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Articles by Category: Strategy Brief

Low-Code Is Not a Backlog Strategy
Strategy Brief

Low-Code Is Not a Backlog Strategy

CIOs need a routing decision before they approve another application platform. Low-code should be treated as a selective delivery tier within application portfolio governance. It is not a general backlog-clearing strategy.
Operational AI: Scale the Service, Not the Model
Strategy Brief

Operational AI: Scale the Service, Not the Model

Operational AI should be funded as a service decision, not a model decision. The immediate risk is not simply inaccurate output. It is AI becoming embedded in enterprise software, connected to internal data and tools, and granted authority before service governance catches up.
Frontier APIs vs. Open-Weight Models: How Financial Services CIOs Can Improve AI ROI Without Mistaking Token Savings for Value
Strategy Brief

Frontier APIs vs. Open-Weight Models: How Financial Services CIOs Can Improve AI ROI Without Mistaking Token Savings for Value

For a regulated financial institution, replacing a token bill with GPUs does not automatically improve return on investment. It can move costs and accountability into capacity planning, model serving, evaluation, cyber controls, resilience testing, specialist staffing, audit evidence, and incident response.
When AI Becomes a Metered Service, CIOs Need More Than a Budget Cap
Strategy Brief

When AI Becomes a Metered Service, CIOs Need More Than a Budget Cap

A budget cap can stop a bill from crossing a threshold. However, it cannot tell a CIO which workloads should use premium models, which prompts are wasteful, when caching matters, whether long context is necessary, or which business unit is consuming AI because usage is easy rather than because it improves an operating result.
AI Coding Gains Are Real. The Hidden Cost Is Moving Downstream
Strategy Brief

AI Coding Gains Are Real. The Hidden Cost Is Moving Downstream

AI coding tools can accelerate development, but the hidden cost often moves downstream into review, validation, release, and remediation. CIOs should scale selectively, fund the control layer, and measure whether the whole delivery system improves. Not just whether developers generate code faster.
AI Token Sprawl: Govern Developer Agents by Workflow Value, Not Consumption
Strategy Brief

AI Token Sprawl: Govern Developer Agents by Workflow Value, Not Consumption

As AI coding tools and agentic workflows become embedded in software delivery, CIOs need to govern AI spend by business value, workflow impact, and platform dependency. Not by seats, prompts, requests, or tokens alone.
Your Data Center Has a Fuel Problem. You Just Don't Know It Yet
Strategy Brief

Your Data Center Has a Fuel Problem. You Just Don't Know It Yet

Aviation shocks do not stay in aviation for long. For CIOs, the real risk is downstream: slower hardware movement, weaker recovery logistics, tighter power assumptions, and cloud resilience that remains more physical than many leaders think.
EAI Reliability: Why Quiet Failures Need Runtime Supervision, Not Better Dashboards
Strategy Brief

EAI Reliability: Why Quiet Failures Need Runtime Supervision, Not Better Dashboards

AI systems can remain available and appear healthy while gradually becoming wrong, brittle, or misaligned. For the C-suite, this shifts the question of EAI’s reliability from a narrow engineering concern to a governance, assurance, and operating-model issue.
Dashboards You Can’t Trust Are Worse Than No Dashboards
Featured

Dashboards You Can’t Trust Are Worse Than No Dashboards

Dashboards shape pricing, investment, and operational decisions, but many rely on fragile, weakly governed data pipelines. Missing records, stale updates, schema changes, and drift create a false sense of certainty and quietly increase financial and governance risk. CIOs and data leaders should treat data quality as a business-critical responsibility, ensuring BI and analytics outputs are reliable and that AI initiatives are built on trusted foundations.
From Autonomy to Accountability: Managing Agentic AI Risks
Strategy Brief

From Autonomy to Accountability: Managing Agentic AI Risks

Agentic AI shifts automation from single-task models to autonomous decision-makers, amplifying risks of misalignment, bias, and data leakage. OWASP’s new guidance equips SMEs with lifecycle security practices, ensuring governance, transparency, and resilience as autonomous agents move from experimentation into production. IT leaders and CISOs should read this article to learn how to secure agentic AI in production using OWASP’s guidance.