3 Strategic Questions Before Embedding AI into Your Estate Planning Workflow

Over the past three years, I’ve worked with hundreds of estate planning and advisory firms evaluating AI-driven software. Nearly every conversation begins with tactical questions:

• How does it handle GRAT substitutions or multi-generational GST allocations?
• Can it reflect state-specific nuances?
• Can the system model a shark-fin CLAT? (had to google this one)

These are important questions. But they are not strategic ones.

Too often, firms focus on feature checklists while underestimating the magnitude of the decision in front of them. Choosing whether and how to adopt AI, particularly in a discipline as complex and consequential as estate planning, is not just a software decision. It is a strategic inflection point.

I should acknowledge up front that I am biased. I lead sales at an AI-centric software company for Wealth Transfer. I spend my days talking about this exact problem. That said, the perspective below is not a product pitch. It is a pattern recognition exercise drawn from hundreds of real evaluation processes.The firms that proactively address these questions arrive at clearer, more assured decisions, rapidly translating them into prospect wins, client deepening and revenue growth. Conversely, those that neglect to do so often find themselves revisiting the same debate a year later.

Based on those experiences, I believe firms should anchor their evaluation around three fundamental questions.

Question 1: What Is Our Firm’s AI Strategy? (If Any at All…)

The first and most important question is not about estate planning. It is about identity.

How does your firm intend to operate in an AI-enabled world?

The nature of knowledge work is rapidly changing across the board. Professionals are moving from being primary producers of first drafts to reviewers, refiners, and strategic decision-makers. In estate planning, this shift is especially pronounced. There are very few analytical tasks where starting from a blank page is more efficient than starting from an AI-generated foundation.

AI does not eliminate expertise. It reallocates, and amplifies it. Instead of spending hours assembling calculations, drafting summaries, or structuring scenario comparisons, planners increasingly spend their time:

• Validating assumptions
• Stress-testing recommendations
• Applying judgment to edge cases
• Communicating insights to clients

The firms that will thrive will intentionally define how AI fits into their workflow, rather than allowing adoption to happen informally through ad hoc tools.

This decision also cannot be deferred indefinitely. Competitive dynamics are already shifting. When one firm can produce deeper analysis, clearer deliverables, and faster turnaround times because AI handles the heavy lifting, that advantage compounds.

An AI strategy does not need to be complex. It does need to be deliberate.

Question 2: Should We Build or Buy?

Once a firm commits to incorporating AI into its operations, the next strategic decision emerges. Build internally or purchase a specialized platform?

For a small subset of firms with significant engineering resources, building may appear attractive. Control, customization, and perceived cost advantages can be compelling. But timing and commitment to maintenance are the variables most firms underestimate.

AI models are not evolving in incremental, traditional software cycles. Improvements are frequently step changes in reasoning capability, speed, context handling, and reliability. Each major model generation has meaningfully expanded what is practical in a production environment.

Over the past several years, leading AI model families have demonstrated substantial improvements across benchmarks measuring reasoning, coding ability, mathematical performance, and complex instruction-following. In many cases, performance gains between generations have not been marginal. They have been material. Tasks that were unreliable or impractical in one generation often become viable in the next.

This pace has practical implications. An internal build that feels sophisticated today can feel dated quickly if it is not continuously re-architected to leverage new model capabilities. Firms that choose to build must be prepared for:

• Ongoing model evaluation and upgrades
• Infrastructure and security oversight
• Prompt engineering and workflow orchestration
• Continuous quality assurance and compliance review

Building is not a one-time initiative. It is a product strategy that requires dedicated ownership and sustained investment.

For many firms, buying a specialized estate planning platform allows them to focus on client relationships and advice while leveraging a team dedicated full time to maintaining, upgrading, and refining the AI layer.

The key is not which path is universally correct. It is whether the firm fully understands the commitment required by either.

Question 3: Should Estate Planning Live Inside a Purpose-Built Software Environment?

This question is frequently overlooked.

Many firms experimenting with AI begin with general-purpose tools layered on top of existing workflows. These tools often generate documents, summaries, or basic analyses. While useful, they typically create another artifact. Another memo. Another PDF. Another draft.

But estate planning is not merely a drafting and client education exercise.

It is a workflow discipline that includes:

• Multi-scenario tax modeling
• Asset-level analysis
• Trust structure comparisons
• State-specific considerations
• Downstream deliverables
• Client-facing visualizations
• Alignment with a firm’s reporting standards and aesthetics

The fundamental data issue stemming from documents trapped in isolated sources has prevented software from addressing downstream workflows. Simply creating more, albeit shorter, documents will not resolve the core problem.

The real leverage of AI is not just synthesizing data. It is integrating that synthesis into a structured environment that:

• Preserves assumptions
• Allows scenario iteration
• Produces client-ready outputs
• Reflects the firm’s intellectual capital

Time savings do not come from generating a paragraph faster. They come from collapsing the distance from document to data and from data to client-ready-deliverable.

When estate planning lives inside a purpose-built platform, AI can operate across the entire workflow rather than being confined to a narrow use case such as summarization.

That integration is what ultimately drives:

• More consistent client experiences
• Stronger prospect presentations
• Increased planner capacity
• Firm-wide scalability

Closing Perspective

Investing in AI-powered estate planning software is not about keeping up with technology trends. It is about deciding how your firm will compete over the next decade.

Yes, I clearly believe purpose-built software matters. But even if Luminary did not exist, the strategic questions would remain the same.

The tactical nuances will always matter. Estate planning is complex and high stakes.

But before asking what the software can do, firms should ask:

1. What is our AI strategy?

2. Should we build or buy?

3. Should estate planning live inside a dedicated software environment?

Answer those correctly, and the feature comparisons become far clearer. Ignore them, and even the most sophisticated software evaluation risks missing the point entirely.

If you’re exploring how AI could fit into your estate planning workflow, Luminary was built for exactly that purpose. Our platform brings AI-powered analysis into a purpose-built wealth transfer environment so planners can move from complex data to client-ready deliverables faster. If you’d like to see how it works in practice, you can request a demo.