According to data from J.P. Morgan, sixty-five percent of family offices say artificial intelligence is a top priority, and Bank of America reports that fifty-seven percent already use it for research. Yet most of those same offices have done very little to actually build it into how they invest and operate. That distance, between naming AI as a priority and putting it to work, has become one of the more revealing vulnerabilities of the moment, because the cost of standing still no longer stays flat. It compounds.
Susan Lindeque has spent her career at the exact place where capital meets technology, and she sees that gap up close. As the Founder and CEO of Avestix Group, a Chartered Accountant, and the Chief Investment Officer of her own family office, she has invested across public markets, venture, real estate, and digital assets. That range matters here, because the conversation about AI in wealth is usually framed as either an investment question or a technology question, and Susan Lindeque treats it as both at once. What follows is less a warning than an education, a practical look at why so many capable people get stuck, and what it actually takes to move.
When “We’re Prioritizing AI” Really Means Opening a Chatbot Once
The first thing Susan Lindeque wants people to understand is that saying and doing are two entirely different acts. Naming AI a priority is easy. Executing on it is hard, and she breaks the difficulty into three honest parts. It takes literacy, the working knowledge to understand what these tools really do. It takes governance, meaning the security, protocols, and architecture that surround the technology. And it takes a person who will actually do the job and carry the execution through. Most offices, she points out, are still in the earliest stages of all three.
The most common pattern she sees is people using AI as a chatbot and nothing more. They open it and ask how to plan a trip to Europe, or what the weather will be in Australia, then conclude they have adopted AI. What they have skipped, Susan Lindeque explains, is the harder and far more valuable step of using AI as an execution layer that lifts real productivity inside the business. That step feels intimidating for a reason. It requires knowing the governance and the architecture around the tool, including how to deploy it carefully so it does not, for example, reach into systems it should not touch or erase documentation built up over decades. The caution is fair. The mistake is letting the caution become the whole strategy.
The practical takeaway is simple to picture. If your relationship with AI looks like asking it occasional questions, you are using a fraction of it. The real shift begins when you stop treating it as something you talk to and start treating it as something that does work for you.
The First Gap Is in the Portfolio
Susan Lindeque argues that most family offices are failing at two separate AI gaps at the same time, and the first lives in the portfolio. When these offices reach for AI exposure, they almost all reach for the same handful of public technology giants, the so called Magnificent Seven names like Google, Microsoft, Tesla, Meta, and Nvidia. The trouble is that those companies are correlated, meaning they tend to move in the same direction together, and the gap between that small group and the rest of the market has grown remarkably wide. An office that owns only those names has concentrated, correlated, and largely late exposure, with little else underneath it. Meanwhile, as she notes, large institutional players such as BlackRock have moved further into alternative assets like real estate, venture capital, and private equity, where a great deal of capital is now flowing.
More than half of family offices say they want more private market AI exposure but cannot source quality deals, and Susan Lindeque is blunt about the real bottleneck. It is not capital, because capital is plentiful. The bottleneck is due diligence. Family offices that built their fortunes in real estate or private equity often have never operated in venture capital, and evaluating a company built on disruptive technology, whether artificial intelligence, cybersecurity, or robotics, is genuinely difficult. Hand many of them a term sheet and ask them to value a company on the strength of its technology, she says, and they would struggle, because the muscle was never developed.
That is why she draws a sharp line between narrative and architecture. Almost every company now claims to be an AI company. The way to tell the real ones apart, according to Susan Lindeque, is to look under the hood with specific questions. Are you using AI agents? Are you running automated workflows? Do you have the cybersecurity in place to control your AI? A true AI company has built the technology into its actual architecture, not just its pitch. She also points to a quieter opportunity that many investors overlook, the infrastructure that all of this runs on. Data centers, energy, semiconductors, and cooling are the plumbing of artificial intelligence, and energy in particular has become the central bottleneck. For the first time in roughly thirty years, she observes, demand is outstripping supply, and some customers are willing to pay a year in advance to secure access. Her comparison is to the early internet, when websites and apps went from novelty to default. The same trajectory, she believes, is now underway with AI.
The Second Gap Is in the Operation
The bigger problem, in Susan Lindeque’s view, is not AI in the portfolio but AI in the operation. Her line is that many family offices are running 2016 infrastructure in a 2026 threat environment. So much of how these offices function still rests on legacy systems, the way work was done ten or fifteen years ago, simply because no one made the effort to keep up. She describes restarting her own computer one morning and finding that her AI application, not a browser or a stream of ads, was the first thing to load. It had quietly become her operating system, because she uses it across nearly everything she does.
What that looks like day to day is a meaningful departure from spreadsheets and quarterly reports. Susan Lindeque no longer builds financial models in Excel. She builds them with AI, and she is careful to say it is not only about prompts. It is about directing and looping AI agents, training them the way you would patiently train a capable intern so the same work never has to be done by hand twice. You establish your models, your business plan, your marketing plan, your branding and logos once, and from there the system can fetch data and pull from your documentation on its own. She mentions, almost in passing, that she cannot remember the last time she saved a Word document, and that the productivity gain is not incremental but on the order of ten times or more.
The clearest place that gain shows up is the back end. Work that once required teams of people for data capture, optimization, and reporting on a monthly or quarterly cycle can now run continuously, giving an office a live, real time picture of its investments rather than a rearview snapshot of where things stood three months ago. The result is twofold. Costs fall, because automated workflows replace expensive manual labor, and leaders are freed to focus on the work a business actually exists to do, which is strategy, partnerships, growth, and serving people well, instead of drowning in administration.
The Security Blind Spot No One Budgets For
There is a cost to running old systems in a new world, and Susan Lindeque is direct about it. Legacy operations are easier to breach, and modern AI gives bad actors the means to do it. The threats people rarely budget for are the ones built on impersonation. Artificial intelligence can now copy a person’s voice and face convincingly enough to stage a phone call that appears to come from a trusted executive. Imagine a call that sounds exactly like your chief financial officer, asking you to approve a wire transfer. Approve it, and the money is simply gone. She describes the rise of what she calls deepfakes as a service, organized purely to extract money from targets as quickly as possible.
AI can now basically duplicate your voice, it can duplicate your face.
Susan Lindeque, Founder and CEO of Avestix Group
The vulnerability is widened by how little protection most people use. When Susan Lindeque asks audiences how many run a private VPN on their phones, the honest answer is almost no one, even though AI already lives on those phones. Her analogy is to living in Florida and knowing the region is prone to hurricanes. If you wait until the storm arrives to put your house in order and secure insurance, you have waited too long. The lesson is the same for a family office. Communication security is no longer a technical afterthought, it is part of protecting the wealth itself, and the only sensible posture is a proactive one.
Why Capable People Freeze, and How to Move Without Being Reckless
If the intent is real, what freezes these families in place? Susan Lindeque names fear and a lack of literacy. Many of the people who built the wealth belong to an older generation, and concepts like cybersecurity, artificial intelligence, and blockchain feel foreign or even threatening. She notes how often blockchain is still confused with cryptocurrency or dismissed as a scam, when it is really just a timestamped digital ledger that she expects most companies will eventually run on. Underneath the fear sits a question about trust, both whether AI can be trusted and who can be trusted to implement it, and the experts who genuinely have that knowledge are in very high demand.
Her answer to the fear is not recklessness but structure. You move faster by putting clear controls around the technology rather than by avoiding it. Speaking for herself, Susan Lindeque has given AI broad access to tools like her email and connected applications such as Notion and Canva, but only inside a defined framework that states plainly what the AI may and may not do. It is not permitted to touch investments or legal matters, and it must ask her every time. She compares it to the segregation of duties any family already understands intuitively. You would not give a ten year old the same access you give a thirty year old, and AI is no different. Around that framework she layers the fundamentals of security, asking where the data sits, who can reach it, what the firewalls and VPN look like, and where privacy is protected.
This is also how she separates genuine prudence from what she calls inaction dressed up as prudence. The first is thoughtful and structured. The second is a bucket over the head, a decision that the whole thing is too complicated or too frightening to begin. The way out, she says, is to start small and take minor steps rather than opening everything at once. She points to marketing as an accessible entry point, where these tools can now produce a website, a PDF, or a one page document that once required a full time hire and a significant budget, with a professional perhaps stepping in for an hour or two of polish. The fear shrinks the moment the work starts.
The Question Stopped Being Whether
To understand what waiting truly costs, Susan Lindeque returns to the reason family offices exist in the first place. For her it comes down to one thing, legacy, the preservation of wealth that someone worked relentlessly to build, so it can pass to children and grandchildren who get a better life than the one before them. She tells the story of a New York taxi driver who had spent twenty five years behind the wheel and spoke with pride about putting both of his children through university, one studying law and one studying medicine. The instinct to give the next generation more is universal, and protecting capital is how families honor it. Lose that capital, she warns, and it takes roughly twice as long to climb back to where you were.
That is why she believes the old debate is finished. The question is no longer whether to bring AI into a business, it is when, and her answer is today. She encourages families to stop using AI as a chatbot and to start using its deeper capabilities, to find a community or a trusted guide rather than going it alone, and to begin with something as ordinary as watching tutorials and experimenting with the tools directly. And she offers one more piece of advice that is easy to overlook. The next generation inside a family is a kind of gold mine, young enough to experiment and unburdened by the fears their elders carry. Hand them a small task, let them explore, and you build the family’s competence from the inside.
The thought to carry into your day is this. Adoption is not a single dramatic leap, it is a sequence of small, governed steps taken sooner rather than later. As Susan Lindeque makes plain, the families who treat AI as something to talk about will keep falling behind the ones who quietly started building, and the gap between them only grows with time.
