There is an industry trend of vendors prioritising AI assistants, which are likely to be dominant features whenever fund marketers assess new platforms.
TL:DR: implement industry-proven AI assistants in stages.
MarTech.org is a leading institution keeping track of product launches. Across its October coverage, there have been dozens of vendor announcements that point to a popular variation-on-a-theme: autonomous AI agents that connect to marketing and sales data.
Most evident is a boom in Model Context Protocol (MCP) at vendors based in research, events and customer loyalty. It is a standard that lets AI assistants read from, and act inside, other software.
This correlates very closely with the substantial industry finding of Salesforce’s planned acquisition of Braze, itself a platform focused on agent-focused beta features.
Prominent launch examples include:
- A Model Context Protocol (MCP) connector for Validity Engage, the email platform from the deliverability specialist.
- FollowOS Email released by Follow Inc.; agentic email for targeted copy and subscription list segmentation.
- Iterable’s updated marketing automation platform adding persistent contextual memory.
- Tealium launched Ads Connectors for ChatGPT advertising.
- Zeta Global’s AthenaOS, an enterprise intelligence operating system to optimise campaign strategy.
- Sales-side agents at ZoomInfo and Apollo.
Such launch-list roundups as these are announcements from the vendors themselves, and mostly indicative of blanket trends seen in the marketing technology sector. There are no adoption figures given for these features.
So, what does this mean for fund marketers?
Agentic AI is nothing new; in fact, it is a prominent buy-side factor on the roadmap of any marketing leader that approaches a new email platform or CRM. AI-assistant connectors will be either default features at some vendors, or offered as a paid add-on.
It is easy to let ‘AI hype’ skew whether to invest in certain exciting vendors, albeit hype does not prove results. The agentic AI rise now opens up a few questions for fund marketers today.
Use AI connectors as a data-access decision informant
These assistants can read and, in some cases, write to prospective investors, so involve IT and compliance teams before enabling them. Ask what data leaves the system, where it is processed, if it trains AI models, and how access is limited and logged.
Preserve your data trail
Final, human decision making and auditing (particularly under financial services’ compliance chokehold) remains absolute. It is therefore wise to check how an agent drafts personalised communications, and how this content is captured for review and record-keeping. Anything that bypasses approvals is a risk.
Experiment with low-risk uses
This brings up the fact that there are safer places to use agentic AI than automated investor outreach. After asking vendors for regulated financial services client testimonials, or a pilot with clear success measures, internal tasks can be automated such as segmenting lists, tidying contact data, flagging deliverability issues, and summarising campaign metrics.
As with a lot of AI, confidence builds from starting small and seeing engagement success by degrees. The hype period is over, where actionable AI use cases will determine how funds can reach LPs with tailored offerings through efficient workflows.
Sources
MarTech.org, “The latest AI-powered martech news and releases”
CMSWire, “Why Is Salesforce Buying Braze, and What Happens to Marketing Cloud?”
