IQM Quantum Computers began trading on the Nasdaq Global Select Market this week under the ticker IQMX, closing a business combination with Real Asset Acquisition Corp. and becoming the first European quantum computing company listed on a major U.S. exchange. The listing is less about a funding milestone and more about a bet on a specific architecture: full-stack, on-premises superconducting quantum systems that customers own and operate themselves, rather than access purely through the cloud.

Forrester has released the full conference agenda for its B2B Forum EMEA 2026, set for 28–29 September 2026 in London. The event’s theme, “Hello, GTM Singularity,” centers on a claim that’s hard to dismiss: Forrester puts the share of business buyers now using generative AI or conversational search during the buying process at roughly nine in ten. That shift is reshaping how B2B organizations approach discovery, evaluation, and purchase across marketing, sales, product, and customer success functions.

Four enterprise software franchises that defined the last software decade entered the summer of 2026 carrying the same wound. Salesforce trades near $152, down roughly a third on the year and off 38% from where it stood twelve months ago. ServiceNow, having executed a five-for-one split in December, changes hands around $95 after surrendering more than half its value from the 2025 peak. Atlassian sits in the low $80s, down some 45% year to date and roughly 74% off its one-year high. Monday.com, the smallest of the group, has lost about 73% over a year and trades in the mid-$60s against a 52-week high near $317.

The May–June 2026 technology conference window is unusually dense, reflecting the sector’s attempt to process several simultaneous inflection points: the industrial deployment of AI beyond the pilot phase, the semiconductor supply chain’s geographic rebalancing, and the maturing of open hardware architectures from academic interest into commercial infrastructure. For investors and operators tracking these transitions, the conference calendar functions less as a social calendar and more as a forward indicator — announcement timing, speaker cancellations, and floor traffic are often more informative than the formal presentations.

xAI is running its 550,000-GPU Colossus fleet at roughly 11% model FLOPs utilization. An internal memo from infrastructure lead Michael Nicolls confirmed the figure and set a target of 50% — a number that sits at the high end of an industry range where even the best operators, Meta and Google, are only achieving 43–46%. For a company that built the world’s largest AI supercomputer in 122 days, the software stack is still catching up to the hardware.

Every few months the same headline resurfaces: DevOps is dead. In 2026, the claim has more surface credibility than before — job boards are brutal, the title is disappearing from org charts, and AI is eating the work that once defined the role. None of that means the function is gone. It means the floor dropped out and the ceiling went up.

The mechanics are straightforward. AI systems built into cloud platforms now handle what junior and mid-level DevOps engineers spent most of their time on — infrastructure provisioning, pipeline configuration, alert triage, rollback decisions. That work isn’t being done by fewer people out of choice. It’s being done by machines out of necessity, because the scale modern systems operate at makes human-in-the-loop intervention a bottleneck. Netflix deployed 4,000 times daily last quarter. Amazon pushes a change every 11.7 seconds across global infrastructure. No headcount solves that. Automation does.

Not dead — but under serious pressure, and some of its foundational assumptions are eroding fast.

Agile was designed around the scarcity of working software. Writing code is slow, human attention is finite, and iteration is expensive. Its rituals — sprints, standups, story points, velocity tracking — exist to manage that scarcity. AI coding assistants like GitHub Copilot, Cursor, and Claude Code dramatically compress the time from intent to working code. When a sprint’s worth of boilerplate takes an afternoon, the sprint cadence starts to feel like bureaucracy.

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.

Systal Technology Solutions has been named a Leader in the 2026 Gartner Magic Quadrant for Managed Network Services, marking its third consecutive inclusion in the report and reinforcing its position within the global managed services landscape. The recognition reflects how enterprise network environments continue to evolve toward highly complex, multi-vendor and hybrid cloud architectures where operational stability, automation, and security integration are becoming inseparable priorities. In this context, Systal has been highlighted for its approach of acting as an embedded extension of enterprise IT teams, supporting both day-to-day operations and longer-term transformation initiatives.

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.