Artificial intelligence has indeed led tech-forward firms (including those in this year's
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.
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.
