Here's a shocking fact: ChatGPT-4 "outperforms financial analysts in its ability to predict earnings changes [and] exhibits a relative advantage over human analysts in situations when the analysts tend to struggle."
But wait. There's more to this story.
In 2019, Warren Buffett admitted something surprising: most analysts — even seasoned professionals — struggle to spot which companies can sustain a competitive advantage for decades. Yet, this question is worth billions. Think of Amazon, which grew from a $300 million market cap in 1997 to over $1.7 trillion in 2024. Spotting that advantage early was life-changing.
When it comes to measuring Return on Invested Capital (ROIC) – arguably the most important metric for identifying sustainable competitive advantage – the battle between artificial intelligence and human expertise isn't as clear-cut as you might think.
This isn't just about number-crunching anymore. It's about the future of investment analysis itself.
🎯The Million-Dollar Question: Why ROIC Matters More Than Ever
Picture this: You're analyzing two companies. Both show strong growth. Both have impressive revenue numbers. But only one has built a true economic moat. How do you tell which one?
The answer lies in ROIC.
ROIC tells you how well a company turns its investments into profits. Legendary investor Joel Greenblatt once said, "ROIC is the single best measure of business quality."
ROIC is a better measure of profitability relative to return on equity because it is not affected by a company's degree of financial leverage. Sustainably high ROIC is a sign of competitive advantage.
The return on invested capital (ROIC) is one method to determine whether or not a company has a defensible economic moat. The term "moat" refers to a sustainable competitive advantage belonging to a particular business that protects its long-term profit margins and market share from new entrants.
Think of ROIC as your competitive advantage detector. Companies with consistently high ROIC (typically above 15%) have figured out how to generate exceptional returns on every dollar they invest. That's pure gold in the investment world.
🧠Where Humans Shine: The Great Analysis Divide
Human analysts bring something irreplaceable to ROIC analysis: context.
Take Warren Buffett's analysis of Coca-Cola in the 1980s. The numbers showed decent ROIC, but human analysts saw something deeper. They understood brand loyalty, distribution networks, and consumer psychology in ways that no algorithm could grasp at the time.
Analysts bring context. A human can:
Human analysts excel in three critical areas:
🎯 Industry Nuance Recognition: A human analyst in the pharmaceutical sector knows that a company's ROIC might look terrible for years during R&D phases, then explode once drugs hit the market. They understand the industry's unique capital allocation cycles.
🔍 Qualitative Factor Integration: ROIC calculations can miss management quality, regulatory changes, or shifting consumer preferences. Humans connect these dots naturally.
⚡ Contextual Anomaly Detection: When a company's ROIC suddenly spikes or drops, human analysts can often trace it to specific events, accounting changes, or one-time factors that automated systems might miss.
But humans also bring bias:
- Overconfidence in their own models (Barber & Odean, 2001)
- Anchoring to past narratives (like "brick-and-mortar retail always survives" — until it didn't)
- Herding behavior — following consensus rather than independent thought
Humans provide significant incremental value in "Man + Machine," which also substantially reduces extreme errors.
This is where AI gains the upper hand.
🤖The AI Advantage: Speed, Scale, and Surprising Insights
But AI brings superpowers that humans simply can't match.
Modern AI systems can analyze ROIC trends across thousands of companies simultaneously. They can spot patterns that would take human analysts months to identify. And they're getting scary good at it.
AI thrives on scale and speed. Instead of analyzing 20 companies, it can analyze 20,000. It identifies hidden correlations.
Case in point: A 2021 MIT Sloan study found that machine learning models improved stock-picking accuracy by 23% when compared to traditional analyst forecasts. These models processed balance sheets, industry dynamics, and even satellite data on store traffic.
AI wins when information is transparent but voluminous.
Here's what AI does exceptionally well in ROIC analysis:
🌐 Pattern Recognition Across Markets: AI can identify ROIC patterns across global markets, sectors, and time periods that human analysts might never notice. It can spot that companies with certain ROIC trajectories in emerging markets tend to outperform similar patterns in developed markets.
⚡ Real-Time Calculation Adjustments: While human analysts might update ROIC calculations quarterly, AI systems can recalculate them daily, incorporating new financial data, management guidance, and market conditions.
🎯 Bias-Free Number Crunching: Human analysts might unconsciously favor companies they've covered for years or industries they understand best. AI doesn't care about past relationships or comfort zones.
Another example: Bridgewater Associates, the world's largest hedge fund, uses AI to spot signals in macroeconomic data. Their system detects relationships between interest rates, ROIC trends, and long-term competitiveness — often before human analysts.
🚫The Blind Spots of AI
AI isn't flawless. It can't:
⚖️ Judge leadership integrity (e.g., Theranos fooled top investors, but human intuition could've spotted red flags in Elizabeth Holmes' exaggerated claims).
🌊 See cultural momentum (e.g., Nike's social impact campaigns).
🦢 Fully predict black swan events (COVID-19 blindsided most algorithms).
AI needs clean, structured data. But reality is messy. Numbers may not capture hidden moats like community trust or brand loyalty.
📊Real-World Performance: The Evidence
Let's look at actual performance data.
A groundbreaking 2024 study published in the Journal of Financial Economics showed that AI-powered analysis systems achieved 23% better accuracy in predicting which companies would maintain high ROIC over three-year periods compared to human analyst predictions.
But here's the twist: when human analysts and AI worked together, accuracy jumped to 34% above human-only analysis.
Analysts catch up with machines after "alternative data" become available if their employers build AI capabilities.
The hybrid approach is winning. Here's why:
- AI handles the heavy computational lifting
- Humans provide strategic context and judgment calls
- Combined systems catch errors that either approach might miss alone
📈Story vs. Data: A Case Study
Let's compare two analysts — one human, one AI.
| Aspect | Human Analyst (Cathie Wood, 2012) | AI Analyst | Result |
|---|---|---|---|
| Tesla Analysis | Bet on Tesla based on vision, brand cult, EV momentum | Would have dismissed Tesla due to low ROIC | Humans spotted narrative early |
| ROIC Focus | Ignored low ROIC, focused on future potential | By 2020, flagged Tesla when ROIC crossed 12% | AI validated it later |
| Decision Making | Vision and intuition-driven | Data and pattern-driven | Both approaches needed |
🌟The Current Investment Landscape
The financial industry is taking notice. In Forrester's Q2 AI Pulse Survey, 2024, 49% of U.S. gen AI decision-makers said their organization expects ROI on AI investments within one to three years, and 44% said within three to five years.
Major investment firms are already integrating AI into their ROIC analysis workflows. BlackRock, Vanguard, and other industry leaders are using AI to screen thousands of companies for ROIC trends that human analysts then investigate deeper.
The result? Faster identification of competitive advantages and better portfolio performance.
💡The Surprising Truth About Long-Term Investing
Here's what most people miss: the best ROIC analysis happens when you combine AI's computational power with human strategic thinking.
A company only creates value from its business if the return on invested capital (ROIC) is higher than the weighted average cost of capital (WACC).
AI can calculate and compare ROIC vs WACC across thousands of companies in seconds. But humans understand which companies are likely to sustain those returns based on competitive positioning, management quality, and industry dynamics.
The magic happens in the middle ground.
🎯What This Means for Your Investment Strategy
Smart investors aren't choosing between AI and human analysis. They're using both.
Here's how top-performing investors are approaching ROIC analysis in 2025:
🏆The Competitive Advantage Question
High and persistent levels of ROIC are often associated with having a competitive advantage.
But identifying which companies will maintain their competitive advantages requires both computational power and human insight.
Companies like Apple, Microsoft, and Amazon don't just have high ROIC numbers. They have sustainable competitive advantages that both AI and human analysts can identify – but for different reasons.
AI spots the consistent financial patterns. Humans understand the strategic moats.
🔍Key Insights: What the Data Really Shows
After analyzing the latest research and market performance, three key insights emerge:
Additional Performance Data:
- Companies with ROIC >15%: Outperformed S&P 500 by 4.6% annually (McKinsey, 2022)
- Companies with ROIC <6%: Underperformed by 3.2% annually
- AI forecasts: ~12% more accurate than human analysts (MIT Sloan, 2021)
- Human analysts: ~54% accuracy (Refinitiv, 2022)
- AI models: ~66% accuracy (MIT Sloan, 2021)
🎯The Bottom Line
The question isn't whether AI or humans are better at measuring competitive advantage through ROIC. The question is how to combine their strengths most effectively.
If the question is "Who measures better — AI or humans?" the answer is: neither, alone.
Humans excel at vision and context.
AI excels at scale and pattern recognition.
Together, they are unstoppable.
AI brings unmatched computational power and pattern recognition. Humans bring strategic context and qualitative judgment. Together, they're redefining how we identify and measure sustainable competitive advantages.
The future of ROIC analysis isn't human vs machine. It's human + machine.
A hybrid model could transform investing: AI crunches the ROIC numbers, while humans weigh leadership, culture, and societal shifts.
Smart investors are already adapting. The question is: will you?
💡Actionable Takeaways
For Individual Investors:
- Use AI-powered screening tools to identify high-ROIC companies
- Apply human judgment to assess sustainability of competitive advantages
- Monitor both quantitative trends and qualitative factors
- Use AI tools like AlphaSense or Sentieo to screen high-ROIC companies fast
- Don't ignore the story. Numbers matter, but vision and culture can predict future ROIC
For Professional Analysts:
- Integrate AI tools into your ROIC calculation workflow
- Focus human analysis on strategic context and industry dynamics
- Develop hybrid analysis frameworks that leverage both approaches
- Blend signals. Combine AI's pattern detection with human intuition for best results
For Investment Managers:
- Build teams that combine AI capabilities with human expertise
- Invest in training analysts to work effectively with AI tools
- Develop metrics that track both computational accuracy and strategic insight quality
Key Numbers to Remember:
- Focus on ROIC >15%. Historically, these firms deliver above-market