What Every IT Leader Should Know About AI in Financial Decision Making

The future of financial advice is on the brink of exciting transformation. With the WealthTech solutions market expected to grow from $7.7 billion in 2025 to $37 billion by 2035, IT leaders have a unique opportunity, but also the responsibility, to shape how AI redefines financial advisory for years to come.

Clients today expect financial guidance that is real-time, personalized, and seamlessly integrated into their digital lives. Advisors , meanwhile, are under pressure to do more with less, while outdated systems and fragmented data make scalable, compliant innovation difficult. For technology leaders, this creates the challenge of balancing innovation with risk and automation with trust.

AI in financial decision-making is emerging as the path forward. But the real opportunity lies in knowing how and where to make it work.

Driving Personalization and Efficiency with AI in Financial Decision Making

AI enhances human expertise by automating time-consuming tasks like portfolio rebalancing, tax optimization and planning. It leverages behavioral data - from transaction patterns to digital engagement - to deliver tailored recommendations that align with investors' financial goals. That's why over 90% of investors say they're open to using AI for product research, and more than 80% believe it can enhance portfolio management.

This is echoed across the industry, with 62% of wealth management firms expecting AI to transform operations over the next few years. When AI automates manual processes, it improves consistency and responsiveness - two key expectations for today's digital-first clients. Morgan Stanley's Debrief assistant is a good example, using generative AI to summarize financial advisors' meeting notes and draft follow-up emails. It captures context and automates documentation, enabling smoother handoffs and consistent client communication.

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Why It’s Time to Rethink Traditional Advisory Models

Clients aren't walking away from banks. They are, however, walking toward options that promise more tailored, always-on support. Looking at the numbers, AI-powered tools like PortfolioPilot have already attracted over $20 billion in assets, simply by offering digital-first advisory at scale.

Even the most loyal users are growing impatient, expecting personalized advice that adapts to their goals and life events as they happen. They've grown used to predictive suggestions from Spotify and Netflix and are demanding the same from their financial guidance. To keep clients engaged, organizations must move beyond generic recommendations and deliver timely, relevant insights tied to milestones such as retirement, career shifts, or family changes.

On the other hand, financial advisory teams are overwhelmed. Manual processes dominate onboarding, portfolio rebalancing, and risk profiling, consuming valuable time that could be spent with clients. As a result, advisors struggle to scale their efforts, and customer satisfaction inevitably suffers. What technology leaders need is a roadmap forward that balances innovation, stability, and compliance - one that frees advisors from repetitive tasks while ensuring every interaction remains accurate, secure, and client-centric.

How Can Technology Leaders Prepare for AI in Financial Advisory?

There is certainly no shortage of AI tools, but technology leaders know implementation is where great ideas go to die, especially when dealing with fragmented systems, compliance pressure, and growing cyberthreats.

Here's where smart execution comes in.

Start by evaluating your current state

Map out key advisory processes and assess system readiness for AI integration. Then turn to the foundation: data readiness. Clean, connected, privacy-compliant data underpins every successful AI model. As Accedia’s Engineering Director, Peter Ivanov notes: “ Each sector handles different data types…Still, one thing remains the same: you can’t build a successful AI strategy on poor data”. A practical way to begin is with a financial data audit across departments. This helps you see where your data comes from, how it’s being used, and where problems like duplication, inconsistency, or siloed ownership exist.

How Financial Data Quality Management Powers AI in Banking

Choose the Right Model for AI Adoption

Decide whether to build in-house or partner with an external development team. Not every organization has the capacity or need to create proprietary AI from scratch, and attempting to do so can quickly drain time and budget. Partnering with specialized firms offers a smarter path - bridging talent gaps, accelerating delivery, and providing the right expertise without the overhead of building everything internally.

Thus, many CTOs are now exploring partnerships with specialists who can accelerate implementation while maintaining oversight. A good example from Accedia’s experience comes from our work with a financial services company navigating strict regulatory requirements. Together, we introduced AI-driven automation for time-consuming processes such as compliance checks and client reporting. This allowed their advisory teams to shift focus from manual administration to higher-value client interactions, while ensuring every output met rigorous regulatory standards. The end result was greater efficiency, reduced risk, and faster delivery of new digital services.

Build Trust Through Explainable AI

Explainability is another non-negotiable. Under recommendations from the U.S. Securities and Exchange Commission (SEC), AI-generated advice must be traceable and auditable. In practice, this means every output needs a clear reasoning path that both regulators and clients can understand.

The risks of neglecting explainability are significant. Without it, AI models may unintentionally favor firm incentives over client outcomes, opening the door to regulatory scrutiny and long-term reputational damage. Firms that lead in this space are those that design AI systems capable of justifying their recommendations in clear, accessible terms. This allows financial advisors to build stronger client relationships while staying firmly within compliance standards.

What’s Next for AI in Financial Advisory?

We're entering a new phase of emerging technologies - generative AI, personalized ESG portfolio suggestions and predictive tools for financial well-being are already fast-approaching realities. Deloitte estimates that by 2028, 78% of retail investors will rely on generative AI for some form of financial advice. At the same time, usage of traditional financial websites could fall sharply - from 28% today to just 9% - if generative AI lives up to its promise of delivering more accurate, user-friendly, and contextual support.

For banks, the implications are significant. Those that move early with platform-enabled AI gain a compounding edge - more data leads to smarter models, and smarter models make it increasingly difficult for competitors to keep pace. But capturing this advantage takes more than rolling out a chatbot or a portfolio app. It requires modern infrastructure, strong security, and a commitment to upskilling, so that financial advisors and AI can complement each other rather than compete.

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Taking Strategic Steps to Capture the AI Advantage

One of the most promising developments in the realm of AI in financial advisory is the rise of predictive financial wellness tools. These go beyond basic tracking to anticipate when a client might miss a savings goal, face liquidity pressure or be able to take advantage of a market opportunity - prompting timely, tailored advice without the need for a scheduled review.

At the same time, generative AI is beginning to transform how users engage with financial services. From onboarding to education to goal planning, intelligent assistants can answer complex financial questions, explain investment strategies and walk users through key decisions in real time. This creates a layer of always-available, hyper-personalized support that complements, not replaces, human advisors.

Finally, advisory is becoming increasingly embedded. As platforms shift toward modular, API-first architectures, organizations are beginning to deliver advice-as-a-service, tailored investment guidance across everyday digital touchpoints that investors already use daily. Whether that's through a partner fintech solution, an employee benefits portal or a digital wallet, AI allows advisory services to meet investors where they are.

To wrap up

AI in financial decision-making is a leadership opportunity. Your journey starts with a clear-eyed assessment of current capabilities, identifying where automation can drive the most impact and ensuring systems and data are AI-ready. Don't wait for disruption to force your hand. If you want to take the lead in building scalable, adaptive wealth advisory services that meet rising expectations, reach out to us to explore how we can support your journey.

This article was originally published by Dimitar Dimitrov, Managing Partner at Accedia, as a contribution to the Forbes Technology Council.