The quiet weight of AI expectations and what to do about it

Zennify Team

You can feel it in every meeting. The pressure to modernize. The push to “use AI.” The sense that everyone else is moving faster. For many financial planners, AI feels more like a burden than an advantage. The tools are unfamiliar. The path is unclear. And the expectation to figure it out grows heavier by the day.

Our latest whitepaper, Managing the AI Overwhelm, was built with that reality in mind. It explains the types of AI that matter, outlines a practical approach to getting started, and helps you identify which efforts are worth your time.

You don’t need a massive transformation to get started, you just need to start smart. Here’s a preview of what’s inside and how to take the first step.

What’s driving the pressure?

The push for AI in financial services is real. According to a recent study by KPMG, 82% of businesses are already integrating AI into finance functions, ahead of the global average of 71%. That includes areas like accounting, planning, tax operations, and treasury.

For financial planners, this shift brings both opportunity and a quiet but growing pressure. Clients expect faster insights and more personalized service. Regulators and firm leaders are urging teams to build frameworks that balance innovation with ethical risk management.

Still, many planning professionals feel stuck, especially when considering the massive opportunity to capitalize on the generational wealth transfer already underway. In the U.S., more than $124 trillion is expected to transfer by 2048—$105 trillion to heirs and $18 trillion to charities.

Opportunities like this only intensify the weight planners are feeling. AI offers a way to keep up and stay ahead, but not without clarity, structure, and the right focus.

What AI tools are worth your time

One way to ease the pressure is to get clear on what AI is and what it isn’t. For financial planners, two types of AI matter most: Generative AI and Agentic AI. Each serves a different purpose, and understanding that difference helps you focus on tools that actually support your workflow.

Generative AI creates content. It writes summaries, emails, or reports. It’s effective for communication tasks but only responds when prompted.
Agentic AI takes action. It monitors portfolios, triggers alerts, schedules meetings, and interacts with your systems. It’s designed to complete tasks across workflows.

Most firms need both. Generative AI helps reduce manual effort. Agentic AI helps improve execution. Knowing the difference will help you focus your investments in the right areas.

How to choose the right use case

AI success doesn’t start with the flashiest use case. It starts with the ones that make your team’s day-to-day easier and your client experience stronger.

Focus on use cases that are:

Time-intensive
Repetitive
Based on structured data
Closely tied to business value

Common examples in financial planning include:

Generating personalized check-ins or updates
Preparing meeting agendas and summaries
Tagging and summarizing incoming documents
Flagging top leads for outreach
Monitoring portfolios and suggesting next steps

These small steps add up. They take work off your team’s plate, increase responsiveness, and create momentum without adding to the strain.

Start with tools that deliver now

You don’t need to build from scratch. Most planners see better results by starting with tools that already exist. Off-the-shelf AI solutions are faster to implement, cost less up front, and integrate with the platforms you already use. They also give you room to test, learn, and adjust without committing to a large investment.

Custom builds have their place, but only when your needs are unique and you have the resources to support development long-term. If the pressure already feels heavy, the smarter move is to avoid complexity and start with what works.

Don’t skip the data work

No AI system will deliver value if the data behind it is disconnected or out of date. Accuracy and access matter. Before introducing new tools, take a hard look at your data environment. Before scaling, ask:

Is your data connected and easy to access?
Is it accurate and current?
Do your systems support secure, compliant usage?

Cleaning and organizing your data may feel unglamorous, but it’s foundational. It sets the stage for consistent, reliable results and builds internal trust in the tools you deploy. Without a strong data foundation, AI can’t deliver and the weight of expectations only grows.

From pressure to progress

The pressure to adopt AI is real. But it doesn’t have to feel like a burden. You don’t need a perfect plan or a major overhaul. You just need to start with what’s clear, what’s doable, and what makes a difference.

Focus on the tasks that slow your team down. Use the data you already trust. Choose tools that work today, not someday.

Managing the AI Overwhelm was written for planners who feel stuck and want a way forward. If you’re ready to take the next step, this guide will help you do it with clarity and confidence. Download the whitepaper to get started.