Jira Pliankharom/iStock via Getty Images Market overview U.S. equities posted a loss of -4.33% in the first quarter, as measured by the S&P 500 Index. Stocks initially traded in a range through January and February, as investors balanced the continued tailwinds of positive economic growth, expectations for falling interest rates and robust corporate earnings against concerns around stress in the p...
Jira Pliankharom/iStock via Getty Images Market overview U.S. equities posted a loss of -4.33% in the first quarter, as measured by the S&P 500 Index. Stocks initially traded in a range through January and February, as investors balanced the continued tailwinds of positive economic growth, expectations for falling interest rates and robust corporate earnings against concerns around stress in the private credit space and the disruptive impact of artificial intelligence ( AI ) across a range of industries. However, the beginning of the U.S.-Iran conflict at the end of February marked a shift in tone, as a disruption in Middle East shipping led to spiking prices for crude oil and fertilizer, among other key commodities. As a result, the outlook for U.S. Federal Reserve rate cuts was called into question as inflation expectations and Treasury yields moved higher. The result was a sharp downturn in stock prices during March. Notably, growth stocks led the sell-off, reflecting underperformance by mega-cap technology-related companies as well as software companies whose businesses could be displaced by AI. By contrast, the value category was supported by the market's rotation toward companies with hard assets that are less vulnerable to AI-related disruption, as well as energy stocks which rose in tandem with crude oil prices. Columbia Cornerstone Equity Fund Top holdings (% of net assets):as of March 31, 2026 NVIDIA ( NVDA ) 8.71 Alphabet-C ( GOOG ) 6.14 Microsoft ( MSFT ) 6.04 Apple ( AAPL ) 4.94 Amazon.Com ( AMZN ) 4.41 Broadcom ( AVGO ) 3.65 JPMorgan Chase ( JPM ) 2.90 Meta Platforms ( META ) 2.79 Chubb ( CB ) 2.22 Equinix ( EQIX ) 2.21 Top holdings exclude short-term holdings and cash, if applicable. Fund holdings are as of the date given, are subject to change at any time, and are not recommendations to buy or sell any security. Click to enlarge Top five contributors - Effect on return (%) as of March 31, 2026 Valero Energy ( VLO ) 0.38 Equinix 0.37 Entergy ( ETR ) 0...
Since early 2023, Nvidia (NASDAQ: NVDA) has been one of the undisputed beneficiaries of the mad dash to adopt artificial intelligence (AI). The company's graphics processing units (GPUs) -- which were developed to render lifelike images in video games (hence the name) -- have since become the gold standard for AI training and inference. In little more than three years, Nvidia stock has soared 1,41...
Since early 2023, Nvidia (NASDAQ: NVDA) has been one of the undisputed beneficiaries of the mad dash to adopt artificial intelligence (AI). The company's graphics processing units (GPUs) -- which were developed to render lifelike images in video games (hence the name) -- have since become the gold standard for AI training and inference. In little more than three years, Nvidia stock has soared 1,410% (as of this writing), resulting in significant gains for shareholders. Yet fears of an AI bubble, rising competition, and geopolitical uncertainty have kept the stock rangebound, with the stock rising just 10% over the past six months. However, Nvidia says it has clear visibility into demand over the next couple of years, and investors should be paying attention. Continue reading
Presented by EdgeVerve For most enterprises, AI adoption began with a straightforward ambition: automate work faster, cheaper, and at scale. Chatbots replaced basic service requests, machine‑learning models optimized forecasts, and analytics dashboards promised sharper insights. Yet many organizations are now discovering that deploying individual AI solutions does not automatically translate into ...
Presented by EdgeVerve For most enterprises, AI adoption began with a straightforward ambition: automate work faster, cheaper, and at scale. Chatbots replaced basic service requests, machine‑learning models optimized forecasts, and analytics dashboards promised sharper insights. Yet many organizations are now discovering that deploying individual AI solutions does not automatically translate into enterprise‑level impact. Pilots proliferate, but value plateaus. The next phase of AI maturity is no longer about deploying more models. It is about adapting AI continuously to changing business objectives, regulatory expectations, operating conditions, and customer contexts. This shift is particularly critical for complex, globally distributed organizations such as Global Business Services (GBS), where outcomes depend on orchestrating work across functions, regions, systems, and stakeholders. From automation to adaptation AI can no longer be treated as a standalone tool to accelerate discrete tasks. To remain competitive, enterprises must move from isolated, single‑purpose models toward systems that can sense context, coordinate actions, and evolve over time. This is where adaptive AI ecosystems come into play. An adaptive AI ecosystem is a network of interoperable AI agents, models, data sources, and decision services that work together dynamically. These ecosystems integrate capabilities such as natural language processing, computer vision, predictive analytics, and autonomous decision‑making, while remaining grounded in human oversight and enterprise governance. For GBS organizations, the relevance is clear. GBS operates at the intersection of scale, standardization, and variation, managing high‑volume processes across markets that differ in regulation, customer behavior, and operational constraints. Static automation struggles in such environments. Adaptive AI, by contrast, allows GBS teams to orchestrate end‑to‑end processes, intelligently route work, and continuously...
Presented by Apptio, an IBM company AI spending is surging, but the full impact often remains an open question. Closing the gap requires clear answers to how AI is governed, measured, and tied to business outcomes. ROI uncertainty isn’t unique to AI: In the Apptio 2026 Technology Investment Management Repor t, 90% of technology leaders surveyed said that ROI uncertainty has a moderate or major imp...
Presented by Apptio, an IBM company AI spending is surging, but the full impact often remains an open question. Closing the gap requires clear answers to how AI is governed, measured, and tied to business outcomes. ROI uncertainty isn’t unique to AI: In the Apptio 2026 Technology Investment Management Repor t, 90% of technology leaders surveyed said that ROI uncertainty has a moderate or major impact on overall tech investment decisions, a 5-percentage point year-over-year increase. In other words, tech leaders are increasing their reliance on ROI – even if they don’t fully know how to measure it. And AI economics involves new and unpredictable costs, further complicating ROI calculations. Faced with increasing uncertainty and increasing budgets, technology leaders need a clear, reliable framework for evaluating AI ROI. Organizations increasingly expect scaled AI to pay its own way, at least partially. According to Apptio’s technology investment management report, 45% of organizations surveyed intend to fund innovation by reinvesting savings from AI-driven efficiencies. That model assumes that such savings are both achievable and quantifiable. Meanwhile, the two-thirds of organizations planning to reallocate existing budget capital to AI will need clarity on the trade-offs involved. Much like the early days of public cloud, AI costs and returns are difficult to predict. Pricing varies widely across providers and continues to evolve, while consumption is unpredictable. The pressure to adopt quickly is also formidable as organizations navigate the threat of disruption by more agile competitors. The new math of AI ROI Considering the many variables, tech leaders should view AI ROI as a matter of optimization. At a high level, the implementation of AI initiatives is inevitable. The question is how to achieve the greatest possible returns — both financial and organizational. Start with the business problem. There are many ways AI can deliver positive impact, but organiza...