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Your analysts have competition – and it is not human.
The six AI models were recently headed with experienced equity analysts to produce SWOT analysis, and the results were striking. In many cases, AI did not just make his grip; This exposed the risks and missed strategic gaps of human experts. This was not the principle. My colleagues and I conducted a controlled test of the leading large language model (LLM) against the consensus of analyst on three companies: Dutash Telecom (Germany), Daichi Shanko (Japan), and Kirby Corporation (USA). Every February 2025 had the most positive rated stock in its region – the kind of “fixed condition” that analysts have highly supported.
We deliberately chose the market favorite because if AI can identify the weaknesses where humans only see strength, it is a powerful sign. This suggests that AI not only has the ability to support analyzer workflow, but also changes the way to challenge consensus thinking and possibly change the way of investment research.
Uncomfortable truth about AI performance
What you should do here: With sophisticated signal, some LLM crossed human analysts at the depth of uniqueness and analysis. Let that sink go.
The machines produced more elaborate, comprehensive swot than professionals who have spent years in the industry. But before you eliminate the need of human analysts, there is an important warning. While AI excels on data synthesis and pattern recognition, it cannot read the body language of a CEO or not detect the subtext under the “careful optimistic” guidance of management. As a portfolio manager told us, “Nothing for management to understand how they actually think of their business.”
40% difference that changes everything
The most striking search? Advanced signal improved AI performance up to 40%. The difference between “Give me a swot for deseush telecom” and providing detailed instructions is the difference between Wikipedia summary and institutional-grade research. This is no longer optional – Prompt Engineering is becoming as necessary as Excel was in the 2000s. Investments that mastery in this skill will extract more prices from professional AI tools. Those who will not see the contestants produce better analysis in a fraction of that time.
Model hierarchy: not all AIs have been made equal
We tested and ranked six state -of -the -art models:
- Gemini of Google Advanced 2.5 (Deep Research Mode) – Clear winner
- OPENAI’s O1 Pro – Close another with extraordinary argument
- Chatgpt 4.5 – Solid behind leaders but especially
- Grocke 3 – Elon Musk shows Challenger Promise
- Deepsek R1 – China’s dark horse, fast but less refined
- Chatgpt 4o – base line for comparison
Logic -friendly models (with “intensive research” abilities) improved standard versions such as chat -4o. He provided more references, better facts-stages and less general statements. Think as a senior analyst vs. a junior analyst as a junior analyst – both can do the work, but one requires low handholding. Time also matters. The best models took 10 to 15 minutes to produce wide swot, while simple models were distributed in less than a minute. There is a direct relationship between the time of thinking and output quality – some human analysts have always known.
European AI Loss: A Strategic Vibrations
Here is an uncomfortable reality for European readers: for tested models, there are five Americans and one is Chinese. The absence of Europe from the AI leadership board is not just shameful – it is strategically dangerous. When the lampsac came out of China with competitive performance at a fraction of western costs, it said “Sputnik Mal” for AI.
The message was clear: AI leadership may be faster shift, and those without domestic abilities risk technical dependence. For European fund managers, it means relying on foreign AI for significant analysis. Do these models really understand ECB communication or German regulatory filing and at the same time they understand the fed statement? The jury is out, but the risk is real.
Practical integration playbook
Investing professionals to use these devices to our research indicating a clear four-step approach
1. Hybrid, not replacement: Use AI for heavy lifting – initial research, data synthesis, pattern identification. Reserve human decisions for the need for real insight into interpretation, strategy, and management thinking. Optimal Workflow: AI Draft, Human refined.
2. Prompt library is your new alpha source: Develop standardized signs for general functions. A well -designed Swot Prompt is intellectual property. Share the best practices internal but protect your best signs like trading strategies.
3. Model Selection Case: For intense analysis, pay for logic-unfamiliar models. For quick summary, standard models are sufficient. Using GPT-4O for complex analysis is like bringing a knife into a gun battle.
4. Continuous evaluation: The new models launch almost weekly. Our six-post evaluation framework (structure, credibility, specificity, depth, cross-checking, meta-assessment) provides a consistent way to assess whether the latest model actually improves its predecessors. Please see full research report for more information: “Better performance by AI: Time to change your analyst?” (Michael Shopf, April 2025).
Beyond Swot: Extension Frontier
While we focus on SWOT analysis, implications extend to the entire investment process. We list some of these below, but there are many more:
- Call brief and analysis in earnings, not in hours
- ESG red flag identity throughout the portfolio
- Regulatory filing analysis on scale
- Competitive intelligence
- Market sense synthesis
Each application frees human analysts for high-value work. The question is not whether to adopt AI – how soon you can integrate it effectively.
Uncomfortable question
Let’s address what many people are thinking: “Will AI replace analysts?” Not completely, but it will replace analysts who do not use AI. The combination of human + AI alone will either perform better. “Can I rely on AI output?” trust but verify. AI can hail facts or miss references. Human monitoring is necessary, especially for investment decisions. “Which model should I use?” Start with Gemini Advanced 2.5 or O1 Pro (or heir) for complex analysis. But given the speed of change, re -accept the quarterly. “What if my competitors use AI better?” Then you will play a catch-up while they are getting alpha. Staying on the sideline when constructing competitors means eliminating the ground in a rapid competitive scenario.
Way forward
The genie is out of the bottle. LLM has displayed that they can do analytical functions in seconds which used to take day. They bring the basis of speed, stability and huge knowledge. Effectively used, they are like having a tireless team of junior analysts that never sleep. But here is the key: Success requires thoughtful integration, not wholesale adoption.
Treat an AI output as you draft a junior analyst – valuable input requiring senior reviews. Master Prompt Engineering. Choose the model wisely. Maintain human monitoring. For European professionals, there is an additional imperative: push for domestic AI development. Technical dependence in significant financial infrastructure is a strategic vulnerability that no region can tolerate.
Mastery in equipment – or be carried forward by them
Embrace these devices wisely or leave the contestants behind. The winners in this new scenario will be those who combine the computational power of AI with human insight, intuition and relationship skills. The future of investment analysis is not human or AI – it is human and AI. Those who recognize it and work accordingly will thrive. Those who will not find themselves not by machines, but by humans who learn to work with them.
Your next analyst may still require that coffee brake on rent. But they better know how to indicate the LLM, evaluate its output, and the human insight to be added that turns the data into alpha. Because in 2025, this is the new standard. The equipment is here. There are structures. The winners will be the ones who know how to use them.
Complete studies can be found here:
