AI as a Force Multiplier Rather Than a Labor Reducer

At iCapital’s main stage, the AI conversation focused less on labor market disruption and more on pragmatic direction: how AI can strengthen human expertise and make better decisions possible at scale.

Moderated by Chief Investment officer Sonali Basak, the discussion centered on how intelligent infrastructure, when paired with experienced judgment, can help firms move faster, think more broadly, and execute with greater precision. To explore where AI is truly headed, and what that means for financial services, Sonali was joined by Ananya Chadda, founder and CEO of Hypersonic and an investor and engineer deeply embedded in the AI ecosystem.

AI is everywhere, even when we don’t notice

One of the main takeaways was that AI is not a niche technology operating at the margins. It is already embedded across industries, and often in ways people don’t immediately recognize.

In financial services, AI’s core strength lies in its ability to process unstructured data and trigger workflows as a result. That can mean monitoring portfolios and drafting rebalancing actions, identifying market events that prompt client outreach, surfacing insights during live sales conversations, or generating first drafts of communications based on existing documentation and style guidelines. Across marketing, sales, investment analysis, and client engagement, AI is increasingly operating as connective tissue by linking information, insight, and execution.

Importantly, Chadda emphasized that these use cases are not hypothetical. Large institutions across asset management, exchanges, and wealth technology are already deploying AI-enabled tools within existing platforms, integrating them into day-to-day workflows rather than treating them as standalone experiments.

When seeing doesn’t necessarily mean believing

To illustrate how quickly AI capabilities have advanced, Chadda shared a series of short video ads—spanning financial services, consumer apps, and beauty brands—and asked the audience to identify which were real. Shockingly, none were.

Every face, voice, and script had been generated by AI. The people didn’t exist, nor had they been trained on footage of real individuals. As AI-generated content becomes increasingly indistinguishable from real media, trust, verification, and ethical use become central considerations—not abstract concerns.

While the risks are real, Chadda also pointed to the upside. Used responsibly, AI can lower barriers to entry, help under-resourced teams compete, accelerate innovation, and even save lives, whether that’s through safer autonomous driving systems or faster drug discovery.

Debunking the largest AI myths

Much of the discussion focused on separating signal from the noise that has permeated the space as of late. Among the most persistent myths: that AI will replace advisors, eliminate entry-level roles, or improve endlessly through sheer scale.

In practice, Chadda argued, trust remains the defining factor. Even in industries where AI-driven automation is highly effective, clients still want accountable humans on the other side of the relationship. Across her own company’s experience, fully automated models consistently underperformed compared with AI-enabled services paired with human oversight. AI may streamline the back end, but relationships still drive outcomes.

In reality, the industry is entering what many leaders describe as a new “age of research,” where architectural innovation—the redesign of underlying system structures, models, and workflows—rather than brute-force scaling will determine progress. Even the most advanced models have shown diminishing returns, reinforcing the need for measured expectations.

Concerns around job displacement were also addressed with historical context. Technological shifts have always reshaped roles but not eliminated the need for people. Just as industrial machinery created new forms of skilled labor, AI is changing how work gets done, elevating strategic thinking, judgment, and creativity rather than rendering them obsolete.

Practical adoption starts small

Perhaps most importantly for financial professionals, Chadda emphasized that realizing value from AI does not require massive transformation or long implementation cycles. Many early benefits come from targeted, incremental use cases such as testing tools, augmenting specific workflows, and learning what works before scaling.

Today’s AI platforms make experimentation accessible, enabling teams to prototype ideas, automate discrete tasks, and build confidence without overhauling core systems of record. Those foundational platforms such as Customer Relationship Management systems (CRMs), Enterprise Resource Planning systems (ERPs), and data infrastructure—remain critical anchors precisely because trust and data integrity take time to build.

Chadda closed with a reminder that periods of uncertainty often coincide with periods of greatest opportunity. The current moment in AI, she suggested, echoes the early days of the internet: adoption now may not always be visible or immediately celebrated, but it can define long-term competitiveness.

For firms willing to engage thoughtfully, pairing intelligent tools with human insight, the upside is significant. AI will never be a sweeping replacement for expertise or human judgment. Instead, it is a force multiplier for those prepared to use it well rather than lean on it to cut headcount.