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IBM (NYSE: IBM) recently announced strong results for its second quarter that surpassed analyst estimates driven by a boost in its AI business.
IBM’s Financials
Revenues for the second quarter grew 2% to $15.8 billion, beating the Street’s estimates of $15.62 billion. Adjusted earnings of $2.43 per share were ahead of the analyst estimates of $2.2 per share.By segment, Software revenue grew 7% to $6.7 billion, ahead of the Street estimates of $6.48 billion. Consulting revenue was down 0.9% to $5.2 billion. Infrastructure revenue was up 0.7% to $3.6 billion. Financing revenues fell 8.3% to $0.2 billion.During the quarter, the company returned $1.5 billion to shareholders in dividends. IBM ended the second quarter with $16 billion in cash and marketable securities, up $2.5 billion from the end of 2023. Debt, including IBM Financing debt of $11.1 billion, totaled $56.5 billion, flat since the end of 2023.Based on the first half results, IBM now expects over $12 billion in free cash flow for the year. It continues to expect revenues to grow in mid-single digits for the full year. Analysts expect IBM to end the year with $63.5 billion in revenue or a 3% growth and a net income of $9.6 billion.
IBM’s AI Platform Strategy
It has been a year since IBM introduced watsonx and its generative AI strategy to the market and has booked business over $2 billion. The mix is roughly one quarter software and three quarters consulting signings.IBM has infused AI in all its segments from the tools clients use to manage and optimize their hybrid cloud environments to its platform products. For example, in software, its suite of automation products like Apptio and watsonx Orchestrate are leveraging AI. Red Hat is bringing AI to OpenShift AI and rhel.ai. In transaction processing, it is seeing early momentum in watsonx Code Assistant for Z. In infrastructure, IBM Z is equipped with real-time AI inferencing capabilities. In consulting, it is helping clients design and implement AI strategies. While large general-purpose models are great for starting on AI use cases, its clients are finding that smaller models are essential for cost-effective AI strategies. Smaller models are also much easier to customize and tune. These fit-for-purpose models can be approximately 90% less expensive than large models. IBM’s Granite models ranging from 3 billion to 34 billion parameters and trained on 116 programming languages have consistently achieved top performance for a variety of coding tasks. IBM continued to strengthen its AI ecosystem. It recently announced a series of new AI partnerships with industry leaders like Palo Alto Networks, Adobe, AWS, Microsoft, Meta, Mistral, Salesforce, and SAP. Palo Alto Networks will be acquiring IBM’s QRadar SaaS assets and will migrate customers to the Cortex Xsiam product. IBM’s AI strategy is a comprehensive platform play where Rhel.ai and OpenShift AI are the foundation of its enterprise AI platform. They combine open-source IBM Granite’s LLMs and InstructLab model alignment tools with full stack optimization, enterprise-grade security and support and model indemnification. On top of that, it has an enterprise AI middleware platform with watsonx and an embed strategy with its AI assistance infused through its software portfolio and those of its ecosystem partners. It is currently trading at $191.98 with a market capitalization of $176.4 billion. It touched a ten-year high of $199.18 in March this year and a 52-week low of $135.87 in October 2023.More By This Author:Cloud Stocks: Oracle Cloud Infrastructure Benefits From AIAI Unicorns: xAI Racing To Catch-Up With OpenAIIPOs 2024: Tempus AI Addresses Medical Diagnostic Use Case
