Exponential advances in AI and adjacent technologies are set to revolutionize how wealth and asset management firms serve their clients and manage their businesses. By transforming their strategies, business models, and operations through AI-enabled digital solutions, firms will not only create frictionless and engaging experiences for clients, but also boost productivity and savings, minimize fraud and risks, and drive new levels of profitability and growth.
To investigate these developments, ThoughtLab is conducting a multi-client research program, The AI- Powered Investment Firm: How wealth and asset management providers will transform their businesses through AI. As part of the study, we asked industry experts and study sponsors their views on how the industry will change in the AI era. This is the first of a set of articles providing their perspective on the wealth management industry’s AI-powered future.
Question: What do you see as today’s most effective AI use cases in the industry and how are they driving value? How will firms reimagine their business strategies and models in the next three years around advanced forms of AI such as GenAI and Agentic AI?
John Blackman, Head of Products, FNZ
To see how AI is adding value right now, look at onboarding. It used to be a slow, manual process, with forms, back-and-forth, and compliance delays. But with the right platform and automation in place, that whole experience can be turned into a near-instant, fully digital journey. That reduces the cost-to-serve, speeds up time-to-advice, and gives clients a much better first impression.
Beyond that, AI is starting to flow through into areas such as proposal generation, suitability assessment, and reporting, all of which can now be built from a cleaner data set and delivered with far more personalization. The result is that advisors are better equipped, clients feel more understood, and firms can operate with much greater consistency and scale.
Looking forward, AI is only going to get smarter. In the future, it won’t just support decisions—it will start making low-risk decisions within guardrails. That might mean nudging clients to rebalance or proactively flagging a change in their financial goals.
Erik Smith, CFP® Senior Vice President, Wealth Planning Product Management, LPL Financial
The most effective use cases for advisors will help them improve client relationships. Client meetings take up a large amount of advisor and staff time between scheduling, preparing, and follow-up and this is an area where AI could deliver large efficiency gains while also providing a more personalized client experience. AI can prepare relevant information about the client before a meeting, prompt key questions or topics, take notes during the meeting, structuring the data and copying those notes into relevant systems, and create and execute follow-up tasks such as to-dos and meeting recaps.
However, AI can potentially go much deeper in the future, looking for non-financial events relevant to the client (age, geographic location, interests), further analyzing client investments prior to an annual review, summarizing performance attribution, and recommending portfolio changes. Generative AI can also distill complex such as account statements, trusts, wills, tax returns, insurance documents and a host of other financial information to digestible summaries with key points and even insights to help advisors manage wealth more holistically.
Beyond serving clients, we see AI assisting with investment research, automating supervision, streamlining operations, which will ultimately have a downstream effect on advisor satisfaction and productivity, and on the business model of a financial services firm.
Steve Wray, Executive Director, Block Center for Technology and Society, Carnegie Mellon University
The most effective use cases include AI agents that serve as personal research assistants and analysts for advisors, increasing their productivity and responsiveness. This gives everyone their own team, allowing smaller firms to more effectively compete with the traditional leaders of the industry. In addition, new and innovative models and portfolios can be created using Gen AI, personalized to the needs and interests of clients.
Brie Williams Global Head of Advisory Solutions & Wealth Intelligence, State Street Investment Management
Today, AI is driving value in the front office—streamlining research, improving data quality, and accelerating scalable solutions like model portfolios. Institutional investors expect GenAI to provide the most value by helping firms define investment objectives, create new strategies, or adjust products and select strategies.
Next, firms will move from pilots to full integration. GenAI and agentic AI won’t just optimize tasks; they’ll reshape collaboration, how insights are packaged, and how intellectual capital is allocated. Success will be measured by productivity gains, innovation speed, and improved client outcomes.
Richard Doherty, Title: VP, Asset & Wealth Management Lead, Publicis Sapient
The most impactful AI use cases today are those where agentic AI is embedded into end-to-end processes. Examples include modernizing thousands of mainframe screens in months, rather than years, AI-enabled trade surveillance that decommissions manual exception management, and colleague companion agents that provide real-time client insights, compliance guidance, and personalized coaching for advisors.
These are not just proofs-of-concept—they are delivering measurable bottom-line results, from halving modernization costs to boosting sales performance by double digits. In the next three years, forward-looking firms will reimagine their business models around these capabilities. This will mean shifting from isolated AI pilots to enterprise-scale agent ecosystems; designing products, services, and client journeys that are AI-native; and adopting operating models that allow humans and agents to co-create value continuously. Firms that act decisively will not only optimize current operations—they will redefine the competitive landscape.
Jamie Solomon, Head of Technology, North America & Head of Data and AI, FNZ
Some of the most effective use cases right now are the really practical ones: things like summarizing emails, spotting gaps in forms, or sorting documents during onboarding. They’re not flashy, but they save hours of effort and reduce mistakes. That’s where firms are already seeing a real return—quicker processes, cleaner data, and happier teams.
Where it gets exciting is what’s coming next. Generative AI combined with enterprise and cross-enterprise data is going to be a game-changer for advisors, going far beyond just helping them prep for meetings, explain things in plain language, or write quick follow-ups that actually sound human. This combination of AI and data can create real insights that just weren’t possible two years ago.
But for any of that to work, the tech needs to be built into the business, not just layered on top. Firms need platforms that can evolve, that have good data underneath, and that are designed with people in mind. It’s not about AI for the sake of it, but about building smart tools that people actually want to use. If it makes work simpler, smarter, and more human–that’s when it delivers real value.
Peter Smith, Director, Customer Strategy, LSEG
Today’s most effective AI use cases include personalized content delivery, predictive analytics, and intelligent portfolio monitoring. Tools like AI-powered alerts and digestible market summaries help advisors stay informed and client-ready. These capabilities drive value by improving advisor productivity, enhancing client engagement, and enabling more consistent, data-driven decision-making.
Looking ahead, advanced AI—especially GenAI and agentic AI—will push firms to rethink their business models. We’ll see a shift toward advisor-centric ecosystems that prioritize interoperability, automation, and scalable personalization. Firms will invest in open platforms, integrated workflows, and collaborative environments that allow advisors to co-create solutions and deliver differentiated experiences. The goal isn’t just efficiency—it’s empowering advisors to deliver more value, with greater confidence, at scale.
Shane O’Neill, Partner, Technology Advisory, Grant Thornton
There are lots of effective use cases and all are driving value in some shape or form. We have seen use cases have impact across all the main domains with industry across investment research, client onboarding, portfolio management, trading and execution, risk management, and many more. All of these are having impact. The most important thing is having an effective AI strategy and framework to enable adoption and progression across companies and resolve key challenges of prioritizing areas for investment. Companies must undertake AI use cases that will increase their literacy, enhance awareness of AI potential, and enable better understanding of the guardrails and prerequisites they need to make these use cases effective.
Marc Butler, Financial Planner and Advisor, Anthony Petsis & Associates; Owner, Marc Butler Consulting; Owner, Wealth Management GPT
Today’s most effective AI use cases in wealth and asset management fall into three broad categories: efficiency gains, enhanced personalization, and intelligent decision support.
On the advisor side, tools for tax return analysis, summarizing client conversations, and generative AI planning assistants are already helping reduce time spent on prep and paperwork—allowing more time to be invested in deepening and personalizing client relationships. In investment management, AI models are increasingly being used to screen securities, build portfolios based on client preferences, and monitor risk exposure dynamically. Compliance teams are also leveraging AI for trade surveillance and document review, reducing manual oversight burdens and catching issues earlier.
Looking ahead, generative AI and agentic AI will shift the industry from reactive automation to proactive orchestration. Rather than waiting for an advisor or client to trigger an action, agentic AI systems will be capable of initiating workflows—such as reaching out to a client who’s veering off-plan, suggesting a tax-loss harvesting opportunity, or drafting a market commentary customized for different client segments.
Over the next three years, firms will need to reimagine core elements of their strategy. Client experience will be dynamic and real-time, not static and episodic. Advisor capacity will no longer be the bottleneck to firm growth. Operating models will shift from people-heavy to AI-augmented, with teams focused more on oversight, exception handling, and experience design.
Importantly, AI will become part of the firm’s strategic DNA—not just a layer of technology. That means leadership must plan for new talent models, data strategies, compliance frameworks, and cultural shifts that support responsible and effective AI adoption.
Karan Gulati, Partner, Financial Services Advisory, Grant Thornton
Wealth management is undergoing a profound transformation, with AI unlocking a growing array of high impact use cases that are redefining industry standards. For investment research, AI can analyze vast volumes of unstructured data—ranging from social media sentiment and real-time web trends to satellite imagery—enabling managers to spot emerging opportunities, identify risks sooner, and make data-driven investment decisions with unprecedented precision. The advisory space is evolving as well. Advanced conversational AI chatbots deliver 24/7 responsiveness, empowering clients to access investment advice, self-service tools, and tailored financial wellness resources anytime, anywhere. Cutting-edge generative AI can craft timely and personalized investment commentary, elevating client engagement and deepening trust.
AI’s monitoring capabilities are equally transformative. Sophisticated algorithms oversee portfolios and user behaviors in real-time, instantly generating alerts and actionable investment recommendations based on dynamic market factors and individual client interests—even leveraging search history to anticipate needs. Meanwhile, advanced machine learning models continuously scan client accounts for signs of cyber threats and fraud, enhancing security and building client confidence.
Looking ahead, the evolution toward fully autonomous, agentic AI processes promises a new era—where self-directed AI agents manage complex tasks end-to-end, seamlessly embedded within the workforce model. Forward-thinking firms are already engineering workflows where AI is not just an assistant, but a vital partner and enabler of innovation, efficiency, and superior results. In this new landscape, AI is poised to fundamentally redefine wealth management—empowering advisors, protecting clients, and delivering smarter outcomes at every step.
Chris McDonald, Capital Markets Specialist, AWS:
The most impactful AI use cases in wealth management today center on advisor augmentation and operational efficiency. We’re witnessing a significant transformation in how advisors prepare for and engage with clients, driven by intelligent Agentic AI platforms that provide real-time insights and predictive analytics.
Utilizing agentic workflows, advisor dashboards have emerged as a key area of innovation. These gen AI-enhanced platforms are revolutionizing pre-meeting intelligence gathering, reducing what once took hours to just minutes. They’re also identifying client retention risks through sophisticated pattern recognition in behavior and market conditions, enabling more proactive and timely client engagement.
Data platforms and GenAI are being used for lead generation and prospecting as well as complaints management. Meanwhile, the ability of large language models to understand multiple data forms and extract data, combined with agentic workflows, will lead to great improvements in efficiency and client experience in the onboarding processes.
Looking ahead to the next three years, we anticipate that generative AI and agentic AI will further reshape business strategies. We expect to see AI agents that can autonomously handle routine client queries and portfolio adjustments, freeing human advisors to focus on complex strategy and relationship building.
The most successful firms are those that effectively combine AI capabilities with human expertise, leveraging technology to enhance their core value proposition rather than completely redefining it. As we move forward, we expect to see a wealth management industry that’s more personalized, proactive, and accessible, powered by the thoughtful application of AI and cloud technologies.
Dr. Henning Stein, Senior Partner, 1Business World; Finance Fellow, Cambridge Judge Business School
The most valuable AI use cases today are not necessarily the most visible ones, and that’s a crucial insight, especially for wealth platforms. In compliance and regulatory reporting, AI is already driving measurable ROI. Institutional firms are integrating agentic AI into KYC, AML, and suitability workflows, areas where scale, speed, and auditability matter most.
In portfolio analytics, machine learning-enhanced platforms are delivering real-time look-through analysis of fund-level carbon signals and peer benchmarking for ESG investing. That’s particularly attractive for wealth advisors working with sustainability-minded heirs. On the client-facing side, GenAI is beginning to power story-based reporting, personalized dashboards, meeting summaries, and forward-looking simulations.
Looking ahead, GenAI and agentic AI will not just support advisors, they will co-pilot with them. I expect firms to embed these systems into their front-to-back value chains, enabling continuous nudging and engagement based on real-time client behavior, automated synthesis of complex data sources, legal, tax, ESG, to drive smarter planning, and full-cycle advisory assistants, from onboarding to execution and review.
But to get there, firms must start with data redesign, not dashboards. Unifying disparate datasets across service, product, and regulatory functions is the real foundation for AI-powered reinvention.
David Murphy, Head of Financial Services, EMEA & APAC, Publicis Sapient
Almost 80% of wealth and asset management firms have moved beyond initial exploration of AI.
The biggest wins are coming from AI use cases embedded in end-to-end investment workflows: AI agents automating client onboarding across multiple regulatory jurisdictions, intelligent trade surveillance reducing manual exception handling, and personalized portfolio recommendations that adapt in real time to market conditions.
In the next three years, firms will move from pilot programs or POC’s to AI-native business models, where product design, investment strategies, and client engagement are built from the ground up with AI in mind. Cross-border collaboration between AI agents will allow firms to harmonize compliance, reporting, and risk management across diverse markets while tailoring client experiences locally. The leaders will be those who treat AI not as a bolt-on to existing processes, but as the operating fabric of the AI-powered investment firm.