AI, automation, and the human touch: Finding the right balance in financial services

AI is changing financial services fast. From underwriting to client service, automation is helping teams work smarter and faster. But there’s a clear pattern emerging across the industry:

Most organizations are early in their AI journey – and many struggle to move from ideas to real impact.

Data from the 2025 McKinsey LIMRA Insurance 360 Benchmark survey shows that fewer than 20% of carriers have AI fully scaled in any business area, and while many teams can build a pilot quickly, half take more than a year to scale it.¹ The reason isn’t the technology. It’s focus, execution, and change management.

The good news? You don’t need to overhaul everything to get started. And you don’t need to lose the human touch to do it right.

Why financial services can’t automate everything

Financial services are built on trust. Clients want speed and clarity, but they also want to feel understood – especially when decisions affect their money, family, or future.

AI is great at:

• Analyzing data. Reviewing large volumes of information quickly and consistently.
• Spotting patterns. Identifying trends or anomalies that might be missed manually.
• Saving time on repeat tasks. Automating administrative and workflow steps that slow teams down.
   

Humans are better at:

• Judgment. Making decisions that require nuance and trade-offs.
• Context. Understanding the full personal or business picture behind a decision.
• Empathy. Supporting clients through emotional or high-stakes moments.
• Explaining trade-offs. Translating options into clear guidance people can act on.
   

The goal isn’t to replace people. It’s to support them – using AI to remove friction so advisors, underwriters, and service teams can spend more time where human insight matters most.

Start small (and actually start)

Many organizations delay AI adoption because they believe they need perfect data, custom-built models, or a fully defined long-term strategy before taking action. In reality, waiting for ideal conditions often means falling behind.

A more effective approach is to begin with what’s already available. Low-cost tools and vendor solutions can deliver value quickly when you apply them to the right problems. Starting small allows teams to learn, test, and adapt – building momentum instead of getting stuck in planning mode.

Progress with AI comes from starting, not over-engineering.

Practical AI use cases in financial services today

When AI is applied thoughtfully, it can improve efficiency without weakening relationships. Here are several practical ways financial services teams are using AI right now.

1. Faster preparation for client conversations

AI can review:

• CRM notes. Highlighting key history and client context.
• Email history. Pulling the most relevant threads and recent updates.
• Previous meeting summaries. Surfacing action items and open decisions.
   

...and produce a short briefing before a call.

This helps you show up prepared, focused, and informed, without spending hours on manual prep.

2. Smarter lead prioritization

AI can analyze engagement patterns, demographics, and interaction history to help teams prioritize outreach.

This doesn’t replace relationship-building. It helps you focus your time on conversations that are more timely and relevant, rather than relying on volume-based outreach.

3. Clear, personalized policy comparisons

Generative AI can create tailored summaries and comparisons based on a specific client’s situation.

You remain in control of the recommendation and the conversation, while AI reduces the time spent creating background materials.

4. “What if” scenario support

AI can model different scenarios and surface considerations such as:

• Potential coverage gaps. Highlighting areas where protection may fall short.
• Risk trade-offs. Showing how different choices shift risk and outcomes.
• Financial impacts under different outcomes. Helping teams explore multiple scenarios quickly.
   

These insights support decision-making, but the final judgment always stays with a human.

5. Internal knowledge and research support

Instead of searching through long documents or policy manuals, teams can use AI to:

• Summarize regulatory updates. Reducing the time it takes to stay current.
• Scan policy details. Pulling key clauses or requirements quickly.
• Answer internal questions quickly. Supporting faster decisions and fewer bottlenecks.
   

This keeps teams aligned and reduces delays without increasing risk.

Why focus matters more than scale

One common mistake organizations make is trying to apply AI everywhere at once. Spreading efforts across too many use cases often leads to slow progress and unclear results.

A better approach is to:

• Choose one or two high-impact areas. Start where results will be measurable.
• Define what success looks like. Create clear goals and decision points.
• Measure results before expanding. Build evidence before scaling.
   

Focused effort leads to stronger outcomes and makes scaling easier later.

Learn how to use AI well

AI is only as effective as the instructions it’s given. Clear goals, strong context, and well-defined expectations lead to better outputs and fewer errors.

Organizations that invest in teaching teams how to work with AI (not just access it) see faster adoption, better results, and greater confidence across the organization.

Keep humans at the centre

The strongest AI strategies in financial services are built with people in mind.

That means:

• Clear boundaries around where AI is used. Protecting trust and managing risk.
• Easy access to human support. Ensuring decisions stay grounded and accountable.
• Transparency with clients and teams. Helping people understand what AI is doing and why.
   

When AI supports (rather than replaces) human expertise, organizations gain efficiency without losing trust.

Ready to put AI to work, without losing the human touch?

AI doesn’t have to be overwhelming, expensive, or disruptive to deliver real value. With the right focus and guidance, financial services teams can start small, move quickly, and build confidence along the way.

That’s where we can help.

George Robertson, our Fractional CTO, works with financial services organizations to cut through the noise and identify practical, high-impact ways to use AI and automation, without compromising trust, compliance, or human connection. His role isn’t to push tools or technology for technology’s sake, but to help teams make smart, informed decisions that actually work in the real world.

If you’re curious where AI could support your business or want a second opinion on what you’re already exploring, book a free 30-minute consultation with George. It’s a no-pressure conversation focused on your goals, your challenges, and what’s realistically achievable.

Start building clarity and confidence in your AI approach.

Schedule your free 30-minute consultation

Sources

2025 McKinsey LIMRA insurance 360 benchmark – industry productivity trends: Workforce benefits. October 12, 2025. LIMRA. https://www.limra.com/globalassets/limra-loma/landing/common-assets/mckinsey-webinars/mckinsey-limra-2025-insurance-360-industry-trends_workfoce-benefits-_11112025.pdf.