No technology is moving faster, nor has a greater capacity for changing the world than AI. It has grabbed countless headlines over the past few years, but even before ChatGPT, it has been changing the way we search the web, find new music, and monitor our credit. AI encompasses a diverse array of approaches, including Generative AI and Supervised AI (Machine Learning). TIFIN AMP and AG focus on the latter, delivering data-driven predictions that can modernize your decision-making.
Because of the immense complexity that comes with AI, TIFIN has found that the capabilities of AI are not always well understood. By addressing these misconceptions head-on, organizations can develop more realistic expectations, make better-informed decisions about AI adoption, and ultimately achieve greater success with their AI initiatives.
So let’s bust 4 common myths about AI…
Myth #1: AI is Magic.
AI systems are often portrayed as possessing almost magical capabilities – autonomous entities that can solve any problem without human guidance, learn without constraints, and create solutions beyond human comprehension.
Truth: AI systems are sophisticated tools built on mathematics, statistics, and computer science principles. They require:
- Careful problem definition: AI excels at specific, well-defined tasks rather than general problem-solving
- High-quality data: AI systems learn from existing data and reproduce patterns found within it
- Human expertise: Skilled data scientists and engineers must design, train, and maintain AI systems
Take supervised machine learning—one of the most practical applications of AI today. If we want to estimate a household’s investable assets, we don’t need a crystal ball. A well-trained model can analyze factors like income, age, household size, and employment status to make accurate predictions. AI isn’t “thinking”—it’s learning relationships in data to make better predictions.
Myth #2: AI is a Black Box.
Many believe that AI systems are inherently unexplainable “black boxes” whose decision-making processes cannot be understood or audited by humans. This perception creates concerns about accountability, regulatory compliance, and trustworthiness.
Truth: The “black box” reputation of AI comes from its complexity, but modern techniques make AI more explainable than ever. One of my favorite tools for breaking open the Supervised AI black box is SHAP (SHapley Additive exPlanations). It helps pinpoint the exact factors influencing a prediction.
Example: Let’s say TIFIN AG’s AI is predicting someone’s investable assets. SHAP can reveal that:
✔ Their age (34 years) lowers their score (older investors tend to have higher assets).
✔ Living in the Midwest increases their score.
✔ Their zip code’s average income and wealth lower their score (suggesting lower local economic levels).
Here is a similar example from SHAP’s documentation showing the impact of each feature on a prediction.
This level of transparency is critical—especially in wealth management, where trust is everything. There are other methods as well that work beyond a single record.
- Feature Importance: A technique that measures how important an input is to the final prediction.
- Process Transparency: Clear documentation of data sources, preprocessing steps, and model training procedures
By prioritizing explainability and transparency, businesses can build trust in their AI systems and more effectively integrate them into existing workflows.
Myth #3: AI Tools Work Right Out of the Box.
There’s a common misconception that implementing AI is as simple as purchasing and installing software – that pre-built AI solutions can be deployed without customization and immediately deliver value.
Truth: Purpose built AI requires data, preparation of that data, and training. This doesn’t include models like ChatGPT, which are generalized. At TIFIN AG & AMP we build supervised AI models built for you and your data. Our implementation typically involves:
- Data preparation: Wrangling and cleaning data are essential because we can only expect accurate results if we provide the model with high value inputs.
- Integration: AI systems must connect with existing business systems and workflows (i.e. directly into your CRM).
- Training and feedback: AI models require training on use-case-specific data and a continuous feedback loop.
- Monitoring and Testing: We monitor feature drift and test for data discrepancies before they can be used to generate new predictions.
Myth #4: AI is going to replace all of our jobs.
Truth: AI will reshape our work, but it won’t eliminate the need for human expertise—especially in industries requiring decision-making, emotional intelligence, and relationships.
The World Economic Forum’s “Future of Jobs Report” predicts AI and automation will displace 85 million jobs—but also create 97 million new ones. That’s a net gain.
Look at what happened with computers:
- Early on, roles like typists and bookkeepers changed or disappeared.
- But computers also created entire industries—IT, software engineering, cybersecurity.
AI is following the same pattern. In wealth management, AI isn’t replacing anyone—it’s making us more efficient, helping scale personalized insights, and giving better data-driven recommendations.