Category: 3. Business

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McDermott Will & Schulte is exploring a restructuring that would allow it to sell a stake to private equity groups, a move that would test ethics rules preventing non-lawyers from owning legal firms.

The reorganisation would involve creating a complex structure giving investors a slice of the law firm’s revenues without breaking traditional ownership rules, according to five people with knowledge of the matter.

Such a move, by one of the world’s largest law firms by revenue, could set a precedent for other large players in an industry that — in the US — has been impervious to outside investment.

Zack Coleman, the son of the firm’s chair Ira Coleman, joined McDermott from private equity and venture capital group Odyssey Investment Partners in July and had been sounding out bankers, advisers and private equity executives about the structure, the people said.

However, no agreement had been finalised and no commitments had been made, the people cautioned, with some saying it was at an exploratory stage.

The younger Coleman, who started his career at the investment bank Moelis in 2015 and joined McDermott as senior director of business opportunities, is considering a model in which some of the revenues that lawyers generate will be diverted to buy services from a separate entity in which outside investors could own a stake.

It is similar to models that have already been used to prise open medical practices and accounting firms to private equity ownership in recent years.

“As one of the fastest-growing, most successful modern law firms, we are constantly approached and we always listen to new ideas,” Ira Coleman told the Financial Times. “We’re excited to learn from other leading organisations as we challenge the status quo.”

The Chicago-headquartered firm, formed in a merger of McDermott Will & Emery and Schulte Roth & Zabel this year, has $3bn in revenues, it said in August. That would put it in the top 20 firms globally by revenue.

The structure under consideration would split it into two parts: a business giving advice to clients that is fully owned by its lawyers, and a separate “managed service organisation” that the lawyer-owned firm would buy services from. That could include back-office work, licensing its brand and buying IT services. Investors could buy a stake in the MSO, giving them a revenue stream designed to be attractive to private equity investors.

There has been an explosion of interest in the potential use of the structure in law this year, but no large firm has adopted it and opponents believe it could breach professional ethics rules designed to keep commercial considerations out of the provision of legal advice.

Ethics rules set by the American Bar Association that ban non-lawyer ownership of US firms are being questioned after some states, such as Arizona, have explicitly licensed alternative business structures that can include private equity control. The potentially more controversial MSO structure has so far been used by only a handful of small practices or start-up law firms.

Law is one of the last areas of the professional services sector that has not so far opened up to private equity and some investors sense an opportunity to buy up firms on the cheap by being in the first wave of deals.

Proponents of outside investment say it could give law firms capital to invest in new technology such as artificial intelligence, pay for the expensive hires and help tie rainmakers to them through equity awards in an industry that bans non-compete clauses.

Burford Capital, which pioneered litigation finance, has said it is interested in buying minority stakes in US law firms.

Lawyers at Holland & Knight wrote in a note that MSOs could “assist law firms in innovating and professionalising their operations”, pointing to a Texas ethics ruling in February that indicated support for the structure.

Tokyo, November 12, 2025 – Mitsubishi Power, a power solutions brand of Mitsubishi Heavy Industries, Ltd. (MHI), has received an order for an H-25 gas turbine for Chang Chun Petrochemical Co., Ltd. through Mitsubishi Corporation Machinery, Inc.

Chang Chun Petrochemical is advancing plans to convert the cogeneration facility at its Miaoli Factory, located in Miaoli City in north-central Taiwan, from existing heavy oil and coal-fired boilers to a high-efficiency natural gas-fired system. The H-25 gas turbine will serve as the core of the new gas-fired cogeneration facility, with an output of approximately 30 MW. Operation is scheduled to begin around the end of 2028, supplying electric power and steam for manufacturing processes at the factory. Mitsubishi Power will deliver the main gas turbine and auxiliary equipment, as well as dispatch engineers to support equipment installation and commissioning.

Mitsubishi Power previously received an order for an H-25 gas turbine for the Miaoli Factory in 2023, with operation planned to start around summer 2026. This order marks the second H-25 gas turbine to be installed at the same factory. Through improved plant efficiency and reduced CO₂ emissions, this project will contribute to the realization of Taiwan’s energy policy objectives.

Chang Chun Petrochemical is a core company of Chang Chun Group, a major petrochemical conglomerate in Taiwan. Headquartered in Taipei, Chang Chun Group comprises numerous group companies and factories in Taiwan and around the world.

Since 1984, Mitsubishi Power has supplied major equipment to power generation and cogeneration facilities at Chang Chun Group factories, including nine steam turbines and five boilers. Mitsubishi Power continues to provide after-sales services and various solution offerings. This latest order reflects the strong trust earned through this track record and the long-standing positive relationship between the two companies.

The H-25 gas turbine has demonstrated high reliability supported by extensive long-term operational experience. Since receiving the initial order for the H-25 in 1987, Mitsubishi Power has delivered and operated more than 200 units worldwide. Utilizing the H-25 in cogeneration facilities enables improved plant efficiency and reduced CO₂ emissions compared to conventional boiler-based systems that burn fuel directly.

Mitsubishi Power will continue to actively expand its business in the distributed power generation market, including industrial private power generation and cogeneration systems worldwide, contributing to the strengthening of industrial infrastructure and the reduction of energy-related environmental impacts globally.

The federal government could “explore” using artificial intelligence programs to write sensitive cabinet submissions or business cases, as part of a major initiative to embed AI across the public service, despite concerns about the technology increasing risks of security and data breaches.

The finance minister, Katy Gallagher, also announced on Wednesday the public service would build its own special AI program for government workers, spruiking productivity benefits for rolling out generative programs such as ChatGPT, Copilot and Gemini to departments.

While many public servants said trials of AI had helped their work, others voiced alarm at the poor quality and inaccuracies in AI-generated work, the potential slashing of entry-level jobs, and public fears about automated decision-making in the wake of the robodebt scandal.

“Interviews with Australian government agencies highlighted that women could be disproportionately impacted as they currently comprise most APS administration staff,” stated a government report into a trial of Microsoft Copilot.

Gallagher, also minister for the public service, told a conference the government wanted to see AI “widely taken up across government in every department” in a bid to “take hold of the opportunities that AI presents”.

Gallagher also launched a “whole of government AI plan”, and results from a six-month trial of Microsoft’s Copilot across government.

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The report’s main finding was: “There are clear benefits to the adoption of generative AI but also challenges with adoption and concerns that need to be monitored.”

Surveys found most trial participants were positive, noting management and executives specifically had reported productivity benefits and saving up to an hour each day through AI helping summarise information, write first drafts, or search for information.

Some 69% said it helped them work faster and 61% said it improved the quality of their work.

However, a range of negatives were also reported, including “inaccuracy” of AI output, with 60% of participants saying they had to make “moderate to significant” edits to the AI-generated work. There were also concerns about “Copilot’s potential unpredictability and lack of contextual knowledge”.

The AI plan sets out a goal “that every public servant will have training and access to generative AI tools”. It says the to-be-developed GovAI Chat program will be rolled out widely, expected to be in the first part of 2026, but the government will also develop guidance on public servants using public platforms, such as ChatGPT, Claude and Gemini, to handle government information up to the “official” level of classification.

That included “novel” suggestions from participants to use AI for assessing documents, writing public documents, and “drafting content for business cases and Cabinet submissions”. The report included these ideas under a heading of “opportunities to further explore use cases”.

But the Copilot report also raised security issues with data, with instances of “Copilot inappropriately accessing sensitive information”.

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“Use of Copilot enabled some participants to access documents that they should not have had permission to access. Trial participants raised instances where Copilot surfaced sensitive data that staff had not classified or stored appropriately,” it said.

“This was largely because their organisation had not properly assured the security and storage of some instances of data and information before adopting Copilot. Without the appropriate data infrastructure and governance in place, the use of Copilot may further exacerbate risks of data and security breaches in the APS.”

The report also noted public expectations around managing data.

“In the wake of the Royal Commission into the Robodebt Scheme, public scrutiny around the APS’ use of technology and automation to support decision-making is particularly high,” it stated.

Gallagher pledged ongoing consultation with staff and unions about job changes, stressing that the government “does not view widespread AI adoption across the APS as a way of replacing people”. The Community and Public Sector Union was contacted for comment.

Vivek Puthucode, Microsoft’s general manager of public sector, backed the government’s plan and said the new technology could “help improve service delivery, strengthen policy outcomes and make government more responsive to community needs”.

November 12, 2025

NTT DATA Group Corporation

TOKYO – November 12, 2025 – NTT DATA, a global leader in AI, digital business and technology services, today announced that it has once again been named a Leader by Everest Group in its 5G Engineering Services PEAK Matrix^® Assessment 2025 report (PDF : 551KB). This is the second consecutive time NTT DATA has been recognized as a leader in the assessment This distinction underscores NTT DATA’s consistent growth in market impact, vision and capabilities.

The report evaluated 20 leading providers across the 5G engineering services ecosystem, assessing their vision, capabilities and market impact. Everest Group highlighted NTT DATA’s robust offerings across the value chain, from equipment and network orchestration to engineering solutions and use case deployments, positioning the company as a true provider of end-to-end solutions.

Everest Group praised NTT DATA for its globally distributed delivery model, which offers clients local proximity, access to regional talent and cost efficiency at scale. The recognition also reflects NTT DATA’s ongoing investments in intellectual property (IP) development, strategic partnerships, skills development initiatives and its global network of labs and Centers of Excellence (CoEs).

“NTT DATA has been recognized as a Leader in Everest Group’s 5G Engineering Services PEAK Matrix^® Assessment 2025, supported by its rapid client expansion, combined capabilities post-merger and a robust services portfolio spanning across the 5G value chain. Its strengths include end-to-end solutions from networks to use cases, in-house IP and frameworks and investments in labs and CoEs that enhance service quality. NTT DATA is diversifying its portfolio across private 5G, AI, NTN and monetization-oriented use cases. Clients have appreciated NTT DATA’s telecom heritage, delivery model efficiency and ability to execute with global consistency,” said Nishant Udupa, Practice Director at Everest Group.

The report also highlighted NTT DATA’s ability to capitalize on emerging technologies such as Artificial Intelligence (AI), Non-terrestrial Networks (NTN), Multi-access Edge Computing (MEC), Application Programming Interfaces (APIs) and monetization strategies as key strengths, report also noted the company’s strong presence in private 5G.

As enterprises adopt private 5G to power intelligent operations, digital transformations and new revenue streams, NTT DATA is delivering innovative, outcome-driven solutions.

Across industries such as manufacturing and automotive as well as smart cities, clients are already benefiting from private 5G networks for AI-driven analytics, real-time monitoring and enhanced connectivity. A recent example includes the City of Brownsville, Texas which is using NTT DATA’s Private 5G network to improve city operations and community services.

“Enterprises today expect far more from private 5G than just connectivity, they look to it as a catalyst for AI-based operations such as robotics and Physical AI,” said Shahid Ahmed, Global Head of Edge Services, NTT DATA, Inc. “This recognition from Everest Group underscores our ability to deliver on those expectations. By securing contracts from world-class clients, strategic partnerships and a globally distributed delivery model, we help clients accelerate time to market, enhance performance and unlock the full value of private 5G.”

The PEAK Matrix^® assessment is based on Everest Group’s RFI submissions, briefings, client references and ongoing research in the 5G engineering space. Leaders are distinguished by significant investments in building IP, automation frameworks and CoEs to support full-stack 5G engineering across RAN, core, private 5G and monetization use cases, including with hyperscalers, NEPs and OEMs.

For more information, find the full report here.

About NTT DATA

NTT DATA is a $30+ billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world’s leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. Our consulting and industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 70 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is part of NTT Group, which invests over $3 billion each year in R&D.
Visit us at nttdata.com

About Everest Group

Everest Group is a leading global research firm helping business leaders make confident decisions. Everest Group’s PEAK Matrix^® assessments provide the analysis and insights enterprises need to make critical selection decisions about global services providers, locations, and products and solutions within various market segments. Likewise, providers of these services, products, and solutions, look to the PEAK Matrix^® to gauge and calibrate their offerings against others in the industry or market. Find further details and in-depth content at www.everestgrp.com

Disclaimer

Licensed extracts taken from Everest Group’s PEAK Matrix^® Reports, may be used by licensed third parties for use in their own marketing and promotional activities and collateral. Selected extracts from Everest Group’s PEAK Matrix^® reports do not necessarily provide the full context of our research and analysis. All research and analysis conducted by Everest Group’s analysts and included in Everest Group’s PEAK Matrix^® reports is independent and no organization has paid a fee to be featured or to influence their ranking. To access the complete research and to learn more about our methodology, please visit Everest Group PEAK Matrix^® Reports.

By Bill Peters

One analyst says payment delays across states are likely, even after the government’s reopening

McDonald’s last week said disruptions to SNAP payments could be putting “additional pressure” on consumers.

As the U.S. government shutdown constrains SNAP food assistance for millions of low-income Americans this month, corporate executives have started to get nervous, even as some say they have yet to feel the impact.

Their remarks on recent earnings calls have come amid what has been the longest government shutdown in the nation’s history. A bill to keep the government open until Jan. 30 cleared the Senate on Monday; the House of Representatives could vote on it Wednesday, but it could still take days for services to be fully restored.

The pause on some of the SNAP benefits, along with recent cuts to the program, also arrived after more than three years in which lower-income customers have felt the impact of price increases more acutely. More executive commentary on that issue, and the shutdown’s impact on SNAP, could come next week when Walmart Inc. (WMT) reports results.

At McDonald’s Corp. (MCD), Chief Executive Chris Kempczinski said last week that the disruption in SNAP benefits added to the stress that lower-income consumers, in particular, were facing.

“If you’re not in that segment and you’re higher income, you don’t feel it as acutely – but lower income, for sure, you’re feeling it acutely,” Kempczinski said during the burger chain’s earnings call. “And I think some of what’s going on most recently with SNAP and other things might be additional pressure on that.”

Kristina Lambert, chief growth officer at poultry producer Tyson Foods Inc. (TSN), said on Monday that “we do see consumer spending patterns again changing from nonfood to more food categories.” She added that the company had a “wide range of product offerings at different budget levels.”

The government shutdown began on Oct. 1, and disruptions to SNAP payments – short for Supplemental Nutrition Assistance Program, although the benefits are still commonly referred to as food stamps – began this month, overwhelming food banks and deepening concerns about food security in the nation. A court battle has ensued over how much of that funding people can receive while the government is shut down. The agreement that advanced through the Senate on Monday would keep SNAP payments flowing through next September.

More than 40 million Americans receive SNAP benefits. Robert Moskow, an analyst at TD Cowen, said in a research note last month that SNAP accounts for 12% of spending on food and beverages. The One Big Beautiful Bill Act that President Donald Trump signed into law over the summer included cuts to that program, expanded work requirements and other restrictions.

Still, Moskow said payment delays across states were likely after the government reopened. If people who receive those benefits cut back their grocery bills by 15%, total grocery sales would fall 1.8%, he estimated.

Elsewhere on recent earnings calls, Instacart (CART) said it didn’t expect a big impact from missed or delayed SNAP payments, noting the program accounted for a small part of its business. Hershey Co. (HSY) said it hadn’t seen a big impact yet, and poultry producer Pilgrim’s Pride Corp. (PPC) said it believed the impact would be temporary.

Dole (DOLE) said it had “not seen any trends out of the shutdowns,” while food producer B&G Foods Inc. (BGS)- known for brands like Crisco, Green Giant and Ortega – said last week that it was too early to gauge the impact.

More broadly, some companies have been more cautious with their forecasts overall to account for any possible impact from the shutdown. But for others, the SNAP disruptions proved too difficult to predict.

Grocery Outlet Holding Corp. (GO) said on its earnings call last week that “any potential disruption to sales resulting from delayed or missed SNAP benefits” was not currently factored into the company’s financial outlook. Chief Financial Officer Christopher Miller noted that the percentage of the grocery chain’s sales that came from electronic benefits transfer, or EBT, payments – which are often tied to SNAP benefits – was around 9% last year.

Grocery Outlet CEO Jason Potter said that its independent store operators would be raising money for food banks. He noted that cuts to SNAP benefits in 2023 didn’t affect sales, and that payments of the benefits typically led to immediate spending.

But at Kraft Heinz Co. (KHC), CEO Carlos Abrams-Rivera said last month that consumers’ difficulties would likely extend into next year, as they continue to grapple with higher costs of living.

“With sentiment worsening, costs continuing to rise, the SNAP-related headwinds expected to intensify, we see these pressures as persisting beyond the fourth quarter, leading to a longer path to consumer recovery,” he said.

-Bill Peters

This content was created by MarketWatch, which is operated by Dow Jones & Co. MarketWatch is published independently from Dow Jones Newswires and The Wall Street Journal.

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Oil prices rallied yesterday, with ICE Brent settling more than 1.4% higher on the day, moving back above $65/bbl. The recent strength in the oil market has been driven by refined products, with gasoline and gasoil cracks surging amid concerns about supply. Continued Ukrainian drone attacks on Russian refineries are a concern for the market, particularly middle distillates. Meanwhile, US sanctions on Lukoil and Rosneft, and what they mean for refining assets outside of Russia, is also a concern for broader product markets. While the outlook for oil is bearish, the strength in the refined products market is proving to be a significant obstacle.

There’s still plenty of uncertainty over Russian crude oil flows due to sanctions. Ship tracking data indicate that flows have slowed in recent weeks, with the 4-week average at its lowest level since mid-September. Additionally, data show a significant decline in volumes to China and India. Yet, there’s a significant share of Russian seaborne shipments for which the destination is currently unknown, and which could ultimately end up in India and/or China.

Today, OPEC will release its monthly oil market report, offering its outlook for the remainder of this year and into 2026. The Energy Information Administration will release its latest Short-Term Energy Outlook, which includes forecasts for US oil and gas supplies. And finally, the American Petroleum Institute will publish its weekly US crude and refined product inventory numbers. They were delayed by a public holiday in the US yesterday.

Thank you for the opportunity to speak to you today.¹ It is an honor and a pleasure to be here with you in Singapore, a crossroads for global trade and finance, to discuss the transformational nature of artificial intelligence (AI). Like other central banks around the world, including the Monetary Authority of Singapore, we at the Federal Reserve have been exploring the use of AI in our operations for quite some time as well as considering the implications of AI’s adoption for the financial sector and the broader economy.

At the Fed, we’ve been interested in the effects of AI on the economy and its role in the financial system for decades. Remarkably, in 1997, Governor Susan Phillips delivered a speech noting the use of AI in consumer loan underwriting.² In the past year, no fewer than seven speeches by Fed Governors have had “AI” in the title.

Today I’ll discuss the opportunities presented by AI relevant for central bankers as well as the risks it may pose that policymakers should consider. I will leave you with three main takeaways.

The State of AI Innovation and Deployment

My first main point is that while AI is a big deal that will transform economies, there are a range of outcomes for how it could do so.

AI—algorithms that mimic human thought, communication, and choices—has been with us for decades, but AI entered a new era with the launch of ChatGPT in late 2022. Generative AI (GenAI) captured our imagination with convincing conversations in which it is possible to go deep on a wide range of topics. Earlier forms of AI were often the bailiwick of digitally native companies, but GenAI is spreading rapidly through the economy. As of 2024, three in four large companies were using GenAI, though some report it has yet to improve their bottom line.³ Smaller companies have been slower to adopt GenAI, with adoption rates reported in the high single digits, albeit with a high degree of heterogeneity among sectors. Also, one could surmise, based on other surveys of individuals, that work-related use is more widespread among employees than their CEOs realize.⁴

A recent survey by the Federal Reserve Bank of New York showed that firms plan to retrain their workforces to take advantage of AI to enhance productivity, with widespread layoffs limited.⁵ But survey respondents also report that AI has led firms to scale back hiring, a development that may be contributing to the recent low levels of job creation in the U.S. economy, a concern for many workers and particularly for newer entrants to the job market.

Taking a step back, as I have noted in the past, I see two basic scenarios for how AI can transform the economy.⁶ In the first scenario, there is incremental adoption of GenAI that augments existing tasks and jobs. In the second scenario, a revolution occurs. GenAI transforms the nature of work and leisure, boosting the efficiency of research and development, remaking industries, and creating firms with new—perhaps radically new—business models.

Right now, it is difficult to predict which scenario (or perhaps one or more intermediate scenarios) will come to pass.

We can already see incremental change as GenAI is increasingly integrated with standard workplace software. With its natural language interface, GenAI is inherently user friendly, so few workers need special skills or unusually onerous training to use it. At the same time, some start-ups have a more revolutionary flavor because they are centered on AI from the outset. One indicator of how the labor market is evolving toward deeper integration with AI is the skills mentioned in job postings. While overall the share of job listings that mention AI-related skills is small—about 5 percent—in the information sector, it is about 20 percent. The financial sector, where firms are always looking for a technological edge, is not far behind, with 1 in 10 job postings mentioning AI.⁷ So we can see that the skills needed in some key sectors are already changing. The speed of that change is likely to increase. If the AI changes happen gradually, workers and firms will have time to adjust, but if they happen rapidly, there may be significant dislocations in the short term.

A massive wave of data center investment has begun, pointing to signs of confidence among leading AI companies that the use of AI at scale throughout the economy is just around the corner. If they’re right and AI is useful enough to keep what is currently projected to be $3 trillion of new data center capacity utilized effectively, we can expect significant changes in economies. Investment in capital generally raises labor productivity and offers the potential for higher output growth without pressure on inflation over the longer term. As I have discussed in previous remarks, if these changes are significant, they can also affect the conduct of monetary policy.⁸ Of course, it may be the case instead that investment exceeds short-term demand, in which case there may be losses and adjustments to the AI sector.

AI and the Financial Sector

The second key point I would like to make is that the financial sector is adopting AI quickly, and while there are many benefits to this adoption, the risks will need to be managed carefully.

So far, AI adoption in the financial sector appears to be most concentrated in areas that can enhance operational efficiency, including applications that involve text analysis, classification, and information search inside the firm, as well as customer-facing functions. These incremental improvements to common business functions are a key reason to be hopeful about AI raising labor productivity in that sector.

At the same time, there is significant investment in experimentation with AI for core functions for financial services. Data-driven financial-sector-specific tasks, including credit decision support, fraud detection, and trading are using AI-specific tools. Ensuring that AI is used appropriately for these functions faces appreciable challenges.

First, the amount of organizational change needed by financial services firms to utilize GenAI may be substantial. History suggests progress may be slow. Adoption of machine learning, an AI technology that preceded GenAI, was concentrated in firms that were highly digitized from their founding—and even in those cases, adoption was a long process.⁹ Fintech firms organized to exploit AI from their founding can play a key role in driving efficiency forward in the sector, providing services to the incumbent firms.¹⁰ But productivity may even decrease in the short term, as heavy investments in business-process improvements take time to play out to productivity gains.

A second challenge is the practical constraints of rushing into AI for core business activities in the financial sector, as firms need to ensure that the resulting processes and outcomes are consistent with relevant laws and appropriate risk management. Large institutions are exploring the use of GenAI, including agentic AI, in their financial models—but doing so requires care. To successfully leverage the potential of GenAI on a sustainable basis, decisions based on those models must be well controlled, numerically and legally precise, explainable, and replicable. AI developers still struggle to some extent with all of those criteria. We need to reduce the risk that AI reinforces biases in consumer lending. And we also need to guard against the risks that could result from the use of AI in financial markets. For example, profit maximization by AI-powered trading algorithms may result in tacit collusion, market manipulation, or trading strategies that result in significant market volatility or even systemic risk.¹¹

We will need innovation that is responsive to these risks to see additional advances in the use of AI for a broad array of core financial services functions.

AI and Central Banking

A third and final point I would like to leave you with is that central banks, including the Fed, need to keep up with AI by increasing our speed of adoption for our own operations.

The nature of central banking work is inherently careful, considered, and measured when evaluating anything new. This is particularly true for any new technologies.¹² But it seems clear already that the many advantages offered by AI could assist central banks in at least some of their operations, and the speed at which this technology is moving makes it appropriate to proactively engage in using AI for our own operations. That is why the Federal Reserve is using AI, where appropriate, to increase staff efficiency and effectiveness. My view is that GenAI is a transformative technology that central banks need to remain engaged with to ensure an ability to execute as technology evolves.

As we push ahead on efficiency gains, it is important that we leverage the right tools for the task at hand, recognizing that GenAI is not always the best choice. Some of the challenges that we face can be addressed by robotic process automation or traditional AI methods. These are the same kinds of questions that every business and organization considering AI should be weighing.

At the Federal Reserve, we have focused on ensuring we can leverage AI capabilities by establishing an AI program and governance framework for the use of AI technologies.¹³ We are taking an enterprise-wide approach of learning-by-doing and broadly adopting high-value uses of GenAI, such as writing, coding, and research activities. We have taken a “hands on keys” approach to having staff engage with it. We are identifying the business processes that can be improved and transformed with the technology.

One internal application of GenAI I am particularly excited about that helps us achieve all these goals is technology modernization. We are applying GenAI-enabled tools within clear guardrails to translate legacy code, generate unit tests, and accelerate cloud migration. So far, the result of this usage is faster delivery, improved quality, and enhanced developer experience. And it will likely mean better outcomes in support of the American people.

Given AI’s current and prospective role in economic activity, we are devoting the necessary resources to understanding it, including by analyzing not only AI’s economic and financial implications, but also exploring how AI can enhance our financial stability work, strengthen supervisory and regulatory capabilities, and ensure the smooth functioning of our payment systems.

These are just some of the ways that the Fed, like other organizations, is using AI to make us more productive and capable. These efforts may also help us understand the effect of AI on the economy, the banking system, and the payment system. That task will be a major job for central banks in the years ahead. AI has the potential to fundamentally change the economy and society. And as central bankers, we need to keep up.

Thank you.

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1. The views expressed here are my own and are not necessarily those of my colleagues on the Federal Reserve Board or the Federal Open Market Committee. Return to text

2. See Susan M. Phillips (1997), “Risk Management,” speech delivered at the Asset/Liability and Treasury Management Conference of the Bank Administration Institute, Chicago, November 4. Return to text

3. See Aditya Challapally, Chris Pease, Ramesh Raskar, and Pradyumna Chari (2025), “The GenAI Divide: State of AI in Business 2025,” MIT NANDA Report, July, https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf. There are a range additional surveys with various results; see, for example, Richard Horton, Jan Michalski, Stacey Winters, Douglas Gunn, and Jennifer Holland (2025), “AI ROI: The Paradox of Rising Investment and Elusive Returns,” October 22, https://www.deloitte.com/uk/en/issues/generative-ai/ai-roi-the-paradox-of-rising-investment-and-elusive-returns.html. Return to text

4. On adoption by large firms, see McKinsey & Company (2025), “The State of AI: Agents, Innovation, and Transformation,” November 5, https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai. On adoption by smaller firms, see the Business Trends and Outlook Survey from the U.S. Census Bureau, which is available on its website at https://www.census.gov/programs-surveys/btos.html. On adoption by individuals, see Alexander Bick, Adam Blandin, and David J. Deming (2024), “The Rapid Adoption of Generative AI,” Working Paper Series 32966 (Cambridge, Mass.: National Bureau of Economic Research, September; revised February 2025). Return to text

5. See Ben Hyman, Jaison R. Abel, Natalia Emanuel, Nick Montalbano, and Richard Deitz (2025), “Are Businesses Scaling Back Hiring Due to AI?” Federal Reserve Bank of New York, Liberty Street Economics (blog), September 4. Other surveys have shown similar results; see, for example, Jeremy Korst, Stefano Puntoni, and Prasanna Tambe (2025), “Accountable Acceleration: Gen AI Fast-Tracks Into the Enterprise (PDF),” 2025 Report, October 29. Return to text

6. See Michael S. Barr (2025), “Artificial Intelligence: Hypothetical Scenarios for the Future,” speech delivered at the Council on Foreign Relations, New York, February 18. Return to text

7. Job-posting statistics are based on the classification by Lightcast and are calculated using the methodology developed in Daron Acemoglu, David Autor, Jonathon Hazell, and Pascual Restrepo, “Artificial Intelligence and Jobs: Evidence from Online Vacancies,” Journal of Labor Economics, vol. 40 (April), pp. S293–340. Data are available by subscription from Lightcast at https://lightcast.io. Return to text

8. See Michael S. Barr (2025), “Artificial Intelligence and the Labor Market: A Scenario-Based Approach,” speech delivered at the Reykjavík Economic Conference 2025, Central Bank of Iceland, Reykjavík, Iceland, May 9. Return to text

9. See Timothy Bresnahan (2024), “What Innovation Paths for AI to Become a GPT?” Journal of Economics & Management Strategy, vol. 33 (Summer), pp. 305–16. Return to text

10. See Michael S. Barr (2025), “AI, Fintechs, and Banks,” speech delivered at the Federal Reserve Bank of San Francisco, San Francisco, April 4. Return to text

11. The most recent Financial Stability Report is available on the Federal Reserve Board’s website at https://www.federalreserve.gov/publications/files/financial-stability-report-20251107.pdf. Return to text

12. A report from the Bank of International Settlements notes that central bankers have the prospect of improving data quality, enhancing operations, and improving decisionmaking with the use of AI and provides a framework for considering questions of governance and risk management when doing so; see Bank of International Settlements (2025), “Governance of AI Adoption at Central Banks (PDF),” January. Return to text

13. See the “Board of Governors of the Federal Reserve System Compliance Plan for OMB Memorandum M-25-21,” which is available on the Federal Reserve’s website at https://www.federalreserve.gov/publications/compliance-plan-for-OMB-memorandum-m-25-21.htm. Return to text