The Future Is Now for AI adoption across Investment Banks. Banks have invested heavily in AI expecting productivity gains, which must begin to be realized. AI is often presented as a versatile, one-size-fits-all solution, but banks struggle to adapt this general-purpose tool to their specific needs and regulatory requirements. Many have pivoted to tailored solutions like RAG for narrow functions like improved enterprise search. With many banks announcing those roll-outs, today’s focus should be on role-specific productivity tools. Companies dedicated to this task, like ModuleQ, ultimately will deliver these specific tools to investment bankers.
With banks evaluating a myriad of solutions that purport productivity gains, the big question is where is the proof: what is banker usage, how is it measured, and does it move the needle in terms of productivity or business development? These are the big questions that banks need to ask vendors who pitch them tools that promise much but can often fail to deliver.
At ModuleQ, we have achieved 70% Daily Active Usage (DAU) across our AI solutions for Investment Bankers. This is a higher DAU to MAU ratio than Facebook. We’re proud of that fact because Investment Bankers are notoriously cagey with adopting new technologies. They are some of the busiest knowledge workers in the world. Switching and ramp costs are significant, often higher than the contract value of the solution. As a result, many new technological initiatives that look promising on the outside die on the vine, as bankers don’t bother to learn a new user interface, or they cannot expend the time and energy to change their existing workflows. The result: usage is low.
Across banks, inertia is often the biggest enemy to adopting a new tool. Why learn a new workflow when the value-add is unknown? Even when the initiative is promoted from the top down, disinterest can lead to begrudging or failed adoption. The examples of failures from investment banking's past range from messaging services to new information portals. There is a recurrent stickiness with outdated ways of approaching work, given the expensive energy of activation.
New solutions must be built with this in mind. At ModuleQ, we have designed a product for bankers and private wealth managers. Because we know that high-value knowledge workers don’t have time to learn new interfaces and they shouldn’t have to constantly context-switch (which ironically saps productivity), we designed our AI insights engine to be unprompted: a push paradigm. This means the right information is delivered to the right banker at the right time. Compare this to a pull paradigm, where the user navigates to a portal, prompts a query, or searches for information. Pushing information reduces the friction of adoption, which is the first step toward usage. Bankers simply sign up for ModuleQ within Microsoft Teams, and they’re off to the races.
Ease of activation mixed with reducing navigation friction can get a banker to sign up. But will they keep leveraging the product? This is the key litmus test for value-add. Bankers will only keep using these solutions if their value is additive to their existing workflows. That means it can’t be a general-purpose technology but must be tailored to who they are and what they do. Think of a computer without application software. If you put an early PC on a banker's desk in the 1970s, they might gaze at it with wonder, but without a spreadsheet or word processor, its adoption would be curtailed.
If AI is the computer of the 2020s, then Unprompted AI is the application layer for disseminating its analytical horsepower. Our Investment Banking clients find our Unprompted AI insights extremely valuable and go back to them on a daily basis. This is because our patented personal-data fusion merges their individual persona (who they are, what they work on) with their role, schedule, and feedback. The goal is to deliver higher-quality information, exactly when they need it.
This is unlike common alerts or notifications, which struggle with context and filtration, or force the user to parametrize clunky rules that never work quite right. They are often tapping weaker data sources, or they can’t adequately deal with nuance. They can’t break data silos or connect dots. Because our Unprompted AI draws insight from a banker’s existing data resources (across multiple repositories), it delivers immediate value. Because it draws on the information that already matters to them, it is immediately accretive to their process. Information overload will be a defining characteristic of knowledge work this decade. Our ability to serve up the right information at the right time is the key to cracking this problem.
Cadence is also key to usage. Because we package our insights with the demanding schedule of our banking and private wealth clients in mind, they are designed to provide quick access to the right information, saving a banker’s time. That cadence can be customized. For example, with our Pre-Meeting Alerts, bankers can have their briefings sent to them an hour before a meeting, or at a pre-determined interval, batched together for the day. This saves them time before every meeting in their busy schedule while ensuring they step in prepared, increasing their return on time. Unprompted AI serves them valuable contextual information, exactly when they need it, like magic.
When thinking about crossing the chasm from AI activation to engagement, the medium is often the message. Tools like Unprompted AI will be key in helping organizations such as investment banks cross that bridge, reducing the failure rate of AI onboarding, improving the ROI on existing data spend, and driving productivity. The proof is in the usage.