TBR’s Business Insights

Fujitsu’s investments in Fujitsu Kozuchi have equipped the company well to appeal to clients’ needs around the technology, providing opportunities to supply analytics with associated text, vision and trust in support of business operations. While AI technology evolves rapidly to include new capabilities, Fujitsu’s approach to developing the platform and leveraging partners and internal capabilities gives it an advantage in offering a wider set of services.

The new federal fiscal year began with a continuing resolution (CR) that extended government funding until Dec. 20, when a subsequent CR was enacted to fund federal operations until March 14, 2025. Further CR extensions in federal fiscal year 2025 (FFY2025) would cause budget delays that could impede the ability of federal IT contractors to convert backlog into revenue, but most vendors expect revenue growth to remain on a solidly upward growth trajectory in FFY25.

Vendor consolidation and enterprise optimization of existing digital stacks have compelled IT services companies and consultancies as well as their ecosystem partners to think strategically about who to partner with and how to secure and expand their position within the ecosystem. As a result, aligning business priorities with alliance partners will allow IT services companies and consultancies to develop a more empathetic approach to technology-fatigued buyers. Additionally, understanding pricing and commercial structures backed by common knowledge management programs will elevate the value of joint services and appeal to enterprise buyers’ appreciation of a separation of labor, supported by greater transparency and accountability.

Developing a client-ready AI-first portfolio is not a strategy unique to Infosys, but keeping pace with the rapidly evolving generative AI (GenAI) market highlights the company’s appetite for innovation and helps it strengthen stakeholder trust. Over the past 24 months, a large portion of vendor-client discussions focused on experimenting with developing and running large language models (LLMs), often fed with either public or nonessential data. Growing adoption of the technology has introduced the need for developing SLMs that are either function or industry specific.