The RIA AI adoption gap has changed.
For the last two years, many advisory-firm conversations started with a basic question: should we be using AI at all?
That is no longer the right question. AI adoption for financial advisors is accelerating, advisor technology vendors are adding AI capabilities, and most firms have at least started experimenting with tools like ChatGPT, Claude, Microsoft Copilot, meeting note takers, CRM AI features, planning software enhancements, and workflow automation.
The new gap is not awareness. It is execution.
The firms that win the next phase will not be the firms with the most tools. They will be the firms with the clearest workflows, strongest guardrails, best adoption habits, and most disciplined measurement. That is the shift from Artificial Intelligence to Advisor Intelligence.
The market has moved past "should we use AI?"
Schwab's 2026 RIA & AI Research Study makes the market shift hard to ignore: 63% of advisors are already using AI, with efforts unfolding at different stages across firms.
That matters because AI is no longer a future-state technology conversation. It is already showing up in meeting prep, note taking, email drafting, client communications, research, operations, marketing, compliance support, and team training.
At the same time, access keeps getting easier. Generative AI tools are widely available. Enterprise productivity suites are embedding copilots. Advisor-tech vendors are releasing AI and agentic capabilities for planning, CRM, documentation, workflow support, and client-service automation.
That creates a more practical challenge for RIAs and independent advisory firms. A firm can have access to multiple AI tools and still have no coherent AI strategy. It can test five vendor demos and still not know which workflow should change first. It can give team members access to AI and still create inconsistent usage, unclear review standards, and avoidable compliance anxiety.
AI access is no longer the scarce resource. Operating discipline is.
What the Schwab data really says
The headline number is adoption: 63% of advisors are using AI.
But the more important signal is maturity. Schwab says most firms are still in experimentation mode, while some are moving toward a more strategic approach. The press release also notes that, among advisors using AI, only about one in ten are fully integrating it into business strategy.
That is the execution gap in plain view.
Advisors increasingly believe AI matters. Nearly six in ten advisors, 59%, believe AI will have a direct, measurable impact on client relationships within the next year. More than two-thirds, 68%, expect AI to be transformative to the future of financial advice within three years.
Those expectations are useful, but they also raise the bar. If AI is expected to affect client relationships and transform advice delivery, firms cannot treat it as a side experiment owned by whichever team member is most curious.
AI implementation for RIAs now needs leadership, workflow selection, data readiness, governance, adoption planning, and measurement.
Experimentation is not the same as operating leverage
Experimentation is necessary. It helps teams learn what AI can and cannot do. It gives advisors a low-risk way to test prompts, summarize information, draft content, and reduce administrative friction.
But experimentation without structure creates inconsistency.
One advisor may use AI for meeting prep. Another may use it for client emails. A client-service associate may use it to draft SOPs. A marketing team member may use it for newsletter ideas. Someone else may avoid it entirely because the rules are unclear.
That kind of usage can create pockets of value, but it rarely creates operating leverage. Operating leverage requires repeatable workflows, shared standards, clear ownership, and feedback loops.
The better question is not "which AI tool should we buy?" The better question is "which workflows should we improve first, and what guardrails make that safe?"
That is why RIA AI readiness matters before a firm starts stacking more tools into the technology environment.
Why leadership and culture now matter more than tool access
Schwab's research is clear that leadership and culture will define success. At the most successful firms, leadership sets a clear AI vision, models usage, and empowers and upskills teams.
That is exactly right for advisory firms.
Leadership needs to define where AI is allowed, where it is encouraged, where it is restricted, and how usage will be reviewed. Without that clarity, team members are left to guess. Some will move too fast. Others will not move at all.
Good governance should not kill experimentation. It should make safe experimentation possible.
For RIAs, that means answering practical questions:
- What client, prospect, and firm data can be entered into each AI tool?
- Which outputs require advisor, principal, or compliance review?
- Which workflows are internal-only, and which can support client-facing work?
- Where do records need to be retained?
- Who owns the playbook after the pilot ends?
- How will the firm measure adoption, time savings, quality, risk reduction, or client-experience improvement?
AI governance for RIAs should feel like a business enabler, not a wall. The goal is to let the team move with confidence.
The agentic AI wrinkle: more capability, more complexity
Schwab also notes that interest in intelligent agents is growing as firms look to personalize service and simplify complex workflows, even though adoption remains low.
That tracks with what is happening across advisor technology. More vendors are releasing AI and agentic features that promise to help with planning, CRM updates, workflow routing, task support, research, documentation, and client-service coordination.
This is exciting, but it also makes execution harder.
AI agents for financial advisors will not be evaluated only by whether they can complete an impressive task in a demo. They need to fit the firm's data environment, approval standards, compliance requirements, client-service model, and day-to-day team behavior.
The more capable the tool becomes, the more important the operating model becomes.
That is the same point behind Anthropic's finance agents and the AI execution gap. When AI moves from chat into workflow execution, firms need more than tool access. They need a clear view of where automation belongs, where human judgment stays central, and how work product moves through review.
What this means for RIAs and independent advisory firms
RIA leaders should evaluate AI by workflow, not by feature list.
The most useful starting workflows are usually practical, repeatable, and close enough to daily work that the team can see value quickly. For many firms, that may include:
- Meeting prep and agenda support
- Follow-up drafts after client meetings
- CRM hygiene and note cleanup
- Client segmentation and service-tier analysis
- Advisor education and internal research
- Prospect research and first-call preparation
- Service calendars and proactive outreach prompts
- Internal SOP creation and knowledge-base maintenance
- Compliance documentation support and review checklists
These workflows are not glamorous, but they are where AI workflow automation for financial advisors can start turning into measurable value.
The firm does not need to automate everything. It needs to choose the right first use cases, define the rules, train the team, and measure whether the workflow improves.
That is also why generic lists of "best tools" can only go so far. Tool selection should follow workflow selection. The best AI tool for one firm may be a poor fit for another because the data, people, compliance posture, and operating model are different. A more practical starting point is to compare tools against the firm's actual work, as outlined in The Best AI Tools for RIAs: Where to Start.
The ThrivAI view: execution is now the differentiator
ThrivAI's view is simple: AI access is getting easier, but AI execution is becoming more valuable.
Advisory firms do not need another abstract AI briefing. They need help translating capability into practical advisor workflows.
That work includes assessing readiness, prioritizing workflows, evaluating vendor fit, setting governance, building playbooks, training teams, and measuring results. It also includes deciding what not to automate yet.
The firms that benefit most from AI will not be the ones chasing every new release. They will be the firms that know which client, advisor, and operations workflows matter most; which data is trustworthy enough to use; which outputs require review; and which metrics prove that the work is actually improving.
That is the difference between scattered AI experimentation and Advisor Intelligence.
Advisor Intelligence means AI is applied to the real operating moments that shape the firm: preparing for better meetings, following up with more consistency, improving CRM data quality, helping advisors learn faster, supporting service discipline, strengthening internal documentation, and giving leaders clearer visibility into where work is happening.
AI consulting for RIAs should help firms build that operating muscle, not simply recommend tools.
What advisory firms should do next
Advisory firms should start by making the AI conversation more specific.
Do not begin with the market map. Begin with the work. Which workflows are repetitive, high-friction, visible to clients, important to growth, or dependent on one person's memory? Which tasks are safe to improve now? Which require more governance or data cleanup first?
Then choose one or two workflows and define the operating model:
- The business outcome the firm wants to improve
- The data and systems involved
- The AI tool or vendor being tested
- The human review steps required
- The team members responsible for usage
- The compliance and recordkeeping expectations
- The metrics that will determine whether the workflow is worth expanding
That is how firms move from curiosity to capability.
ThrivAI helps RIAs and independent advisory firms assess AI readiness, prioritize the right workflows, evaluate tools, set governance, build practical playbooks, train teams, and measure results.
Sources: Schwab, "2026 RIA & AI Research Study: Advisor AI in Action"; Schwab press release, "Schwab Study Reveals RIA AI Adoption More Than Doubles — But Most Firms Still in Early Stages".