Sanctuary Wealth CTO warns AI vendors straying from core competency creates 'anarchy'. Data coherence is the key filter for fintech investments. Zocks $45M Series B signals flood.
Wealth management technology officers at the BNY INSITE conference this week delivered a direct warning: the stampede toward artificial intelligence is creating a mission creep problem that threatens to undermine the very efficiency AI promises. The core message was not anti-AI. It was a call for discipline–specifically, discipline around data coherence and core competency.
The sharpest warning came from Sanctuary Wealth Chief Technology Officer Bob Coppola. Speaking on a panel, Coppola described a growing tension between advisory firms and AI-forward providers. Sanctuary had been forced to “drive integration” through its customer relationship management (CRM) system simply to maintain order.
Coppola's use of anarchy is not rhetorical. A vendor originally building a portfolio rebalancing engine that suddenly offers a client onboarding chatbot introduces data flows crossing internal silos without a common schema. The firm, not the vendor, bears the cost of reconciling the mess.
Steven Latow, chief strategy officer at Zocks, offered a complementary view from the vendor side. Latow acknowledged that RIAs and their service providers face pressure to “AI everything.” The combination of abundant venture funding and demand creates a market where speed to market often outweighs problem definition.
The underlying mechanism that makes mission creep dangerous is data incoherence. AI systems are statistical pattern matchers. If the same security is labeled three different ways across a firm's CRM, portfolio system, and tax software, the model cannot learn correctly. It produces outputs that look plausible yet contain hidden errors.
Coppola made the point directly: “If your data is inconsistent and you're calling a security something different in three different places, the AI is going to be confused.”
For a trader evaluating wealth management fintech stocks or private placements, this is a critical distinction. The naive bull case for AI in wealth tech assumes that automation reduces costs and errors. The better market read is that AI amplifies existing data quality problems before it improves anything. Firms with strong data governance–usually the largest custodians and broker-dealers–will extract value. Firms with patchwork legacy systems will see AI as an expensive accelerant of bad data.
Ainslie Simmonds, an executive platform owner with BNY Pershing Wealth Services Platform, observed that the industry is making a “pretty dramatic shift” toward “fewer, more connected partners.” She said firms now have a “real awareness of the importance of their data layer,” yet many are still building strategies around it.
A practical framework for evaluating any AI vendor in this space begins with a single question: what is the vendor's primary data domain?
This is not a theoretical exercise. Sanctuary Wealth explicitly forced integration through its CRM to prevent vendors from writing directly to its core systems. That decision carries an execution cost–integration takes time and engineering–yet it creates a single source of truth.
Chip Kispert, a managing partner with Beacon Strategies, identified a structural bifurcation. Large firms can support building internal AI assistants because they have dedicated technology teams and multi-year budgets. Mid-size broker-dealers and RIAs, he argued, “should not be tech shops. They should not be tech shops, and they should be stewards of their tech.”
That distinction has direct implications for vendor market share. Vendors targeting large firms need to be best-in-class components that can be dropped into a bespoke architecture. Vendors targeting mid-size firms need to be end-to-end platforms with pre-built integrations, because the client lacks the engineering bandwidth to stitch pieces together.
The divergence also affects valuation. A vendor that succeeds with large institutions earns recurring revenue with sticky, long-term contracts. A vendor that wins mid-size firms earns volume yet faces churn when custodians or larger competitors roll out competing features at lower cost.
Zocks itself fits the mid-size target. Its $45 million Series B and 5,000+ firm client list, including Carson Group, Osaic, and Kestra Financial, suggest it has built a broad base. Latow's own comment about “solution looking for a problem” applies to Zocks as well: it now offers CRM tools, financial planning integration, tax software, and portfolio management workflows. The more features it adds, the more it risks becoming a generalist that does no single thing better than a specialist.
For an equity analyst or private market investor, the Zocks round is one data point in a wave. Latow noted that the Kitces Advisory Technology Map of fintech providers tripled in size over 18 months. That surge in supply coincides with a moment when enterprise clients are growing more skeptical, not less.
CRM (Salesforce Inc.) offers a proxy for the broader market's view on wealth tech AI integration. Salesforce's Alpha Score of 57/100 (Moderate) reflects a company that dominates CRM yet is expanding aggressively into AI agents. If Salesforce's own AI expansion leads to mission creep–bloating its wealth management module with features that duplicate specialist tools–it could create integration friction for firms. Lower adoption would pressure Salesforce's revenue growth in the segment. Track the CRM stock page for signs of this dynamic.
Conversely, the shakeout scenario benefits custodians and platform operators. BNY Pershing and Charles Schwab can afford to build internal AI layers because their data sets are already harmonized across custodial, trading, and reporting systems. They become the default AI vendors for the mid-size firms Kispert described.
The most concrete catalyst to watch in the next 12 months is the rollout of custodian-native AI products. If BNY Pershing, Schwab, or Fidelity embed AI features directly into their platforms–assistant, document processing, portfolio analysis–they will effectively commoditize the middle layer of independent wealth tech vendors. Mid-size firms would then face a straightforward choice between a free or low-cost custodial AI tool and a paid specialist tool that requires separate data integration.
The panel did not offer a timeline. Simmonds' description of “fewer, more connected partners” is consistent with a multi-year trend. Firms that have not already invested in their data layer will find it harder to switch vendors after adoption, creating a first-mover advantage for custodians that move quickly.
For a portfolio manager considering exposure to wealth management fintech, the long position is in custodians and established platform providers. The short candidate or underweight is in any VC-backed AI vendor that cannot articulate its core competency in one sentence. The mission creep warning from the BNY INSITE conference is a signal to re-read vendor investor presentations with the data consistency question in mind.
Finally, for a trader designing a watchlist, the BNY INSITE warnings create a concrete risk to monitor. Track any public announcement of a wealth management AI vendor losing a major client due to data issues. Track the Kitces map count quarterly–if it stops growing or shrinks, the shakeout is underway. And track the CRM (Salesforce) stock page for signs of Salesforce's own AI mission creep affecting its wealth tech sales cycle.
The conference speakers were not arguing against AI. They were arguing for discipline. The firms that enforce data coherence now will capture the compound returns of AI later. The firms that chase every shiny vendor will find themselves paying for anarchy.
Prepared with AlphaScala research tooling and grounded in primary market data: live prices, fundamentals, SEC filings, hedge-fund holdings, and insider activity. Each story is checked against AlphaScala publishing rules before release. Educational coverage, not personalized advice.