5 Ways to Optimize Application Sourcing Costs in an AI-Enabled Market
Application sourcing —software, support, labor and managed services — presents a prime opportunity to reduce spend and improve value realization.
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Learn MoreThe shift toward a Zero Trust Network Access (ZTNA) framework is a strategic move for enterprises aiming for an enhanced security posture. ZTNA operates on the principle of "never trust, always verify," ensuring that every user and device is authenticated and authorized before accessing resources. In this context, Privileged Access Management (PAM) emerges as a critical enabler for an effective ZTNA implementation.
ISG Software Research is expanding our ISG Buyers Guide™ coverage to include the topic of sustainability. Our buyers guide will focus on sustainability exclusively rather than the full spectrum of environmental, social and governance topics. One reason is that gathering, managing, analyzing and reporting environmental data under the various frameworks and statutes presents enterprises with considerably greater challenges than the other two. For most, it is likely the least well defined and the hardest for acquiring and managing environmental-related data. ISG Research asserts that through 2027, one-half of enterprises will have insufficient data and software to adequately measure their environmental, social and governance metrics to inform their governance strategy, risk management and targets.
Frontline work is being reshaped from every direction: tighter labor pools, unpredictable demand and a workforce that expects more than just a paycheck. Yet too many organizations are still treating workforce scheduling like a game of Tetris—moving blocks around and hoping they fit. It’s time to admit that smart scheduling isn’t just a nice-to-have—it’s a frontline imperative. And if the enterprise is still thinking of it as “just logistics,” it's already falling behind.
I have previously described how data as a product was initially closely aligned with data mesh, a cultural and organizational approach to distributed data processing. As a result of data mesh’s association with distributed data, many assumed that the concept was diametrically opposed to the data lake, which offered a platform for combining large volumes of data from multiple data sources. That assumption was always misguided: There was never any reason why data lakes could not be used as a data persistence and processing platform within a data mesh environment. In recent years, data as a product has gained momentum outside the context of data mesh, while data lakes have evolved into data lakehouses. It has become increasingly clear that data lakehouses and data as a product are well matched, as the data intelligence cataloging capabilities of a lakehouse environment can serve as the foundation to enable the development, sharing and management of data as a product.
Conversational automation is one of those software segments that means something different depending on who you are or your role in an organization. According to my colleague Jeff Orr, the core of the idea is that conversational automation tools benefit from artificial intelligence (AI), allowing software agents, chatbots and virtual assistants to automate customer interactions and internal processes. This broad definition hits the mark, I think, because it identifies the core functions without putting too tight a straitjacket on the technology itself, which is developing very quickly. The software provider landscape is analyzed in the ISG Buyers Guide for Conversational Automation.