The other day, I read an article about a broader adoption of artificial intelligence (“AI”). The Wall Street Journal reported that Amazon.com, Inc. (AMZN) is accelerating investments into its hardware, which includes the Kindle e-reader, the Echo smart speaker, and other products.
One of the products is Alexa, powered by Generative AI.
According to the Wall Street Journal, Amazon’s emphasis on Generative AI in Alexa reflects its ambition to advance the technology and expand its application beyond existing corporate cloud customers.
The move aims to reposition Amazon in the AI landscape, potentially bridging gaps perceived by investors.
I believe that this is just the start, as all major tech companies are now accelerating AI investments and finding new ways to bring AI solutions to market.
Below is one of the charts that The Journal has used. Since the launch of ChatGPT, the tech stocks have been on the rise. Although there are other factors that influence the tech stocks’ performance, this is a significant one, especially when it comes to the FANG+ companies that are competing against each other for AI dominance.
One of the companies most investors do not think of when investing in technology is the International Business Machines Corporation (NYSE:IBM).
I have never owned the stock because it was performing so poorly! I’ve also witnessed all of my friends sell their IBM shares over the past few years.
Investors preferred to buy tech leaders instead of companies that were doing just fine.
Having said that, IBM isn’t dead. No, far from it.
Please bear in mind that this performance includes its 4.5% yielding dividend.
In this article, I will not make the case that IBM will suddenly turn into a high-flying tech stock. That would be misleading and likely be the wrong call.
No, I will make the case that IBM is an underappreciated and somewhat undervalued high-yield tech stock, especially in light of the massive AI trend.
That’s why I started this article discussing AI.
So, let’s dive into IBM!
Exploiting AI In A Big Way
With its $140 billion market cap and more than 280 thousand employees, IBM is a giant in the tech industry.
Founded in 1911, the company has a highly diversified business model consisting of three major pillars.
|USD in Million||2021||Weight||2022||Weight|
|24,141||42.1 %||25,037||41.4 %|
|17,844||31.1 %||19,107||31.6 %|
|14,188||24.7 %||15,288||25.3 %|
|774||1.3 %||645||1.1 %|
|405||0.7 %||453||0.7 %|
Paraphrasing its 10-K, the company is dedicated to empowering clients through the potent combination of hybrid cloud technology and artificial intelligence. Its robust hybrid cloud platform and AI capabilities are instrumental in aiding clients with their digital transformations.
Essentially, they enable the reimagining of pivotal workflows at scale and the modernization of applications, amplifying agility, fostering innovation, and generating operational efficiencies.
Again, that’s how IBM sees it.
During the recent annual Bank of America Securities Global AI Conference, the company elaborated on its plans to expand in this attractive industry.
Essentially, IBM has been investing in generative AI since 2020, focusing on enterprise data rather than consumer applications.
They have built models based on datasets they know best, including code, natural language processing, IT data, and more.
IBM introduced watsonx, their AI and data platform, which is now generally available.
Their tech stack includes open-source tools delivered through Red Hat OpenShift AI, data services for managing and delivering trusted data, watsonx as the core platform, and AI systems like watsonx Assistant and Code Assistant.
IBM is also offering consulting services to support clients in their AI endeavors.
While listening to the conference, I felt like the company had finally found its edge. The focus on enterprises instead of consumers is key, as competing for consumer-focused AI solutions is just too competitive (I think).
Having said that, IBM has identified three primary use cases for generative AI that offer significant ROI.
- The first is talent automation, which involves automating repetitive tasks, leading to a 40% improvement in productivity. This use case extends beyond HR into finance, procurement, and supply chain functions.
- Customer service is the second use case, where IBM’s watsonx Assistant achieves a 70%+ containment rate in call center interactions, automating responses and providing an ROI.
- The third use case is app modernization, particularly around code, using watsonx Code Assistant. This has led to a 30% productivity gain, with an 85% code acceptance rate in early work around Ansible. IBM plans to expand this capability to other programming languages.
On top of that, watsonx is a platform with multiple uses.
- Watsonx.ai enables the training, tuning, validation, and deployment of AI models, offering a wide selection of open-source models. IBM also partners with Hugging Face and Meta (META) to provide model choices. This is one of the benefits I like so much of the enterprise-focused, as it takes away some competition risks.
- Watsonx.data focuses on making data ready for AI by using open-source query engines and integrating a vector database capability.
- Watsonx.governance, which is set to launch later this year, addresses governance concerns around AI models, data lineage, and transparency.
So far, this doesn’t just sound like a good foundation for success, but it is already paying dividends.
According to the company, it has seen good momentum with clients, primarily driven by productivity increases.
Examples include Truist Financial Corporation (TFC) automating labor-intensive summarization tasks, Samsung SDS using generative AI for product delivery, SAP implementing natural language queries powered by watsonx, and NASA creating a unique geospatial data model in partnership with IBM.
With that in mind, we all know that AI is rapidly evolving. Ideas and technologies that may be groundbreaking today could be ancient just 12 months from now.
IBM knows this and used the conference to explain where it is headed with its technologies.
According to the company, AI is expanding beyond natural language processing in 2023, with governance becoming mainstream in 2024.
By 2025, AI is expected to become more energy and cost-efficient.
In 2027, foundation models will start to scale uniquely, with AI building AI and taking over some aspects of new use cases and outcomes.
Growth & Valuation
At this point, it’s important to mention that IBM won’t turn into a one-trick pony. As impressive as watsonx is, the company has more in store.
During its Q2 2023 earnings call, the company covered the introduction of OpenShift AI, which is a unified solution for AI model management, and IBM hybrid cloud mesh, a SaaS solution streamlining application-centric connectivity in multi-cloud environments.
Additionally, IBM demonstrated the use of quantum computers for solving complex problems, indicating progress in practical quantum computing for areas like risk analysis, finance, and materials science.
During the call, the company also mentioned research from McKinsey, which found that AI could add up to $4.4 trillion to the global economy – annually.
The research focused on key areas that IBM addresses through watsonx and related solutions (emphasis added).
We estimate that generative AI is likely to deliver its biggest impact in banking, high tech, and life sciences as a percent of overall industry revenue. In banking, for example, the technology could create value equal to an additional $200 to $340 billion a year if all use cases were implemented. That’s not to say other industries won’t realize big value from deploying generative AI. All told, for instance, retail and consumer packaged goods companies could see an additional $400 billion to $660 billion in operating profits annually from the use of generative AI.
Having said that, in the second quarter, the company’s software revenue growth accelerated to 8%, with strong performance in Hybrid Platforms & Solutions, Transaction Processing, and Data & AI. Red Hat and Data & AI were significant growth contributors.
As a result, IBM reaffirmed its full-year expectations for 2023, anticipating constant-currency revenue growth of 3% to 5% and free cash flow of about $10.5 billion.
The company also expects operating pre-tax margin to expand by about 0.5 points, driven by product mix and productivity initiatives.
Software revenue growth is anticipated to be at the high end of the mid-single-digit range, with consulting revenue growth in the range of 6% to 8%.
Infrastructure revenue is expected to remain roughly flat over the mid-term model horizon.
Overall, IBM aimed for higher growth, higher-value business, and strong cash generation for the year.
Looking at analyst expectations, we see mid-single-digit annual EBITDA growth expectations through 2025.
The company’s margins are expected to gradually rise to 25.3%.
Although these growth rates aren’t as high as one might have expected given the positive watsonx developments, they finally break the company’s long-term EBITDA decline.
With regard to the company’s valuation, it used to trade close to 8x EBITDA before the pandemic. That was more than justified, given the long-term decline in EBITDA.
Now, the company is trading at 11.5x 2023E EBITDA. It’s trading at 10.3x 2025 EBITDA. Applying a 12x multiple, which I believe to be appropriate, we’re dealing with a fair price target of $176 per share, which is 17% above the current price.
The current consensus price target is $144.
However, the market is getting increasingly bullish. On September 20, RBC gave the stock an outperform target ($188).
RBC Capital analyst Matthew Swanson, who also put a $188 price target on IBM, said he is “impressed” with the depth of the company’s software platform, especially around enablement. Swanson also pointed out that after the pandemic, networks have become broader and “increasingly complex,” setting IBM up to benefit. – Via Seeking Alpha.
It also needs to be said that the company has an implied 2024E free cash flow yield of more than 8%, which protects its 4.5% dividend yield.
The company has a 2023E net leverage ratio of 2.5x (EBITDA), which is very healthy. IBM enjoys an A- credit rating.
With all of this in mind, I do believe that IBM will likely continue to outperform the S&P 500.
The only reason why I’m not in it is because I haven’t made up my mind about how I want to play future tech trends. While IBM is certainly a good play, I’m also considering buying a growth-focused ETF at some point over the next few quarters.
Needless to say, I’ll keep readers up-to-date!
In a tech world buzzing with AI advancements, IBM stands as an underappreciated giant, steering towards an AI-driven future.
Their platform, watsonx, focuses on generative AI and caters to enterprises, unveiling exceptional ROI in talent automation, customer service, and app modernization.
IBM’s strategy, outlined at the recent Bank of America Securities Global AI Conference, reveals a deep understanding of the evolving AI landscape, with a vision stretching to 2027.
Although not a high-flying tech stock, IBM, with its diverse offerings, including OpenShift AI and quantum computing, is poised for growth, backed by McKinsey’s estimations of generative AI’s potential impact across industries.
From a financial standpoint, IBM’s growth expectations and valuation appear promising. With strong margins, healthy free cash flow, and a solid credit rating, IBM is likely to continue outperforming the S&P 500.