Will AI Really Change How We Invest?

Recently, when we asked our community what they wanted us to talk about next, one question topped the poll: How to pick stocks using AI?
It’s a fair question. AI is everywhere, so why not in investing? But before rushing to find answers, it’s worth asking questions that tell whether we really need AI for stock picking in the first place.

Q1. Do You Really Need New Stocks Every Day?

Most of us treat stocks like groceries, as if we must keep shopping for new ones. But for a rational investor, over a lifetime, even 20-50 good stocks are more than enough.

“The next new idea” on AI, social media, or news often just distracts us. If you’ve already found strong businesses, the smarter move is to keep adding to them. Realistically, even 2-5 new ideas a year can build a healthy basket of 25 stocks over five years.

Q2. So, Is AI Useless For Investors?

No no. It depends entirely on how often you analyse stocks and what your goals are.

For professionals:

Analysts, wealth managers, and advisors have to study hundreds of companies. AI can make that grunt work faster. It can help them sort mountains of information quickly, spot anomalies, and free up time for deeper thinking. In these roles, AI acts more like a productivity tool than a stock-picking oracle.

For retail investors:

Most of us look at only 5-10 stocks in a year. At this pace, building an AI system just to filter or analyse isn’t necessary; the overhead outweighs the benefit. You’re better off with mutual funds, a trusted advisor, or doing focused DIY research within your circle of competence (businesses you already understand through your work, education, or interests). Sticking to familiar sectors simplifies decision-making, reduces mistakes, and keeps you grounded.

Even if AI could flag an “undervalued” stock, it doesn’t replace a firsthand understanding of a business. For retail investors, knowing the companies, their markets, and their customers is crucial. Something an AI can only scratch the surface of. 

Q3. Can It Not Help With Data?

Here’s something we’ve seen time and again: 90% of investing research is qualitative. Apart from the balance sheet, it’s about:

• The company’s industry and positioning.
• Its website and communication style.
• Whether management kept promises made years ago.
• What customers and vendors say about them.
• The company’s culture, ethics, and reputation.

Financials come in later, only to verify the story.

Take Royal Enfield (Eicher Motors) as an example. On paper, the competition always looked threatening; Harley-Davidson, BMW-TVS tie-ups, and whatnot. But the reality? Royal Enfield thrives because of something no spreadsheet can show: a community of riders and a sense of belongingness. That’s its moat.

So, it can generate neat reports. But it also builds a layer of separation between the investor and the company. And when it comes to things like a loyal community, a brand’s culture, or the feeling a product creates, instead of firsthand insight, you get boilerplate answers. 🚩

Q4. Is AI Completely Useless?

To be fair, AI does have some valid use cases for retail investors. Two stand out:

1. Query answering

• Sometimes you need an answer that’s not easy to Google or Ctrl+F in an annual report. AI can dig that up quickly. For example, it can consolidate scattered information on management statements, regulatory filings, or past news events into one readable summary.
• But this is more of a time-saver, not a game-changer. Over-relying on it risks making investors lazy and pulling them away from the scuttlebutt approach, that “on-the-ground” feel of knowing a company.

2. Educational clarity

• When you don’t understand a ratio, a financial term, or a concept, AI can explain it in plain language.
• It can also help interpret company-specific questions, like why a ratio behaves a certain way, or what a management commentary really means.
• While AI can’t replace judgment, it can quickly flag companies or sectors that meet basic quantitative criteria. For example, it can shortlist companies with strong cash flow, low debt, or consistent revenue growth, giving you a smaller set of candidates to evaluate qualitatively.

3. Tracking news and developments efficiently

• AI can summarise relevant news or trends related to your portfolio companies, saving hours of manual reading.
• This helps you stay informed, but it’s important to treat these summaries as signals, not verdicts.

Q5. What Can AI Not Replace?

At the heart of long-term investing is a direct connection with the businesses you own. No AI can replicate:

• Subscribing to a company’s newsletter.
• Calling customer support to see how they respond.
• Visiting service centres or showrooms.
• Asking shop owners how payments are handled.

These little checks add up to an understanding that AI can’t provide. And here’s another trap: most investors run to complicated screeners or over-analysis when they’re venturing into businesses they don’t truly understand. Pharma one day, IT the next, Steel the day after. That “Sarvagyani” urge to know everything often backfires.

It’s far better to stay in your own lane. If you know a sector inside out, you have an edge. Trying to compete outside your circle of competence is a game others are better positioned to win.

The Bottom Line

Investing works best when you focus on companies you understand. Most of the market becomes irrelevant on its own; out of 5,000 listed companies, around 100 may fit your circle of competence, and only 15–30 of those need to form your portfolio. This approach makes decision-making clearer and more manageable.

AI can assist with queries, explain concepts, or track numbers, offering useful support along the way. But as you know, success in investing is about sticking with companies you understand.