Can Artificial Intelligence Help You Plan for Retirement? A Comprehensive Look
The Growing Role of AI in Financial Planning
Artificial intelligence has become deeply woven into the fabric of everyday life, helping people with tasks ranging from creating workplace presentations and online shopping to supporting complex scientific research. Now, Americans are increasingly turning to AI to tackle one of life’s most critical financial questions: Can I afford to retire? According to recent research, approximately 20% of Americans are already using chatbots for financial advice, based on a September study conducted by AI company Pearl. Even more striking is that workers who use AI in their professional lives are twice as likely to use it for retirement planning compared to those who don’t use AI at work, according to findings from the MissionSquare Research Institute. This trend reflects both the growing accessibility of AI tools and the urgent need for retirement guidance among American workers who face an increasingly uncertain financial future.
The appetite for retirement planning assistance couldn’t be more real or pressing. Americans now report expecting to work four years longer than they’d prefer, driven by escalating living costs and insufficient savings. The statistics paint a sobering picture: the median retirement account balance for workers is just $40,000, falling dramatically short of the $1.5 million that people say they need to retire comfortably. Adding to these concerns, Social Security—the financial safety net that millions of Americans are counting on—faces a potential crisis, with projections suggesting monthly benefits could be reduced by as much as 20% within the next six years unless Congress takes action to stabilize the program. Against this backdrop of financial anxiety and uncertainty, it’s understandable why people are looking for help wherever they can find it, including from AI chatbots like ChatGPT and Claude.
Where AI Shows Promise in Retirement Planning
Some financial experts believe that artificial intelligence can serve as a valuable starting point for addressing basic retirement questions and concerns. Luke Delorme, who serves as director of financial planning and is a Certified Financial Planner at Tableau Wealth in Great Barrington, Massachusetts, has experimented with using AI for financial planning tasks. He explains that he can ask AI systems to generate financial planning ideas or even conduct Monte Carlo simulations to determine safe annual spending rates. While acknowledging that the technology isn’t perfect yet, Delorme believes it’s beginning to produce genuinely valuable output that could benefit people seeking retirement guidance.
Monte Carlo simulations represent one area where AI’s computational power could prove particularly useful. These sophisticated mathematical models run thousands of potential scenarios for an individual’s retirement portfolio, accounting for both best-case and worst-case possibilities, such as how a bear market might impact savings. The model then calculates the probability that a person’s retirement savings will last throughout their entire lifetime. As Delorme points out, these simulations “are the perfect thing for a computer program to do,” and he believes that AI-powered versions of these tools will eventually become quite powerful. For the roughly two-thirds of Americans who don’t work with professional financial planners, AI could provide an accessible entry point for understanding important financial concepts and beginning to engage with retirement planning in a more structured way.
Significant Limitations and Concerns
Despite these potential benefits, experts are raising significant concerns about the readiness of AI to handle the complex web of issues involved in comprehensive retirement planning. Laurence Kotlikoff, a noted economist from Boston University and recognized retirement expert, has warned that AI may actually do more harm than good when dispensing retirement advice. He argues that current AI systems struggle to grasp the nuances of Social Security and other retirement considerations, and worse, they’re built on what he considers fundamentally flawed traditional financial planning advice. According to Kotlikoff, who has developed his own retirement planning tool called MaxiFi, AI is “being trained on Wall Street’s guidance, and Wall Street’s guidance is all about maintaining and collecting and expanding its assets under management, so that has nothing to do with proper economic-based advice.”
One critical flaw Kotlikoff identifies is how AI programs estimate retirement savings needs. Most AI systems, following conventional financial planning wisdom, base their calculations on expected longevity—essentially how long people are likely to live based on statistical averages. However, Kotlikoff argues that retirement planning should be based on a person’s maximum potential life expectancy to adequately protect against the risk of outliving one’s money. Additionally, he has discovered that AI often provides incorrect information when projecting Social Security scenarios, which can be extraordinarily complex given that the federal program operates under 22,000 pages of rules and regulations. When the foundational analysis is wrong, “then you are off to the races of having the wrong analysis done for you,” Kotlikoff cautions. He’s frustrated by what he sees as uncritical enthusiasm for AI, stating bluntly, “I don’t give a s*** about feeling cool—I’m here to make people feel safe.”
Andrew Lo, a finance professor at MIT Sloan School of Management, has identified additional areas where AI falls short. He notes that AI struggles with tax optimization, fails to understand regulatory nuances, and—unlike human financial advisers—isn’t subject to legal requirements such as the fiduciary duty to act in a client’s best interest. Lo emphasizes the importance of asking critical questions when using AI for retirement advice, such as prompting the system to identify where it might be wrong and to explicitly list its assumptions and uncertainties. Without this critical engagement, users may place unwarranted trust in advice that contains significant blind spots.
Testing AI with a Real-World Scenario
To better understand how AI handles actual retirement planning questions, consider a test case involving a hypothetical 50-year-old single woman earning $70,000 annually. She has accumulated $185,000 in retirement savings—the median amount for someone her age—mostly invested in S&P 500 index funds. She’s contributing 12% of her income to retirement accounts, and at her full retirement age of 67, she expects to receive approximately $2,400 monthly in Social Security benefits. When this scenario was presented to three popular AI systems—Anthropic’s Claude, OpenAI’s ChatGPT, and Perplexity—asking whether this woman could comfortably retire at 65 and what advice they would offer, the responses revealed both capabilities and concerning limitations.
Claude and ChatGPT provided similar assessments: the woman could potentially retire, but it would be financially tight, and under certain circumstances, she might risk running out of money in retirement. Perplexity took a more pessimistic view, suggesting that she likely could not retire comfortably at 65 without either significantly reducing her spending or finding ways to increase her income before retirement. When specifically asked about their assumptions—following Professor Lo’s advice to probe AI systems critically—the chatbots revealed several important limitations. They acknowledged basing their models on the woman living to age 90 rather than considering a maximum potential lifespan of 100 years. They also admitted they weren’t modeling exact tax implications, which can significantly impact retirement income. Perhaps most significantly, the AI systems revealed they weren’t factoring in potential long-term care costs, which can be substantial and often represent one of the largest expenses retirees face.
After these assumptions were highlighted, the chatbots walked back some of their original conclusions. Claude specifically acknowledged that its original planning horizon was too short, changing its assessment from “a tight but doable retirement” to “meaningfully underfunded without course correction.” This reversal demonstrates both the importance of critical questioning when using AI for financial advice and the systems’ current limitations in providing comprehensive analysis without significant user intervention and financial knowledge.
The Behavioral Challenge Beyond Technology
While much of the discussion about AI and retirement planning focuses on technical capabilities and limitations, there’s a deeper issue that technology alone may not be able to solve: the emotional and behavioral barriers that prevent people from engaging with retirement planning in the first place. Luke Delorme points out that many Americans fear investing, which can lead to costly mistakes such as keeping retirement savings in cash or certificates of deposit. These conservative options often provide returns lower than inflation rates, meaning that the purchasing power of savings erodes over time, significantly increasing the risk of running out of money in retirement.
Delorme believes that AI could potentially help the approximately two-thirds of Americans who don’t work with financial planners begin to understand investment concepts and feel more comfortable engaging with retirement planning. However, he expresses skepticism that AI alone can overcome the deep-seated anxieties many people have about financial matters. “It’s much more behavioral than it is a technical lack of knowledge,” Delorme explains. “I don’t know if today that’s going to help people overcome their fears of things, like the fear of investing, which is such a huge obstacle.” This observation highlights a fundamental limitation of AI-based solutions: while chatbots can provide information and run calculations, they may not be able to address the emotional support, personalized reassurance, and behavioral coaching that human financial advisers can offer—elements that are often crucial for helping people make and stick with long-term financial decisions.
The Bottom Line: Use AI Cautiously and Critically
The current state of AI for retirement planning presents a mixed picture. On one hand, these tools offer unprecedented accessibility to retirement planning concepts and calculations that were once available only to those who could afford professional financial advisers. AI chatbots can run complex simulations, provide instant answers to basic questions, and help people begin thinking more concretely about their retirement readiness. For someone just starting to consider retirement planning, AI can serve as a useful first step in understanding the landscape and identifying important questions to explore further.
On the other hand, the limitations are significant and potentially dangerous for those who rely too heavily on AI without recognizing its shortcomings. Current AI systems may provide advice based on flawed assumptions, miss critical considerations like maximum longevity and long-term care costs, offer incorrect information about complex programs like Social Security, and fail to account for important regulatory and tax implications. Perhaps most importantly, they lack the legal obligations and accountability that come with human financial advisers, and they cannot provide the emotional support and behavioral guidance that often make the difference between financial success and failure.
For now, the wisest approach appears to be treating AI as a starting point rather than a comprehensive solution. Use chatbots to explore basic scenarios and understand fundamental concepts, but approach their conclusions with healthy skepticism. Ask critical questions about assumptions, limitations, and potential errors. Recognize that AI cannot yet replace the nuanced judgment, comprehensive analysis, and legal protections that come with qualified human financial advisers, particularly for complex situations. And perhaps most importantly, understand that successful retirement planning involves not just technical calculations but also behavioral change, emotional preparation, and long-term commitment—human dimensions where AI still has a very long way to go.
