From Wall Street to AI: How GenerativeX's ex-bankers are reshaping finance with AI agents

Can you briefly describe your current role and background?

Ueda: I oversee all aspects of our US operations including business development, sales and delivery, while driving our firm’s growth strategy. The focus remains consistent: delivering bespoke agentic AI solutions to enterprises. 

Previously, I spent six years structuring and executing investments in financial institutions in emerging markets at the International Finance Corporation in Washington, D.C. I began my career at JP Morgan, where I was involved in IPO executions and M&A advisory. 

When a former colleague, now our CEO, asked me to help expand GenerativeX’s business to the US, I was surprised but intrigued. 

Morita: I lead sales operations in the US, building client relationships and exploring new opportunities. Before that, I spent over a decade on equities trading desks at Citigroup, following roles at Goldman Sachs and Morgan Stanley. 

Like many in finance, I initially kept my distance from AI, but the rapid changes and market volatility around it sparked my curiosity and ultimately drew me into the field.

How are you finding AI so far?

Ueda: Coming from a non-engineering background, I was unsure about whether I could code and create applications. However, AI-powered coding tools quickly proved more capable than expected, allowing me to use natural language to code, without learning a slew of programming languages from scratch. 

In many financial institutions, AI is still limited to basic chatbots that struggle with answering real queries. I have first handedly experienced that, too. Since joining, I’ve seen how fast technology is advancing, and how much more capable AI tools are becoming every day. 

At GenerativeX, we use AI extensively in our internal workflows. Meeting notes are quickly turned into presentation drafts, internal information sharing is enhanced with AI tools, providing useful insights at once. 

These shifts have made our work more client focused. We spend our time understanding client needs, designing solutions, and supporting implementation, while AI tools handle the rest.

What are the bottlenecks in financial institutions?

Morita: In equities markets, legacy infrastructure remains a major challenge. Its scale and complexity make new initiatives difficult, given security and legal constraints. Since markets run continuously, banks can’t pause trading for upgrades, so transformation stays gradual. 

Banks also remain hesitant to share data or delegate rule-setting to AI. Even routine tasks such as quick client updates still involve typing out information and reading through materials. 

IT departments may have access to agentic AI models, but access for front-office teams remains strictly limited, as a single mistake could expose sensitive information. Strong controls and safeguards are essential, but even within these constraints there is room to do more with AI. 

Ueda: In investment banks, much of the information involved in M&A, IPO and due diligence is strictly confidential and must remain secure. But I wouldn't call that a bottleneck; it's a requirement we have to live with, from both a regulatory and an operational risk perspective. The real challenge is the sheer volume of information that has been manually assembled and structured within those constraints. 

Another common challenge across industries is the gap between IT and the business front; solutions built by IT often drift away from business needs. By the time they reach the end-users, the original intent is lost through the organizational layers.

How can GenerativeX help them drive change?

Ueda: Some companies rely on engineers to produce the agentic AI solutions, but we take a different approach. Business professionals lead both the consulting and development of the application, ensuring precise understanding of client needs while preserving intent. 

This allows us to move faster and avoid long iteration cycles. We can deliver demos within hours, prototypes in days, and full solutions within weeks or months. 

In line with rapid AI advancement, we continuously update our products, built by former investment bankers and management consultants with strong attention to detail and a focus on client-ready outputs. 

Our solutions are already delivering proven ROI in Japan's highly regulated industries. In one engagement, we reduced sales preparation time by 70% across thousands of employees — equivalent to approximately $10 million in annual impact, achieved within half a month from POC to production.

Morita: It’s relatively rare for AI startups to have people from non-tech backgrounds. Coding may seem challenging, but it no longer is. Many people still underestimate how quickly AI is evolving. 

As a young company, we’re agile and quick to act. If a client needs a meeting in Texas, we will fly out immediately, prioritizing in-person engagement. This responsiveness is rooted in our investment banking backgrounds, where speed and execution are the norm. 

Ueda: We often have a desk inside the client’s office, working side by side. This is a forward-deployed engineer model, where our consultants are embedded in the client’s environment, working together. 

While this approach is becoming more common in the US, we combine it with a hands-on, hardworking culture closer to investment banking than typical tech firms.

How does your background create value for clients?

Ueda: I recently built a product for a client focused on automating their portfolio monitoring process. Some features may seem trivial, like allowing users to upload multiple documents at once, but they matter when the client is dealing with 100 files and do not want to upload files one by one. Moreover, the key in the quality of output was granularity. You don’t want everything listed in detail, but you also don’t want to lose important signals. It needs to be summarized at the right level, for which the standard is not necessarily explicit. 

Client needs are rarely explicitly stated; they are implicit and only become clear through experience. My finance background, and experience in conducting similar work, helped shape outputs and prompts in context, and the client was satisfied with the coverage and framing.

My mindset is simple: what would I have wanted as a user? If I find it useful, chances are the client will too.

Morita: In markets, most information is publicly available through media such as Reuters and Bloomberg. In this commoditized data environment, competitive advantage comes from people and process, creating opportunities to help optimize workflows and scale capability. 

AI can play a major role in training talent. Rather than manually gathering data, AI can surface insights instantly. Many institutions are still early in embedding AI into workflows. 

As highlighted in the movie Margin Call, success in finance often comes down to being either smart or first. AI can enhance both by accelerating analysis and enabling faster, more informed decision-making.

How do you see the future in the AI era?

Ueda: We won’t use AI tools like chatbots in the same way anymore. AI is becoming agentic and ambient, more like infrastructure embedded in daily work, similar to spell check in Word. AI solutions will be attached to what you already use, and you would not have to activate a separate tool to use these solutions.  

Organizational transformation, from reskilling to redesigning workflows, must happen alongside AI adoption. AI won’t replace companies, but those that adopt it effectively will replace those that don’t. 

We enable this transition to be secure and scalable, so leaders can shape how their organizations evolve. 

Morita: The future is bright, but companies will always need a partner who can move quickly with them and guide them through this fast-evolving environment. 

We stay closely aligned with clients. We don’t just sprint—we “sprint a marathon” with them. We may fall, but we recover quickly and keep moving forward together.