The strategy stories enterprise IT leaders actually read.
Volume 342, Issue 3 · 20 articles from 7 issues
New research shows CIOs and CTOs now lead AI initiatives as enterprises report measurable ROI.
Dresner Advisory Services research finds CIOs and CTOs oversee AI in more than 50% of organizations surveyed. Fifty-eight percent report ROI above 12%, with 9% exceeding 50%.
As AI ties to business outcomes rather than experimentation, responsibility shifts from CDOs toward technology leadership. Organizations prioritizing custom AI capabilities over vendor solutions report stronger competitive differentiation, and data maturity remains critical to success.
MSSP Alert argues AI turns employees into software builders, creating visibility, access and governance gaps.
This MSSP Alert commentary argues AI is turning many more employees into software builders, whether they think of themselves that way or not. People can now create applications, connect systems and build agents without going through the usual engineering process.
That means security teams have considerably more to watch and far less visibility into what exists.
The article covers the resulting challenges around visibility, access and governance. For MSSPs, it frames the shift as both a risk to manage and a service opportunity.
Capturing metadata at collection time prevents the loss of context that accurate AI models require.
Organizations pursuing AI face a paradox: overly cleaned data removes context essential for accurate predictions. Rather than sanitizing data after collection, experts recommend annotating and tagging information at the source to preserve lineage, provenance, and situational detail.
In-stream labeling captures what is free to capture and unrecoverable later, such as system origin, while allowing lost context to be reconstructed.
Companies including LinkedIn and Miro achieved substantial improvements in AI accuracy by implementing schema validation and metadata attribution earlier in data pipelines, turning governance into a proactive practice rather than reactive cleanup.
CIOs escaped the feared midyear AI reckoning, but boards are intensifying scrutiny of returns.
While CIOs avoided mass layoffs after the midyear AI ROI deadline passed, organizations are tightening focus on AI spending and measurable outcomes. Only 27% of organizations expect near-term ROI even though 71% plan to increase AI spending. Successful CIOs are narrowing their approach to specific use cases tied to metrics rather than broad experimentation.
The pilot-to-production gap remains the persistent obstacle.
Rising IT professionals describe how business acumen and soft skills open the path to C-suite technology roles.
CIO.com's Next CIO Award winners discuss tactics for progressing from mid-level IT management into senior leadership.
Key strategies include developing business understanding alongside technical expertise, building cross-organizational relationships, taking high-impact stretch assignments, learning from failure and cultivating mentors.
Leaders emphasize that communication, empathy and the ability to solve business problems rather than only technical ones are critical for reaching CIO and other executive roles.
AI agents are moving beyond enterprise boundaries to interact across organizations, requiring new infrastructure for secure coordination.
As AI agents begin operating beyond individual companies and coordinating with external systems, enterprises face new challenges around identity, trust, and interoperability.
The emerging internet of agents infrastructure, exemplified by initiatives like AGNTCY, aims to enable secure agent-to-agent communication across organizational boundaries through standardized discovery, identity verification, messaging, and observability.
Without proper foundational layers for identity, data interoperability, and cross-enterprise protocols, agents will remain siloed within individual applications.
Enterprise leaders must establish agent identity frameworks, design for interoperability, and prepare data for seamless exchanges.
Gartner finds only 21% of C-suite executives consider their peers AI savvy.
Gartner research finds only 21% of C-suite executives consider their peers AI savvy, which is a leadership gap rather than a training gap. The argument is that CIOs need to move past general employee enablement and build executive-level understanding that pairs AI knowledge with strategic judgment.
The goal is not to make executives technical, but to let them question assumptions, evaluate trade-offs, and decide on governance, investment, workforce transformation, and competitive positioning.
Organizations that invest in targeted executive education convert AI initiatives into measurable business results more reliably.
CIOs need unified AI control planes to govern models, tools, and agents across enterprise deployments.
Enterprise AI deployments span multiple teams using copilots, agents, and integrated workflows, creating governance gaps without centralized oversight.
An AI control plane provides CIOs with one operational layer to manage model access, data exposure, agent actions, and compliance across the organization.
The article outlines how CIOs should map AI assets, establish tiered policies based on risk levels, implement routing by sensitivity and cost, and maintain comprehensive audit logs.
Effective governance balances freedom for innovation with necessary controls over data exposure and business impact before AI adoption outruns accountability.
Despite growing MFA adoption, vulnerabilities persist across authentication methods requiring comprehensive security strategies.
While multifactor authentication usage has increased to 87% among larger enterprises, implementation remains inconsistent and often ineffective against sophisticated attacks.
The article details common MFA compromise methods including fatigue attacks, social engineering, cookie theft, and targeting weak authentication systems.
Security teams must adopt adaptive authentication approaches, implement FIDO protocols for sensitive systems, conduct regular workflow analysis, secure password reset processes, and prioritize high-value user accounts to strengthen MFA defenses against evolving threats.
What starts as a small AI experiment can quickly spiral into budget-busting invoices -- here's how CIOs are taking control before costs explode.
AI costs mirror early cloud computing challenges. Because usage scales unpredictably rather than under fixed contracts, traditional budgeting methods are ineffective and lead to surprise invoices that exceed planned budgets.
Hidden expenses emerge from multiple sources, including decentralized employee subscriptions, agentic AI performing complex multi-step tasks, API consumption and shadow AI purchases that bypass IT approval processes.
CIOs share their insight on how to control costs, including establishing AI cost centers with chargeback models, setting real time usage thresholds, monitoring autonomous agent activity, standardizing model selection and applying FinOps principles with predefined success criteria.
CTOs align technology investments with business goals through phased planning and scalable architecture.
Technology roadmaps serve as blueprints connecting technical investments to long-term business objectives.
CTOs begin by understanding revenue goals and operational priorities, then assess current technology stacks to identify technical debt and security gaps.
Scalable strategies emphasize modular designs, microservices, and cloud-native approaches that enable incremental expansion.
Phased implementation addresses infrastructure stabilization first, then automation and data platforms, with a long-term focus on AI and competitive innovation. Success measurement relies on performance metrics including system availability, deployment frequency, and customer satisfaction.
CIOs must evolve from technical experts to business leaders who build teams and drive transformation.
The CIO role has shifted fundamentally from managing technology to driving business transformation through strategic leadership.
Modern CIOs succeed by building teams with collective expertise rather than possessing all the answers themselves, fostering environments where constructive disagreement and candor drive innovation.
Today's CIOs must balance technology knowledge with business acumen, understanding customer needs, supply chains, and operations to deliver measurable value. As AI makes technology more accessible, the question shifts from whether something can be built to whether it should be.
The most successful CIOs connect technology with strategy and help organizations adapt faster than competitors.
ChatGPT, Claude, Grok and Copilot all degraded together on one Azure region failure, exposing concentrated dependency risk.
On September 3, a failure in Microsoft Azure's East US region degraded or darkened ChatGPT, Claude, Grok and Microsoft's own Copilot within roughly 90 minutes.
Downdetector logged more than 37,000 reports for ChatGPT alone, and combined counts passed 66,000 before mitigations landed. What made the event notable was not scale but simultaneity: three fiercely competing AI labs that each spend billions differentiating their models all buckled at once because they shared a regional dependency.
Gemini stayed largely upright because Google runs it on its own vertically integrated stack, which the author calls architecture rather than luck.
Enterprise IT complexity grows from years of reasonable decisions that become difficult to untangle.
Enterprise IT environments become increasingly complex not through poor planning but through the accumulation of smart decisions made under real pressure.
Author Michael Shonholz describes three phases: accumulation, as capability is added to meet business demand, drift, as workarounds appear outside official channels, and lock-in, where interdependencies make change risky.
Organizations waste roughly 28% of cloud spending maintaining overlapping capabilities. Successful simplification requires starting with contract audits, building comprehensive environment maps, and aligning the work with business cycles rather than IT timelines.
Physical AI systems are moving beyond prototypes into enterprise deployment across manufacturing, logistics, and infrastructure.
Physical AI, valued at $92 billion today, is projected to exceed $489 billion by 2030. Strong use cases require labor-intensive tasks in constrained environments with repeatable, measurable outcomes.
Seven key applications include manufacturing robots and quality inspection, autonomous vehicles and drones, warehouse coordination systems, surveillance and security, smart buildings, and smart city infrastructure.
Companies such as Boston Dynamics and Symbotic have deployed thousands of autonomous systems across multiple industries, demonstrating practical viability beyond experimental pilots.
AI is collapsing the skill barrier to nation-state-grade attacks, forcing defenders toward autonomous response.
The piece frames AI-enabled attack tooling as the latest step in a long historical pattern - firearms, crossbows, irregular warfare, drones - where new technology lets small actors challenge far larger ones.
It points to a disclosed largely autonomous AI-enabled attack in Taiwan in August 2026, in which eight agents mapped systems and harvested data in parallel, and to earlier China-linked use of coding agents for reconnaissance, exploitation and exfiltration with minimal human involvement.
The argument for CISOs is that human-analyst-paced security cannot match attackers running continuously and in parallel. The author calls for defensive AI operating under mission-command style guardrails, and warns the advantage goes to whoever learns to use it first.
If We Want to Implement AI Successfully, We Need to Completely Change How We Do Business
The author argues that successful AI adoption requires fundamental business transformation, not simply bolting AI onto existing workflows.
Organizations should establish centers of excellence empowered to vet, test, and validate AI tools across the company, preventing AI sprawl and shadow AI usage.
Drawing parallels to the delayed productivity gains from electricity adoption, the piece contends that general-purpose technologies require organizational restructuring.
Companies that succeed will give middle-level teams authority to experiment with and implement AI solutions, creating a working rhythm for adoption rather than leaving it to scattered pilots or executive guesswork.
Negotiating AI Licensing and Costs: A Step-by-Step Guide for CIOs
As AI becomes embedded in enterprise software, vendors are charging premium prices for capabilities that should be standard features.
CIOs must separate baseline improvements from genuinely new capabilities, identify hidden costs in consumption-based pricing models, and develop negotiation strategies involving legal, procurement, and engineering teams.
Key tactics include conducting proofs of concept, securing at-will termination clauses, and accounting for integration and data preparation expenses that often exceed AI license costs.
Organizations should avoid rushed vendor negotiations and focus on determining which features deliver measurable value before committing to scaled usage.
Cost-cutting that looks good on a spreadsheet can quietly erode the people, flexibility, and resilience that keep IT running.
Steven Goodman of Marquette University argues that leaders who optimize visible cost reductions often miss the hidden expenses of that thinking.
Outsourcing vendors deliver contractual obligations but not the institutional knowledge lost when experienced engineers leave. He cites GE Medical Systems creating a 'Specials' department to handle unexpected customization, illustrating how built-in headroom prevents costly emergency responses.
He warns that salaried time appears 'free,' enabling burnout through endless meetings, and that CIOs who succeed by spreadsheet metrics can preside over deteriorating organizational resilience.
Why Cisco Is Redefining Its CIO Role
Thimaya Subaiya, Cisco's EVP of operations, discusses how AI agents and Model Context Protocol connectors are fundamentally reshaping the CIO role.
Traditional IT leadership skills like applications integration are becoming obsolete as agents have replaced the interface. Cisco seeks CIO candidates with backgrounds in product management, innovation, and transformation rather than conventional IT experience.
The ideal candidate should question whether processes need to exist at all rather than simply optimizing them. Security has been separated as its own function to maintain focus, while AI governance will eventually merge back into IT operations.