The Hackett Group Reports Accelerated AI Adoption in GBS

Apr 9, 2025
The Hackett Group's latest study reveals that 42% of global business services organizations piloted generative AI in 2024, with 63% reporting productivity and cost-saving gains.

The Hackett Group has released findings from its 2025 Key Issues Study, revealing that global business services (GBS) organizations are rapidly advancing their AI adoption. In 2024, 42% of GBS organizations piloted generative AI (Gen AI), and 63% of these early adopters reported measurable gains in productivity, cost savings, and service quality announced in a press release.

The study highlights that GBS leaders are moving from early adoption to scaling Gen AI across their operations. Two-thirds of GBS leaders believe that Gen AI, including AI agents, will fundamentally reshape structured work this year. As a result, GBS technology budgets are set to increase by 10%.

The Hackett Group's report also identifies key challenges to AI adoption, such as process complexity, unrealistic expectations, and data quality issues. To overcome these obstacles, the report recommends cultivating a business culture that embraces co-intelligence, setting realistic performance expectations, and reskilling talent.

The Hackett Group emphasizes the importance of Gen AI in achieving business objectives like cost leadership and value creation, with plans to accelerate deployment across finance, IT, HR, and procurement shared services in 2025.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Enterprise AI Brief or Daily AI Brief.

Also, consider following us on social media:

Enterprise AI AI Brief AI Brief (X)
Free newsletter

Enterprise AI Brief

Weekly report on AI business applications, enterprise software releases, automation tools, and industry implementations.

Guide
Hosted Speech Recognition APIs

Hosted Speech Recognition APIs

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.