ServiceNow has expanded its platform into manufacturing with a suite of AI-native capabilities covering the full value chain from sales and configuration through quality, warranty, and factory-floor operations. The announcement, made alongside the unveiling of Industrial Connected Workforce and ServiceNow EmployeeWorks for manufacturers, positions the company directly against the fragmented stack that characterizes most industrial enterprises.

The core argument is structural. Manufacturing AI investments have accelerated over the past two years, but quality data, warranty claims, order workflows, and shop-floor processes typically run on separate systems with no shared governance layer. ServiceNow’s pitch is that AI cannot deliver end-to-end execution when the data it needs is distributed across siloed tools—and that a single platform with unified workflows changes that calculus.

The manufacturing sector’s AI problem has never been a shortage of tools — it has been the absence of tools that understand what manufacturing actually is. Infor and AWS are now pushing a direct answer to that gap, announcing a collaboration to deploy industry-specific AI agents built natively on AWS infrastructure, targeting discrete and process manufacturing enterprises that need to move from pilot programs to production at scale.

The core argument is that generic AI fails on the shop floor. Manufacturing workflows involve bill of materials hierarchies, vendor pricing cycles tied to annual model changes, returns processing chains, and procurement-to-payment pipelines that off-the-shelf models cannot navigate without substantial domain grounding. Infor’s position is that it supplies the industry-specific intelligence while AWS provides the enterprise infrastructure — Amazon Bedrock AgentCore, Amazon Bedrock, and Amazon SageMaker — to run it at mission-critical reliability.

Kyndryl (NYSE: KD) has been recognized as a Leader in three categories of the 2026 ISG Provider Lens™ Mainframes — Services and Solutions Report: Mainframe Technology Consulting, Mainframe as a Service (MFaaS), and Application Modernization Services. The triple recognition from ISG, an independent technology research and advisory firm, positions Kyndryl among the top-tier providers supporting enterprises running mission-critical mainframe workloads through a period of compounding modernization pressure.

In the Mainframe Technology Consulting quadrant, ISG cited Kyndryl’s strategy and assessment-led methodology, its targeted infrastructure optimization approach, and its hybrid cloud integration focus. The report highlighted the role of Kyndryl Bridge and agentic AI-enabled automated discovery in delivering operational insights across discrete engagements and multi-year transformation programs alike. For MFaaS, ISG noted Kyndryl’s large global outsourcing footprint and the breadth of its consumption models, which range from customer-owned environments to shared platforms including zCloud and C4i, alongside continued investment in AI-driven autonomy and observability. In Application Modernization Services, ISG recognized the company’s end-to-end approach spanning structured assessments, iterative implementation, and hyperscaler alliances, again underpinned by Kyndryl Bridge and its governance and agentic AI capabilities.

Hardware asset management — knowing what physical devices the organization owns, where they are, who has them, what software is installed on them, and when they need to be refreshed or retired — is foundational to almost every other IT function. Security teams need accurate asset inventory to understand their attack surface. Support teams need device configuration data to resolve issues efficiently. Finance teams need asset records for depreciation and insurance. Procurement teams need lifecycle data to plan refresh cycles.

Low-code and no-code platforms arrived with a promise that has been partially delivered and significantly oversold: that business users without programming backgrounds could build the software applications they needed without depending on IT development teams. The partially-delivered part is real. Workflow automation tools like Power Automate, Zapier, and Make have genuinely enabled business users to build integrations and automations that previously required developer time. The oversold part is the claim that this capability extends to applications of arbitrary complexity.

The IT support model that existed before 2020 was built around physical proximity. The helpdesk sat in the office building. Employees who needed support walked to the helpdesk or the helpdesk walked to the employee. Hardware issues were resolved by hand. The model had inefficiencies — the helpdesk was idle when nobody needed support, and wait times were unpredictable — but it had a ceiling on support complexity that physical access naturally enforced.

The cloud versus on-premises debate has settled into a more nuanced position than its early framing suggested. The argument that all workloads should move to cloud and that on-premises infrastructure would become obsolete was oversimplified. The organizations that moved all workloads to cloud and discovered that certain workload categories are more expensive to run in cloud than on-premises have been quietly repatriating those workloads for several years.

The current reality is a hybrid infrastructure landscape where the economic decision about where to run a workload depends on its specific characteristics — compute intensity, data volume, access patterns, regulatory requirements, and predictability — rather than on a blanket preference for either delivery model. Server hardware investment in this context requires the same rigor as any capital investment: a specific business case for the specific workloads that the hardware will run.

Vulnerability management programs have a dirty secret that annual security assessments and compliance audits politely decline to examine: the remediation backlog. Organizations that have deployed vulnerability scanners — Tenable, Qualys, Rapid7 — know their vulnerability count precisely. Most of them have more open vulnerabilities than they will remediate in the coming year. Many have more open vulnerabilities than they will remediate in the next three years at their current remediation pace.

Enterprise IT has adopted AI-assisted tools at an uneven pace across the four functional areas. The adoption unevenness reflects a genuine difference in the maturity of AI applications across contexts — some IT functions have clear, measurable AI use cases with documented productivity gains, while others have AI vendor claims that have not translated to operational reality at the scale most enterprises require.

The honest assessment of where AI is saving time in enterprise IT is narrow but real: specific use cases within IT support, security operations, and software development assistance have demonstrated consistent productivity gains. The broader claims — AI transformation of IT operations across all functions — remain future-oriented rather than present-tense.