Meta Invests Heavily in Enterprise AI Development

Meta's CFO Susan Li highlighted the vast potential of enterprise AI during the company's third-quarter earnings call on Wednesday. The space is rapidly becoming a major battleground, and Meta believes it remains largely untapped.

Traditional companies in this sector have not yet secured a strong hold on the market, according to Li. In contrast, newer entrants have made significant investments in model development, which has given them an edge. This trend is evident among some leading companies that did not exist just a few years ago.

Meta itself has been investing heavily in AI model development, said Li. The company's efforts have yielded increasingly competitive models that can benefit both small businesses and enterprises alike. These advancements are being leveraged to support the launch of Meta's Enterprise Platform, announced on September 28.

The platform is being built out by Chirantan "CJ Desai, former CEO of MongoDB, who has joined Meta for this purpose. By combining its AI models with business-facing products and tools such as APIs and integrated AI agents in messaging and social platforms, Meta aims to compete across parts of the enterprise software and AI markets.

Li emphasized that the company's goal is to enable businesses to adopt AI agents for a wide range of tasks including software development, productivity, sales, marketing, and customer service. This could potentially become a new diversified recurring revenue stream for Meta, supporting multiple business models.

Meta's expansion of consumer AI offerings is a clear indication that the company is taking a long-term approach to adoption and product economics.

The AI ambitions are being driven by significant demand, with more than 10 million people in the U.S. and Canada setting up Muse personal agents within six weeks of launch. This rapid uptake suggests that users are eager to integrate AI into their daily lives, providing a solid foundation for future growth. A quarter of these users are active on any given day.

Meta's advertising business is fueling its AI ambitions, with third-quarter revenue soaring 26% year over year to $64.6 billion. This exceeded analysts' expectations of $63.3 billion, indicating strong ad performance and favorable macroeconomic conditions. The global average price per ad increased by 14%, reflecting the growing demand for Meta's advertising services.

The company is also exploring innovative funding mechanisms, including a new Class C stock share class. This will enable Meta to fund its AI buildout, pay employee stock compensation, and make strategic acquisitions, providing a flexible financial framework to support its long-term growth strategy.

Meta's capital expenditures have been on a significant upward trajectory, reaching $34.9 billion in the third quarter of this year. This represents a substantial increase from the same period last year, when capex stood at $19.4 billion.

The company has revised its full-year capital expenditure forecast upwards to $135 billion, while maintaining the upper end of the estimate at $145 billion. This indicates that Meta's investment in infrastructure and other areas is accelerating, driven by growing demand for its services and products.

Total expenses have also surged, rising 68% year-over-year to $51.6 billion. This increase reflects higher spending on infrastructure, as well as costs associated with third-party AI token purchases and a significant charge related to an agreement with multiple state attorneys general in August.

Meta has provided guidance for its fourth-quarter revenue, forecasting between $73 billion and $76 billion. This range is slightly below the consensus estimate of $73.75 billion among analysts, but it remains to be seen whether the company will meet this target.

In other news, several companies have announced changes to their chief financial officer (CFO) roles. Wiser Solutions has appointed Rick Buffington as its new CFO, bringing with him experience in finance and operational leadership from his previous roles at MultiTracks.com, Accruent, and Community Brands.

A recent report by Skillsoft highlights a concerning trend in corporate America: despite acknowledging skills gaps within their organizations, many managers struggle to address them effectively.

The Workforce Readiness Report: Execution Gap Edition" surveyed over 2,000 employees, managers, and executives across the US, UK, and Germany, revealing that while 77% of managers recognize skills gaps, only a minority are taking concrete steps to rectify them. In fact, an alarming 54% admit to frequently or always assigning tasks to employees who lack the necessary skills.

This disconnect between awareness and action has serious consequences for business performance, including quality issues, delayed adoption of AI technologies, slower service delivery, employee burnout, and customer dissatisfaction. Companies must take a more proactive approach to understanding their workforce's capabilities and aligning learning and development with business priorities.

To bridge this execution gap, companies need to provide timely support and resources that enable employees to develop the skills required for success. This involves moving beyond mere training programs and focusing on delivering meaningful business outcomes through workforce readiness. By doing so, organizations can unlock the full potential of their employees and drive long-term growth.

The stakes are high: a recent wave of AI-fueled cyberattacks has highlighted the risks associated with inadequate workforce readiness. As experts warn of an increasing threat landscape, corporate America must prioritize workforce development to stay ahead of emerging challenges.

As experts warn of an increasing threat landscape, corporate America must prioritize workforce development to stay ahead of emerging challenges.

The longevity of AI models and their underlying infrastructure remains uncertain, according to Ming Lu, a senior advisory partner at KKR and former executive chairman of KKR Asia Pacific. He suggests that the process of developing long-lasting data centers, semiconductors, and AI models will take decades, rather than months or years. This implies that companies must be prepared for continuous investment in these areas. The future of AI adoption will therefore depend on sustained corporate commitment to innovation.