Imagine algorithms not only establish specific client preferences from past investment patterns but also autonomously execute financial portfolio modelling and stress-testing against numerous historical and projected market scenarios.
Today, new financial products are legally structured with significantly less human involvement. Tax opinions and rulings are requested electronically, and all necessary documentation is automatically generated. This entire process, including all regulatory filings, can now be completed without the direct input of a lawyer.
Following the legal groundwork, AI systems integrate market evaluations and coded corporate positioning from all involved firms to create marketing materials and promotional content. This includes everything from term sheets for pre-marketing and pitch decks to fully interactive reporting suites that integrate seamlessly into the client’s preferred systems.
A fully automated launch campaign is then planned, which uses sophisticated algorithms to craft contextually relevant messages and schedule briefings. It also automatically blocks time slots in spokespersons’ calendars. Content assets sourced from research, white papers, and videos are curated, and any gaps are filled by ordering new content production. Conference slots are pre-booked, and time is reserved for roadshows among investment and sales staff.
AI-driven systems also craft teaser emails, letters, and phone scripts for the initially created prospect list.
Whether you view this as an ideal future or a scenario of concern, much of this technology is operational today, with numerous FinTech startups pushing these capabilities further.
Since my last update in 2020, the conversation about the role of humans in AI-driven industries has evolved from sensationalist debates to more nuanced discussions. However, two key human skills remain irreplaceably valuable: complex problem-solving and empathy.
Decades of research in artificial intelligence and machine learning have indeed produced specialized AI applications for various challenges. While there’s no “master algorithm” as Pedro Domingos conceptualized, ongoing advancements in AI research continue to blur the lines between human and machine capabilities.
Empathy, in particular, highlights the complex interplay between humans and machines. It involves understanding and responding to human needs—a domain where many still prefer the nuanced understanding of a fellow human over even the most advanced AI systems.
As we move forward, it is crucial for us to enhance our skills in solving complex problems and fostering empathetic connections, preparing ourselves for a future where AI partners with us to tackle both new opportunities and enduring challenges.
