Dive Brief:
- Companies are struggling to scale AI capabilities, according to Accenture Chair and CEO Julie Sweet, speaking Thursday, during a Q2 2024 earnings call.
- “Clients are coming to grips with the investments needed to truly implement AI across the enterprise and nearly all are finding it difficult to scale, because the AI technology is a small part of what is needed,” Sweet said.
- Though organizations are laying the groundwork for AI adoption, Accenture saw its clients prioritize large-scale transformations while cutting back on smaller, discretionary projects during the three-month period ending Feb. 29, according to Sweet.
Dive Insight:
Generative AI’s potential remains locked up in the data LLMs are eager to devour.
As organizations move into the adoption phase, vendors and service providers are racing to provide industry-tuned models, implementation expertise and, perhaps most importantly, data modernization solutions.
Accenture has already sunk billions into its AI shop.
In addition to pouring $3 billion into enterprise-grade solutions last year, the firm announced plans to acquire Udacity earlier this month, a move triggered by its broader $1 billion push to expand upskilling capabilities.
Accenture will apportion the investment in its LearnVantage client training platform over the next three years, Sweet said Thursday.
To bolster its portfolio, Accenture partnered with AWS and Anthropic to build industry-specific AI solutions and ease enterprise adoption, the companies announced Wednesday.
While Accenture’s quarterly revenues were flat year over year, the company booked over $600 million in generative AI-related business, bringing the total to $1.1 billion for the first half of the fiscal year.
Client bookings also reached record levels, according to Sweet.
The company inked $21.6 billion in new bookings, the second largest quarterly volume on record, including $10 billion in North America, the highest ever in the company’s 35-year history.
“We see clients continuing to prioritize investing in large-scale transformations which convert to revenue more slowly, while further limiting discretionary spending, particularly in smaller projects,” Sweet said.
Tech stack modernization is now table stakes for AI implementation.
“An enterprise-wide AI rotation requires a strong data foundation,” Sweet said, adding, “You can’t just jump to the great data foundation. You need to be in the cloud. You’ve got to have modern platforms.”
