Practical AI for Private Markets

Unlocking the Value in Meeting Notes: Actionable Date

By Andrey Volosevich
March 08, 2024

Setting the Stage: Real-World AI Applications
Welcome to the inaugural post of our series on the practical application of AI in the Private Markets. Our goal is to illuminate how AI can bring about significant, practical improvements to daily workflows for deal sourcing, fundraising, and investor relations professionals. As we transition from the theoretical AI hype of 2023 into the pragmatic, result-driven ethos of 2024, we are focusing squarely on delivering tangible benefits.

We kick off this series with posts focusing on specific use cases for practical AI, to be followed by a broader overview into InvestorFlow’s approach to AI. The central theme of this discussion is the optimization of existing data within your organizations. Despite the abundance of valuable, albeit unstructured, data amassed through emails, meetings, and notes, leveraging this data effectively remains a challenge. Our initial venture into AI application aims to address this very issue.

The Simple Yet Significant: Actionable Date
Our users take detailed notes as part of their daily conversations. Deal professionals collect information when meeting with prospective investment targets and sponsors. Fundraisers collect similar notes from their LPs and prospective LPs. Often a single note may include information about multiple companies or LPs. These notes are gold mines of information, often hinting at the optimal next engagement opportunity with potential investment targets or limited partners. Identifying the Actionable Quarter, or Actionable Date from these conversations is crucial but manually updating records post-meeting is time consuming and is rarely done consistently across the organization. This has real impact. Deals get missed when busy professionals are not able to track large target lists that aren’t optimally and continually prioritized. Let’s see how AI assists in this process.

Example Note: A Deal Professional's Conversation with a Sponsor
Consider a scenario where a Deal Professional captures meeting notes from a discussion with a Sponsor. There is a treasure trove of valuable information in those notes. Let’s focus on the simplest takeaway first: Actionable Date – the ideal time for the next engagement.

The data is there. However, manually entering it across multiple company records is inefficient and unreliable. By employing AI, we can automate this process, ensuring that engagement timelines are accurately captured and updated without any manual intervention from the user. Let’s look at a sample note:
2024.02.12 - HealthTech w/ Emerson Partners
Overall sentiment: “Expecting more activity compared to last year”

  • Strivve
    • Michael is the lead on this one
    • Increase in business + change management and blended professional development services
    • Nurse education space
    • Solid growth, not a ton of competition
    • Thinks they won’t be ready till 2025
  • BestHealth
    • Comprehensive view of the entire patient
    • Ran a process last winter, decided not to proceed
    • Coming back out to market after Labor Day; process is being run by Baseline Capital
    • Size is around $30m
  • TeleDoctor
    • Exceeded most benchmarks after investing in the infrastructure and rebuilding the team since Emerson divested them
    • Thinks that LTM could take to market as soon as this summer
    • Transacted for 98m of enterprise value with 50-70m of bookings
    • Acquiring ZenDoc
  • Holistik
    • Coming back around likely end of the year
  • Clever Path
    • Probably coming back in market soon
    • Lots of conversation about interest there
* Please note, the company names here are fictitious. Any similarity with real business is coincidental

Manually, a user would have to search for 5 different company records and populate 5 fields. Doesn’t sound like an impossible task but how consistently, if at all, is this being done in your CRM system today?

Now let’s imagine we have a system that discovers these actionable events from notes, understands which companies they relate to, finds those companies and updates respective records accordingly. Wouldn’t that be helpful? We think, yes. Now, our Deal Professional will get a nicely and automatically prioritized Pipeline View like the screenshot below without tedious data entry.
Practical AI for Private Markets
What to Look for Next
This is a very simple and small feature but one that can make a difference. It enables professionals to concentrate on fostering relationships and securing exceptional deals by eliminating data entry and automatically organizing their prospecting lists. This ensures they can consistently target the right companies at the optimal moment. In our next article, we will build on this example and look at more sophisticated AI-powered features for extracting, collecting, organizing and leveraging company KPIs.

A Note on Privacy and Security
In leveraging AI to enhance our workflows, we, at InvestorFlow, never lose sight of the confidentiality and privacy these processes entail. Our platform is designed from the ground up to meet the highest standards of privacy and security, a commitment we are eager to discuss in detail with those interested.

Stay tuned for more insights as we continue to explore AI's practical applications within the Private Markets, making our daily tasks easier and our strategic decisions more informed.

Click here to learn more.

icon for back button

Back to Resources

Share this
icon for share linkedin icon for share twitter
The Alternatives Cloud
Request Info