IT strategy, leadership in digital organisations, and talent and capability management – curated from analysts, CIOs and technology leaders, with a link to every original source.
TopicsIT ProfessionalIT ManagementAI & MLRisk & securityCloud computingStrategy and trend
Raman Mehta became CIO of Goodyear in December 2025, aiming to transform the 128-year-old company through a startup mindset. He emphasizes speed, data quality, and alignment of technology with business goals. Mehta’s initiatives include leveraging AI for cross-functional challenges and fostering collaboration between technology and domain experts to enhance manufacturing processes.
The article matters to IT leaders because it reframes legacy systems as strategic assets for AI, not automatic replacement candidates. The key decision is whether to fund rip-and-replace programs or prioritize data readiness, governance, and targeted AI augmentation with faster, more measurable returns.
The article matters because maintenance backlog is often an ungoverned business-risk portfolio, not just an IT workload. CIOs should reassess who can accept deferred-risk decisions, how exposure is prioritized against transformation funding, and whether AI automation is being layered onto weak operational controls.
ConnectOne’s vendor screening process highlights a broader IT management issue: innovation sourcing must be governed as a portfolio and risk decision. Leaders should examine whether their intake model can test emerging vendors quickly without weakening compliance, security, accountability, or architectural coherence.
The article is relevant to IT leaders because it exposes a governance gap between AI vendor claims and enterprise proof standards. Leaders should decide how to validate AI-generated findings, manage trust and provenance risk, and measure value beyond inflated “breakthrough” narratives.
As AI workloads move into steady production, CIOs must decide when pay-per-use becomes a budgeting risk and capacity investment becomes viable. The real test is governance: can the organization forecast demand, keep utilization high, and tie AI capacity decisions to measurable business outcomes?
This article is relevant to CIOs and transformation leaders because agentic supply chains depend on governed, trusted data before automation can scale. The immediate decision is how to sequence investment between AI use cases and the master data, semantics, and controls needed to manage execution risk.
As AI arrives through vendors, platforms, and employee tools, CIOs need governance that tracks capabilities, data access, and actions continuously. The core decision is how to establish visibility, control rights, and vendor accountability before shadow AI and embedded agents create security or compliance exposure.
As AI oversight matures, enterprise leaders must decide which systems need independent assurance, who can credibly provide it, and how procurement and governance will validate assessors. The risk is not only weak AI controls, but relying on evidence stakeholders do not trust.
Companies that redesign processes around physical AI will gain significant advantages. Automation examples, like the robot lawn mower, highlight the need for shifting from traditional work cycles to continuous operations. Success requires capturing operational data and building trust through incremental adoption. The future lies in tailored automation, enhancing process efficiency one step at a time.
Delivery Hero’s Flink migration shows that real-time measurement is really a state-management problem: deduplication, attribution, enrichment and replay all move into the stream processor. For IT teams, the lesson is how latency, billing correctness, model quality and operational resilience depend on event-time discipline and stateful architecture choices.
AI-agent incidents expose a management gap between legal liability and enterprise accountability. IT leaders should define internal disclosure thresholds, strengthen vendor audit rights, and clarify decision ownership before agentic systems create cyber, compliance, or reputational risk that regulation does not yet fully address.
As AI agents move from pilots to production, IT leaders need governance that ties access decisions to business purpose, accountability, and transaction risk. The key decision is whether current IAM can support delegated, contextual, auditable machine actions without creating new security and compliance gaps.