As AI reshapes industries, finance is beginning to tap its potential. A recent Swiss Financial Market Authority survey found that about 50% of institutions use or are developing AI, with another 25% planning to adopt it within three years. While sectors like healthcare and e-commerce already leverage AI for real-time decisions and efficiency, finance is still catching up. By learning from these early adopters, asset managers can use AI to enhance, not replace, human expertise.
While financial services are rapidly adopting artificial intelligence (AI), they’re not necessarily leading the way. Industries like healthcare, logistics, and e-commerce have spent over a decade refining AI systems under real-world constraints: regulatory scrutiny, real-time operations, and hyper-personalized customer engagement.
For asset managers and fintech innovators, these sectors offer practical blueprints. As the financial industry shifts toward dynamic, data-driven investment strategies, there’s clear value in adapting lessons from AI’s early adopters.
“AI in finance is not about replacing human expertise. It’s about amplifying it. The real power lies in using AI to streamline decision-making, uncover hidden opportunities, and enable optimized, personalized strategies.”
— Stefan Klauser, Co-Founder and CEO, Aisot Technologies
Healthcare: Explainability is a requirement, not a feature
In healthcare, doctors and patients need to trust AI systems before they are willing to use them. That means these systems must clearly show how they make decisions. One common way to do this is with tools like SHAP, which breaks down how much each piece of information influences the AI’s recommendation. A recent 2025 study published in BMC Medical Informatics and Decision Making found that these kinds of explanation tools are used in many medical AI systems to make their decisions more transparent and easier to understand.
In finance, models that predict market movements or allocate capital are often complex. But regulatory bodies are moving toward mandating explainability – especially in the EU under the AI Act, and increasingly in the U.S. under SEC scrutiny of algorithmic trading.
Why it matters: Institutional investors need to understand why an AI made a certain decision. For example, a signal that predicts a 0.7% upside in a stock should come with context: sentiment score, event trigger, confidence level.
aisot application: Using time-boxed large language models (LLMs), our systems restrict predictions to only the data available at prediction time – eliminating look-ahead bias and improving auditability. On top of that we apply the Arbitrage Pricing Theory (APT), which assesses the fair price of an asset by considering multiple factors or risk sources affecting its return. Just like the healthcare industry, we use Shapley values to ensure explainability by showing how each factor (like market trends or sentiment) influences the outcome. This is crucial for both investors and their clients. In short, our AI decisions are transparent and easy to understand. Watch our video.
Logistics: Real-time data should drive real-time decisions
AI in logistics is a mature field, and UPS’s ORION system is a prime example. As of 2016, UPS estimated it had optimized 55,000 routes, achieving fuel savings of 10 million gallons, reducing CO₂ emissions by 100,000 tons, and saving between $300 and $400 million in costs (Source: Harvard).
Why it matters: Long-only portfolios don’t need to react to every intraday fluctuation, but they benefit from more adaptive, event-aware updates. Shifts in sentiment, earnings surprises, or macro developments can build over hours or days – and catching these, without overtrading, supports more resilient, forward-looking positioning.
aisot application: Our system enables asset managers to move to data-driven, context-sensitive adjustments – enhancing decision-making without abandoning the discipline of long-term investing.
E-commerce: Personalization isn’t novel – it’s normal
McKinsey reported that Amazon’s recommendation engine accounted for 35% of the company’s sales in 2013—a figure that has likely increased significantly since then—by offering customers highly personalized product suggestions (Source: Forbes). Netflix’s algorithm reportedly saves the company $1 billion per year in customer retention (Nasdaq).
Most asset managers offer broad risk tiers (e.g., “conservative,” “balanced,” “aggressive”), but few dynamically adjust based on user behavior, goal drift, or changing preferences.
Why it matters: A 2023 EY survey found that 79% of retail investors – and 61% of institutional allocators – expect their portfolios to reflect both financial and non-financial preferences (e.g., sustainability, impact).
Aisot application: We allow for per-user customization on asset class inclusion, volatility targets, factor exposure, and ESG tilt – on a per-strategy basis, delivered as index- or fund-ready outputs.
Manufacturing: Human-in-the-loop isn’t a weakness
Machines inevitably break, and relying solely on worker intuition is no longer enough as equipment grows more complex and costly.
Predictive maintenance uses big data to identify early signs of failure, enabling proactive action that can cut downtime by 30–50% and extend equipment life by 20–40%. Still, expert judgment is essential to determine the right time to intervene. (Source: McKinsey)
Why it matters: A McKinsey study shows that successful AI adoption depends on aligning strategy, tools, and talent to boost returns, efficiency, and risk management. Technology should support—not replace—human decision-making, freeing teams from routine tasks to focus on strategic priorities.
Aisot application: Our AI Insights Platform offers overrideable, explainable suggestions to human portfolio managers - supporting, not replacing, investment decision-making. In addition, the platform has proven to deliver up to 90% time savings, allowing portfolio managers to shift their focus from routine analysis to higher-value activities like strategy development and idea generation.
Conclusion: translate, don’t replicate
AI adoption in finance is no longer a question of “if,” but of “how.” Industries that have tackled high-stakes decision-making, personalized user experiences, and real-time operations offer a head start.
The real opportunity lies not in copying their methods wholesale, but in adapting them – intelligently and ethically – to the unique demands of investing.
Are you a qualified investor and want to see our AI Insights Platform in action? Schedule a demo today to learn more.
