SIX artificial intelligence (AI) models recently went head-to-head with seasoned equity analysts to produce Swot (strengths, weaknesses, opportunities and threats) analyses, and the results were striking.
In many cases, the AI didn’t just hold its own; it uncovered risks and strategic gaps the human experts missed. This wasn’t theory. My colleagues and I ran a controlled test of leading large language models (LLMs) against analyst consensus on three companies – Deutsche Telekom (Germany), Daiichi Sankyo (Japan), and Kirby Corporation (the US). Each was the most positively rated stock in its region as of February 2025 – the kind of “sure bet” that analysts overwhelmingly endorse.
We deliberately chose market favourites because if AI can identify weaknesses where humans see only strengths, that’s a powerful signal. It suggests that AI has the potential not just to support analyst workflows, but to challenge consensus thinking and possibly change the way investment research gets done.
The uncomfortable truth about AI performance
Here’s what should make you sit up: With sophisticated prompting, certain LLMs exceeded human analysts in specificity and depth of analysis. Let that sink in.
The machines produced more detailed, comprehensive Swots than professionals who have spent years in the industry. But before you eliminate the need for human analysts, there’s a crucial caveat. While AI excels at data synthesis and pattern recognition, it can’t read a CEO’s body language or detect the subtext in management’s “cautiously optimistic” guidance. As one portfolio manager told us, “Nothing replaces talking to management to understand how they really think about their business.”
The 40 per cent difference that changes everything
The most striking finding? Advanced prompting improved AI performance by up to 40 per cent. The difference between asking “Give me a Swot for Deutsche Telekom” and providing detailed instructions is the difference between a Wikipedia summary and institutional-grade research. This isn’t optional anymore — prompt engineering is becoming as essential as Excel was in the 2000s. Investment professionals who master this skill will extract exponentially more value from AI tools. Those who don’t will watch competitors produce superior analysis in a fraction of the time.
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The model hierarchy: Not all AI is created equal
We tested and ranked six state-of-the-art models:
1. Google’s Gemini Advanced 2.5 (Deep Research mode) – The clear winner
2. OpenAI’s o1 Pro – Close second with exceptional reasoning
3. ChatGPT 4.5 – Solid but notably behind the leaders
4. Grok 3 – Elon Musk’s challenger showing promise
5. DeepSeek R1 – China’s dark horse, fast but less refined
6. ChatGPT 4o – The baseline for comparison
The reasoning-optimised models (those with “deep research” capabilities) consistently outperformed standard versions such as ChatGPT-4o. They provided more context, better fact-checking, and fewer generic statements.
Think of it as hiring a senior analyst versus a junior analyst – both can do the job, but one needs far less handholding. Timing matters too. The best models took 10 to 15 minutes to produce comprehensive Swots, while simpler models delivered in less than a minute. There’s a direct correlation between thinking time and output quality – something human analysts have always known.
The European AI deficit: A strategic vulnerability
Here’s an uncomfortable reality for European readers: Of the models tested, five are American and one is Chinese. Europe’s absence from the AI leadership board isn’t just embarrassing – it’s strategically dangerous. When DeepSeek emerged from China with competitive performance at a fraction of Western costs, it triggered what some called a “Sputnik moment” for AI.
The message was clear: AI leadership can shift rapidly, and those without domestic capabilities risk technological dependence. For European fund managers, this means relying on foreign AI for critical analysis. Do these models truly understand European Central Bank communications or German regulatory filings as well as they grasp US Federal Reserve statements? The jury’s out, but the risk is real.
The practical integration playbook
Our research points to a clear four-step approach for how investment professionals should use these tools:
1. Hybrid, not replacement: Use AI for the heavy lifting – initial research, data synthesis, pattern identification. Reserve human judgment for interpretation, strategy, and anything requiring genuine insight into management thinking. The optimal workflow: AI drafts, humans refine.
2. Prompt libraries are your new alpha source: Develop standardised prompts for common tasks. A well-crafted Swot prompt is intellectual property. Share best practices internally, but guard your best prompts like trading strategies.
3. Model selection matters: For deep analysis, pay for reasoning-optimised models. For quick summaries, standard models suffice. Using GPT 4o for complex analysis is like bringing a knife to a gunfight.
4. Continuous evaluation: New models launch almost weekly. Our six-criteria evaluation framework (structure, plausibility, specificity, depth, cross-checking, meta-evaluation) provides a consistent way to assess whether the latest model truly improves on its predecessors.
Beyond Swot: The expanding frontier
While we focused on Swot analysis, the implications extend across the entire investment process. We list a few of these below, but there are many more:
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Earnings call summarisation and analysis in minutes, not hours
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ESG red flag identification across entire portfolios
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Regulatory filing analysis at scale
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Competitive intelligence gathering
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Market sentiment synthesis
Each application frees human analysts for higher-value work. The question isn’t whether to adopt AI – it’s how quickly you can integrate it effectively.
The uncomfortable questions
Let’s address what many are thinking:
“Will AI replace analysts?” Not entirely, but it will replace analysts who don’t use AI. The combination of human plus AI will outperform either alone.
“Can I trust AI output?” Trust but verify. AI can hallucinate facts or miss context. Human oversight remains essential, especially for investment decisions.
“Which model should I use?” Start with Gemini Advanced 2.5 or o1 Pro (or the successors) for complex analysis. But given the pace of change, reassess quarterly.
“What if my competitors use AI better?” Then you’ll be playing catch-up while they’re finding alpha. Staying on the sidelines while competitors build AI advantage means ceding ground in an increasingly competitive landscape.
The path forward
The genie is out of the bottle. LLMs have demonstrated they can perform analytical work in seconds that once took days. They bring speed, consistency, and vast knowledge bases. Used effectively, they’re like having a tireless team of junior analysts who never sleep. But here’s the key: Success requires thoughtful integration, not wholesale adoption.
Treat AI output as you would a junior analyst’s draft – valuable input requiring senior review. Master prompt engineering. Choose models wisely. Maintain human oversight. For European professionals, there’s an additional imperative: Push for domestic AI development. Technological dependence in critical financial infrastructure is a strategic vulnerability no region can afford.
Master the tools – or be outpaced by them
Embrace these tools intelligently, or watch competitors leave you behind. The winners in this new landscape will be those who combine AI’s computational power with human insight, intuition, and relationship skills. The future of investment analysis isn’t human or AI – it’s human and AI. Those who recognise this and act accordingly will thrive.
Those who don’t will find themselves outperformed not by machines, but by humans who learned to work with them.
Your next analyst hire might still need that coffee break. But they’d better know how to prompt an LLM, evaluate its output, and add the human insight that transforms data into alpha. Because in 2025, that’s the new standard. The tools are here. The frameworks exist. The winners will be the ones who know how to use them.
This content has been adapted from an article that first appeared in Enterprising Investor at https://blogs.cfainstitute.org/investor
The full study can be found here
The writer, CFA, is chief investment officer at MHS CapInvest, where he employs advanced AI tools to enhance allocation, stock selection, portfolio construction, and risk management for different market capitalisations, He trains teams at DAX-listed companies on generative AI integration and helps investment professionals leverage tools like ChatGPT and Gemini to enhance their performance.
