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Can T. Rowe Price turn AI Adoption into a competitive advantage? 47 minutes ago Key Takeaways In sync with this, last week, TROW announced AI leadership changes as it looks to expand the use of AI across the company. T. Rowe Price has dedicated teams focused on investment applications, distribution, engineering and risk management, while integrating AI into research, portfolio analysis, sales and client-service workflows. Within its investment organization, the company created an Investment AI Solutions organization led by Vinit Agrawal to develop AI capabilities across asset classes. The unit will focus on agentic AI products, education, strategic partnerships and research to support investment professionals. TROW has also introduced Chat TRP, formerly known as Investor Copilot, and is exploring agentic AI, which can execute multistep tasks with greater autonomy. By combining these capabilities with proprietary research and institutional knowledge, TROW aims to improve the efficiency of investment professionals without compromising differentiated human judgment. AI adoption is also gaining traction across distribution. TROW's Global Distribution AI Strategy and Transformation team, led by Sal Dhanani, is focused on improving client experiences, sales effectiveness and employee capabilities, while T. Rowe Price Labs evaluates emerging technologies and helps scale promising applications. Dedicated AI risk and governance functions are intended to support responsible deployment and regulatory readiness. During the second-quarter 2026 earnings call, management noted that TROW had deployed more than 130 AI solutions by the end of June 30, 2026, with adoption exceeding 70% of employees. The scale of deployment suggests that AI is increasingly becoming embedded in the company's day-to-day operations rather than remaining confined to pilot programs. From a financial standpoint, broader AI adoption could strengthen operating leverage. Automating repetitive tasks, accelerating research and data analysis, and enabling employees to focus on higher-value activities may improve productivity and help contain expense growth over time. For TROW, however, widespread adoption alone will not determine success. The key will be translating AI use into measurable productivity gains, better client outcomes and stronger investment capabilities. If the company can do so while maintaining effective governance and investment discipline, AI could become a meaningful competitive advantage and an important component of TROW's broader efficiency and growth strategy.AI Adoption by Other Financial Firms Beyond its core underwriting platform, Pagaya is also expanding its broader AI capabilities to enhance risk management, automation, portfolio optimization, and data-driven decision-making across the credit ecosystem. Robinhood Markets' (HOOD Quick QuoteHOOD - Free Report) Robinhood has rapidly expanded its adoption of artificial intelligence, making it a central part of both its internal operations and customer-facing products. In May 2026, Robinhood took its AI strategy further by launching agentic trading, allowing customers to connect AI agents that can analyze portfolios and place trades through dedicated accounts with safety controls. Overall, HOOD's AI adoption shows a shift from using AI mainly to improve efficiency toward making AI an integral part of investing and financial decision-making.TROW Price Performance & Zacks Rank Price PerformanceThe company currently carries a Zacks Rank #3 (Hold). You can see the complete list of today's Zacks #1 Rank (Strong Buy) stocks here. Zacks' Research Chief Names "Stock Most Likely to Double" Its team of experts has just released the 5 stocks with the greatest probability of gaining +100% or more in the coming months. Of those 5, Director of Research Sheraz Mian highlights the one stock set to climb highest. This top pick is a little-known satellite-based communications firm. Space is projected to become a trillion dollar industry, and this company's customer base is growing fast. Analysts have forecasted a major revenue breakout in 2025. Of course, all its elite picks aren't winners but this one could far surpass earlier Zacks' Stocks Set to Double like Hims & Hers Health, which shot up +209%. Free: See Its Top Stock And 4 Runners Up

Custody bank stocks reported strong Q2 results, with 16 tracked companies beating revenue consensus estimates by 3.2% on average. Share prices rose 4.7% since earnings announcements. T. Rowe Price reported revenues of $1.91 billion, up 8.6% year on year, but missed analyst expectations by 1%. The stock fell 7.1% following the results and currently trades at $110.87. Hamilton Lane delivered the strongest performance, reporting revenues of $275.3 million, up 56.5% year on year, exceeding expectations by 21%. The stock rose 9.8% post-earnings and trades at $104.26. The custody bank sector faces fee pressure from large clients and requires substantial technology investments, whilst opportunities exist in global asset growth and blockchain adoption for settlement efficiency.

Databricks closes $5 billion round at a $190 billion valuation. Ad powered by advergic.com Databricks has closed a $5 billion funding round at a $190 billion valuation, the company confirmed on August 13, 2026. The round was led by Coatue Management with participation from Blackstone, MGX and T. Rowe Price, and it arrives alongside Databricks saying it has crossed a $7 billion revenue run-rate. It is the second financing the data and AI platform company has completed this year, and it lifts its valuation well above the $134 billion mark it carried after its previous round. What Databricks announced. In its own newsroom statement, Databricks said revenue grew more than 80 percent year over year in the second quarter and that the business has now passed a $7 billion annualised run-rate. The company also pointed to growth in newer products including Lakebase, its operational database layer, Genie for natural language data queries, and the Unity AI Gateway that governs how enterprise models access company data. The $5 billion raise was larger than originally planned. Chief executive Ali Ghodsi has said publicly that the company set out to raise roughly $1 billion and that investor demand pushed the round several times higher after word of the fundraise circulated during the company conference in June. Full details of the round were published by Databricks and reported by CNBC. Why investors are paying this much. Databricks sits in a part of the AI stack that has turned out to be unusually durable. Companies building AI products still need somewhere to store, clean, govern and query their data before any model is useful to them. That layer, often called the data lakehouse, is where Databricks competes directly with Snowflake and with the data warehouse products of the large cloud providers. The pitch to investors is fairly simple. Model providers keep changing, and enterprises keep switching between them, but the underlying data platform tends to stay put once it is installed. That makes revenue from the data layer stickier than revenue tied to any single model vendor, which is part of why private market investors have been willing to pay a valuation of this size while the company remains private. Ad powered by advergic.com What it signals about the AI funding market. The size of the round is a useful marker for anyone trying to read where the AI money is going in 2026. A lot of the attention in the last two years went to model labs and consumer AI apps. Deals like this one show capital moving further down the stack, into the infrastructure and data plumbing that AI systems depend on. It also underlines how long companies of this size are now staying private. Databricks has raised repeatedly at rising valuations without going to public markets, which is a pattern several large private technology companies have followed. For employees and early investors, that means liquidity increasingly comes through secondary sales attached to rounds like this one rather than through an IPO. Why it matters for professionals and business owners. If you work in data, analytics or AI engineering, the practical takeaway is about skills. Demand at the data platform layer is not slowing down. Familiarity with lakehouse architecture, SQL at scale, data governance and the tooling around retrieval and model access continues to be some of the most transferable technical experience available right now, and it is not tied to whichever model happens to be leading the benchmarks this month. For business owners and freelancers serving clients, the signal is that enterprise AI budgets are still being approved, and a growing share of them is going to data readiness rather than to model subscriptions. Companies are discovering that their AI plans stall because their data is messy, not because their model is weak. Anyone who can help fix that gap, whether as a consultant, a contractor or an internal hire, is selling into a market that investors clearly believe is still expanding. The round is a private financing, not a public listing, so the valuation reflects what a small group of institutional investors were willing to pay rather than an open market price. That distinction is worth keeping in mind when comparing it to the market capitalisation of listed competitors. Sharing is caring!