We all procrastinate sometimes – some more skillfully than others. In Tim Urban’s talk ‘Inside the mind of a master procrastinator’ he explains how the ‘instant gratification monkey’ overrides our ‘rational decision-maker,’ making us chase what’s ‘easy’ and ‘fun’ instead of what’s necessary.  Retirement planning is neither easy nor fun, so it’s easy to see why so many of us may disengage from planning decisions far longer than we should – until it’s often too late.  This disengagement carries real human costs. Consider that 70% of retirees wish they had started saving earlier, while 51% of Americans worry they’ll run out of money in retirement. These aren’t just statistics – they represent millions of Americans in financial anxiety. In fact, many rank retirement alongside life’s most stressful events, including job loss and divorce. The challenge, then, is clear: how might we help people overcome the procrastination trap and engage with critical planning choices well in time for them to help secure a comfortable retirement?    

How the power of AI and behavioral economics unlocks opportunity 

This blog leverages analysis on emerging market trends, insights on evolving customer behavior and in-depth interviews with 11 senior executives from leading incumbent and challenger retirement and record keeping firms. Our research reveals that if, through a better experience, more people participated in their retirement plans, and contributed more to these plans, this could unlock an incremental $405B in assets under administration (AUA) within the sector over a 10-year horizon in the US. The key to capturing this opportunity and boosting engagement lies in reimagining the participant experience through the combinatorial power of AI and behavioral economics. While AI has dominated headlines with its potential to create efficiencies and cost savings, we believe its true power comes to the fore when it’s merged with behavioral science principles to create experiences that don’t just inform participants but also make them think and drive action. Behavioral economics provides insightful principles around how people make decisions. Three elements are especially relevant and deserve a rethink in how retirement plans are presented:     

Choice architecture is around how choices are presented (defaults, categories, visual organization). For example, highlighting the most appropriate option for an individual based on their age, demographics, life stage or already stated preferences. 

Information architecture is around how information is presented, using framing, reference points, labeling, and visuals to influence behavior. For instance, expressing calorie counts in hours of exercise needed to burn them off could influence healthier eating habits. 

Thinking architecture is around encouraging slower, deliberate thinking for complex or infrequent tasks. For example, using tax checklists to ensure an individual is maximizing all eligible tax deductions, credits, etc. to reduce their tax bill. 

Reframing key savings choices and decisions around these architectures could help participants make better decisions. Dr. Steven Shu, Professor of Practice of Behavioral Economics at Cornell University, collaborated with us on our analysis. He has a track record of success in this field, as evidenced by this study where he presented an option to boost emergency savings as “$5 per day” instead of “$150 per month.” This approach doubled participation among higher earners and increased it more than sixfold among lower earners. Operationalizing the above-mentioned principles, at scale and with personalized guidance, used to be challenging. This is now where AI comes in. It could help people make better decisions based on their context and allows for personalization of guidance to their circumstances.  

Imagining the future: Meet Lisa 

To understand the potential of AI and behavioral economics in transforming the retirement participant journey, consider the journey of Lisa, a character we created to articulate a vision for how AI and behavioral economics can work together to deliver hyper-personalized experiences. Lisa embodies the competing priorities many Americans face: 

“Between work, managing my kids’ activities, planning for their college, and everyday expenses, retirement feels distant and complicated. I know I should focus on it more, but it’s easy to postpone when there are so many immediate demands on my time and money.” 

Lisa contributes to her 401(k) but hasn’t increased her contribution in two years. She occasionally checks her balance but rarely makes changes. Her experience probably represents millions of Americans who know retirement planning matters but struggle to make it a priority amid life’s complexities.  In the near-term, when, for example, Lisa gets a raise, an AI tool could send her a behaviorally-optimized message to increase her 401(k) savings rate:  

“Lisa, congrats on your recent raise! If you increase your 401(k) savings by just 2% today, you’ll boost your projected retirement balance by $75,000—without even noticing a difference in your paycheck. Most people like you do this within 30 days of a raise. Tap below to update your savings.” 

The right timing (after a positive event), social relevance (“most people like you do this”) and one-click implementation could help motivate Lisa to take action. 

In the longer-term, we envision AI agents to become capable enough to serve as an always-on, hyper-personalized coach. For example, seven years after buying her home, interest rates have fallen, and Lisa’s credit score has risen. Her AI coach notices this and sends her a nudge:  

“Lisa, interest rates have dropped, and based on your mortgage balance and credit score, you might qualify for a 3.2% rate. If you refinance now, you can reduce your monthly mortgage payment by $500—without extending your loan term! And redirecting that $500 per month into your 401(k) can grow your retirement savings by $200,000+ by the time you withdraw.” 

Again, the right framing (not just a rate drop, but $500 monthly turning into $200,000 in wealth), smart defaults (reinvest mortgage savings into 401(k)) and one-click actions can help Lisa overcome procrastination.  

Overcoming legacy hurdles 

We acknowledge that achieving a vision of real-time, personalized interactions in retirement services might be challenging due to legacy technology infrastructure, fragmented data architecture, and limited funding for participant experience investments. However, AI paired with machine learning could help overcome these obstacles by creating unified participant profiles, improving data quality, and reducing legacy migration costs.  

The time is now to experiment, test and learn

So, the question arises… what are you waiting for? You may need to resist your own temptation to procrastinate. Instead, you might want to inject a dose of AI and behavioral economics into your customer experience now. You can help your customers to resist whatever their procrastination tool of choice is – and direct them to set up their retirement policies or increase their contributions. A win-win. We’d love to chat with you – please feel free to contact us on LinkedIn at Tim Hoying or Saurabh Wahi.