AI is not the answer that wealth management thinks it is

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As more wealth management firms experiment with artificial intelligence, a central question is whether adoption is translating into meaningful operational improvements. Orion’s 2026 State of the Advisor report found that 63% of registered investment advisers use AI in some capacity, more than double the 2023 rate. Yet most remain at the stage of individual experimentation rather than firm-wide strategy.

The industry’s deeper technology challenge may be the gap between integration and genuine unification. According to Orion’s 2026 Advisor Wealthtech Survey, only 3% of advisers said their firms’ data was fully unified and flowed seamlessly across every system. CRMs, planning software, reporting tools, portfolio systems, and custodial platforms can appear connected while still requiring advisers to transfer information or reconcile records manually.

This fragmentation raises practical questions about how effectively AI can operate across an advisory firm. When client details and planning assumptions are recorded differently across systems, tools used for meeting summaries or drafting may still require additional review and reconciliation by advisers. Each additional AI feature may automate one activity while preserving the transfers, reconciliations, and repeated data entry surrounding it.

Recent industry surveys have also examined how AI might be used beyond isolated tasks. McKinsey reports that more than 62% of independent advisers surveyed in 2024 intended to use AI for efficiency, while only about 20% intended to use it for client-facing activities. McKinsey argues that value will increasingly depend on redesigning cross-functional workflows and orchestrating technology throughout the adviser-client journey.

Praetorian is Clark Etheridge’s response to that gap. Led by Etheridge as managing founder, the wealth-technology company is developing a centralized operating system intended to replace fragmented adviser tools by bringing data, workflows, and financial insights into one architecture. Its model reflects Etheridge’s argument that firms must address the infrastructure beneath AI before expecting it to transform productivity.

Within that model, Praetorian is being designed to connect communication, data visibility, and decision support across financial functions. A shared operating environment could allow AI to work across complete advisory processes while giving advisers a clearer view of the information supporting each decision.

“Financial services already has an extraordinary amount of technology, but advisers still spend too much time transferring the same information among systems,” Etheridge says. “From my perspective, adding intelligence to individual products may produce limited gains when those products interpret data differently, and advisers must still connect the workflow themselves.”

He illustrates the problem through a common planning process. An adviser may record or transcribe a client meeting, store the notes in a CRM, copy relevant information into separate planning software, enter individual figures into calculators, and then transfer the results into a reporting tool. When information changes or was missing from the first conversation, portions of that sequence may have to be repeated.

Etheridge believes this is why firms should assess their architecture before purchasing another AI product. A useful review would examine where client information originates, how many times it is re-entered, whether updates flow through every relevant application, and which decisions still depend on employees reconciling conflicting records. That process can reveal whether an AI investment is addressing a workflow or merely improving one step within it.

The broader technology sector is reaching a similar conclusion. Deloitte’s 2025 analysis of legacy system modernization explains that organizations can use AI to improve current processes, reengineer their digital core, or redesign business capabilities. Its analysis also emphasizes modern data structures that allow AI and enterprise systems to consume information consistently.

For wealth managers, unified infrastructure could allow advisers to spend more time interpreting decisions, developing relationships, and guiding clients through consequential financial events. Technology would carry information through the process while the adviser contributes judgment and context.

The conversation around wealth-management AI may therefore need to reach beneath the chatbot. Etheridge’s perspective invites firms to ask whether their data can move reliably through the full advisory process before choosing another tool. For an industry surrounded by intelligent software, the most consequential technology question may still concern the foundation carrying it all.