Not All AI Is the Same AI: TradAI vs GenAI
Has AI officially arrived in Wealth and Asset Management?
I mean, it’s everywhere. It’s in mission statements, vendor roadmaps, boardroom discussions, a pre-requirement for all jobs, and increasingly, becoming seen as headcount. But while the appetite for AI is high, the understanding of what AI actually is and what it isn’t, is still often muddled.
Specifically…Traditional AI (used to streamline, automate and bring structure to processes) vs Generative AI (which immitates human reasoning and generates new content).
These are not interchangeable tools. They serve different purposes, require different inputs, and deliver different outcomes. And so for leaders and people trying to drive change across functions such Market Data, Performance, Risk, Research, or Operations, distinguishing between the two is essential.
Traditional AI: The Automation Engine
Standardly referred to as ‘Predictive AI’ or ‘Analytical AI’, this is workhorse and primarily focuses on automation, doing its best work in structured environments where rules are set and repeatable. These systems don’t “think” or create; they execute logic at scale, faster and if the parameters are set, then more accurately than humans.
Example use cases would be:-
Market Data: Analyse structured spend data by vendor, track renewal dates, monitor license utilisation vs entitlement
Operations: Automated trade matching, reconciliation exception handling, post-trade reporting validation.
Performance: Return calculation engines, benchmark comparisons, attribution modelling.
Risk: Real-time compliance alerts, rule-based exposure monitoring, VaR calculations.
Research: Screening financial instruments based on filters (e.g. P/E ratio, yield thresholds).
In each of these, the AI is pretty simple: feed it data and it will give you the same result every time based on the rules given.
Generative AI: The Creative Collaborator
Built on large language models (LLMs) and neural networks, they copy human language and create content based on patterns it has learned, typically from large datasets. This isn’t about automation; it’s about enhancement. It helps people explore ideas and navigate complexity.
Example use cases would be:-
Market Data: Summarising vendor spend patterns, comparing vendor contracts side-by-side, drafting RPF questions
Operations: Drafting client comms or internal incident summaries based on raw input data.
Performance: Generating initial fund commentary drafts for factsheets or reports.
Risk: Creating narrative summaries of stress scenarios or risk committee outputs.
Research: Summarising market outlooks, generating views on economic trends, or translating complex data into plain English.
It’s powerful but not foolproof, as the sources and output require constant validation.
For a deeper dive into TradAI vs GenAI, I’ve put together my own list of use cases and outputs from my most recent consultancy project with PwC and previous roles within Market Data here.
Why This Distinction Matters
Too often, senior leaders look at Generative AI and expect it to deliver automation. Or they see Traditional AI and assume it can provide insights. But conflating these can lead to:-
Inappropriate application and use of AI in areas requiring high accuracy when the tech used focuses on association
Misallocated resources where companies invest in for example GenAI for tasks that suited to traditional automated tasks
Risk of reputational or regulatory errors if the tech is misapplied or misrepresented
Data leaks and privacy violations where sensitive data is uploaded into public tools, resulting in data breaches
Poor adoption rates and disappointed users in the business
Having a clear understanding of the difference and having the right governance, means you’re more likely to use the right tool for the right task and empower teams and people to do the same.
The Human Side of AI: Two Critical Questions People Must Ask
For all the headlines about AI and the reduction in entry level white-collar jobs, the reality is, it was designed by and is for the benefit of people. And if you’re not learning about or using it in some capacity, then you’re missing out.
However, with it increasingly being used across businesses to improve operational efficiency, client experience and decision making, we must consider the risks and challenges that come with AI adoption and usage.
1. What does AI mean for our mental capacity and cognitive thinking?
One risk of relying too heavily on AI, especially Generative AI, is the gradual erosion of our own analytical and critical thinking skills. Using AI is very much encouraged, but if people simply accept outputs without scrutiny, they risk outsourcing not just the tasks but also judgement.
Recent research backs this up. Studies published in Nature Human Behaviour and by MIT Sloan show that people who overrely on AI tools often experience cognitive offloading, where the brain reduces its own problem solving effort when it expects technology to do the heavy lifting.
The danger? Less questioning, less creativity, and a decline in independent decision-making over time.
For leaders, this means AI adoption should go hand-in-hand with embedding a healthy challenge culture: encouraging teams to interrogate outputs, compare against their own reasoning, and treat AI as a starting point, not the final word.
2. Is the future of AI about asking the right questions?
The real power of AI lies not just in the answers it gives, but in the questions we ask it. Framing problems effectively, providing clear inputs, and probing outputs critically is what makes AI useful.
In my own work within Market Data, I’ve used AI directly for:-
Summarising Contracts: Pulling out key clauses, but only if I know what contractual nuances matter from the licensing agreements.
Auditing Licensing: Highlighting exceptions, but it’s my job to define what “compliance” should look like in the first place.
Forecasting Fees: Modelling different fee scenarios, but it’s my role to sense-check assumptions against market benchmarking.
Each of these requires not just technical literacy, but also the ability to probe and to ask: What’s missing? Where could this be wrong? How do I validate this?
Final Reflection
As AI becomes more embedded into businesses, the real differentiator won’t be who has access to the tools but who uses and how to use them with judgment, curiosity, and detail. Traditional AI and Generative AI both bring huge value, but only when leaders and businesses frame them correctly and ensure their teams and people stay engaged, raising the bar for critical thinking and questioning, to elevate the art of what is possible.
Note: TradAI is not a standard industry term. It is commonly referred to as “Predictive AI” or “Analytical AI”. However, for ease and comparison within this blog, I have referred to it as TradAI
