Accenture PLC investors experienced a significant downturn on Thursday, marking their toughest day in four years. The management consultancy revised its full-year earnings forecast downward, citing decreased client spending and prolonged delays in corporate decision-making as primary factors.
Despite these challenges, the company, trading as ACN, -8.10%, underscored its advancements in the generative artificial intelligence (genAI) market. It announced plans to double its AI workforce within the next two years, indicating a strong commitment to this burgeoning sector.
In the fiscal second quarter, new bookings for generative AI exceeded $600 million, bringing the total to $1.1 billion for the first half of the fiscal year. CEO Julie Sweet reiterated the company’s dedication to expanding its footprint in the genAI domain.
During the post-earnings call with analysts, Sweet emphasized Accenture’s ambitious plans for expanding its data and AI workforce.
“We presently boast over 53,000 proficient data and AI practitioners, aiming to double our data and AI workforce from 40,000 to 80,000 by the conclusion of fiscal year 2026,” Sweet stated, as cited in an AlphaSense transcript.
Despite this optimistic outlook, Accenture faced a significant setback as its stock plummeted by 7.3% to reach a two-month low during morning trading. This decline made Accenture the top decliner in the S&P 500 index, overshadowing other stocks.
Notably, this marked the most substantial single-day decline since its 8.4% drop on March 16, 2020, during the peak of the pandemic panic.
The stock’s sharp decline followed Accenture’s mixed fiscal second-quarter earnings report. While the company exceeded profit expectations, it fell short on revenue. Moreover, Accenture revised its fiscal 2024 guidance downwards, contributing to investor concerns and the subsequent stock sell-off.
As 2024 unfolded, Sweet remarked on “another shift towards constrained spending by our clients.” She attributed this to changes in the composition of new bookings, diverging from initial projections.
“We’re seeing clients persisting in prioritizing investments in extensive transformations, leading to slower revenue generation and additional constraints on discretionary spending, particularly in smaller projects,” elaborated Sweet. “Moreover, we’ve encountered ongoing delays in decision-making and a slowdown in spending pace.”
For the quarter ending on Feb. 29, quarterly net income soared to $1.68 billion, or $2.63 per share, up from $1.53 billion, or $2.39 per share, in the corresponding period the previous year. Adjusted earnings per share, excluding exceptional items, reached $2.77, surpassing the consensus estimate of $2.66.
Despite surpassing revenue expectations, experiencing a slight dip to $15.8 billion from $15.81 billion, Accenture fell short of the FactSet consensus, which predicted a rise to $15.85 billion.
New bookings saw a decrease of 2%, amounting to $21.58 billion. Consulting bookings totaled $10.52 billion, while managed-services bookings reached $11.06 billion.
Looking ahead, the company anticipates third-quarter revenue ranging from $16.25 billion to $16.85 billion, below the current FactSet consensus of $17.02 billion.
In fiscal 2024, Accenture adjusted its guidance range for revenue growth to 1%-3%, down from the previous 2%-5%, and for adjusted EPS to $11.97-$12.20 from $11.97-$12.32. The FactSet EPS consensus remains at $12.24, with a revenue consensus of $66.17 billion, suggesting a 3.2% growth.
Year to date, Accenture’s stock has only seen a 0.5% increase, compared to the S&P 500’s 10.1% rally.
Accenture’s CEO, Sweet, emphasized the widespread recognition of AI’s importance, but noted the varying degrees of AI application scalability, depending on the strength of clients’ digital infrastructure.
“While most clients acknowledge the investment needed for comprehensive AI implementation, scaling remains a challenge, as AI technology is just one part of the equation,” she stated.
Sweet stressed the importance for clients to transform technology, data, and AI. She highlighted the significance of a solid “digital core,” operational changes, employee training, and the development of AI-centric capabilities, all while fostering a deep understanding of AI mechanisms.
