With TBR’s Digital Transformation market and competitive intelligence research, gain insight into how technology and services firms are investing, going to market, partnering and more as they help enterprises transition to digital solutions.
Learn how IT and business buyers approach digital applications, platforms and services within the context of disruptive market trends and emerging opportunities. Additionally, examine and dissect IT vendors’ ecosystem management as well as investments, performance and go-to-market strategies in both mature and emerging areas, including blockchain, Industrial IoT, analytics, customer experience and innovation centers, and cloud.
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Trends we’re watching in 2025:
- Digital transformation is back in fashion
- GenAI upends pyramids, even as enterprises slow their AI roll
- Ecosystem intelligence becomes a strategic advantage
Explore TBR Digital Transformation Coverage
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The Stargate Project: A Manhattan Project for the AI era
/by Alex Demeule, Research AnalystBeing the largest single technology endeavor in recent history, the Stargate Project will have a notable influence on the budding AI market, and in TBR’s opinion, no company stands to benefit more than OpenAI. OpenAI’s ultimate goal is to reach AGI, which the company defines as “highly autonomous systems that outperform humans at most economically valuable work.” While estimates about when OpenAI will reach this goal vary widely, the Stargate Project fulfills a critical requirement in the pursuit of AGI, making OpenAI a top contender to reach AGI before other firms.
Infosys’ Future: Scaling GenAI and SLM Innovation to Drive Growth and Stakeholder Trust
/by Bozhidar Hristov, Principal AnalystDeveloping 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.