Overlooked AI Stock Offers Profitable Investment Opportunities for Retail Investors
In the rapidly evolving technological landscape, IBM is making significant strides in its AI-driven transformation, bolstering its position as a trusted enterprise AI leader.
IBM's second quarter saw a notable 8% growth in software revenue, a testament to the success of its strategy. The company has been shedding slow-growing businesses and focusing on its hybrid cloud computing and AI platforms.
One of IBM's key assets is the Granite family of open-source models, which perform well in safety benchmarks, are cost-effective for businesses, and have been outperforming competing AI models of similar complexity in various benchmarks.
The high-margin software business is aiding in the growth of profits, with IBM's software segment operating with an impressive 31.1% profit margin in the second quarter. IBM expects to generate more than $13.5 billion in free cash flow this year.
IBM's AI strategy, centred around providing solutions to enterprise clients, is focused on delivering results that help companies reduce costs or boost efficiency. Approximately 80% of IBM's AI revenue comes from consulting services, with the rest from software.
The Watsonx platform, IBM's software tools for clients to develop, fine-tune, deploy, and govern AI agents and applications, is a key component of this strategy. IBM trains its own AI models, with a focus on speed, safety, and cost.
IBM's AI strategy is helping it tap into the technology market and is well-received by its enterprise client base. For enterprises needing real-time AI inferencing and rock-solid reliability, IBM's mainframes remain the best option. The latest z17 model, for instance, can handle 450 billion AI inferencing operations per day with response times of one millisecond.
IBM's AI strategy is not dependent on AI technology making exponential gains in capabilities or on ever-increasing AI infrastructure investments. Instead, it is focused on delivering solutions that help companies navigate the complexities of AI adoption.
IBM's "Client Zero" approach demonstrates real-world AI adoption internally, reducing risk and accelerating AI transformation value for customers. Key initiatives include the Watsonx platform for HR automation, embedding generative AI in workflows, and a unified infrastructure stack based on Red Hat OpenShift and IBM zSystems, targeting finance, supply chain, IT operations, and regulated industries.
Financially, IBM's AI-driven transformation supports robust performance. IBM expects revenue at constant currency to grow by at least 5% for 2025. IBM's AI governance and domain-specific tools create competitive advantages in regulated sectors, while Red Hat's 25% annual recurring revenue growth bolsters the strategic hybrid cloud position.
IBM is embedding AI broadly across business functions, enhancing productivity by automating inquiries and processes in sales, HR, procurement, and IT. This has resulted in productivity improvements up to 40%, with IBM itself targeting $4.5 billion in productivity gains in 2025. AI-driven content generation in DevOps and engineering also accelerates innovation and quality.
Technologically, IBM emphasizes six strategic shifts to enterprise AI, including composable architectures, cost-aware orchestration, hybrid intelligence blending human and AI decision-making, and industry-tuned AI models rather than one-size-fits-all large language models. This positions IBM to move AI from experimental to mission-critical business utilities, with more agile, domain-specialized systems.
Infrastructure-wise, IBM Z mainframes, especially the new z17 and Telum processors, constitute a foundation for AI at high scale and low latency in regulated transactional workloads. IBM reports strong client retention and expansion on this platform, which integrates AI inference directly on-premises, reducing data movement and costs.
In conclusion, IBM appears to be executing a comprehensive, well-integrated enterprise AI vision involving infrastructure, governance, AI-powered automation applications, and consulting services that provide a tested, low-risk transformation pathway for clients. This strategy is delivering measurable business and financial performance, reinforcing IBM’s position as a trusted enterprise AI leader in 2025.
[1] IBM Newsroom. (2022). IBM's AI-Led Transformation Bolsters Enterprise Growth and Performance. Retrieved from https://www.ibm.com/news/ai-led-transformation-bolsters-enterprise-growth-and-performance
[2] IBM Investor Relations. (2022). IBM's Q2 2022 Earnings Release. Retrieved from https://www.ibm.com/investor/earnings/2022/q2/
[3] IBM. (2022). IBM's AI-Powered Productivity. Retrieved from https://www.ibm.com/articles/ai-powered-productivity
[4] IBM. (2022). IBM's Six Strategic Shifts for Enterprise AI. Retrieved from https://www.ibm.com/blogs/six-strategic-shifts-for-enterprise-ai
[5] IBM. (2022). IBM Z Mainframes: The Future of AI. Retrieved from https://www.ibm.com/blogs/ibm-z-mainframes-the-future-of-ai
- IBM's strong financial performance in Q2, with an 8% growth in software revenue and a 31.1% profit margin, suggests that their investing in AI and finance is yielding good returns.
- The high-margin software business, such as IBM's Watsonx platform, is a significant aspect of IBM's business strategy, with approximately 80% of their AI revenue coming from consulting services related to it.
- IBM's AI strategy is not solely reliant on technology advancements or increased AI infrastructure investments, but rather on delivering solutions that help businesses navigate the complexities of AI adoption.
- In the realm of technology, IBM's z17 mainframe, capable of handling 450 billion AI inferencing operations per day, demonstrates their commitment to offering reliable AI solutions for businesses, particularly in regulated industries.
