A growing number of organizations are appointing chief AI officers and seeing an average of 10% greater return on investment in AI spending and 24% greater innovation compared to their peers — but most organizations remain stuck in pilot mode and struggle to scale AI initiatives more broadly. Those are among the findings of a new study by the IBM Institute for Business Value with the Dubai Future Foundation and Oxford Economics. The survey reveals that organizations with CAIOs see positive returns but face strategic, technical and organizational obstacles to optimizing the role’s value. Improved metrics, teamwork and cultural modifications are needed. About one-quarter of the organizations surveyed reported having a CAIO, up from 11% in 2023. Two-thirds of respondents expect most organizations will have a CAIO within the next two years. Organizations that have appointed CAIOs say the primary drivers are to accelerate AI strategy and adoption. AI spending increased 62% as a share of information technology budgets over the past three years, and CEOs expect 31% annual increases through 2027. Nevertheless, 60% of organizations are still investing primarily in pilots, and only 25% of AI initiatives have delivered the expected ROI since 2023. The report delineates a clear shift in operating models as AI projects scale. Initial efforts tend to be decentralized, but advanced organizations shift to centralized hub‑and‑spoke models. That approach moves twice as many pilots into production compared to a decentralized structure and realizes 36% higher ROI. That’s because centralization provides clearer ownership, according to Mohammed Al Mudharreb, CAIO of Dubai’s Road and Transport Authority. The study found that the factors that separate high-performing CAIOs from their peers are measurement, teamwork and authority. Successful projects address high-impact areas like revenue growth, profit, customer satisfaction and employee productivity. The most effective teams combine AI specialists, machine‑learning engineers and business strategists, with AI experts embedded across functions to avoid the emergence of shadow AI operations.
‘White-space’ opportunity for buying or originating fintech loans is estimated at US$280 billion over the next five years; banks are partnering with private-credit funds to originate and distribute loans off balance sheet expanding fee-based revenue while offloading credit risk
Fintech founders are facing a significant challenge as giant private-credit funds are lining up with term sheets so large they would have broken cap tables a year ago. A joint study from BCG and QED Investors puts the “white-space” opportunity at US$280 billion over the next five years: capital earmarked for buying or originating fintech loans. Private credit has grown nearly ten-fold since 2010 to roughly US$1.5 trillion of assets under management (AUM) in 2024, and consultants expect it to hit US$3.5 trillion by 2028, a compound annual growth rate north of 19%. Big banks are partnering with private-credit titans to originate and distribute loans off balance sheet, such as Citi x Apollo and Citi x Carlyle. The model is simple: banks keep the origination and servicing fees, funds take the credit risk, and regulators get comfort that risky assets live outside the deposit-backed system. Private-credit exuberance has pushed unitranche pricing down, but funds can lever those assets 1.5-2x and still net solid returns, making it an attractive asset class. However, many CEOs prefer a 14 percent cost of capital that preserves ownership over a 35% down round in an unforgiving venture market. The venture capital power-law reset threatens that maths, as private-credit recycling threatens that maths. Funds are aggressively scouring growth markets where banking pull-back is most acute, such as India, Southeast Asia, and Latin America, where dollar funding married to local-currency wallets is a tantalizing carry trade, provided FX hedges hold. However, there are several risks to consider, including credit deterioration, funding squeeze, regulatory shock, and FX blow-ups in emerging markets. To mitigate these risks, investors should monitor the spread compression Pace, Reg-Tech Build-Out, Private-Fund Reporting, Basel Endgame Final Rule, and Structured-Credit Revival.
New hires at PwC to be like managers, reviewing and supervising AI perform routine, repetitive audit tasks like data gathering and processing and focusing on “more advanced and value-added work”
New hires at PwC will be doing the roles that managers are doing within three years, because they will be overseeing AI performing routine, repetitive audit tasks, Jenn Kosar, AI assurance leader at PwC, told. “People are going to walk in the door almost instantaneously becoming reviewers and supervisors,” she said. PwC, one of the “Big Four” accounting and consulting firms, is deploying AI to take over tasks like data gathering and processing. This is leaving entry-level employees free to focus on “more advanced and value-added work,” Kosar said. AI has got PwC rethinking how it trains junior employees. Kosar said the technology meant PwC was changing how it trains its junior employees, adding that entry-level workers have to know how to review and supervise the AI’s work. Where the Big Four firm once focused on teaching young employees to execute audit tasks, it’s now focused on more “back to basics” training and the fundamentals of what an audit should do for a client, she said. There’s more time in the programming to teach junior employees deeper critical thinking, negotiation, and “professional skepticism,” she said, adding they previously would have been trained in these soft skills later in their careers. PwC’s “assurance for AI” product, which works with clients on ensuring the AI they use is operated responsibly, has only existed since June. For all its potential, AI is challenging the Big Four’s long-held business models, organizational structures, and day-to-day roles. Firms are having to consider outcomes-based pricing models based on results instead of billing clients by the hour. Alan Paton, a former partner in PwC UK’s financial services division who’s now CEO of a Google Cloud solutions consultancy, previously told that automation could increasingly cause clients to question why they should pay consultants big money when they can get answers “instantaneously from a tool.” Partners and managers will have to adapt to new types of requests from clients, who are asking how AI can fully take over certain business tasks, she added. Kosar acknowledged there was fear AI would reduce critical thinking capabilities and replace jobs, but she said she thought AI would lead to better-informed, faster-developing professionals.
Ai2 releases an open AI model that allows robots to ‘plan’ movements in 3D space
AI research institute Ai2, the Allen Institute for AI, released MolmoAct 7B, a breakthrough open embodied AI model that brings intelligence to robotics by allowing them to “think” through actions before performing. Ai2 said MolmoAct is the first in a new category of AI models the company is calling an action reasoning model, or ARM, that interprets high-level natural language and then reasons through a plan of physical actions to carry them out in the real world. Unlike current robotics models on the market that operate as vision language action foundation models, ARMs break down instructions into a series of waypoints and actions that take into account what the model can see. “As soon as it sees the world, it lifts the entire world into 3D and then it draws a trajectory to define how its arms are going to move in that space,” Ranjay Krishna, the computer vision team lead at Ai2. “So, it plans for the future. And after it’s done planning, only then does it start taking actions and moving its joints.” Unlike many current models on the market, MolmoAct 7B was trained on a curated open dataset of around 12,000 “robot episodes” from real-world environments, such as kitchens and bedrooms. These demonstrations were used to map goal-oriented actions — such as arranging pillows and putting away laundry. Krishna explained that MolmoAct overcomes this industry transparency challenge by being fully open, providing its code, weights and evaluations, thus resolving the “black box problem.” It is both trained on open data and its inner workings are transparent and openly available. To add even more control, users can preview the model’s planned movements before execution, with its intended motion trajectories overlaid on camera images. These plans can be modified using natural language or by sketching corrections on a touchscreen. This provides a fine-grained method for developers or robotics technicians to control robots in different settings such as homes, hospitals and warehouses. In the SimPLER benchmark, the model achieved state-of-the-art task success rates of 72.1%, beating models from Physical Intelligence, Google LLC, Microsoft Corp. and Nvidia.
Regulatory pullback heightens fintech risk: uncertain fee caps and federal delays force fragmented state rules, bilateral data deals, rising integration costs, and unpredictable bank data access
Last week, a federal court struck down the Federal Reserve’s Regulation II debit interchange fee cap, upending a framework that defined payment economics for more than a decade. The Consumer Financial Protection Bureau (CFPB) paused its open banking rule under Section 1033 of Dodd-Frank and delayed small business lending data collection under Section 1071, both responding to litigation. The Supreme Court’s Loper Bright decision eliminated Chevron deference, sharply curtailing agencies’ ability to interpret ambiguous laws. From a distance, this looks like a deregulatory moment. For many fintech business models, it creates a high-risk period of uncertainty that can be more damaging than the rules themselves. The Regulation II ruling illustrates the problem. Companies that built their economics around debit interchange fees now face uncertainty. Some use those fees to fund rewards programs. Others share them with banking-as-a-service partners or use them to offer zero-fee accounts. The Fed could rewrite the rule to favor merchants, which would slash interchange rates. But that process could drag on for years, with appeals and maybe even congressional hearings. Meanwhile, companies are trying to plan budgets and investor presentations without knowing what their core revenue stream will look like. This pattern extends beyond payments. Credit card rewards, routing rules, and data rights all face similar risks. When a business model depends on a particular legal framework, and that framework gets sent back to the drawing board, companies operate in uncertainty until something new emerges. What’s likely to emerge is fragmentation. Private companies will cut bilateral data-sharing deals. Different states will write their own rules. Banks will use different technical standards depending on who’s asking for data. For fintechs that need bank data, the complications are significant. Integration costs increase. Product launch timelines extend. And there’s always the risk that key data sources will change terms or cut access entirely. Recent developments illustrate this risk, with major banks beginning to charge fintechs for customer data access through aggregators. Industry executives warn these fees could be devastating for early-stage startups and make certain financial transactions economically impossible for consumers. Without clear federal rules, banks can essentially set their own terms for data access.
Tokenizing private equity unlocks a $15 trillion opportunity by enabling fractional, programmable ownership and broader investor access beyond 80% of retail exclusion barriers
Tokenization, a technology that has gained prominence in the cryptocurrency industry, has the potential to revolutionize finance by redefining access to capital. Currently, private markets remain less transparent, more expensive to access, and off-limits to over 80% of investors. Tokenizing private equity could remake capital formation, unlocking a massive new level of financial inclusion. Today’s system limits access to high-growth private companies to accredited investors and institutions, leaving retail investors locked out of early-stage growth opportunities. Blockchain infrastructure can represent ownership digitally and enable programmable transfers, making it possible to securely fractionalize, trade, and settle these assets without the friction of traditional intermediaries. This would lower the cost and complexity of fundraising while unlocking the door for everyday investors to participate in their growth. By the end of 2025, private markets will represent a projected $15-trillion-walled-off opportunity, dwarfing public equities’ growth potential. Enabling companies to tokenize shares before $300 million in revenues would give millions of people access to innovation-stage companies that have historically been the domain of VCs and hedge funds. Tokenization doesn’t mean throwing out safeguards; more transparency results in better outcomes, and blockchain technology offers that. Access is the ultimate asset, and tokenizing private equity could rewrite the rules of participation, opening a massive new addressable market for companies and dismantling a system where only accredited investors are trusted to take risks. It also creates a two-way unlock: startups can tap new global capital sources, and investors worldwide can participate in economic growth from day one. Tokenized private equity could be one of the biggest democratizations of wealth creation in history, shifting the center of gravity from a handful of gatekeepers to a global network of contributors.
MIT report on 95% failure applies to custom enterprise builds; however shadow AI is booming as 95% of staff quietly use consumer LLMs multiple times daily despite limited official subscriptions
The most widely cited statistic from a new MIT report has been deeply misunderstood. While headlines trumpet that “95% of generative AI pilots at companies are failing,” the report actually reveals something far more remarkable: the fastest and most successful enterprise technology adoption in corporate history is happening right under executives’ noses. The researchers found that 90% of employees regularly use personal AI tools for work, even though only 40% of their companies have official AI subscriptions. “While only 40% of companies say they purchased an official LLM subscription, workers from over 90% of the companies we surveyed reported regular use of personal AI tools for work tasks,” the study explains. “In fact, almost every single person used an LLM in some form for their work.” The MIT researchers discovered what they call a “shadow AI economy” where workers use personal ChatGPT accounts, Claude subscriptions and other consumer tools to handle significant portions of their jobs. These employees aren’t just experimenting — they’re using AI “multiples times a day every day of their weekly workload,” the study found. The 95% failure rate that has dominated headlines applies specifically to custom enterprise AI solutions — the expensive, bespoke systems companies commission from vendors or build internally. These tools fail because they lack what the MIT researchers call “learning capability.” Far from showing AI failure, the shadow economy reveals massive productivity gains that don’t appear in corporate metrics. Workers have solved integration challenges that stymie official initiatives, proving AI works when implemented correctly. “This shadow economy demonstrates that individuals can successfully cross the GenAI Divide when given access to flexible, responsive tools,” the report explains. Some companies have started paying attention: “Forward-thinking organizations are beginning to bridge this gap by learning from shadow usage and analyzing which personal tools deliver value before procuring enterprise alternatives.”
Specialized consultancies turn AI into packaged services (from slide deck to software) for SMBs and enterprises, pressuring Big Four playbooks on speed, price, and scope
A new set of consulting firms is leveraging AI to challenge the classic consulting model. Xavier AI describes itself as the world’s first AI strategy consultant. Filipe said Xavier AI has its own proprietary reasoning engine that is tailor-made for business use cases and can provide detailed sources without the hallucination you might find with other chatbots. He said Xavier can provide both strategy recommendations and actionable plans for implementation. “We created Xavier AI so that anyone could have the power of a consulting firm at their hands when they need it.” Consulting IQ positions itself as an AI-powered boutique consultant dedicated to the needs of SMBs. Once a user registers on the platform, they provide a few basic details about their business — who they are, where they operate, and their challenges. Then they’ll see a list of over 5,000 preloaded prompts in topics ranging from branding to business strategy to sales. Users can converse with the tool for insights on how to optimize their operations. Perceptis aims to help smaller and midsize firms compete with bigger industry players by using AI to streamline some of the more tedious processes in consulting, like proposal writing. Genpact, a professional services company that expects to generate $5 billion in revenue this year, has made a major push over the past year to position itself as a leader in AI strategy. Last year, Genpact launched “Client Zero,” an initiative to design, test, and refine AI solutions in-house before rolling them out to its 800-plus clients. SIB specializes in helping clients like restaurant groups, hospitals, universities, and government agencies find savings in fixed costs — expenses that remain static regardless of how much a company produces. SIB CEO Shannon Copeland told that these are often found in areas that “escape scrutiny,” like fees for telecommunications, utilities, waste removal, shipping, and software licenses. Monevate focuses on pricing strategy for software-as-a-service and high-growth tech companies. It also works with private equity firms to assess the commercial viability of potential investments. James Wilton, Managing Partner said clients usually turn to Monevate when they’ve hit a wall with their current strategy because their product has changed or the market has evolved. Keystone is a strategy consulting firm that advises technology companies, life science companies, governments, and law firms. While many consulting firms are embracing generative AI, which is often used to automate day-to-day work like writing emails or reviewing documents and contracts, Keystone is focusing more on operational AI.. Fusion Collective is an IT consulting firm that offers a range of consulting services to clients, including strategy and management advice, cloud transformation, and AI alignment. Founder Blake Crawford, said that in her experience, clients are seeking AI advice from consulting firms before they’re ready.. Slideworks isn’t necessarily going after consulting firms’ business, though it focuses on something many of the big guys are known for: making powerful slides. Slideworks offers what it calls “high-end” PowerPoint templates and “toolkits” created by former consultants for Bain, BCG, and McKinsey. The idea is to offer access to a library of slides and spreadsheets for areas including strategy, supply chain management, and “digital transformation.” Unity Advisory: Some top UK executives from Ernst & Young and PwC are joining forces to launch a new firm called Unity Advisory. The firm will be chaired by Steve Varley, and led by CEO Marissa Thomas.
Homeownership financial hurdles reshape GenZ’s goals, student debt career instability and maintenance costs deter buyers and 64% choose to live without roommates to keep control and flexibility
Homeownership has long been seen as a marker of stability and success. But for many in Generation Z — the youngest group entering the housing market — that dream feels increasingly out of reach. Rising mortgage rates, high home prices, student debt and career uncertainty are reshaping what housing looks like for this generation — and many are renting for the long term. A recent survey of more than 2,000 U.S. renters conducted in January 2025 by Entrata in collaboration with Qualtrics sheds light on how Gen Z views renting, homeownership and financial priorities. The biggest obstacles to homeownership for Gen Z are financial. More than half of respondents (57%) said rising mortgage rates are a key factor preventing them from buying a home. About 52% cited escalating home prices, while others pointed to student loan debt and career instability as the main reason a mortgage feels out of reach. Many also expressed reluctance to take on the responsibilities of home maintenance and repairs. Roughly one in three renters said these costs and responsibilities were enough to steer them away from homeownership. “Many can’t afford the upfront costs associated with home ownership like down payments for (private mortgage insurance) if they’re unable to meet the (loan-to-value ratio) necessary to eliminate the requirement for mortgage insurance,” the report explained.
Merrill and Bank of America Private Bank launch new alternative investments program to Ultra-High-Net-Worth Clients, to build an expanded allocation to alternatives as part of a diversified portfolio
Merrill Wealth Management and Bank of America Private Bank today announced the launch of the Alts Expanded Access Program, a new private market program available to ultra-high-net-worth (UHNW) clients with a net worth of $50 million or more. Available in fall 2025, the program is designed to complement the investment options available through Merrill’s and Bank of America Private Bank’s core Alternative Investments platform, offering qualified investors avenues to build an expanded allocation to alternatives as part of a diversified portfolio. “Traditionally, private market alternatives were the domain of institutional investors, but as wealth building needs have evolved, we’re seeing more clients seek non-traditional investments, fueled by market changes and the desire to diversify,” said Mark Sutterlin, head of alternative investments for Merrill and Bank of America Private Bank. Key Features of the Alts Expanded Access Program: Selective access: These funds are not broadly distributed and provide access to specialized opportunities in emerging themes, niche strategies and evolving sectors. Supported recommendation: Clients’ advisor or team helps them understand the process and provides them with access to fund manager materials. Client-directed: Clients conduct due diligence, make investment decisions, and invest directly with fund managers. Insights from the 2024 Bank of America Private Bank Study of Wealthy Americans underscore the growing interest in alternatives, particularly among young HNW investors: Alternatives comprise 17% of their current portfolio allocations. 93% plan to increase their allocate to alternatives in the coming years. “This program is part of our broader commitment to meet the evolving needs of UHNW clients with increasingly complex financial goals,” added Sutterlin. The new offering builds on the successful launch of other UHNW capabilities, such as Premium Access Strategies, a dual-contract investment advisory program that has grown to over $60 billion in client assets in under three years.