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 2026:
- Customer experience becomes the battleground of AI for growth
- Data as the new (old) currency
- How vendor portfolio offerings evolve to avoid the GIGO (garbage-in-garbage-out) effect when on clients’ site
- Discretionary spend takes a new shape as IT buyers continue to shoulder more risk than the business, presenting an opportunity for vendors to deliver on service-level outcomes enabled by agents
Explore TBR Digital Transformation Coverage
Market & Competitor Benchmarks
Market and competitor benchmarks provide a comparison of vendor performance in a market, including analysis on vendor strategies, financial performance, go-to-market and resource management. The research graphically portrays comparisons of vendors by myriad metrics, calling out leaders, laggards and business models. Defensible, data-informed views of market opportunity and operational best practices are highlighted in each publication. TBR also provides benchmark data in Excel pivot tables.
Current Market & Competitor Benchmarks:
- Digital Transformation: Analytics Professional Services Benchmark
- Digital Transformation: Digital Marketing Services Benchmark
Customer Research
Ecosystem Reports
Market Landscapes
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Infosys Moves From AI Experimentation to Enterprise Orchestration as Infosys Topaz Takes Center Stage in the Company’s Next Chapter
/by Bozhidar Hristov, Principal AnalystInfosys hosted more than 200 clients, partners, analysts and advisers during its flagship event, Infosys Americas Confluence 2026. The packed agenda included Partner Day and Analyst Day tracks, client stories, partner presentations and Living Labs demos of both Infosys- and partner-developed solutions and services. AI dominated the agenda, but the conversations were notably different than those at Confluence 2025. Last year, Infosys and its clients largely focused on the prerequisites for enterprise AI adoption, including data readiness, governance, architecture and talent. This year the discussion shifted toward what happens when enterprises actually begin scaling AI; the need to address rising complexity, agent governance, token economics, cybersecurity and model choice; and the ever-difficult task of proving business value. Infosys used the event to reinforce its ambition to become a leading enterprise AI services provider, positioning Infosys Topaz — and increasingly Infosys Topaz Fabric — as the enabling layer connecting models, applications, data business processes and partner technologies.
Gemini for Industries: Context, Governance and the Changing Role of SI Partners
/by Catie Merrill, Senior AnalystThroughout 2026, Google’s Gemini for business has evolved from a set of components to a unified solution for practical AI. Today, it is used by 90% of the Fortune 100 and has become the fastest-growing product in Google Cloud history. Two complementary pieces exist: the application — which is designed to be the “front door” for the workplace, drawing on context from Workspace as well as other productivity apps (e.g., M365, Slack) — and the platform for building, customizing and operating AI. New FinOps capabilities and the inclusion of the Antigravity harness in all subscriptions are recent examples of how Google Cloud is addressing both sides of the portfolio, unlocking a strategic flywheel where Gemini creates demand and fuels usage of the Gemini platform. If Google Cloud can effectively deliver both as part of a unified solution, helping customers combat agent sprawl while staying true to its roots of openness and flexibility, it will take a big step to address one of AI’s central challenges: how to translate capable AI models into something that can scale at enterprise levels.