AI for Advisors newsletter
A few weeks ago, we tested Deep Research for a member of the National Association of Active Investment Managers (NAAIM). His challenge: Could it deliver a CFA-level white paper on surge investing and attribution analysis?
Nine minutes later, the system returned a fully cited, multi-section report—based on 185 live web searches across 28 sources.
This wasn’t a typical AI response. Deep Research planned, searched, and synthesized like a junior analyst. The result? Something advisors can actually use.
Most advisors who’ve tried tools like ChatGPT think of it as a smart assistant—good for writing, marketing, and brainstorming. But behind the scenes, this new generation of tools has emerged with the ability to conduct autonomous research.
Known as Deep Research, this new capability can break down complex prompts into sub-tasks, retrieve and evaluate content from multiple sources, and synthesize information into structured outputs like full reports, tables, and charts that would traditionally take humans many hours to complete.
What is Deep Research?
Think of Deep Research as a tireless junior analyst. It can cut 80–90% of the time typically spent on research-heavy tasks.
You give it a prompt—whether it’s developing a niche or prepping for a complex client case—and it pulls live sources, analyzes the data, and delivers a structured, sourced report.
OpenAI’s Deep Research is the most prominent tool today, but similar capabilities now appear in Gemini, Claude, and Perplexity Pro.
How it’s different from Google search
Deep Research aren’t keyword search tools. They’re automated research assistants, capable of cross-referencing hundreds of sources and producing intelligent synthesis.
The difference between a Google search and a Deep Research report is like comparing a stack of books to a book report. Google gives you the links. Deep Research reads everything, highlights the key points, and summarizes what matters most.
For advisors, ideally, this means fewer open tabs, less manual skimming, and faster insights to complex, research-heavy questions. Instead of cobbling together notes from articles and PDFs, you prompt the AI—and get back something usable.
Five strategic applications for advisors
From investment research to estate planning, Deep Research equips advisors with faster, smarter ways to tackle complex questions. Here are five high-impact applications that can elevate your thinking and your client service.
1. Investment strategy research
Deep Research excels at investigating complex investment topics that require synthesizing information from multiple perspectives. For instance, consider researching emerging market opportunities in renewable energy infrastructure. Rather than spending hours reviewing analyst reports, regulatory filings, and industry publications, you could prompt Deep Research to analyze the investment landscape, regulatory environment, risk factors, and performance metrics across different geographic regions.
The research tool can examine everything from sovereign debt ratings and currency stability to specific infrastructure projects and their financial backers, delivering a detailed analysis that would typically require a team of research analysts. This capability is particularly valuable when exploring alternative investments or niche market opportunities that don’t receive extensive mainstream coverage.
2. Tax and estate planning strategy
Tax and estate planning represent areas where regulatory complexity meets individual circumstances, creating research challenges that Deep Research handles well. An advisor working with high-net-worth clients could use the research to investigate sophisticated planning strategies, such as analyzing the implications of recent tax law changes on charitable remainder trusts or researching state-specific estate tax optimization techniques.
For instance, when exploring opportunities for clients considering relocation, Deep Research can simultaneously analyze multiple states’ tax environments, estate planning laws, trust regulations, and practical implementation considerations. The tool can identify unique planning opportunities and potential pitfalls that might not surface in conventional research.
3. Technology vendor due diligence
The technology landscape for advisors changes rapidly, with new solutions emerging constantly. Deep Research can conduct detailed vendor evaluations that go far beyond marketing materials and basic reviews.
When considering a new portfolio management system or client relationship management platform, the tool can research the vendor’s financial stability, regulatory compliance history, integration capabilities, security protocols, and user experiences across different firm sizes.
4. Niche market research and client segmentation
Successful advisors increasingly focus on specific client niches, but entering new markets requires extensive research. Deep Research can analyze potential market segments by investigating demographic trends, regulatory environments, specific financial planning needs, and competitive landscapes.
For example, an advisor considering specializing in serving physicians can use Deep Research to analyze medical professionals’ unique financial challenges, from student loan optimization strategies to malpractice insurance considerations and practice transition planning. The tool can identify specific pain points, regulatory considerations, and service gaps that create opportunities for specialized advisory services.
5. High-net-worth prospect research
When preparing for a first meeting with a high-net-worth prospect, Deep Research can dramatically accelerate your pre-call prep. Rather than relying solely on LinkedIn and Google, advisors can use Deep Research to gather a complete profile across multiple sources—business affiliations, philanthropic activities, board memberships, media appearances, and potential financial complexity signals.
For example, ahead of a meeting with a tech executive referral, Deep Research can uncover insights about recent liquidity events, investment preferences, public speaking topics, or past philanthropic donations. This allows you to enter the conversation with tailored talking points and a nuanced understanding of the client’s potential needs.
It’s especially powerful for referrals, introductions, or prospect meetings where little background is initially available.
What to know before you start
Deep Research tools are powerful but not perfect. Before sharing results with clients, always verify key claims. Not all tools provide transparent citations or up-to-date data, and outputs still require your professional judgment.
Common pitfalls include vague prompts, overreliance on low-quality sources, and unstructured responses. Some tools can’t access premium data or produce shallow synthesis.
Avoid these issues by supplying context, requesting structured output, and giving clear direction.
How to get more reliable results
To get consistent, credible results, take more control of the research process.
- Direct the research: Ask for a plan before the AI begins. Tell it which sources to prioritize (e.g., journals, government data) and which to avoid. You can also ask it to explain discrepancies or assumptions.
- Treat output as a first draft: The first response is rarely the best. Provide feedback, request edits, and iterate. Prompt for clarity, structure, or added depth—AI improves with guidance.
- Build structured workflows: Once you’re comfortable, create repeatable prompting structures. For example: Start with a source list, verify credibility, and then instruct Deep Research to synthesize and flag conflicting data.
A Deep Research prompt template
To get the best output from Deep Research tools, you need a prompt that gives structure, not just direction. This is a little different than our typical Role, Task, Format, Context, Questions, Examples (RTF-CQE) framework.
Unlike general-purpose AI chats, these Deep Research systems benefit from knowing your goals, formatting preferences, and content priorities up front.
Start with this Deep Research Prompt Template. Fill it in before pasting into ChatGPT (with the Deep Research tool activated), or any system with a comparable capability.
- Goal: What do you want the research to achieve?
- Context: Why you’re doing this research and who it’s for.
- Report sections: Desired layout or flow—headings, order, or report format.
- Required content: Specific items to include—comparisons, insights, or criteria.
- Report style: Preferred tone or structure—e.g., bullet points, TL;DR summary, Pyramid Principle.
- Instructions: Ask the AI to outline a research plan and clarify sources before writing the full report.
Example: Surge Investing Strategy Report
- Goal: Produce a white paper-level analysis of attribution methodologies in relation to surge investing.
- Context: For a CFA-audience advisor focused on multi-asset strategies during volatility spikes.
- Report sections: Executive summary, model comparisons, application to surge strategies, historical insights (1900–present), conclusion.
- Required content: Evaluate at least three attribution frameworks, show how each handles sudden inflows, cite at least five peer-reviewed or institutional sources.
- Report style: Pyramid Principle layout with bullet-pointed key takeaways at the top of each section.
- Instructions: Propose a research plan first. List sources and explain how each contributes to the analysis. Proceed only after confirmation.
This small shift in prompting style makes a big difference. You’ll get better-structured reports, more relevant insights, and a smoother workflow. Start using this template as a way to get started with Deep Research.
What’s next
We’re just getting started. Deep Research tools are evolving quickly, with capabilities like autonomous agents that combine research with task execution already taking shape.
And the quality of these tools will only improve, as Ethan Mollick, author of the book Co-Intelligence, reminds us, “Assume current AI is the least advanced it will ever be.”
But even as the technology accelerates, the advisor’s role doesn’t shrink—it sharpens. The real shift isn’t about outsourcing decisions to AI. It’s about offloading the grunt work so you can think more deeply, apply professional judgment, and focus on what matters most.
Used actively and strategically, Deep Research will help you focus more deeply on what matters: interpreting insights, applying professional judgment, and thinking harder with better inputs.
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