The strategy stories enterprise IT leaders actually read.
Volume 343, Issue 1 · 20 articles from 6 issues
Boards want exposure, trend and financial impact, but siloed security tools leave CISOs unable to answer with confidence.
The Hacker News says boards ask three questions most CISOs cannot answer confidently - how secure the organization is, what the financial exposure is, and whether posture is improving - while activity metrics like patches applied and alerts closed measure effort, not risk.
The real problem lies in gaps between identity, cloud posture, EDR, SIEM and SaaS tools, where individually moderate findings can chain into a critical attack path to sensitive data, and AI agents and non-human identities widen those gaps.
The piece argues for exposure-based reporting that connects context across tools rather than buying another console.
CIOs should stop just controlling consumption-based AI spend and act as capital allocators, investing more where AI drives business results.
An advisor writing in CIO.com says AI budget conversations start with token costs and model pricing but should end with which investments drive revenue, margin, customer satisfaction and time to market.
Consumption-based AI behaves like a utility bill rather than an owned asset, and the author calls constraining deployments or renegotiating contracts as costs rise the wrong path.
Instead, CIOs should think like capital allocators, borrow finance disciplines to focus on value realization, and decide where the business should invest more.
Product management placed under one business unit narrows its priorities; it needs independence to serve the whole organization.
A CIO.com author with experience from trillion-dollar enterprises to startups argues that where product management sits matters as much as who is hired and how they are trained.
Smaller firms under pressure for quick technology results often place a new product function under the most influential business unit head, creating a new inefficiency by narrowing the team's priorities.
'Placement eats good intentions for lunch,' the author writes, urging independence so product teams work for the whole organization.
License and prompt counts hide value; tracking who abandons AI tools reveals where workflows break.
AI adoption dashboards count licenses, activated seats and prompts, but they measure distribution rather than value, and the metrics are usually defined by vendors.
With only 31% of leaders expecting to measure generative AI ROI in the next six months, the author argues that abandonment, meaning who tried a tool, how quickly they stopped and where it failed, is the signal employees produce themselves.
Different abandonment patterns need different fixes, and on a 1,000-seat deal, 20% never adopting plus 20% dropping off wastes 400 licenses.
Enterprise AI's problem is often not model quality but that models have no link to real decisions; diagnose that gap before buying more.
A CIO.com columnist describes a capital allocation review that ignored a good predictive failure-risk model because it had no connection to the decision being made, which he calls the central problem in enterprise AI.
McKinsey's State of AI 2026 finds nearly nine in ten firms use AI regularly and eight in ten see higher individual productivity, yet only about 37% report positive enterprise EBIT impact, with BCG and PwC CEO surveys showing similar gaps.
Before buying more AI, leaders should diagnose whether their gap is capability or the link between insight and decisions.
Ford's rehiring of 350 veteran engineers after over-relying on AI quality control shows experienced judgment still matters.
A CIO.com column notes that some companies that cut staff for AI are now reconsidering, citing Ford, which leaned on more than 900 AI-assisted cameras for quality control in 2023 but saw defects persist.
Concluding it had overcorrected, Ford rehired 350 veteran 'gray beard' engineers over three years to retrain the AI, catch failures early and mentor younger staff rather than replace AI.
CEO Jim Farley credits the move with hundreds of millions in warranty and recall savings, Ford topped the 2026 J.D. Power Initial Quality Study, and the author calls it organizational courage.
CSO Online argues CISOs must triage AI risk by business impact because AI arms attackers and defenders equally.
CSO Online frames AI security as a duality: it gives defenders some of the best discovery tooling they have ever had while giving attackers the same capability. That leaves CISOs managing AI on two simultaneous fronts.
Outside the organization, attackers use AI to make phishing more convincing, automate reconnaissance and compress the time between vulnerability disclosure and exploitation. Inside, the organization is adopting AI faster than it can govern it.
The article argues a risk-first CISO decides what to prioritize based on which risks could hurt the business most rather than attempting to address everything.
Gartner finds most firms lack confidence in AI-ready data, so CIOs use AI to manage data lifecycles.
Gartner finds that roughly two-thirds of organizations lack confidence in their data management practices for AI, and predicts 60% of AI projects will fail due to insufficient data readiness.
The article explores how CIOs use AI to strengthen information management across the data lifecycle, from creation through destruction.
Leaders stress that data quality is paramount, requiring information to be findable, understandable, trustworthy, and accessible quickly.
Pioneers see AI's potential to spot data gaps, correct anomalies, and streamline data cleansing, but limitations persist in regulated sectors where probabilistic models cannot guarantee full accuracy, so most organizations are applying AI to specific lifecycle stages rather than full automation.
CIO surveys 13 evaluation tools for continuously testing that agentic systems stay on task.
CIO argues that enterprises building agentic systems need continuous testing to ensure their AI remains on task. The framing is that LLMs remain opaque even to the developers who build them, so teams need a way to peer into the weights and make sense of behavior.
An explosion of tooling has appeared to fill that gap.
The article surveys 13 AI evaluation tools and compares what each is good for. It is written as a buyer's orientation to an emerging and fast-moving tool category.
CIO shows how a model can produce a perfect SHAP explanation and still be wrong because the data and rules are off.
The article opens with a fraud model scoring an insurance claim at 0.23, or low risk. A SHAP explanation lays out exactly why: no prior claims, a modest claim value, an unremarkable claimant profile.
A human adjuster reads the explanation, agrees and signs off, and every box that explainable AI asks us to check has been checked.
The claim is settled and closed. The article then shows what was actually wrong, arguing that a model can explain its answer perfectly and still be wrong when the underlying data and rules are flawed.
A CIO explains how regulated migrations succeed by proving the new system matches old outcomes, guarding against 'quiet' failures.
Drawing on migrations from a VAX minicomputer running a parking operator's 200 New York City sites and a global fintech payments and card issuing core, a CIO.com author explains why regulated migrations fail differently.
Loud failures take systems down, as when TSB's 2018 big-bang migration locked customers out and led to a GBP 48.65 million fine, while quiet failures such as pricing discrepancies, missed filings or lost audit trails may not surface for weeks.
Not breaking the business means proving at every step that the new system produces the same outcomes as the old one.
CIO argues locking into one AI platform too early risks paying for yesterday's technology as capability leapfrogs.
The article traces enterprise AI investment back to late 2022, when businesses began spending across marketing, sales, developer platforms, analytics and automation. That experimentation was justified: use cases were unclear and the cost of not participating felt higher than the cost of overspending.
The situation has now changed, and organizations are under pressure to consolidate and standardize.
The author argues that standardizing too soon carries its own cost, because AI capability keeps leapfrogging and a locked-in platform can leave an organization paying for yesterday's technology. The piece offers a framework for deciding when consolidation is actually safe.
Three practices to help CIOs measure and report AI ROI across their portfolio, starting with a portfolio view tied to AI ambition.
CIO.com notes that most CIOs struggle to show AI value, with 27% in Gartner's AI ROI Vendor Sentiment survey strongly agreeing ROI is hard to quantify and only one in four having a clear evaluation process.
AI apps bring classic concerns like availability and security plus trust attributes such as accuracy, explainability and safety from the NIST AI RMF, and at scale AI also becomes a FinOps exercise.
The author offers three best practices for formalizing measurement and ROI reporting, beginning with a portfolio approach aligned to the organization's AI ambition.
Stated concerns about sovereignty and lock-in may mask a defensive reaction to expertise losing its scarcity value.
MIT's GenAI Divide study found only about 40 percent of organizations had bought official LLM subscriptions while workers at more than 90 percent of those same organizations were already using personal AI tools for work.
The article argues that some CIO caution is a defensive routine, in Chris Argyris's sense: an unconscious move to protect expertise whose value came from scarcity.
Research cited shows novices improve 34 to 43 percent with AI assistance while experienced professionals gain far less, which flattens skill distributions.
Recommended countermeasures include hiring for learning velocity rather than tenure, using the tools daily yourself, and measuring how often junior staff successfully challenge senior decisions.
Why IT Projects Still Fail
Even with alignment between IT and business leadership, technical initiatives still fail at concerning rates, with estimates ranging from one-third to 70% of projects not delivering expected benefits.
Key failure factors include insufficient project management expertise on smaller initiatives, misalignment between IT and business objectives, undefined success metrics, and inadequate business sponsor engagement.
Additional challenges involve poor stakeholder inclusion, slow decision-making processes, inadequate resource allocation, and weak change management strategies.
The article highlights how AI tools, while accelerating delivery, can introduce hidden errors and create bottlenecks when human teams cannot keep pace with automated outputs.
Digital transformation succeeds by developing talent to execute strategy, not by adopting advanced technology.
Digital transformation fails when organizations focus on technology deployment rather than people development. The author argues CIOs must cultivate DX talent across three tiers of employees, people who understand business challenges, customer value and organizational change.
Success requires vertical consistency between management philosophy and DX strategy, horizontal alignment of HR systems, and bridging the knowledge-to-action gap through practical application. Recognition programs help motivate talent and build culture.
An award-winning AI document workflow cost $12-$14 per file versus 80 cents for the manual process it replaced.
An auditor reviewed an award-winning document workflow that cut vendor onboarding turnaround by 90%, after finance noticed operating costs rising each cycle despite flat volume.
Tracing spend to the ledger showed manual review cost about 80 cents per document, while the automated pipeline ran $12 to $14 for the same file, even though the system was stable and dashboards stayed green.
Unit economics were never measured after the pilot, and the lesson is to track post-production unit economics, not just headline speed metrics.
Survey: 53% of executives use generative AI vs. 38% of workers, and bosses are 2.7x likelier to use agentic AI.
A survey of 7,675 American adults found 53.2% of executives and business owners use generative AI compared with 37.9% of other workers, and executives are 2.7 times more likely to use agentic AI (17.5% versus 6.6%).
Leaders are moving from AI-as-search toward AI-assisted creation and knowledge work while much of the workforce has not followed, and a notable subset admit they pretend to know more about AI than they do.
Executives are no more willing than others to let bots handle high-stakes customer service, with about three-quarters preferring a live human.
Vayu's Revenue Intelligence Hub shows CFOs customer by customer whether AI pricing covers the cost to serve.
The Next Web reports that Vayu, an AI revenue management platform, has launched its Revenue Intelligence Hub, a margin and insights layer on top of its metering and revenue agents that shows CFOs per customer whether pricing keeps pace with delivery cost.
CEO Erez Agmon says AI broke the old software math: unlike near-free SaaS seats, a single heavy AI customer can erase a contract's margin through tokens, compute and model calls, and finance often learns a quarter later.
Vayu also published the CFO Signal Report 2026 with PwC and The SaaS CFO on usage-based, hybrid and outcome pricing.
An anonymized Group CIO describes retraining developers as AI orchestrators, cutting an 18-month project to four months.
In this diginomica network profile, a Group CIO explains how retraining developers to work as AI orchestrators using Claude and Devin AI cut an 18-month project timeline to four months.
The team built in-house replacements for third-party tools like N8N and Redgate, eliminating roughly half a million dollars in licensing costs.
The CIO stresses that FinOps discipline and observability tooling are essential to control token-consumption costs, and that keeping "a human in the loop" remains core governance even as development accelerates dramatically.