Big technology companies are betting that a new wave of smaller, more precise AI models will be more effective when it comes to the needs of businesses in sectors like law, finance, and health care.
Despite bold statements, a lot of companies are failing to produce tangible results, according to Edward J Achtner, the head of generative AI for U.K. banking giant HSBC.
“Candidly, there’s a lot of success theater out there,” Achtner said on a panel at the CogX Global Leadership Summit alongside Ranil Boteju — a fellow AI leader at rival British bank Lloyds Banking Group — and Nathalie Oestmann, head of NV Ltd, an advisory firm for venture capital funds.
“We have to be very clinical in terms of what we choose to do, and where we choose to do it,” Achtner told attendees of the event, held at the Royal Albert Hall in London earlier this week.
Achtner outlined how the 150-year-old lending institution has embraced artificial intelligence since ChatGPT — the popular AI chatbot from Microsoft-backed startup OpenAI — burst onto the scene in November 2022.
The HSBC AI leader said that the bank has more than 550 use cases across its business lines and functions linked to AI — ranging from fighting money laundering and fraud using machine learning tools to supporting knowledge workers with newer generative AI systems.
One example he gave was a partnership that HSBC has in place with internet search titan Google on the use of AI technology anti-money laundering and fraud mitigation. That tie-up has been in place for several years, he said. The bank has also dipped its toes deeper into genAI tech much more recently.
“When it comes to generative artificial intelligence, we do need to clearly separate that” from other types of AI, Achtner said. “We do approach the underlying risk with respect to generative very differently because, while it represents incredible potential opportunity and productivity gains, it also represents a different type of risk.”
Achtner’s comments come as other figures in the financial services sector — particularly leaders at startup firms — have made bold statements about the level of overall efficiency gains and cost reductions they are seeing as a result of investments in AI.
Buy now, pay later firm Klarna says it has been taking advantage of AI to make up for loss of productivity resulting from declines in its workforce as employees move on from the company.
It is implementing a company-wide hiring freeze and has slashed overall employee headcount down to 3,800 from 5,000 — a roughly 24% workforce reduction — with the help of AI, CEO Sebastian Siemiatkowski said in August. He is looking to further reduce Klarna’s headcount to 2,000 staff members — without specifying a time for this target.
Klarna’s boss said the firm was lowering its overall headcount against the backdrop of AI’s potential to have “a dramatic impact” on jobs and society.
“I think politicians already today should consider whether there are other alternatives of how they could support people that may be effective,” he said at the time in an interview with the BBC. Siemiatkowski said it was “too simplistic” to say AI’s disruptive effects would be offset by the creation of new jobs thanks to AI.
Oestmann of NV Ltd, a London-based firm that offers advisory services for the C-suite of venture capital and private equity firms, directly touched on Klarna’s actions, saying headlines around such AI-driven workforce reductions are “not helpful.”
Klarna, she suggested, likely saw that AI “makes them a more valuable company” and was consequently incorporating the technology as part of plans to reduce its workforce anyway.
The result Klarna is seeing from AI “are very real,” a Klarna spokesperson told CNBC. “We publicize these results because we want to be honest and transparent about the impact genAI is having in the real world in companies today,” the spokesperson added.
“At the end of the day,” Oestmann added, as long as people are “trained appropriately” and banks and other financial services firm can “reinvent” themselves in the new AI era, “it will just help us to evolve.” She advised financial firms to pursue “continuous learning in everything that you do.”
“Make sure you are trying these tools out, make sure you are making this part of your everyday, make sure you are curious,” she added.
Boteju, chief data and analytics officer at Lloyds, pointed to three main use cases that the lender sees with respect to AI: automating back office functions like coding and engineering documentation, “human-in-the loop” uses like prompts for sales staff, and AI-generated responses to client queries.
Boteju stressed that Lloyds is “proceeding with caution” when it comes to exposing the bank’s customers to generative AI tools. “We want to get our guardrails in place before we actually start to scale those,” he added.
“Banks in particular have been using AI and machine learning for probably about 15 or 20 years,” Boteju said, signaling that machine learning, intelligent automation and chatbots are things traditional lenders have been “doing for a while.”
Generative AI, on the other hand, is a more nascent technology, according to the Lloyds exec. The bank is increasingly thinking about how to scale that technology — but by “using the current frameworks and infrastructure we’ve got,” rather than by moving the needle significantly.
Boteju and Achtner’s comments tally with what other AI leaders of financial services have said previously. Speaking with CNBC last week, Bahadir Yilmaz, chief analytics officer of ING, said that AI is unlikely to be as disruptive as firms like Klarna are suggesting with their public messaging.
“We see the same potential that they’re seeing,” Yilmaz said in an interview in London. “It’s just the tone of communication is a bit different.” He added that ING is primarily using AI in its global contact centers and internally for software engineering.
“We don’t need to be seen as an AI-driven bank,” Yilmaz said, adding that, with many processes lenders won’t even need AI to solve certain problems. “It’s a really powerful tool. It’s very disruptive. But we don’t necessarily have to say we are putting it as a sauce on all the food.”
Johan Tjarnberg, CEO of Swedish online payments firm Trustly, told CNBC earlier this week that AI “will actually be one of the biggest technology levers in payments.”But even so, he noted that the firm is focusing more of the “basics of AI” than on transformative changes like AI-led customer service.
One area where Trustly is looking to improve customer experience with AI is subscriptions. The startup is working on an “intelligent charging mechanism” that would aim to figure out the best time for a bank to take payment from a subscription platform user, based on their historical financial activity.
Tjarnberg added that Trustly is seeing closer to 5-10% improved efficiency as a result of implementing AI within its organization.
Fixed-income markets recorded significant re-pricing during the week ending July 25, 2026, as a convergence of strong labor market metrics and surging energy costs drove U.S. Treasury yields higher across all maturities. The benchmark 10-year Treasury yield climbed toward 4.70%, reaching its highest point in several months. Institutional bond investors rapidly adjusted portfolio durations as expectations for near-term interest rate cuts by the Federal Reserve faded in response to inflation concerns.
The upward shift in sovereign yields reflects a broader fundamental reassessment of global monetary policy. Earlier in the quarter, money markets had priced in a series of rate reductions designed to support economic activity. However, with initial jobless claims falling to 187,000 and crude oil breaching $100 per barrel, fixed-income traders are pricing in a ‘higher-for-longer’ interest rate environment. The inversion between short-term Treasury bills and long-term bonds narrowed, indicating a shift toward term premium expansion.
Rising Treasury yields present both challenges and opportunities for institutional wealth managers. While commercial lenders and mortgage origination volumes face headwinds from elevated borrowing costs, fixed-income investors are locking in attractive real yields on high-quality sovereign and investment-grade corporate bonds. Institutional debt issuers, conversely, are recalibrating their capital structures, opting for shorter-term refinancing instruments or private credit facilities to avoid committing to elevated long-term coupon rates.
Navigating the current bond market landscape demands strict duration management and credit selection. Wealth advisors recommend maintaining flexible fixed-income allocations, combining short-duration Treasuries with inflation-protected securities (TIPS) to shield capital against potential energy-driven inflation spikes while earning dependable nominal income.
Private credit markets reached a pivotal milestone during the week ending July 25, 2026, as non-bank direct lending consortiums captured a record share of middle-market corporate debt originations. With commercial banks maintaining conservative credit standards and public bond yields remaining elevated, corporate borrowers are increasingly turning to private fund managers for customized capital solutions. This expansion marks a permanent structural shift in enterprise finance, establishing private credit as a primary pillar of institutional corporate liquidity.
The acceleration of private credit deals is driven by speed, deal certainty, and flexible terms. Unlike traditional syndicated bank loans that require lengthy underwriting, credit rating approvals, and public roadshows, private direct lenders can structure tailored financing packages within days. Middle-market firms facing upcoming debt maturities are utilizing private debt facilities to execute recapitalizations, strategic acquisitions, and growth capital deployments without risking execution delay in public markets.
However, financial regulators and central bank supervisors are scrutinizing the sector’s rapid growth. Supervisory agencies are evaluating potential systemic risks associated with non-bank leverage, valuation transparency, and liquidity mismatches during economic downturns. Despite regulatory interest, major pension funds, insurance firms, and sovereign wealth entities continue to expand capital allocations to private credit funds, attracted by reliable floating-rate yields that outperform public fixed-income benchmarks.
As private credit matures into a dominant asset class, corporate chief financial officers must evaluate non-bank lenders alongside traditional banking relationships. Direct lending partnerships provide valuable balance sheet resilience, enabling companies to secure flexible financing terms even during periods of public market turbulence.
The landscape of institutional debt markets is undergoing a profound structural shift on July 21, 2026, as major corporate issuers and commercial banks rapidly accelerate the deployment of tokenized debt instruments. Data published by leading capital market consortiums indicates that primary issuances of digital commercial paper and tokenized corporate bonds have reached record volumes this month. By moving legacy debt origination, underwriting, and secondary distribution onto permissioned distributed ledgers, corporate treasurers are unlocking unprecedented operational flexibility and instantaneous cross-border liquidity.
The adoption of tokenized debt is fundamentally altering how enterprise balance sheets manage short-term liquidity needs. Traditional corporate bond settlement cycles historically required multi-day clearing processes involving numerous intermediaries, custodial entities, and clearinghouses. Through programmable smart contracts on distributed ledgers, issuers can now execute atomic settlement—enabling continuous, 24/7 access to institutional capital pools. This instantaneous clearing mechanism drastically reduces counterparty risk, eliminates costly settlement friction, and allows treasury teams to dynamically optimize working capital in real time.
A major catalyst driving this institutional migration is the establishment of comprehensive digital asset regulatory frameworks across major financial hubs. Clear legal guidelines regarding ledger-based securities ownership have provided institutional compliance officers with the regulatory confidence necessary to transition multi-billion-dollar liquidity facilities onto digital platforms. Furthermore, the integration of automated regulatory reporting directly into token smart contracts simplifies ongoing compliance audits, ensuring that secondary market trades automatically enforce investor accreditation limits and tax withholding requirements.
For chief financial officers and institutional portfolio managers, tokenized debt represents a fundamental evolution in fixed-income strategy. Companies that embrace ledger-based debt structures gain direct access to a broader, global base of digital-native institutional investors while substantially reducing borrowing overhead. As ledger interoperability continues to improve across global exchanges, tokenized debt is poised to become the standard infrastructure for global corporate finance.