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Generative AI accelerating product development, increasing competitive pressure says solutions providers

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The generative AI revolution, now several years old, has materially accelerated the software development cycle, allowing solutions providers to design, build and release new products faster than before. But this extra efficiency has not served to reduce stress but rather increase it among leaders as the widespread use of these tools has turbocharged already intense competitive pressures. 

Coding 

Beyond text generation, coding support has been touted as one of the primary use cases for generative AI, with several studies this year finding that software engineers have been more productive (though not necessarily everyone). Overall, there was remarkable uniformity among leaders in just how much faster generative AI has made projects, as everyone when asked this question provided a figure between 10-20%. 

However, there was also remarkable uniformity in saying that code generation capabilities were not the primary factor in why generative AI has sped things up. Indeed, there was a general recognition that generative AI, left to its own devices, does not produce quality code. Chris Szymansky, chief technology officer of accounting and auditing platform Fieldguide, spoke for many when discussing the quality of AI coding. 

“Certain activities are not as useful yet. Like writing high quality code itself, like the code a senior engineer would write, those tools are not helping with that yet,” said Szymansky. 

Rather than writing the code itself, generative AI has instead been an invaluable tool for helping engineers review, analyze and optimize their own code, identifying root causes of bugs and errors, testing and evaluating their work, and making suggestions when they’re stuck, all of which are as important as the coding itself. 

“I think this drives speed into the development process, but also more importantly for us, it drives long term quality improvements into our products as well in terms of how they perform at scale,” said Joel Hron, chief technology officer for Thomson Reuters. 

This, ultimately, has facilitated the prototyping process. Coming up with new products and quickly making a prototype has become much easier, as has making iterative improvements on it, according to Dan Miller, executive vice president of Sage’s ERP division.

“The greatest benefit of generative AI accelerating our product development is the rapid prototyping of new feature sets to ultimately drive the value for our users. Sage customers have always recognized the tremendous value our platforms have been able to deliver relative to cost, and this product acceleration only supports our ability to deliver the best value. By saving development times, we can gain more and more efficiencies to help our customers grow their business by delivering greater value,” he said. 

Non-Coding 

However, product development is more than just code. A project is built on not just the technical aspects but myriad other factors like design, user experience, market research and overall business strategy. Generative AI has had a huge impact in these areas, serving to accelerate the overall product development cycle. Leaders cited uses like summarizing progress meetings, drafting reports, and tracking key metrics and milestones. Enrico Palmerino, CEO of accounting automation solutions provider Botkeeper, spoke for many in saying it has also been valuable for analysis and research in seconds that normally would take days. These insights are then employed to improve product design. 

“If we have a question and we can’t understand what is going on with our users [it can help]. I just did this in an executive meeting recently: [I asked] what is the biggest problem people are experiencing? And before, it used to be we needed someone who would look at all the tickets coming in. Now you can just ask the AI and it will be like ‘16% is this, 35% is that,'” said Palmerino. 

Sage’s Miller, also mentioned analytics as an aid to development, adding that this has greatly facilitated not just prototyping for current products but ideas for future releases as well. 

“From a non-code perspective, we can pipeline product development more efficiently using data from user metrics, such as product features that our users are leveraging more than anticipated and what new features they might benefit from in future releases. In other words, generative AI is facilitating market research for us in the most efficient way possible and uncovering user patterns at a rapid pace,” said Miller. 

Another major non-code aspect is content development. Brian Diffin, chief technology officer for Wolters Kluwer, noted that their own products have a lot of content which needs to be drafted, edited and curated. Generative AI has significantly sped up this process, allowing them to draft materials much faster. 

“Some of our products—let’s say Research for example, where we have editorial people who are finding new legislative content and then curating that content and summarizing it into more digestible language and concepts for our research products—the editors are using generative AI to help them do that and it is saving a lot of time,” said Diffin. 

Jayme Fishman, chief strategy and product officer for Avalara, made a similar point, saying that content generation has been vital not only for documenting use cases “because for everything you build you need to document it,” but for content generation as well. 

“We don’t have a product that does not rely on content, because we are a compliance solution and everything we do is governed by some law somewhere that needs to be translated to business logic, and using it to help in that definitely helps accelerate our ability to do more with less,” he said. 

Time and money

While a project may require fewer labor hours than it did before, this has not necessarily translated into lower development costs. Diffin, from Wolters Kluwer, noted that while projects require fewer labor hours than before, there are still technology costs to consider. For one, generative AI is very compute-intensive, which can lead to higher data fees from cloud providers. It is a challenge, he said, to balance functionality with cost. 

“We’re doing a lot of experiments with this, there’s so many approaches on how you implement a generative AI based piece of functionality in the software—we’re evaluating not just the large language models but what their capacities would provide and what is going to be the cost of that feature when we go into production. … We’re seeing some companies right now develop small language models to lower the cost of compute, so we’re doing a lot of experimentation now on what is the best way to release this from a feature perspective and how we can optimize cost,” said Diffin. 

Hron, from Thomson Reuters, though, felt that costs, whether in terms of labor hours or technology infrastructure, is beside the point. The benefits of increased efficiency and capacity outweigh these kinds of considerations, and vendors are usually more focused on the product’s quality than the speed at which it is brought to market. 

“These things are making it easier than they were before to provide more flexibility on how we deploy our resources across teams, and how we bring people to bear on new problems. I’d emphasize quality in terms of applications—not just shipping things faster but better. I think for us that is as important or even more important than speed,” said Hron. 

And at any rate, even if a project does take fewer labor hours, no one is using the extra time to take a vacation. Everyone, instead, puts that saved time into more work, whether that’s adding features and refining the quality of the existing project or starting up a new one entirely. 

“We’re a startup company so anything we can do to move faster and be laser focused on our customers, that is where we put our power into. If we can do that X percent times more, that is huge. So that is where we’re putting the time: more R&D, more product, shipping more product, faster dev cycles, happier customers,” said Fieldguide’s Szymansky. 

So even if AI is saving people labor, it seems people are working more than ever. Botkeeper’s Palmerino noted that while AI has saved tons of hours in the product development cycle, people—including himself—have even less free time than before. 

“What you will see is people going beyond, because they are trying to benchmark the new output expectations. Inherently, we tend to do more. … I’m not seeing work hours come down. They all said AI would mean we work shorter days, but you actually work longer days,” said Palmerino. 

Competitive pressures

A large factor in this situation is that generative AI has greatly improved efficiency at many companies, including the competition. Consequently, competitive pressures have increased significantly since the introduction of generative AI, as everyone with these tools is developing products at an accelerated rate to the point where this pace is more or less the new baseline. Hron, from Thomson Reuters, said that as much as he’d like to be sitting on a beach sipping mai tais, the current market environment just doesn’t allow that. 

“The interesting dynamic is the degree to which this technology has moved everyone forward in terms of pace, not just Thomson Reuters. The entire market can move faster, and our customers can move faster, and their appetite for more has grown as well. … If anything, I would say it is pushing us to do more, even if we can do each bit a little faster than we were before,” said Hron. 

Avalara’s Fishman noted that this space has always had an “innovate or die” dynamic so the types of competitive pressures they’re facing are nothing new, but what is new is their sheer scope and scale. At this point, pretty much everyone is using AI tools, so adopting the technology can seem less about seeking advantage and more about avoiding disadvantage. 

“AI really has the promise of making your solutions better, strong, faster. But that is the worst kept secret in the world. You can’t turn on the news or read an article in Accounting Today without reading about AI. Everyone’s awareness creates a dynamic where a choice as to whether or not to use AI is an illusion: there is no choice. You have to, or you will become obsolete,” he said. 

Diffin, from Wolters Kluwer, pointed out that beyond incumbent competitors becoming more efficient, AI has also made it easier to launch a startup. With this technology lowering the barrier for entry in this market, there has been an explosion of niche products released at “almost a hypersonic speed because things are now easy to develop.” 

“Someone, just a few programmers, can go to Azure, orchestrate a bunch of services, including OpenAI services, with just a bit of business logic and make a solution they can sell into the market,” Diffin said. Though, this may not be all bad. “We’re seeing evidence of that happening quite a bit. And of course we look at those startups as potential acquisition candidates.” 

What’s a product anyway?

Botkeeper’s Palmerino noted, though, that as AI becomes increasingly intertwined with software development, the concept of a product release might start to lose its meaning. Right now, AI is still highly focused on specific applications, even if one interacts with these AI using natural language. In the future, he envisioned, AI might become advanced enough that it won’t necessarily need a discrete feature to do what users ask, it will just do it. In such a world respect, talking about a development cycle might not be as relevant to the experience of solutions providers as it is now.

Today, for example, someone might ask an AI built for insights how they can make their company more efficient, and the AI will say it found 12 financial institutions across four clients, all of which are capable of connection. Later, it might not only find those 12 financial institutions, it will prompt the user if they want the AI to connect to them now, and if so will just do it, all without a specific feature or functionality built in. It will just know how to operate the software.

“That is where AI is heading: to do full stack task completion for you, which will make it hard to understand releases. We do true releases where there is an update to the version or a very segmented or defined functionality change, but with the open-endedness of AI and the ability to do full completion of tasks, pieces will mostly be behind the scenes, I don’t think you’ll see or hear about them and, in many cases,” he said.

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Accounting

IAASB tweaks standards on working with outside experts

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The International Auditing and Assurance Standards Board is proposing to tailor some of its standards to align with recent additions to the International Ethics Standards Board for Accountants’ International Code of Ethics for Professional Accountants when it comes to using the work of an external expert.

The proposed narrow-scope amendments involve minor changes to several IAASB standards:

  • ISA 620, Using the Work of an Auditor’s Expert;
  • ISRE 2400 (Revised), Engagements to Review Historical Financial Statements;
  • ISAE 3000 (Revised), Assurance Engagements Other than Audits or Reviews of Historical Financial Information;
  • ISRS 4400 (Revised), Agreed-upon Procedures Engagements.

The IAASB is asking for comments via a digital response template that can be found on the IAASB website by July 24, 2025.

In December 2023, the IESBA approved an exposure draft for proposed revisions to the IESBA’s Code of Ethics related to using the work of an external expert. The proposals included three new sections to the Code of Ethics, including provisions for professional accountants in public practice; professional accountants in business and sustainability assurance practitioners. The IESBA approved the provisions on using the work of an external expert at its December 2024 meeting, establishing an ethical framework to guide accountants and sustainability assurance practitioners in evaluating whether an external expert has the necessary competence, capabilities and objectivity to use their work, as well as provisions on applying the Ethics Code’s conceptual framework when using the work of an outside expert.  

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Accounting

Tariffs will hit low-income Americans harder than richest, report says

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President Donald Trump’s tariffs would effectively cause a tax increase for low-income families that is more than three times higher than what wealthier Americans would pay, according to an analysis from the Institute on Taxation and Economic Policy.

The report from the progressive think tank outlined the outcomes for Americans of all backgrounds if the tariffs currently in effect remain in place next year. Those making $28,600 or less would have to spend 6.2% more of their income due to higher prices, while the richest Americans with income of at least $914,900 are expected to spend 1.7% more. Middle-income families making between $55,100 and $94,100 would pay 5% more of their earnings. 

Trump has imposed the steepest U.S. duties in more than a century, including a 145% tariff on many products from China, a 25% rate on most imports from Canada and Mexico, duties on some sectors such as steel and aluminum and a baseline 10% tariff on the rest of the country’s trading partners. He suspended higher, customized tariffs on most countries for 90 days.

Economists have warned that costs from tariff increases would ultimately be passed on to U.S. consumers. And while prices will rise for everyone, lower-income families are expected to lose a larger portion of their budgets because they tend to spend more of their earnings on goods, including food and other necessities, compared to wealthier individuals.

Food prices could rise by 2.6% in the short run due to tariffs, according to an estimate from the Yale Budget Lab. Among all goods impacted, consumers are expected to face the steepest price hikes for clothing at 64%, the report showed. 

The Yale Budget Lab projected that the tariffs would result in a loss of $4,700 a year on average for American households.

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Accounting

At Schellman, AI reshapes a firm’s staffing needs

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Artificial intelligence is just getting started in the accounting world, but it is already helping firms like technology specialist Schellman do more things with fewer people, allowing the firm to scale back hiring and reduce headcount in certain areas through natural attrition. 

Schellman CEO Avani Desai said there have definitely been some shifts in headcount at the Top 100 Firm, though she stressed it was nothing dramatic, as it mostly reflects natural attrition combined with being more selective with hiring. She said the firm has already made an internal decision to not reduce headcount in force, as that just indicates they didn’t hire properly the first time. 

“It hasn’t been about reducing roles but evolving how we do work, so there wasn’t one specific date where we ‘started’ the reduction. It’s been more case by case. We’ve held back on refilling certain roles when we saw opportunities to streamline, especially with the use of new technologies like AI,” she said. 

One area where the firm has found such opportunities has been in the testing of certain cybersecurity controls, particularly within the SOC framework. The firm examined all the controls it tests on the service side and asked which ones require human judgment or deep expertise. The answer was a lot of them. But for the ones that don’t, AI algorithms have been able to significantly lighten the load. 

“[If] we don’t refill a role, it’s because the need actually has changed, or the process has improved so significantly [that] the workload is lighter or shared across the smarter system. So that’s what’s happening,” said Desai. 

Outside of client services like SOC control testing and reporting, the firm has found efficiencies in administrative functions as well as certain internal operational processes. On the latter point, Desai noted that Schellman’s engineers, including the chief information officer, have been using AI to help develop code, which means they’re not relying as much on outside expertise on the internal service delivery side of things. There are still people in the development process, but their roles are changing: They’re writing less code, and doing more reviewing of code before it gets pushed into production, saving time and creating efficiencies. 

“The best way for me to say this is, to us, this has been intentional. We paused hiring in a few areas where we saw overlaps, where technology was really working,” said Desai.

However, even in an age awash with AI, Schellman acknowledges there are certain jobs that need a human, at least for now. For example, the firm does assessments for the FedRAMP program, which is needed for cloud service providers to contract with certain government agencies. These assessments, even in the most stable of times, can be long and complex engagements, to say nothing of the less predictable nature of the current government. As such, it does not make as much sense to reduce human staff in this area. 

“The way it is right now for us to do FedRAMP engagements, it’s a very manual process. There’s a lot of back and forth between us and a third party, the government, and we don’t see a lot of overall application or technology help… We’re in the federal space and you can imagine, [with] what’s going on right now, there’s a big changing market condition for clients and their pricing pressure,” said Desai. 

As Schellman reduces staff levels in some places, it is increasing them in others. Desai said the firm is actively hiring in certain areas. In particular, it’s adding staff in technical cybersecurity (e.g., penetration testers), the aforementioned FedRAMP engagements, AI assessment (in line with recently becoming an ISO 42001 certification body) and in some client-facing roles like marketing and sales. 

“So, to me, this isn’t about doing more with less … It’s about doing more of the right things with the right people,” said Desai. 

While these moves have resulted in savings, she said that was never really the point, so whatever the firm has saved from staffing efficiencies it has reinvested in its tech stack to build its service line further. When asked for an example, she said the firm would like to focus more on penetration testing by building a SaaS tool for it. While Schellman has a proof of concept developed, she noted it would take a lot of money and time to deploy a full solution — both of which the firm now has more of because of its efficiency moves. 

“What is the ‘why’ behind these decisions? The ‘why’ for us isn’t what I think you traditionally see, which is ‘We need to get profitability high. We need to have less people do more things.’ That’s not what it is like,” said Desai. “I want to be able to focus on quality. And the only way I think I can focus on quality is if my people are not focusing on things that don’t matter … I feel like I’m in a much better place because the smart people that I’ve hired are working on the riskiest and most complicated things.”

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