International Business Machines Corporation

Welcome and thank you for standing by. At this time, all participants are in a listen-only mode. Today's conference is being recorded. If you have any objections, you may disconnect at this time. Now, I will turn the meeting over to Olympia McNerney, IBM's Global Head of Investor Relations. Olympia, you may begin.

Thank you. I'd like to welcome you to IBM's second quarter 2024 earnings presentation. I'm Olympia McNerney, and I'm here today with Arvind Krishna, IBM's Chairman and Chief Executive Officer, and Jim Cavanaugh, IBM's Senior Vice President and Chief Financial Officer. We'll post today's prepared remarks on the IBM Investor website within a couple of hours, and a replay will be available by this time tomorrow. To provide additional information to our investors, our presentation includes certain non-GAAP measures. For example, all of our references to revenue and signings growth are at constant currency. We provided reconciliation charts for these and other non-GAAP financial measures at the end of the presentation, which is posted to our investor website. Finally, some comments made in this presentation may be considered forward-looking under the Private Securities Litigation Reform Act of 1995. These statements involve factors that could cause our actual results to differ materially. Additional information about these factors is included in the company's SEC filings. So with that, I'll turn the call over to Arvind.

Thank you for joining us today to discuss IBM's second quarter earnings. We delivered a strong quarter, exceeding our expectations, driven by solid revenue growth, profitability, and cash flow generation. We had strong performance in software and infrastructure above our model, as investment in innovation is yielding organic growth, while consulting remained below model. Our results underscore the continued success of our hybrid cloud and AI strategy and the strength of our diversified business. Let me start with a few comments on the macroeconomic environment. Technology spending remains robust, as it continues to serve as a key competitive advantage in allowing businesses to scale, drive efficiencies, and fuel growth. As we stated last quarter, factors such as interest rates and inflation impacted timing of decision-making and discretionary spend in consulting. Overall, we remain confident in the positive macro outlook for technology spending, but acknowledge this impact. It has been a year since we introduced WatsonX and our generative AI strategy to the market. We have infused AI across the business. From the tools clients use to manage and optimize their hybrid cloud environments, to our platform products across .ai, .data, and .gov, to infrastructure and consulting, you can find AI innovation in all of our segments. For example, in software, a broad suite of automation products like Apptio and Watson X Orchestrate are leveraging AI, and we expect to do the same with HashiCorp once the acquisition is complete. Red Hat is bringing AI to OpenShift AI and rel.ai. In transaction processing, we are seeing early momentum in Watson X Code Assistant for Z. In infrastructure, IBM Z is equipped with real-time AI inferencing capabilities. In consulting, our experts are helping clients design and implement AI strategies. Our enterprise AI strategy is resonating as we evolve to meet client needs. Let me start by discussing IBM models. Choosing the right AI model is crucial for success in scaling AI. While large general purpose models are great for starting on AI use cases, clients are finding that smaller models are essential for cost-effective AI strategies. Smaller models are also much easier to customize and tune. IBM's granite models, ranging from 3 billion to 34 billion parameters, and trained on 116 programming languages, consistently achieved top performance for a variety of coding tasks. To put cost in perspective, these fit-for-purpose models can be approximately 90% less expensive than large models. Hybrid cloud remains a top priority for clients, as flexibility of deployment of AI models across multiple environments, and data sovereignty remain a key focus. We believe in the power of open innovation and recently announced at IBM Think that we open-sourced IBM's Granite family of models, now available under Apache 2.0 licenses on both Hugging Face and GitHub. We see parallels to Linux becoming dominant in the enterprise server space thanks to the speed and innovation offered by open source. We are confident that the same dynamic will play out with AI as we benefit from developer mindshare and community innovation. We also recently launched InstructLab, a tool for more rapid model tuning through synthetic data generation, allowing our clients to more efficiently customize models using their own data and expertise. The last 12 months of AI pilots has made it clear that sustained value from AI requires truly leveraging enterprise data. In summary, our AI strategy is a comprehensive platform play. RHEL.AI and OpenShift AI are the foundation of our enterprise AI platform. They combine open source IBM Granite LLMs and InstructLab model alignment tools with full stack optimization, enterprise-grade security, and support and model indemnification. On top of that, we have an enterprise AI middleware platform with Watson X and an embed strategy with our AI assistance infused through our software portfolio and those of our ecosystem partners. In addition, our consulting services are critical in helping clients build their AI strategies from the ground up. We also continue to see our infrastructure segment play a larger role as clients leverage their hardware investments in their AI strategies. Our book of business related to generative AI now stands at greater than $2 billion inception to date. The mix is roughly one quarter software and three quarters consulting signings. We believe these strong results highlight our momentum and traction with clients. Our early leadership positions us for long-term success and this transformational technology, which is still in the initial stages of adoption. As clients build out AI strategies, the IT landscape is becoming increasingly complex. Labor demographic shifts further emphasize the importance of optimizing IT spend and automating business processes. We continue to innovate and invest and have created a leading automation portfolio to capture this opportunity, which you can see in our results. This includes Aptia for cost management, capabilities for observability and resource management, and with the announced acquisition of HashiCorp, the automation of cloud infrastructure. The powerful combination of Red Hat Ansible and Terraform will simplify provisioning and configuration of applications across hybrid cloud environments. The latest addition to this portfolio is IBM Concert, also announced at Think, a Gen AI power tool which helps clients get end-to-end visibility across business applications. We also recently completed the acquisition of the Stream Sets and Web Methods assets from Software AG. This acquisition brings together leading capabilities in integration, API management, and data ingestion. Let me now spend a minute on the continued strength we are seeing in infrastructure. IBM Z, our mainframe solution, is an integral part of our clients' hybrid cloud environments, driving their most secure and mission-critical workloads. Our latest cycle, Z16, is uniquely tailored to offer clients security, scalability, and resilience, which help clients address both cybersecurity threats and complex regulatory requirements. Z16's Stellum processor is a unique differentiator, driving real-time, in-line AI inferencing at unprecedented speed and scale for applications like real-time fraud detection. Our storage offerings are also benefiting from generative AI as clients address data readiness and need high-speed access to massive volumes of unstructured data. We continue to invest in innovation and make great progress in emerging technology like quantum computing. This quarter, we expanded QuizKit IBM's quantum computing software into a comprehensive stack aimed at optimizing performance on utility-scale quantum hardware. These updates aim to enhance the stability, efficiency, and usability of Qiskit, supporting advanced quantum algorithm development and fostering broader adoption across various industries. This strong momentum and innovation across the portfolio manifests itself in client adoption. In virtually all industries and geographies, clients leverage IBM solutions to help them transform their operations and create better experiences for end users. Names like Virgin Money, Credit Mutual, and Panasonic all turn to IBM in the quarter. We also continue to strengthen our ecosystem. At our Think event, we announced a series of new AI partnerships with industry leaders like Adobe, AWS, Microsoft, Meta, Mistral, Salesforce, and SAP. In May, IBM and Palo Alto Networks announced a partnership to deliver AI-powered security solutions using Watson X. As part of this, Palo Alto is acquiring IBM's QRadar SaaS assets and we are partnering to offer seamless migration for QRadar customers to XM. IBM will train over 1,000 security consultants on Palo Alto network products to drive a significant book of business with them. In summary, we are excited to continue delivering strong results. Given our first-half performance, we are raising our expectations for free cash flow to greater than $12 billion for the year. I will now hand over to Jim to walk you through the details of the quarter. Jim, over to you.