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Read this if you sponsor an employee benefit plan. 

In December 2022, Congress passed the Securing a Strong Retirement Act of 2022, commonly referred to as the SECURE 2.0 Act. The SECURE 2.0 Act includes a multitude of provisions, many of which affect employer-sponsored retirement plans and individual retirement accounts. In this article, we want to specifically focus on changes to catch-up contributions for employer-sponsored retirement plans. 

Catch-up contribution parameters

Currently, salary deferral catch-up contributions are available to participants who are age 50 or older (regardless of when they turn 50 during the calendar year). For 2023, the catch-up contribution limit is $7,500; however, this amount changes annually as it is indexed to inflation. This has historically been a non-issue for plans, payroll providers, and recordkeepers, as payroll provider and recordkeeper portals have been set up to allow contributions over the normal salary deferral limit (currently $22,500) for those participants who are over age 50. These catch-up contributions have traditionally been coded the same as a participant’s regular deferrals—either traditional or Roth.

Effective dates: Roth catch-up contributions requirement

Effective January 1, 2024, catch-up contributions will be required to be made on a Roth basis for participants with wages greater than $145,000 (indexed annually for inflation) in the prior year. However, on Friday, August 25, 2023, the IRS issued Notice 2023-62, which provides a two-year administrative transition period to implement the new catch-up contribution provisions.

Specifically, until taxable years beginning after December 31, 2025, catch-up contributions to participants with wages greater than $145,000 in the prior year will not need to be designated as Roth contributions.

Note that the $145,000 limit is determined by looking at wages for social security tax purposes. That may or may not be the same definition that is used by a plan for other purposes (e.g., salary deferral and employer contributions). This may create an administrative burden for payroll providers and recordkeepers as these vendors will need to be able to differentiate between employees under and over this compensation threshold. Furthermore, for those above the threshold, software and systems will need to be set up to help ensure any catch-up contributions are properly coded as Roth contributions, for payroll and retirement plan reporting. Employees with wages of $145,000 or less may still elect to have Roth catch-up contributions, if allowed by the plan documents.

Recommendations

We recommend reaching out to payroll providers and recordkeepers today to see how they plan to approach compliance with the new provision. These conversations should not be independent of one another—it will take a concerted effort amongst plan management, payroll providers, and recordkeepers to help ensure compliance.

NOTE: There is one other change on the horizon for catch-up contributions. Beginning in 2025, the SECURE 2.0 Act creates an additional “special” catch-up limit for employees who are ages 60 to 63. This special catch-up limit will be the greater of $10,000 or 150% of the regular catch-up amount in effect for the year. This amount will also be indexed for inflation annually.

Additional changes effective in 2024

• Elimination of Required Minimum Distributions (RMDs) for Roth 401(k) and 403(b) plans
• RMDs for surviving spouses
• Student loan repayments matching contributions
• Emergency savings accounts
• Optional Rothification of catch-up contributions for high earners (as discussed above, this will be mandatory in 2026)
• Higher forced rollover limit
• Retroactively amending plan to increase benefits for prior plan year
• Waiver of early withdrawal penalties for certain distributions
• Permanent safe harbor for correcting auto-enrollment and auto-escalation failures
• Uniform rollover forms
• 403(b) hardship distributions conform to 401(k) rules
• Starter 401(k) or 403(b) plans
• Separate top-heavy tests allowed 
• SIMPLE plan updates
• Reform of family attribution rules
• Improved defined benefit plan annual funding notices
• Indexing individual retirement account (IRA) catch-up limit
• Section 529 rollovers
• Retirement savings lost and found

For more information on these changes and others going into effect in 2025, read the previous article on the SECURE 2.0 Act.

If you have questions about the SECURE 2.0 Act, catch-up contributions, or your specific situation, please contact our Employee Benefits team. We're here to help. 

Read this if you are in healthcare and interested in AI.

AI in healthcare is here. While it’s the early days and there is a lot still to figure out in terms of accuracy and risk, it’s undeniable that AI has huge potential in solving some of healthcare’s greatest challenges. 

AI to help alleviate staffing shortages

A February 2023 report to the US Senate drafted by the American Hospital Association painted a startling, though not new, picture of the healthcare staffing shortage: “The result of mounting pressures on the healthcare workforce has created a historic workforce crisis complete with real-time, short-term staffing shortages and a daunting long-range picture of an unfulfilled talent pipeline.” While this report focused on providers such as physicians and nurses, hospitals nationwide are struggling with filling positions across the board. There are just not enough people to fill the need—now and for the foreseeable future. 

AI has the potential to address this critical staffing shortage in a number of ways. The area where most people think of AI efficiencies is in administrative tasks. These, as anyone in healthcare knows, abound in the daily work. Reducing the administrative burden on clinicians means more time with patients, and more patients that can be seen. In this scenario, it’s also possible that this reduced administrative workload will reduce burnout, improve the clinician experience, potentially keep more clinicians in the field—and encourage more to join. This, in turn, can reduce recruiting and retention costs and costs associated with outsourced and travel staff. 

It has been shown that nurses can spend up to 35% of their time on data entry, documentation, and charting. Generative AI can assist in these tasks, freeing up nurses for direct patient care. 

Risks to consider: The biggest risk here is the protection of personal health information (PHI). Safeguards need to be in place to remain Health Insurance Portability and Accountability Act (HIPAA) compliant. Common AI platforms are often hosted by third-parties, or are open-source systems that require proper due diligence to help ensure that any PHI in AI systems is secured. Often, medical billing systems contain PHI that is overlooked as a risk for HIPAA compliance, but can easily result in large scale breaches. Using AI to help with such administrative tasks will save time, making sure that the correct IT controls are put in place and are frequently evaluated for operating effectiveness. 

AI to improve patient experience and outcomes

An added benefit of using AI for administrative tasks and reducing the workload on providers is that patients will have better experiences when providers have better experiences. Patients will, theoretically, have more time with their providers and more focused attention. Another way AI can help with patient experiences and outcomes is through AI diagnostic and image-interpreting tools.

With AI’s ability to sift through and analyze huge amounts of data, it can assist doctors in diagnosing patients and even predict what illnesses a patient is likely to develop—in some cases, with life-saving results. A March 2023 article in the Wall Street Journal reported that AI was able to diagnose sepsis in hospitalized patients two hours earlier on average than humans, which reduced the sepsis mortality rate by 18%.

AI is also proving to be adept at analyzing images. Using Natural Language Processing (NLP), AI can provide high-quality analysis of X-rays and MRIs, leading to precise early diagnoses. GE Healthcare reported a 30% increase in speed and enhanced image quality using this technology.

Risks to consider: As with any technology, results depend on the quality and quantity of data that is being analyzed. Patients may also feel uncomfortable being diagnosed by AI. Any diagnosis should be verified by a human and any technology should be thoroughly vetted and tested prior to use. There is a particular risk when using AI in emergency room situations, where the treating physicians may not have the patient's full medical history for backend algorithms to consider. This could result in a misdiagnosis. However, in a clinical setting, the results of AI diagnosis tools are close to the accuracy rate of a doctor (about 90%). The biggest challenge to the medical profession will be getting patients to trust the use of AI. In a study by Harvard Business Review, surveys showed that patients felt their medical conditions were unique and overwhelmingly preferred meeting with a doctor versus being diagnosed by AI.

AI and revenue cycle optimization

At BerryDunn’s Hospital Summit in October, audience members were asked where they saw AI having the greatest impact in the next five to 10 years. The top answer, with 35% of votes, was in the revenue cycle area. According to a recent article published by the Healthcare Financial Management Association, there is the potential for $9.8 billion in savings by automating revenue cycle functions. Many organizations are already using Robotic Process Automation (RPA) to aid in their revenue cycle functions. Many are now adding predictive analytics and automation using AI. 

AI tools can help revenue cycle operations in a number of ways. One example is using AI to prompt providers on when and how best to document. It can also be used in medical coding to suggest appropriate codes based on relevant clinical data. One hospital reports that they’ve saved over $1 million using AI in their revenue cycle. 

Risks to consider: As with most AI tools, the biggest risks are related to data privacy, security concerns, and concern about the accuracy of data. Like any investment an organization makes in technology, new systems need to be fully vetted and tested, and organizations must follow proper change management practices so that the new technology is properly designed and implemented. Rushing to implement AI may result in incorrect outputs and increase the organization’s risk exposure. 

The healthcare industry is already a front-runner in using AI and there’s an exciting future ahead. Risk management is key to launching any new technology and particularly AI. Organizations should prioritize the following: Inventorying all uses of AI at the organization, having clear policies and procedures in place, and doubling down on compliance.

If you have any questions about AI or your specific situation, please don’t hesitate to reach out to the BerryDunn team. We are here to help.

Read this if you are interested in artificial intelligence (AI).

Everyone is talking about AI right now. With the technology accelerating so fast, companies and individuals are struggling to figure out the impacts: Will it be helpful for businesses? Will it be harmful for employees? Will it change the way we work? 

The thought leaders at BerryDunn have been exploring the technology and ways it may benefit our clients moving forward—both for accounting services and for broader consulting services. Here are five reasons we're optimistic about the future of AI in business. 

1. AI has the potential to complete menial, time-consuming, and error-prone tasks faster and more accurately.
   BerryDunn’s Kathy Parker, Practice Leader for the firm’s Outsourced Accounting Group, recently shared her thoughts with MassCPAs, stating that “Our approach to clients has always been to act as their consultant and advisor, and AI won't change that. What we are starting to see is that AI software has the potential to reduce the manual work we do, which will free up time so we can do what we do best. I expect that AI will start to take care of things more reliably like automatic scanning, basic tax preparation, and some functions that take up a lot of staff time.” 
2. AI may free up resources so businesses can be more strategic.
   AI can help complete time-consuming tasks, saving time and staff resources to focus on more strategic initiatives. Parker is excited to think about adding more value to her clients by providing more strategic planning, benchmarking, and advisory services that will contribute to their success. She shared that “Clients are starting to recognize that AI can reduce the burden on accountants, and they're beginning to expect more from us. They don't just want our calculations, they want our expertise, and that's fantastic. Clients want to know how they compare with their competitors, and they want to know how to be proactive about their growth. They want dashboards to be able to easily see where they stand and they're looking for more sophisticated deliverables.”
3. AI could reduce the price of some services. 
   Parker also shared her view of how AI may affect prices for services such as tax and accounting. “Businesses may also expect to see a different fee schedule, given the potential reduced workload that AI could bring. Of course, that reduced workload will allow service providers to raise the bar in other, potentially more significant, areas of their business.”
4. AI could improve decision making for businesses.
   Tucker Cutter, a Senior Manager with BerryDunn’s higher education consulting team, believes that AI has the potential to significantly improve decision making for any business. His work focuses on the nexus of technology and people, helping higher ed institutions manage large-scale digital transformation projects. “I’m looking forward to seeing how AI will help us provide high quality decision-making guidance through enhanced data analysis and predictive analytics,” he shared. Once tools have been vetted and tested to prove their accuracy and reliability, AI has the potential to analyze data much faster than could be done manually by humans.
5. Clarity on the best use cases for AI will emerge.
   If you’re not inclined to be an early adopter, know that you’re not alone. Just as with any other technological revolution, there are still a lot of unknowns. It makes sense to be cautious, especially when you’re dealing with sensitive data. As the dust settles, you’ll be able to learn from the experience of early adopters about what works and what doesn’t. At BerryDunn, our consultants are keeping an eye on the technology and how it may impact processes, systems, and outcomes. 
   
   Cutter shared his approach to staying current on AI technologies so he can advise his clients, stating that “We need to understand how to use these platforms and tools as well as stay well-versed in AI/technology governance strategies. For the good of our clients, we are still responsible for keeping data strategy at the forefront even if it’s not top-of-mind for our clients.” Clarity won’t emerge overnight, but in the coming months and years, we’re confident that a healthy balance between what’s best for business and what’s best for people will be possible. 

Read this if you are interested in AI and considering using AI in your organization.

Since the debut of OpenAI’s ChatGPT, interest in artificial intelligence (AI), specifically generative AI, has soared. Businesses across industries and sectors are exploring how generative AI can transform their operations, services, and products. Generative AI presents a unique opportunity for companies to hyper-personalize their products and services, monetize their data, and create frictionless customer experiences, among other innovative use cases. The more advanced generative AI becomes, the more it can enhance the value companies bring to their customers. 

Artificial intelligence definitions

Before we specifically discuss generative AI, it is worthwhile to expand the discussion to AI generally. Artificial intelligence is certainly a buzz term and although people love to use it, AI can be used in many contexts and with various intentions, making it difficult to define. Here are some definitions of the more common AI technologies:

1. Artificial intelligence: Refers to the development of systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language understanding.
2. Natural language processing: Enables computers or machines to understand, generate, manipulate, and interact with human language in text or voice form. Think of the last time you called a call center and spoke with an answering machine: Did you have to press the numbers on your phone to navigate through the conversation or could you answer via voice commands, possibly even full sentences? If the latter, that is natural language processing. Other examples include Apple’s Siri or Amazon’s Alexa.
3. Machine learning: Allows computers to learn and adapt without following explicit instructions. An example would be a computer playing chess that learns its opponents’ habits the more games it plays. Transaction monitoring software, often used by financial institutions as part of their anti-money laundering programs, also uses machine learning technology. Over time, the software learns over time to detect anomalies in patterns of transactions. Although humans may intervene to “guide” the software, the software primarily learns on its own.
4. Deep learning: Deep learning is a subset of machine learning. The biggest distinction between machine learning and deep learning is the data each is able to process. Machine learning does best with structured data. For instance, the transaction example above uses structured data—each transaction contains the same data points which can be easily identifiable, for instance, customer name, dollar amount, date, description, etc. Deep learning can analyze unstructured data, like text and images. You may have heard of surveillance cameras being able to identify individuals. The cameras do this through deep learning. Another example of deep learning is its ability to distinguish between types of animals. Over time, deep learning technology can learn which features distinguish a cat from a dog.
5. Large language model: A large language model is a machine learning model, specifically a type of deep learning model, designed to understand and generate human-like text. These models are trained on massive amounts of textual data, allowing the models to learn patterns, grammar, context, and even some factual information.
6. Generative AI: Generative AI is also considered a subset of machine learning and expands upon the applications of machine and deep learning. Where machine and deep learning can analyze data, generative AI is able to analyze data and then produce original text, video, images, and other types of content. It uses a large language model trained on a vast dataset to generate human-like responses in text-based conversations. Currently, generative AI most commonly creates content in response to natural language requests, like ChatGPT, where the user inputs a request and ChatGPT responds with an answer. If the answer is not quite what the user was looking for, they can modify the request and receive a (hopefully) better answer.

An easy way to envision AI is as a tree, with various branches representing different techniques and technologies. One of the branches is generative AI, which includes sub-branches such as ChatGPT and large language models. ChatGPT, powered by a large language model, functions as a conversational agent that can generate human-like text responses. Each branch and sub-branch of the AI tree contribute to the overall growth and understanding of AI.

Generative AI applications 

Generative AI already has a myriad of uses. Some of the more common ones involve using the technology to draft policies or messages—and even review policies or messages. The user can specify the tone, style, and length of the communication. (Author’s note: This article was not written with generative AI). Generative AI can also summarize large amounts of data. For example, a new policy or regulation could be input into generative AI software, and the software could provide only the salient points. Generative AI can also quickly provide responses to questions—think of it as a superpowered Google. Rather than inputting a question and searching through thousands of websites, generative AI will type up a response, as if you are having a conversation with the technology. Generative AI has even been known to help with software coding and Excel formulas. 

These uses are just scratching the surface of generative AI’s capabilities. According to a recent Gartner survey, executives believe the primary focus of generative AI will be on—in this order—customer experience/retention, revenue growth, cost optimization, and business continuity. 
 
If you have any questions about AI or your specific situation, please don’t hesitate to reach out to the BerryDunn team. We are here to help.

In an industry where challenges abound when it comes to serving employees with robust physical and mental well-being support, we wanted to share this article from Construction Executive about construction companies that are taking the lead in creating a “culture of caring.”

As you’ll see in the article, companies who are doing well-being right are taking a variety of actions every day to help ensure that their employees feel supported, including:

• Getting leaders and managers out in the field to talk to employees (don’t just send emails)
• Giving everyone a voice
• Prioritizing mental health
• Finding and addressing root causes of employee burnout and stress
• Looking at well-being from a systemic perspective, the same way you look at workplace safety

As we’ve seen in our work with clients in the construction industry, running a successful company depends to a large extent on a loyal, satisfied, and (physically and mentally) healthy workforce. Companies that take care of their people are the companies well positioned for financial success.

That’s why BerryDunn has a well-being, culture, and engagement consulting team. If you’re looking for a simple way to assess your current employee well-being program, and actionable steps to improve, start by downloading our Well-being Maturity Model or scheduling a call with a member of our team.