Executive Insights for Supplier Intelligence

The 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.

For decades, organizations have treated sales incentive compensation as a separate entity, isolated from the broader total rewards strategy. This isolation stems from historical operations, where sales compensation is designed and managed within Sales, with periodic budget oversight from Finance, while HR focuses on base pay, benefits and equity. Unfortunately, this siloed approach, further reinforced by outdated technology and entrenched business practices, limits companies from fully leveraging their total rewards strategy. As a result, I predict that by 2028, many companies will find their compensation processes ineffective, prompting a critical assessment of their alignment with desired priorities and values.

The six costliest words in managing a finance department are, “We’ve always done it this way.” The record-to-report (R2R) cycle describes the process of finalizing and summarizing the financial activities of a business for a specific accounting period—typically a month, quarter or fiscal year. It is important to note that R2R exclusively covers the activities between recording (keeping the books) and reporting (publishing financial statements and management accounts). It involves completing various tasks to ensure that all revenue, expense and other financial transactions are properly recorded, accounts are balanced and accurate financial statements can be prepared.

Ten years have passed since artificial intelligence (AI) first appeared in sales technology, and the results are mixed. Early tools applied rudimentary machine learning (ML) models to customer relationship management (CRM) exports, assigning win probability scores or advising on the “ideal” time to call. The mathematics was sound, the demos impressive, yet adoption faltered because little thought was given as to how sellers should use this information.

In an era defined by the relentless advance of cyber threats, enterprise security leaders grapple with an overwhelming landscape. Enterprises face complex challenges as sophisticated attacks and increasing responsibilities stretch security teams thin. Many describe daily experiences as perpetual "firefighting," trapped in cycles of reacting to incidents rather than developing proactive strategies to fortify defenses.

Enterprises are transforming their HR operating models to align with evolving talent strategies, EX goals and digital maturity aspirations. With mounting pressure to deliver hyperpersonalized employee journeys, ensure global compliance and drive cost efficiencies, organizations are increasingly turning to multiprocess human resources outsourcing (MPHRO) providers for scalable, agile solutions. Today’s MPHRO engagements extend beyond  mere transactional efficiency; they have evolved into strategic partnerships that help enterprises build future-ready workforces. From recruitment and onboarding to learning, payroll and employee engagement, MPHRO providers are central to modernizing HR delivery models.

Global market dynamics are currently characterized by uncertainty and turbulence, driving organizations to increase their focus on cloud-related investments aimed at improving business visibility, predictability and resilience. Tech spending for 2025, especially related to the Microsoft AI and Cloud ecosystem, is mainly driven by investments in cybersecurity, cloud-enabled (and AI-fueled) innovation, data and analytics, digital transformation and infrastructure modernization, particularly hardware/device refresh initiatives as Windows 10 approaches the end of life.

Particularly with the integration of generative and agentic artificial intelligence (AI) technologies. Generative AI usecases have quickly proliferated across all aspects and phases of engineering- spanning from R&D, design, build, test, run/ operations, to aftermarkets, field services, and ultimately in the digital business platformization services. These innovations have transformed various sectors, including chip design, precision manufacturing, life sciences, and service platforms in banking, financial services, insurance, and retail. Hyperscalers—large-scale cloud service providers—have played a pivotal role in this transformation by offering advanced AI services tailored to engineering design applications.

The global capability center (GCC) market is undergoing a rapid transformation, evolving from traditional cost-saving hubs to strategic enablers of enterprisewide digitalization. Organizations are increasingly looking beyond labor arbitrage and are instead positioning their GCCs as CoEs that drive innovation, enhance CX and optimize business operations.

The chemicals industry drives the U.S. economy by generating high-paying jobs, fueling R&D investments and supplying essential chemicals for daily life. Trade is crucial to the industry’s competitiveness. As the second - largest manufacturing export sector in the U.S., the chemicals industry recorded a trade surplus exceeding $30 billion in 2023, with exports supporting nearly 200,000 jobs.

In an earlier Analyst Perspective, I discussed data democratization’s role in creating a data-driven enterprise agenda. Building a foundation of self-service data discovery, data-driven organizations provide more workers with the ability to analyze and use data. I’ve also examined how generative artificial intelligence (GenAI) could revolutionize business intelligence software by using natural language interfaces to lower the barriers to working with analytics software. Today, however, data democratization ensures that access is not limited to analytics software. Users in different roles should be able to use data through whatever applications or tools best align with their business workflow and objectives. This is the driver behind growing interest in a new category of products that enable headless BI.

For years, HR technology has been seen primarily as an administrative tool—handling records, payroll and compliance. But what if its real power lies in shaping workforce strategy, building community and driving transformation?

There are contact centers, and there is customer experience. They are not the same thing, even though we have a tendency to conflate them or at least discuss them in tandem for operational and planning purposes. This is mostly fine, but it does obscure one basic difference that will matter more as enterprises succeed at integrating contact centers into enterprise CX strategies. That difference is in metrics and, by extension, how we define success.

AI, like analytics, must lead to action. Too often, in both cases, too much of the exercise is left to the reader. We have tools to provide sophisticated analyses, including AI platforms that can be used to predict many types of behavior, but we fall short in helping the workforce know what to do with that information. Some examples are more obvious, such as fraud detection. If a transaction is predicted to be fraudulent, the transaction should be blocked. But even this example is not as cut and dry as you might think. I’m sure many of you have been frustrated standing at a hotel check-in desk or at a retail counter attempting to make a purchase when your credit card transaction has been denied. What is the appropriate action or set of actions that should be taken?

Agents and “agentic AI” are all the rage now, eclipsing last year’s focus on artificial intelligence (AI) and generative AI (GenAI). They are a way to automate work almost effortlessly so that repetitive and boring tasks get done with the least amount of effort and perhaps, more consistently. In business software, a broad range of software providers are claiming agents to be a panacea that can improve performance and lower costs. They are alluring, with an almost unlimited number of potential use cases. Agents are an important evolutionary step in the design of business software, similar to the transition from procedural programming to event-driven programming that accelerated in the late 1980s. That paradigm shift enabled business software to be more flexible and responsive in replicating how work is performed. Adding agents to the considerable body of well-developed business applications will take the capabilities of these applications to the next level.

Conversational automation leverages artificial intelligence (AI)-powered agents, chatbots and virtual assistants to automate both customer interactions and internal processes. These systems understand natural language, sentiment and intent, generating relevant responses and executing actions based on user input. The software provider landscape is analyzed in the ISG Buyers Guide for Conversational Automation.