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From novelty to necessity: How GenAI is reshaping investment accounting

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Imagine a tool so integral to your daily routine that it becomes second nature in your professional life. Generative AI has done that for investment accounting. In just two short years, GenAI’s impact has reimagined how investment accountants interact with data, make decisions and drive financial strategies.

Today, nearly two-thirds of organizations say they regularly use GenAI in at least one aspect of their operations. Such rapid adoption makes it easy to understand why global GenAI spending is set to hit $202 billion — 32% of all AI spending — by 2028. Yet, as the tech continues to take shape and offer more ways to deliver intelligence, its rapid rise has also raised expectations for measurable, higher-level returns on investment. 

In the past year, GenAI has streamlined routine tasks such as document summarization and sifting through mountains of portfolio data to create actionable reports. Beyond these applications, GenAI is tackling more complex work: from demystifying the intricacies of reconciliation work to pioneering multi-country compliance automation. With each breakthrough, we’re eager to see what GenAI can do next — solving data puzzles within middle- and back-office operations is just the beginning.

However, integrating GenAI is a gradual process, with many investment accountants still learning to maximize their return on investment from these tools. The crux of GenAI implementation lies in how it can take very complex work that has involved many teams of experts and engineers harnessing very large datasets and build a data architecture that delivers remarkable output. Thus, the key to unlocking this next level of innovation lies in building a strong data architecture foundation.

Ensuring data integrity and accuracy

 
Much like investment accounting itself, the quality and accuracy of the data inputs into GenAI are essential to the reliability of its outputs. As we pioneer more advanced applications of GenAI, the creation of domain-specific prompts becomes crucial. They act as guardrails, ensuring models capture the granular context of queries and deliver accurate results. Before this can happen, we must ensure our data architecture is not only resilient but entirely without defects.

To prepare for a GenAI-driven future, businesses must maintain impeccable, validated and standardized investment data. Given the heightened regulatory scrutiny they operate in, investment accountants don’t have the luxury of simply writing off minor data errors. Even the smallest hallucination or inaccuracy can escalate into significant regulatory issues, reinforcing the need for rigorous data management practices. With this in mind and to ensure a smooth GenAI deployment, organizations should focus on three key aspects: 

  • Establish a data governance framework. Assigning clear responsibilities and processes is crucial. A formalized structure should define roles in data oversight, specify tasks for data quality control, and ensure compliance, all contributing to a trustworthy data environment.
  • Enhance data preparation. As the demands for GenAI evolve, so must our data management practices. Organizations must elevate their data preparation processes, such as collecting, formatting and organizing raw data into a structured format suitable for analysis. Automation and validation are critical for transforming data into analytics-ready information, quickly rooting out and addressing any anomalies.
  • Break down data silos. Despite more organizations migrating to the cloud, the challenge of unstructured data from disparate systems remains a hurdle for technology success. Centralizing a data story into “data lakes” can boost collaboration, standardize data and streamline data operations, paving the way for a successful GenAI integration.

 

Address legacy technology barriers that stunt AI overhauls

Financial organizations, especially within back-office functions, are still grappling with outdated legacy technology systems. These systems, although familiar, resist large-scale AI transformations. Internal inertia, external constraints and other reasons keep organizations from breaking free from the status quo. As a result, many organizations tiptoe into AI integrations on a piecemeal basis, hindering their ability to scale and evolve.

While modernizing systems involves complexity, the payoff can be significant. A transition to agile, interconnected systems can result in enhanced operational efficiency, a culture of continuous innovation, and a seamless data flow that’s vital for GenAI’s success. It’s about trading in the old for new ways of working that are more in sync with our dynamic digital world.

A phased approach to replacing legacy systems can minimize disruption and facilitate a smoother changeover. Additionally, fostering open collaboration between everyday users and engineering teams is essential. This partnership ensures upgrades are implemented efficiently and in a way that maximizes ROI — turning the complex task of replacing legacy systems into a rewarding journey of transformation.

Enabling strategic alignment before launch

Organizational adoption of bold technologies like GenAI can often feel like embarking on an epic expedition. The journey begins with grand visions, but can run off course due to competing priorities and misalignments between teams and executive stakeholders. A stark reminder of this is the sobering statistic that only 54% of AI projects make it from pilot to production — with even fewer delivering their intended ROI.

To navigate a successful transition, organizations must have a clearly defined outcome-centric roadmap before launching AI projects. This includes clearly outlining what GenAI can achieve in terms of use cases and what lies beyond its current reach. For instance, while GenAI can automate routine tasks and provide data-driven insights, it may not replace the need for human judgment and decision-making.

Such a roadmap should highlight milestones, pitfalls to avoid, deadlines and expected outcomes, bringing the team closer to realizing the project’s full potential using GenAI. Ultimately, the success of GenAI integration depends on strategic alignment and collaboration — ensuring communication lines are open so every team member, from the front line to decision-makers, is informed and vested in the mission.

Fulfilling the promise of GenAI

As we peer into the future, GenAI adoption within the accounting space is set to skyrocket this year and beyond. It’s natural for business leaders to feel the pressure to dive headfirst into AI initiatives. However, it’s crucial to discern between merely adding GenAI to the toolkit and harnessing its potential to general value-added outcomes. Despite GenAI’s transformative promise, it’s not simply a plug-and-play proposition.

Success depends on several pillars: robust data governance, the modernization of legacy systems and a strategy that aligns with the organization’s objectives. Keeping these considerations front and center, investment accounting organizations can rely on a sound foundation necessary for a thriving GenAI ecosystem. By doing so, they stand the best chance to gain ROI that not only fits, but also advances their organization’s strategic objectives in the short and long term.

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AI great at simple tasks but struggles with complexity

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Artificial intelligence has indeed led tech-forward firms (including those in this year’s Best Firms for Technology) to be more efficient and productive in both client-facing and administrative tasks, but at the same time professionals have found the technology still struggles with precision and accuracy, which limits its usefulness for complex work. 

On the positive end, firms such as the Texas-based Franklin Alliance reported that adopting AI technology has dramatically increased their capacities as bots take on repetitive manual tasks with an ease and a speed far past more conventional automation setups, allowing accountants to focus more on higher value tasks. 

“What’s been most impressive about the AI tools we’ve explored is their ability to dramatically reduce the time spent on repetitive, manual tasks—things like document summarization, data extraction, and even early-stage tax prep. In the right context, these tools create real efficiency gains and allow our team to shift focus to higher-value advisory work,” said Benjamin Holloway, co-founder of Texas-based Franklin Alliance. 

Robot AI scale balance

madedee – stock.adobe.com

For some, like Illinois-based Mowery & Schoenfeld, these efficiencies have been most impressive on the internal administration side, with AI effectively taking care of the non-accounting work that nonetheless keeps many firms afloat, especially where it concerns meetings. 

“Truly most impressive and a huge time savings for us has been AI’s ability to record and summarize Team meetings. Circulating notes and reducing administrative burden on such activities has freed up much capacity, both for our admin side and for partners or management who are not able to be at every meeting,” said Chris Madden, director of information technology.

Others, like top 10 firm Grant Thornton, emphasized AI’s benefits in client-facing activities and noted that it has been especially meaningful in its risk advisory services at least partially due to the firm’s recently-launched CompliAI tool, designed specifically for this area. 

“The tool uses generative artificial intelligence and was developed using Microsoft technology, including Microsoft Azure OpenAI Service. CompliAI’s ability to quickly analyze vast datasets and identify potential risks has proven invaluable in combining Grant Thornton’s extensive global controls library with generative AI models and features, including AI analysis, ranking and natural language processing capabilities. As a result, our employees can run control design and assessment tasks in minutes, versus days or weeks. This means clients enjoy faster operational insights, which could amount to a new level of efficiency and a path toward transformative growth,” said Mike Kempke, GT’s chief information officer. 

Another positive frequently mentioned, such as by top 25 firm Cherry Bekaert, has been the accessibility and ease of use for many AI solutions even for those without strong technical capacities. Assurance partner Jonathan Kraftchick said this means they did not need to wait long before they began seeing results. 

“The most impressive aspect of AI has been its ability to add value with minimal ramp-up time. Many of the tools we’ve implemented have a low barrier to entry, allowing users to start experimenting and seeing results almost immediately. Whether it’s drafting content, conducting accounting research, summarizing meetings, normalizing data, or detecting anomalies, AI has consistently helped accelerate tasks and enable our teams to focus on higher-risk or higher-value areas,” he said. 

Several firms, such as California-based Navolio & Tallman, also mentioned improvements to broad strategy and ideation, saying it’s been good for enhancing creativity and accelerating the early stages of their work. 

“We’ve still seen value in AI as a jumping off point for ideas and strategy. It’s been helpful for brainstorming, drafting early versions of client communications, and supporting high-level planning conversations,” said IT partner Stephanie Ringrose. 

Inconsistencies, inaccuracies, insufficiency, and insecurity

At the same time, firms over and over again said that while the strength of AI comes in handling simple jobs, it often lacks the precision and consistent accuracy needed for higher value accounting work. While it can certainly generate outputs at an industrial scale, trusting that those outputs are correct is another story for firms like Community CPA and Associates. 

“AI is incredibly useful for certain types of tasks, such as summarization, data extraction, answering simple questions, drafting communications or documentation, brainstorming ideas, or serving as a sounding board. However, we have observed that most AI tools we’ve tried have difficulty with complex tasks that require lots of context, precision, or domain-specific knowledge. Oftentimes in these cases, AI tools will generate responses that are overly confident or wrong and are missing key information due to not being integrated with other systems or software we have,” said CEO Ying Sa. 

Some, like top 25 firm Armanino, noted that these challenges mean that humans need to devote considerable time to ensuring the quality of AI outputs and intervening when the programs go off track. 

“The primary disappointment stems from the occasional inaccuracies or biases inherent in AI-generated outputs, commonly referred to as ‘hallucinations,’ necessitating continuous human oversight to ensure reliability. Addressing these inconsistencies remains an ongoing challenge,” said Jim Nagata, senior director of  cybersecurity and IT operations. 

Top 25 firm Eisner Amper’s chief technology officer Sanjay Desai noted that these issues with accuracy and consistency can be found across AI solutions, though noted that the technology is still quite new and so many things are still in the process of being refined. 

“The lows come from the gap between what’s possible and what works reliably in practice. We still need strong guardrails to define valid inputs and outputs, especially in sensitive use cases. Technologies like retrieval augmented generation (RAG) haven’t yet delivered the accuracy or consistency we need when working with proprietary or domain-specific data. Even in mature areas like audio-to-text transcription, we see issues—particularly with accurately identifying speakers in multi-person meetings, which affects the quality of recaps and follow-up actions. In short, while LLMs have come a long way, making them enterprise-ready still requires ongoing human oversight, thoughtful implementation, and continuous refinement,” said Desai. 

Another issue reported by several firms was what firms like Navolio & Tallman saw as ongoing security risks from AI solutions that limits their ability to apply the technology to more sensitive use cases.  

“The overall attention to security and privacy is still more limited than our industry requires, vendors have not yet aligned their pricing models with the impact their tools make to the business, and vendors still oversell their AI capabilities,” she said. 

Top 25 firm Citrin Cooperman also noted–among other things–that the security of these solutions could stand to improve. 

“The overall attention to security and privacy is still more limited than our industry requires, vendors have not yet aligned their pricing models with the impact their tools make to the business, and vendors still oversell their AI capabilities,” said chief information officer Kimberly Paul. 

Another issue with AI that firms have reported is that solutions today don’t seem to integrate especially well with other programs, which limits the ability of these solutions to work across multiple systems in a single coherent workflow–under such conditions, AI solutions can wind up being siloed from the very areas it is needed the most. 

“We believe one of the biggest gaps in current AI solutions is the inability to integrate into other AI solutions to work collectively across one process or workflow. There are many cases where one AI solution is very good at a specific task, while another is very good at another process or task, but the gap is the ability to integrate those solutions together to solve for an entirety of a process or a workflow,” said Brent McDaniel, chief digital officer for top 25 firm Aprio. 

There is also the matter of data integration, which is needed for AI systems to gain a more holistic understanding of a firm’s needs. Without such integrations, AI becomes more limited in its ability to develop insights and provide actionable guidance, according to Tom Hasard, IT shareholder for New Jersey-based Wilken Gutenplan.  

“We wish AI tools could fully synthesize all of our internal data and unique expertise—beyond the scope of general internet search—and provide detailed, context-specific answers for our team. In the near term, we envision an internal system that taps into our accumulated knowledge to assist staff in resolving complex client problems more quickly. Over time, this capability could be extended to give clients direct, on-demand access to our specialized insights, effectively scaling our expertise and delivering value in a more immediate and personalized way,” he said. 

Beyond just data, lack of integration also limits the ability for AI to address complex problems due to lack of cross-disciplinary expertise, according to Kempke from Grant Thornton. 

“Current AI solutions lack the deep cross-disciplinary expertise to be able to solve complex issues. AI today is optimized for specific fields and tasks but when it comes to solving problems that span multiple disciplines such as Tax, Legal and Finance, the current solutions are not yet capable of providing meaningful advice and guidance. Grant Thornton is already working with various AI partners on this issue and targets to be a very early adopter of the next iteration of AI that addresses this,” he said. 

The AI wishlist

Many firms hoped that the next generation of AI solutions would address these sorts of problems in a way that will allow them to become true assistants capable of taking on complex tasks that require extensive judgment. 

“We have found that AI currently lacks in the ability to replicate human creativity and complex decision-making. While AI excels at data analysis and task automation, it struggles with tasks requiring creativity and nuanced judgment. If AI could offer more sophisticated support in areas such as accounting and audit services, its value and impact in our daily lives would be significantly enhanced,” said Jim Meade, CEO of top 50 firm LBMC. 

Desai, from Eisner Amper, also pointed out that AI isn’t very good at handling bad data, which is a problem considering that AIs run on data. This means that using AI effectively today still requires a great deal of data processing and sanitation to make information useful. If humans did not need to do so much manual cleanup to get data AI-ready, it would help make the technology even more efficient.  

“One of the biggest gaps in AI today is its limited ability to handle bad data. Since data is the foundation of any AI strategy, it’s a challenge that most organizations still face— dealing with messy, inconsistent, or unstructured data. We wish AI could do more to identify, fix, and improve data quality automatically, instead of relying so much on manual cleanup,” said Desai. 

Finally, Avani Desai, CEO of top 50 firm Schellman, said that AI needs to not only be safer, it needs to be visibly so, as trust and confidence in the technology is often key to adoption. 

“I wish that AI could de-risk itself so that clients would be more open to using it and build client trust. If AI could more clearly demonstrate safety and responsible use, adoption would be much easier. Once people understand it’s here to help—and learn to use it responsibly—the fear will fade,” she said. 

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Staten Island’s Malliotakis open to $30K SALT cap

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Representative Nicole Malliotakis said increasing the state and local tax deduction cap to $30,000 from $10,000 would reduce the tax burden of the vast majority of people in her district, indicating support for a proposal that is dividing Republicans.

“Every member needs to advocate for the particular needs of their district. Tripling the deduction to $30,000 will provide much-needed relief for the middle-class and cover 98% of the families in my district,” Malliotakis, a Republican representing Staten Island, New York and a member of the House tax committee, said in a statement to Bloomberg News on Friday.

Malliotakis’ nod of approval for a $30,000 SALT deduction cap comes as Republicans are fighting among themselves about how high to increase a tax break that has the potential to scuttle President Donald Trump’s entire tax package.

House Speaker Mike Johnson on Thursday said the $30,000 write-off limit is one of several options being discussed. That figure was rejected by several other New York Republicans, including Elise Stefanik, Nick LaLota, Mike Lawler and Andrew Garbarino. California’s Young Kim also rebuffed the idea.

Malliotakis’ district has less expensive property values and lower incomes than some of the other lawmakers pushing for a SALT expansion, making it politically viable for her to accept a lower cap than some of her colleagues.

White House Press Secretary Karoline Leavitt suggested on Friday that Trump would not weigh in on an appropriate level for a SALT cap, leaving it to lawmakers to resolve.

“There’s a lot of disagreement on Capitol Hill right now about the SALT tax proposal, and we will let them work it out,” she told reporters.

House Republicans’ narrow majority means that Johnson needs to win the support of nearly all his members to pass Trump’s tax-and-spending package. 

Several of the SALT advocates have said that they are willing to block the bill unless there is a sufficient increase to the deduction. However, most members have not publicly stated how high the deduction must be to win their support.

The debate over SALT has proved to be a particularly thorny fight because it is a political priority for a small but vocal group of Republicans representing swing districts critical to the party maintaining a majority in the 2026 midterm elections. 

Expanding the write-off is an expensive proposition, and Republicans have little fiscal wiggle room as they are sparring over ways — including cuts to Medicaid and levy hikes on millionaires — to offset the cost of the tax-cut package.

The House Ways and Means Committee is slated to consider the tax portion of the bill on Tuesday, including SALT changes.

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GOP eyes endowment tax hike in escalation of Ivy League feud

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House Republicans are considering increasing taxes on university endowments, a significant threat to some of the nation’s wealthiest schools as President Donald Trump seeks to tighten control over American higher education.

The measure is in a draft of the tax package Republicans are weighing, according to people familiar with the matter who spoke on condition of anonymity to share details on the effort. The proposal would create a tiered system of taxation so that wealthy colleges and universities pay more as the size of their endowment grows, the people said. 

Republicans are considering boosting the 1.4% endowment tax currently on the books to rates as high as 14% to 21%, a person familiar with the matter said.

The bill is not finalized, however, the people cautioned, and the draft could change as Republicans negotiate its terms, a complex task as the party looks to renew and expand tax breaks and find ways to pay for them with only a narrow House majority.

Targeting university endowments would be a major escalation of Trump’s fight with elite colleges and universities, which has seen the administration demand changes to school policies that reflect his priorities. 

The current tax on private-school endowments ensnares many of the richest universities, like Harvard University and Yale University, as well as smaller elite institutions such as Amherst College and Williams College. Some of the wealthiest private colleges in the country boast endowments of at least $500,000 per student. 

Harvard, in particular, with a $53.2 billion endowment, has been locked in a high-stakes fight with the Trump administration over its demands for changes at the school. Harvard has sued several U.S. agencies and top officials for freezing billions of dollars in federal funding. Trump has also threatened the school’s tax-exempt status, though experts say revoking that designation would be a lengthy process involving the Internal Revenue Service and the courts.

A new poll by AP-NORC out Friday shows a majority of Americans disagree with Trump’s demands that higher-education institutions make curriculum and cultural changes or face the loss of federal funding for scientific and medical research or have their tax-exempt status threatened.

The poll found that 62% of Americans support maintaining federal research funding, 72% believe “liberals, students and professors can speak freely to at least some extent,” and 84% are concerned at some level about the cost of tuition, an issue Trump has not focused on.

Trump’s 2017 tax package, which Republicans are moving to renew, implemented an endowment levy of 1.4% on net investment income, similar to one that private foundations pay. That levy generated more than $380 million from 56 colleges or universities in 2023 — though it affected just a small fraction of the 1,700 private, nonprofit US schools. 

House Budget Committee Chairman Jodey Arrington floated a long list of possible budget cuts in January that included raising $10 billion over 10 years by raising the endowment tax to 14%.

Discussions over the Republican tax package are reaching a critical stage. Trump is meeting Friday with the chair of the House Ways and Means Committee — the chamber’s tax-writing panel, according to people familiar. 

Trump and Representative Jason Smith will discuss the draft proposal. The committee is expected to release parts of the bill later this afternoon and the rest of the draft on Sunday night or Monday, the people said.

One of the people familiar cast the effort as a bid by Republicans to ensure that universities spend their endowments on their students and not on other initiatives disfavored by conservatives, such as diversity, equity and inclusion efforts or on challenging the Trump administration’s policies.

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