Artificial intelligence is no longer a distant concept on the horizon of financial services. It is arriving — thoughtfully, unevenly, and faster than most anticipated. This edition looks at where things stand, what is changing, and how Lifemark is thinking about it.
Not long ago, the central question around generative AI was simply: what can it say? That question has shifted considerably. Today, the more relevant question is: what can AI actually do?
Modern AI systems are increasingly capable of summarizing large volumes of information, assisting with research, automating repetitive workflows, and interacting with software and data on a user's behalf. The technology is moving from novelty to infrastructure — embedding itself into the tools professionals use every day rather than existing as a standalone application you visit occasionally.
For financial advisors and staff, this shift matters because it changes what AI adoption actually looks like in practice. It is less about experimenting with a chatbot and more about understanding how the systems around you are quietly becoming more capable — and what that means for how you work, what you verify, and where human judgment remains essential.
One of the clearest signals that AI adoption is accelerating is the sheer volume of corporate investment flowing into AI infrastructure. In September 2026, Reuters reported that Oracle secured more than $30 billion in new AI cloud contracts during a single quarter — a figure that illustrates the enormous resources being committed to building the underlying capacity that AI systems depend on.
Oracle is one data point among many. Across the technology sector, enterprise demand for AI-capable infrastructure — cloud computing, data centers, specialized processing hardware — continues to grow at a pace that few anticipated even two years ago. This is not speculative investment in a future technology. It reflects real, present-day demand from organizations actively integrating AI into their operations.
For those of us in financial services, the practical takeaway is straightforward: the tools and platforms we use are being shaped by this investment wave. AI capabilities are not being added as optional features. In many cases, they are becoming the foundation on which new workflow, research, and productivity tools are built.
New AI cloud contracts secured in a single quarter, per Reuters (Sept. 2026)
Regulatory Oversight Report recognizes growing generative AI implementation across member firms
The next frontier: AI systems that plan and complete multi-step tasks on a user's behalf
The term "agentic AI" is appearing with increasing frequency, and it is worth understanding what it actually means before the terminology becomes noise.
Traditional AI systems — including most of the generative AI tools you may have encountered — operate in a simple loop: you ask, they respond. Each interaction is largely independent. An AI agent is different. It is a system capable of planning and executing multiple steps on your behalf to accomplish a defined objective, rather than simply answering a single question.
Think of the difference between asking a colleague "what is the client's account balance?" and asking that colleague to "prepare a summary of this client's situation, pull relevant account data, flag any compliance considerations, and draft a follow-up email." Agentic AI is designed to handle the latter type of request — completing a workflow, not just answering a prompt.
This is why many observers believe agentic AI represents a meaningful leap in usefulness rather than an incremental improvement. The technology could eventually allow professionals to delegate multi-step administrative work to AI systems while focusing their own attention on judgment-intensive tasks.
You ask a single question. The system answers. Each interaction is independent and discrete.
You define an objective. The system plans and completes multiple steps across tools and data to accomplish it.
NIST has emphasized that security, identity verification and access controls cannot become secondary to functionality as agentic systems expand.
The expanding capabilities of AI systems have understandably prompted serious and legitimate questions — and those questions deserve straight answers rather than dismissal. Organizations operating responsibly in this space acknowledge that the concerns are real, even as they work to capture the genuine benefits the technology offers.
Recent reporting has documented sophisticated AI systems being targeted and misused. As AI becomes more capable, so does its potential for exploitation by bad actors. Security safeguards must evolve alongside the technology itself.
AI systems can produce confident-sounding outputs that are incorrect or entirely fabricated. In financial services, acting on unverified AI output carries real professional and regulatory risk.
AI models trained on historical data can reflect and amplify existing biases. In client-facing or decision-support applications, this can have meaningful consequences for fair dealing obligations.
NIST has specifically highlighted emerging challenges around agent identity, permissions and access — the risk of AI systems acting beyond their intended authority, particularly in agentic configurations.
These are not reasons to ignore the technology. They are reasons to adopt it intelligently.
The following tools represent early, practical examples of where AI is beginning to show up in advisor workflows. Advisors are encouraged to learn about these tools — with the important clarification that Lifemark's evaluation of a platform does not constitute approval of every AI capability that platform may offer. Specific functionality and approved use cases are subject to ongoing review.
Jump AI is designed to help capture meeting notes, organize information from client conversations, identify follow-up items, and reduce the administrative overhead that surrounds client meetings. For advisors managing a high volume of client interactions, tools like Jump can help ensure that important details are captured consistently without requiring the advisor's full attention to be divided between the client conversation and note-taking. The goal is to support better follow-through — not to replace the advisor's own judgment about what matters in a client relationship.
Redtail is a widely used CRM platform in the advisory space, and like many enterprise software providers, it is actively integrating AI-enhanced capabilities into its core offering. The broader evolution of CRM technology is significant: as AI becomes more embedded, CRM systems may increasingly help advisors surface relevant client information, organize historical interactions, reduce repetitive data entry, and identify follow-up opportunities — all within the system advisors are already using daily. Lifemark is monitoring these developments as part of its technology evaluation process.
The goal of AI adoption at Lifemark is not to replace the advisor, the operations professional, or the compliance professional. The most compelling and defensible opportunity that AI presents is far more straightforward: give those people better tools.
Better tools that reduce the time spent on administrative tasks. Better tools that surface relevant information faster. Better tools that support — rather than substitute for — the professional judgment, relationship skills, and regulatory awareness that define what it means to operate well in financial services.
That perspective shapes how Lifemark approaches every technology evaluation. We are watching the space carefully, conducting due diligence on specific platforms, and moving deliberately rather than reactively. The landscape will continue to evolve — some of what is being tested today will prove its value, and some will not. Our job is to identify the difference.
The following reputable sources informed this article. Advisors and staff interested in exploring these topics further are encouraged to consult these resources directly.
2026 Regulatory Oversight Report — covers generative AI implementation trends among member firms and the application of existing regulatory obligations to AI-enabled workflows.
AI Risk Management Framework and recent publications on agentic AI security — including emerging challenges surrounding agent identity, permissions and access controls in enterprise environments.
Reporting on Oracle AI cloud contract growth (September 2026) — cited as illustrative of the scale of enterprise AI infrastructure investment currently taking place across the technology sector.
Ongoing guidance and staff bulletins relating to the use of technology, automated tools and AI in securities industry workflows, supervision and communications.
This article is intended for informational purposes only and does not constitute legal, compliance, or regulatory advice. Advisors with questions about specific AI tools or approved use cases should contact [email protected] or Lifemark's compliance team.