Creating with AI Is Getting Easier. Scaling It Across Your Firm Is Not.

Recently, a wealth planning professional asked me a question I've been hearing more frequently across the industry.

He had been experimenting with Claude to build estate planning calculators. Within minutes, he could generate a financial model, adjust assumptions, and produce something useful for a client.

His question was simple: Why would I need specialized estate planning software if I can build these tools myself?

It's a fair question, especially given how quickly AI has advanced.

In Part 1 of this series, I outlined three strategic questions firms should consider before embedding AI into their estate planning workflows. Since then, the technology and the conversations we're having with firms have evolved considerably.

Today, professionals are using tools like Claude, ChatGPT, and Copilot to build calculators, summarize documents, and generate client deliverables in minutes.

Building something that works for one person is getting easier. Building something that works reliably across an entire firm is not.

That distinction warrants revisiting our original three questions.

Question 1: Are We Solving One-off Tasks or Building a System that Scales?

Generative AI makes it remarkably easy to produce useful work. But an impressive individual output doesn't necessarily translate into a scalable, repeatable process.

Consider an advisor who builds a custom estate planning model for a client.

What happens six months later when another professional needs to update it? Can they verify the assumptions, incorporate changes to the client's financial picture, or reuse the methodology for another client?

These questions become increasingly important as firms move AI beyond individual experimentation.

The bigger opportunity isn't simply making professionals more productive on a one-off basis. It's turning the expertise of your best professionals into repeatable processes that benefit the entire firm.

In estate planning, that requires connected information about a client's assets, ownership, trusts, beneficiaries, and planning strategies.

When information changes, downstream calculations and deliverables should reflect it. Firms also need consistent templates, approval workflows, and outputs that align with their standards of care.

This is where specialized applications and systems of record become valuable.

At Luminary, we convert complex estate planning documents and financial information into structured data that powers calculations, planning strategies, and client deliverables.

The goal isn't simply to produce the next deliverable faster. It's to preserve the underlying work so it can be trusted, updated, and reused across the organization.

Question 2: What Will Serve as Our Firm's AI Control Center?

Once firms establish what they want to accomplish, the next question is where employees will interact with AI.

Will their primary point of entry be the CRM? Microsoft Copilot? Claude or ChatGPT? Perhaps an AI-powered meeting assistant?

As AI evolves from answering questions to executing tasks, this decision becomes increasingly important.

Imagine an advisor finishing a client meeting. Rather than manually moving information between a meeting transcript, CRM, portfolio reporting system, and estate planning application, an AI assistant could coordinate the workflow.

It could retrieve client data, initiate an estate planning analysis, and assemble a draft deliverable for professional review.

This is the promise of the orchestration layer, where AI connects the firm's underlying applications, data, and workflows. Think of it as an AI control center, giving employees a central place to initiate tasks, access specialized capabilities, and coordinate work across systems without having to navigate each application independently.

Importantly, firms don't need to consolidate everything into one platform. Their CRM, portfolio reporting tools, and specialized applications can continue serving their respective functions while AI coordinates work across them.

Where MCP Comes In

One development making this possible is the Model Context Protocol, or MCP.

Think of MCP as a standardized way for AI assistants to connect with external software and its capabilities, reducing the need for custom integrations.

For example, an advisor could ask an AI assistant to initiate an estate planning analysis using structured data and calculations from Luminary, without recreating that analysis in a chat.

The goal isn't to make a general-purpose AI model an estate planning expert. It's to give that model access to trusted expertise, structured data, and validated calculations.

Luminary’s MCP supports this approach, making our wealth transfer data and workflows accessible through compatible AI environments.

We'll demonstrate this in Part 3 of our AI webinar series.

For firms developing their AI strategy, the takeaway is straightforward: Think beyond which AI model you prefer. Consider how your existing systems will connect and work together.

Question 3: Should We Build or Buy? And What Is the Real Cost of Building?

This question has changed dramatically with tools like Claude Code.

Professionals with limited programming experience can now build and refine applications using natural language. Some firms are even recreating functionality that previously required specialized software.

But the initial speed can be deceptive.

We've spoken with firms that quickly developed useful applications, only to discover that operationalizing them required substantially more work around databases, integrations, and complex client scenarios.

The challenge often isn't the first 80%. It's the remaining 20%.

Why Complexity Matters

Large language models are probabilistic systems designed to interpret and generate language. That's powerful, but it introduces challenges when precision matters.

Consider the word equity.

In financial planning, it could mean ownership in a business, equity in real estate, publicly traded stocks, or the residual value of an asset after liabilities.

Those distinctions aren't interchangeable.

A model that misunderstands ownership or applies the wrong tax treatment can produce an analysis that looks convincing but is fundamentally incorrect.

AI-generated code can produce deterministic calculations, but the underlying assumptions and logic still need to be correct.

At Luminary, we've invested in encoding tax and legal knowledge into our platform, combining deterministic calculations with intuitive AI workflows.

And building the application is only the beginning. Someone must maintain tax rules, integrations, security, accuracy, and ongoing support.

For some firms, owning that infrastructure makes sense. For others, specialized providers offer a more efficient path.

The question isn't whether your firm can build the software. It's whether building and maintaining it is the best use of your resources.

Closing Perspective: Build Where You Differentiate. Partner for Support.

The most effective technology stacks will likely combine general-purpose AI with specialized applications.

AI will make it easier to interact with information and automate workflows. Specialized software will provide the structured data, domain expertise, and validated calculations that make those workflows dependable.

As firms revisit their AI strategies, I'd encourage them to ask:

1. What are we trying to accomplish? Are we creating isolated tools or scalable, repeatable workflows?
2. What will serve as our AI control center? Where will employees interact with AI, and how will it connect the applications and data we already rely on?
3. What should we build versus buy? Where should we invest internally, and where can specialized providers deliver greater value?

The firms best positioned to benefit from AI won't necessarily be those that build the most tools.

They'll be the ones that make AI work reliably across their entire organization.