AI Portfolio Management
We developed an AI-powered assistant to streamline portfolio management. Despite time constraints and resource limitations, the team successfully implemented the solution, prioritising immediate needs. The AI assistant aims to reduce support requests and provide users with easier access to their portfolio data, paving the way for a more advanced dashboard solution in the future.
Diligent Equity AI Portfolio Management Assistant Interface
Overview
Diligent is at the forefront of AI innovation, strategically implementing AI features across multiple products to stay competitive in today's rapidly evolving market. As part of this initiative, Equity is prioritising the development of an AI assistant to address the pressing need for easier access to portfolio data.
Equity is Diligent’s equity management product that helps venture capital, private equity, start-ups, companies and service providers to easily manage their portfolio’s cap tables, legal terms and performance.
By leveraging AI, we aim to enhance user experience, increase efficiency, and drive growth within our product offerings.
Defining and researching the problem
Diligent is facing increased pressure from competitors to innovate and deliver cutting-edge solutions. To maintain our market leadership, we must leverage AI to offer a more efficient, intuitive, and valuable product experience. Our current user satisfaction surveys indicate a growing need for more automated and personalised features, while our support ticket volume highlights the challenges users face in managing their portfolios.
"In reality, navigating the beginning of this project felt like swimming in the dark. I needed to understand why we had to focus on the users asking questions & portfolio ownership data."
After some back-and-forth with the team, I highlighted the fact that the underlying issue might actually be users struggling to access and analyse their portfolio ownership data effectively.
Following this initial discovery, we argued the fact that expanding the dashboard’s functionality beyond fund overview, by leveraging AI, would be a more promising solution. While product managers were enthusiastic about this idea, the pressing need for a quick solution made it infeasible for the time being. Although the AI assistant is limited in scope, it represents the most viable option given the constraints and will play a crucial first step toward our broader goal of enhancing user access to their data.
Designing AI solutions demands a strategic approach that addresses the core needs of users, even in the face of challenging circumstances.
Who needs access to portfolio ownership data?
While portfolio ownership assistant was initially selected as a quick solution to address a specific business need, it has become clear that it's also benefiting Private Equity (PE) firms. Understanding the needs of this group helped us refine the AI assistant and ensure it aligned with their specific requirements. This will not only enhance the product's value for PE firms but also provide valuable insights for future development and expansion.
Target user group
Looking at competitors
While our direct competitors may not currently offer a similar solution, I researched other companies’ approaches to AI assistant and data table solutions. Atlassian’s AI strategy for data table format was particularly inspiring, as it seemed to align with the goal of our assistant.
Measuring impact
While our direct competitors may not currently offer a similar solution, I researched other companies’ approaches to AI assistant and data table solutions. Atlassian’s AI strategy for data table format was particularly inspiring, as it seemed to align with the goal of our assistant.
Outcome & feedback
While we eagerly await direct user feedback, the initial response from our customer support team has been overwhelmingly positive. They've expressed enthusiasm for the AI solution, highlighting its potential to significantly alleviate their workload by automating common inquiries such as "how do I identify my top investors?"
The ultimate success of this initiative will be measured by a decrease in customer support requests and the AI assistant's ability to consistently provide accurate and relevant answers. If we achieve these goals, it will not only demonstrate the effectiveness of our AI solution but also pave the way for a more comprehensive, all-in-one dashboard experience that empowers users to access and analyse their portfolio data seamlessly.
The Solution
An AI assistant as first step toward innovative growth
With less than 2 months for product managers, technical writers, engineers and myself to work out a solution, we could only aim for the fastest solution to ship, and only keep the bare minimum required.
Working together to create the best solution
Given the constraints and roadblocks we encountered, we believe we've developed the most effective solution possible. A key player in this process was our technical writer, whose deep understanding of AI proved invaluable. Meanwhile, our developers demonstrated remarkable ingenuity, overcoming seemingly insurmountable technical challenges to bring our vision to life.
Alternative solution (for the future):
Creating an all-rounded AI-powered dashboard experience
With more time to explore, I could have prioritised enhancing users' access to their portfolio data insights. While an AI assistant is valuable, direct access to data is essential for a comprehensive user experience.
A proactive approach would involve equipping users with an AI-powered dashboard, providing them with direct access to their data. The assistant would then complement this experience by answering more niche questions when users require even quicker access.
To identify potential areas for improvement, I leveraged the PMs' established list of questions and employed AI tools to brainstorm additional features and functionalities that could extend the dashboard's capabilities beyond its current focus on fund performance overview.
While these ideas are promising, their implementation is subject to technical feasibility and budgetary considerations.
Constraints Drive Clarity: When faced with a tight launch deadline, focusing on query intent for top investors delivered maximum reduction in support ticket volume.
Data Table Contextual Assistant: Integrating conversational AI directly into equity cap tables eliminated context switching between external spreadsheets and legal terms.
Cross-Functional Synergy: Close alignment between lead design, technical writing, and engineering turned complex financial schemas into simple natural language responses.