Your modern data infrastructure isn’t delivering even after modernization? The limiting factor could be the processes and operations surrounding the systems. Learn how to improve your operating model to best leverage infrastructure and technology
investments.
Learn how leading healthcare, insurance and life sciences companies can unleash their power in the marketplace by using upgraded cloud backbones, improved processes, tighter value realization loops, best of breed providers and DataOps/MLops
to facilitate platform-based consumption of their data.
Despite growing investments and sustained C-suite interest in advanced analytics, BI and data infrastructure, businesses and their customers often are underwhelmed by the capabilities of their data systems. And the competitive pressure from nimbler competitors and new entrants is on the rise. We also can expect growing regulatory requirements in the data realm – from customer privacy, data sovereignty, compliance, cybersecurity, to fairness and inclusivity.
To advance your data capabilities, you must grow your investments and hone your skills. ISG helps you evaluate existing investments, identify opportunities and build a cognitive enterprise.
AI investment is accelerating, but results remain uneven. Only one in four initiatives is meeting revenue impact expectations, at an average spend of $1.3M per use case. Enterprises are no longer asking whether AI works. They are being asked to prove that it pays.
AI investment is shifting decisively toward revenue-generating functions. CRM automation, sales enablement and forecasting have replaced chatbots and IT productivity tools as the leading use case priorities, reflecting enterprise recognition that productivity gains alone do not satisfy board-level scrutiny. At the same time, use cases in production have doubled since 2024, and the portfolio is diversifying rapidly, with over 300 distinct function and industry-specific use cases now in active deployment.
ISG research across 1,200 enterprise use cases shows that the strongest AI returns are currently concentrated in compliance, risk management and quality control, not in the growth and cost outcomes most enterprises originally set out to achieve
The gap between where enterprises are investing and where AI is actually delivering is the defining commercial tension of 2025. Organizations that close it by targeting functions with structured, revenue-attributable data and clear ROI measures will establish performance benchmarks that compress the window for competitors still cycling through pilots. The standard is being set now.
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Learn MoreThe adoption of private and hybrid cloud has gained significant traction in the last four quarters. Private and hybrid clouds have been popular choices over public clouds. However, the infrastructure environments have become more complex and are becoming difficult to manage, with the growing demand for these hybrid solutions. Read more to learn the latest market trends and compare providers.
Find out the key service capabilities dominating the analytics market. Topics include:
This ISG report speaks to organizations that are building data analytics programs that follow a sequence of consulting, architecture and design, infrastructure and integration and finally maintenance and support. Businesses are increasingly assessing their decisions and relying on strong scientific and statistical approaches to strategize growth as well as operational initiatives.
Use data to assess your supplier delivery against regulations and monitor customer-specific requirements. Complexity and regulatory scrutiny of third-party relationships are on the rise. Get a comprehensive and data-driven way to control risk throughout the life of the relationship.
Advanced technologies address some challenges and introduce others. It’s not science fiction to imagine that the technology is based on machine learning, which is based on identifying and repeating certain patterns. Does this mean the software could, therefore, "learn" that most candidates hired come from a specific school or town? Does this encourage bias – or even profiling?
