Executive Insights for Supplier Intelligence

The market loves flashy HR tech, but much of the real value sits behind the interface.

That is easy to forget in a market shaped by polished demos, hot buzzwords and high-visibility features designed to win attention quickly. Copilots, assistants, dashboards and sleek user experiences tend to dominate the conversation. They are easy to see, easy to explain and easy to remember. But some of the most important technology in the HR stack is the part most users never notice at all.

The insurance services market in North America is operating under a different set of structural pressures than many other regions, with the life and retirement (L&R) segment exerting much influence on sourcing behavior. Insurers are managing long-term obligations amid persistent margin pressure, high regulatory scrutiny and limited tolerance for operational disruption. Unlike shortcycle property and casualty (P&C) portfolios, L&R books mandate long-term servicing commitments from carriers, spanning decades, making stability, accuracy and continuity nonnegotiable operational requirements rather than just optimization. These conditions are reshaping the way enterprises assess providers in North America.

This ISG Provider Lens® Specialty Analytics & AI Services – Supply Chain 2026 study assesses providers enabling analytics- and AI‑driven supply chain transformation across planning, orchestration and execution. These providers help improve visibility, strengthen risk management, enhance forecasting accuracy and accelerate informed decision-making across planning, sourcing, manufacturing, logistics, warehousing, inventory, transportation and procurement. The report focuses on providers’ ability to enable AI-powered decision engines, real-time insights and proactive exception management that help enterprises build resilient, responsive and adaptive supply chains. It examines how providers support the evolution from traditional process execution toward intelligent orchestration, autonomous decision-making and outcome-driven supply chain operations.

I recently wrote about the increasing popularity and range of adoption choices for the PostgreSQL database. Having done so, it is also worth taking the time to assess the health of another significant open-source database: MySQL. As with PostgreSQL, potential adopters of MySQL have a variety of different options available, thanks to a broad ecosystem of service, support and software providers. Unlike PostgreSQL, which is developed by an open community, MySQL is owned and developed by a single provider. This has been a source of tension for the MySQL provider and user ecosystem throughout the project’s history. While MySQL-related products have prospered at Oracle since it became the project’s owner in 2010, tension resurfaced this year amid calls from some members of the MySQL community for Oracle to consider handing the MySQL project over to a provider-neutral, non-profit foundation. Given this discussion, it’s timely to evaluate the past, present and potential future of MySQL.

B2B buying has moved into a more fragmented operating model: more stakeholders, more digital research, more internal consensus-building and fewer moments where the seller actually controls the conversation. The implication for CROs is clear: Revenue teams need better technology to manage what happens between meetings, not just what happens inside the CRM. The complication is that many sales organizations still rely on email threads, static decks, scattered links and seller memory to move complex deals forward. So, the question becomes: What technology helps sellers guide a buying group without forcing the buyer back into a seller-led process? The answer is the digital sales room, a shared deal space that gives buyers and sellers one place to collaborate, track progress, surface intent and move from interest to decision.

The U.S. digital engineering midsize landscape has reached a strategic inflection point, where organizations are transitioning from fragmented experimental cycles to high-velocity, autonomous ecosystems. In an era that is defined by survival of the smartest, the focus has shifted from tactical digital intervention to foundational elimination of intelligence debt across the silicon-to-cloud continuum. This structural mandate unifies R&D, operations and CX into a single, AI-led digital thread, ensuring that every physical asset and digital platform functions as a self-optimizing growth engine.

There is a familiar refrain echoing across boardrooms and pipeline reviews that the channel is underperforming, that partners are not delivering and that ecosystems are noisy and inefficient and somehow past their prime. The implication is that something fundamental has broken. That conclusion is convenient, but it is wrong. The channel is not broken. What is broken is how most organizations design, enable and experience their partners. The failure is not structural. It is experiential.

The emergence of cloud computing has had an enormous impact on all segments of the IT industry, including data platforms. All providers of data platform products have enabled their products to be deployed in the cloud and/or consumed as cloud-hosted managed services. To date, the cloud has arguably had the largest impact on analytic data platforms, where cloud infrastructure led to the emergence of a new analytic data platform architecture: the data lakehouse. Now ubiquitous, the data lakehouse decoupled compute and storage and enabled enterprises to take advantage of data lakes based on cloud object storage combined with open file formats, open table formats and analytic data processing engines. Many data platform providers are now looking to bring some of the same advantages of the data lakehouse to operational data platforms.

IT service management (ITSM) is at an inflection point. For two decades, ITSM platforms have operated as structured systems of record and workflow orchestration layers, capturing tickets, routing tasks and enforcing process consistency. That model assumed humans were the primary actors and automation was deterministic, limited and rule-based. That assumption no longer holds.

What I observed at Enterprise Connect was the disappointing specter of an industry that is collectively missing the opportunity to clarify and define its own future. Despite incredible technological advancements, many companies are instead playing defense against threats that they can’t (or won’t) articulate.

The shift roster has always been a place where strategy becomes personal. It decides who works, when they work and how predictable their lives can be from one week to the next. When scheduling becomes more automated, the consequences show up immediately in worker sentiment, manager workload, overtime spend and service levels. That is why the rise of artificial intelligence (AI)-driven scheduling feels different than other HR technology shifts. It is not just another feature in the stack, because it touches time, fairness and trust all at once.

Artificial intelligence (AI) just became the operating system for enterprise security. That was the clearest signal coming out of the RSAC 2026 Conference, and it materially changes how CIOs and CISOs should evaluate security platforms, operating models and investment priorities over the next 12–24 months.

Global capability centers (GCCs) have reached a strategic inflection point. What began as cost arbitrage vehicles is now firmly established as enterprise-grade hubs for innovation, digital transformation and long-term competitive advantages. This ISG Provider Lens® study evaluates the service provider landscape supporting GCCs across two dimensions: design and setup and ongoing optimization and enhancement. The findings reflect a market in transition, where execution capability is rapidly becoming the defining measure of leadership and the basis on which provider relevance will be judged.

The digital engineering space has undergone a fundamental change, shifting from an isolated and segmented value chain automation to a holistic model that drives enterprise-wide business value. In the U.S., this evolution has led to an increasing demand for augmented design and R&D services to achieve siliconto- service innovation, intelligent operations and connected experience services to create autonomous, informed value loops, and integrated platform and application services to ensure AI-enabled platform development across silos by leveraging agentic AI architectures. These services elevate engineering from a back-office function into a primary enabler of enterprise-wide resilience and a measurable ROI engine in a digital-human hybrid economy.

AI is redefining healthcare operations, shifting the focus from isolated innovation to enterprise-scale transformation. What began as experimentation with GenAI is now evolving into structured, outcome-driven adoption across clinical, administrative and payer workflows. The market is entering a phase where value realization, governance and scalability matter more than experimentation. At the center of this shift is the emergence of agentic AI and workflow-embedded intelligence, enabling healthcare organizations to move from assistive AI to accountable, action-oriented systems.

This study shows the life sciences services landscape progressing beyond function-specific transformation toward value-chain-wide modernization, with IT service providers and CROs both repositioning around integrated, AI-enabled and platform-led delivery. Across clinical development, patient engagement, pharmacovigilance, regulatory functions for CROs, and manufacturing, supply chain and commercial operations for service providers, the shift is moving from isolated digital interventions to connected operating models that integrate data, workflows, compliance and outcomes.

The growing strategic importance of sales and operations planning (S&OP) reflects the confluence of two major trends evolving outside of enterprise operations. One is the ongoing disintegration of the post-World War 2 liberal trade environment that began in the early 2010s and has accelerated since. The second is the increasing sophistication and approachability of S&OP software. These applications are designed to align an enterprise’s sales and marketing objectives with its operating environment (including its production and distribution assets as well as its supplier ecosystem) while respecting financial constraints. The availability of more accurate and nuanced machine learning (ML)-based forecasting systems and the growing availability of agents that shorten planning and execution cycles will enable enterprises using software to better adapt to change, anticipate risks and evaluate trade-offs to determine the best course of action faster with greater intelligence.

There is a growing disconnect between what digital commerce platforms were originally built to do and what modern revenue teams now need them to deliver. Most traditional platforms were designed to manage storefronts, catalogs and transactions. They perform well when the goal is to display products, enforce static pricing and process payments. But in today’s revenue environment, the challenge is no longer limited to completing a transaction. It is about orchestrating revenue across multiple moments, touchpoints and decision makers over time. As organizations adopt recurring revenue models, usage-based pricing and faster deal cycles, commerce technology that cannot adapt to these realities becomes a constraint on growth rather than a driver of it.

The mission to enable an autonomous enterprise, as I have articulated, remains aspirational for most organizations. Few have aligned business and technology leadership, strategy and execution to support autonomous operations at any scale. A shared definition is essential. An autonomous enterprise follows an operating model in which intelligent systems can sense conditions, decide actions, execute outcomes and continuously learn, that is orchestrated across the business, with humans setting intent, governance and accountability.

The U.S. healthcare environment is undergoing a structural shift as economic pressure, demographic change and technological acceleration reshape how payers, providers and life sciences organizations operate. With healthcare already representing 17 percent of the region’s GDP and advancing toward a 20 percent share by 2032, rising medical demand is paralleled by an unprecedented expansion in technology investment. Aging populations and the growing burden of chronic disease are increasing healthcare consumption, while intensifying cost pressures are heightening the urgency for new digital operating models that reduce friction and enable more sustainable delivery.