A couple of years ago, if you asked someone whether they "used AI," you probably would have heard about a general-purpose assistant like ChatGPT answering questions or making emails sound more professional. Ask the same question today, and the answer might be dramatically different. AI has become both more sophisticated and easier to use – and even laggard industries are buying in. Some recent estimates put AI adoption among financial advisors at 85%, a major lift since a 2023 survey ranked advisor adoption of AI as the lowest of all surveyed professions.
Two firms can both say they use AI and be describing very different things, which makes AI maturity a more useful measure of success than usage alone. Firms face advisor shortages and growing operational complexity, and client expectations are rising fast. Given reports that consumers are skipping the advisor entirely in favor of general-purpose AI assistants, advisors are turning to AI to sharpen their competitive edge, using tools that help them spend more time on customer experience and provide better insights.
Below, Jump outlines six questions financial firm leaders should be asking about AI maturity.
AI-mature firms are using compliant, purpose-built AI to create capacity without proportionally growing headcount. The most mature use it to change how they grow, govern, and compete, positioning them to win clients and talent.
To better understand where your firm stands, the six questions below, drawn from Jump's AI maturity model for enterprise wealth management, help situate your firm's AI use across six dimensions, from capability to business outcomes.
This question looks at AI capability, meaning how much autonomy and complexity your firm entrusts to AI. An assistant that only responds when prompted leaves the follow-through to people.
Before, AI only answered questions when prompted. It could research, draft, and summarize, and it took no action beyond the conversation. Many firms still use AI exactly this way, with no way of measuring ROI. More mature firms use AI tools to produce structured outputs for real tasks, like a meeting recap. Agents, which carry out multi-step work on their own, execute custom workflows with a human reviewing the result.
Looking ahead, firms will use an orchestrated system of agents that operates across all of the firm's functions, informed by shared context, with people focused on the work that needs trust, empathy, and judgment.
This question looks at specialization, meaning how well your AI is tailored to your firm's industry, workflows, and data. General AI assistants not built for financial services can give inaccurate and nonspecific outputs in situations that require industry expertise.
Before, firms used off-the-shelf AI with no customization. More mature firms use tools built for financial services workflows. In an ideal state, AI will be configured to your firm's own playbooks, segments, and processes, and the most mature firms will partner with their AI provider on a custom roadmap.
A defining requirement of AI built for financial services is compliance. A tool built for the industry has to meet regulatory requirements, and firms that use AI without review risk regulatory and legal exposure and the loss of client trust.
This question looks at compliance and governance, meaning how formally and consistently your firm governs AI usage. AI used without compliance review creates security, regulatory, and legal risk, and it can erode client trust. At a minimum, firms should be able to show what was shared, with which tool, and by whom.
Before industry-specific AI tools became available, some financial professionals may have used generic AI tools without legal or compliance sign-off. More mature firms have compliance approve specific tools and oversee them manually, one tool at a time. The most mature firms will have a firm-wide framework with clear human ownership sets, data retention, consent, and access rules. Compliance becomes continuous and largely automated, with every AI action logged, auditable, and mapped to regulatory requirements in real time.
This question looks at tech stack integration, meaning how your AI tools integrate with the firm's systems and with each other. Disconnected tools leave work siloed, and your answer shows whether AI reduces work for your team or adds another system to manage.
Before, individuals may have used their own AI tools outside firm systems or relied on AI features isolated inside a single existing system. Many software products now include an AI feature, from note-takers to calendar tools, but most of them don't share data with each other. More mature firms use AI tools and agents that connect to each other and integrate directly to systems of record like CRM and planning tools, so data flows automatically.
Without a shared context layer, a change to one system's data format can break a connected workflow, and the firm may not notice until errors surface downstream. The most mature firms have a common context layer across the whole client lifecycle and an orchestration layer that coordinates agents across every firm system, so no one re-keys information.
This question looks at adoption, meaning how deeply embedded AI is across your firm. Advisors adopt tools that solve a long-standing problem, like the manual admin around every client meeting, without asking them to change how they work. The ROI of an AI tool nobody uses is zero, and success in one department doesn't spread on its own.
Before, a handful of employees may have used AI on their own initiative. More mature firms have a specific group or function using a sanctioned tool, and they can measure how long it takes an advisor to go from sign-up to a live client meeting. The most mature firms are AI-enabled across the entire firm, with connected AI sharing data across previously siloed functions and informing decisions at the leadership and board level. A firm-wide owner and a change management plan make it possible.
This question looks at business outcomes, meaning the value AI produces for the firm. It is the result of the other five questions, and it shows up in top-line results like revenue and net new assets, along with households onboarded and accounts opened.
Before, value was anecdotal and hard to quantify ("Look how easy it is to generate an email"). More mature firms see measurable time and cost savings, with the freed capacity going toward client relationships and firm growth, and AI surfaces opportunities like referrals, retention risks, and next-best actions. The most mature firms scale capacity without adding headcount and use the proprietary data they've built up over time to change the firm's economics and pursue new lines of business.
From any starting point, the next step is the same: Find where work is most fragmented, like the handoffs, the re-keyed data, and the workflows that span six systems, and connect them before adding another point solution.
These six questions are a starting point, and your answers may place your firm at different levels on different dimensions.
This story was produced by Jump and reviewed and distributed by Stacker.
