this is not a trivial saving. Compounded over ten or fifteen years, even a 0.5% annual difference in fees has a meaningful impact on terminal wealth.
Equally important is the discipline that automated systems impose. Robo-platforms enforce systematic rebalancing, follow goal-based allocation rules without deviation, and remove the emotional decision-making that causes most retail investors to buy high and sell low. Behavioural biases such as recency bias, overconfidence, and panic-driven selling are substantially reduced when a rules-based system handles the routine mechanics of portfolio management rather than a human susceptible to short-term noise.
At the research and data level, AI tools operate at a scale and speed that no human team can match. The ability to process thousands of earnings filings, market signals, and news items in real time gives AI-augmented advisors a genuine information edge, particularly in identifying early warning signals or emerging sector themes before they become consensus views.
For India’s expanding base of first-generation investors, these platforms have also dramatically widened access to structured financial planning, making goal-based investing available without the minimum ticket sizes or relationship prerequisites that traditional advisory has historically required.
Perhaps the most important limitation of AI in wealth management is that it cannot replicate human judgement in situations that are complex and deeply personal. Estate planning, family governance, business succession, and intergenerational wealth transfer all require a combination of legal, emotional, and contextual intelligence that no algorithm currently possesses. These are not edge cases for India’s HNI community. They are the norm.
Closely related is the problem of model risk, which tends to be underappreciated until it becomes expensive. Quant strategies are built on historical data, and when market conditions shift in ways the model was not trained to anticipate, underperformance can be sharp and sustained. The global market dislocations of 2020 and 2022 exposed this vulnerability across quant funds worldwide, and Indian investors in certain quantitative strategies experienced similar drawdowns in early 2025.
India’s regulatory and tax environment adds another layer of complexity that automated platforms are simply not built to handle. SEBI rule changes, capital gains tax revisions following the 2024 Union Budget, and product-level regulatory shifts all require timely human interpretation and proactive communication with clients. A roboplatform will process these changes eventually, but it will not call you, walk you through the implications for your specific situation, or recommend a restructuring before the financial year closes.
Finally, concentrated and illiquid holdings remain a significant blind spot for automated systems. A standard portfolio questionnaire cannot capture the true risk profile of a client with a large stake in a private business, a pending IPO, or a portfolio of unlisted bonds. And when a model-driven portfolio does underperform, the lack of transparency in how most AI systems arrive at their decisions makes it genuinely difficult for investors to assess whether to stay the course or act – a frustrating position to be in when real money is at stake.
AI is not a threat to good financial advice. It is a test of it. The advisors and firms that embrace these tools thoughtfully will deliver faster, sharper, and more consistent work. Those who ignore them will fall behind. But the tools themselves cannot replace the judgement, trust, and human understanding that sit at the heart of a genuinely useful advisory relationship. For most HNI and emerging affluent investors in India, the question is not whether to use AI in your financial life. It almost certainly already is present in some form. The real question is whether the human expertise guiding your wealth is keeping pace with it.
We have been thinking carefully about how to integrate the best of these tools into the advice we give our clients, not to replace the relationship, but to deepen it.
| Indices | 01-02-2026 | 28-02-2026 | High | Low |
|---|---|---|---|---|
| BSE S&P SENSEX | 82,388.97 | 81,287.19 | 85,871.73 | 79,899.42 |
| NIFTY 50 | 25,333.75 | 25,178.65 | 26,341.20 | 24,571.75 |
| Particulars | AUM As On 31-01-2026 | Fresh Fund Mobilized During Feb-26 | Redemption During Feb-26 | AUM As On 28-02-2026 |
|---|---|---|---|---|
| Total AUM of all mutual fund schemes | 80.83 | 13.55 | 12.60 | 81.78 |
| AUM of equity oriented (growth) schemes | 35.13 | 0.62 | 0.36 | 35.30 |
(INR. In Lakh Crore)
Source: Association of Mutual Fund of India (AMFI)
| Month | SIP Contribution | SIP AUM |
|---|---|---|
| Feb-2026 | 29,845 | 16,64,085 |
(INR. In Crore)
FII’s selling in the month is 0.07 Lakh.
DII’s buying in the month is 0.39 Lakh
| FII / DII | Gross Purchase | Gross Sale | Net |
|---|---|---|---|
| FII | 3.39 Lakh | 3.46 Lakh | (0.07 Lakh) |
| DII | 3.47 Lakh | 3.08 Lakh | 0.39 Lakh |
(INR. In Crore)
All statistics, contents, comparisons, conclusive discussions, other data, etc provided in this bulletin are based on facts and figures available in the public domain. We does not favour any particular product, fund, securities, stocks, investment mode, strategy or fund manager, etc. Comparison of any products, strategies, etc does not intent to favour any particular product, strategy, etc. Investment is a matter of independence and subjectivity. Investors are kindly requested to use their judgment, and decision or consult your financial advisor before making an investment decision.
