What it takes to step into a C-level technology role
You’ve led several digital transformation initiatives and delivered financial impacts. Executives recognize your change leadership competencies, having improved both customer and employee experiences. The architectures you helped roll out are now platform standards and are foundational to your organization’s data and AI strategies. Now, you’re asking whether you’re ready for a CIO role, or another C-level role in data, digital, or security. CIO.com’s 24th annual State of the CIO reports that over 80% of CIOs say their role is becoming more digital- and innovation-focused, that they are more involved in leading digital transformation, and that the CIO is becoming a changemaker . If you’re checking these boxes, you should be asking how you can step up into a C-level job. Transformation leade
Could not retrieve the full article text.
Read on CIO Magazine →CIO Magazine
https://www.cio.com/article/4154063/what-it-takes-to-step-into-a-c-level-technology-role-2.htmlSign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
modeltrainingrelease
Why Some AI Feels “Process-Obsessed” While Others Just Ship Code
I ran a simple experiment. Same codebase. One AI rated it 9/10 production-ready . Another rated it 5/10 . At first, it looks like one of them is wrong. But the difference is not accuracy — it’s philosophy. Two Types of AI Behavior 1. Process-Driven (Audit Mindset) Focus: edge cases, failure modes, scalability Conservative scoring Assumes production = survives real-world stress 2. Outcome-Driven (Delivery Mindset) Focus: working solution, completeness Generous scoring Assumes production = can be shipped What’s Actually Happening Both are correct — under different assumptions. One asks: “Will this break in production?” The other asks: “Does this solve the problem?” You’re not comparing quality. You’re comparing evaluation lenses . Failure Modes Process-driven systems Over-analysis Slower shi
OpenAI Bought TBPN Because PR Can’t Keep Up With AI
OpenAI is deeply embedded in political and social discourse because AI itself is. A traditional way to shape public opinion, PR, is not effective enough to help OpenAI deal with the kinds of challenges the company faces. So OpenAI is buying a media company, TBPN. Read All
Reflections on the largest AI safety protest in US history
On a sunny Saturday afternoon two weeks ago, I was sitting in Dolores park, watching a man get turned into a cake. It was, I gather, his birthday and for reasons (Maybe something to do with Scandanavia?) his friends had decided to celebrate by taping him to a tree and dousing him with all manner of liquids and powders. At the end, confetti flew everywhere. It was hard not to notice, and hard not to watch. Something about the vibe was inspiring… I felt like maybe we should be doing something like that. I was there celebrating with another fifty or so people from the Stop the AI Race protest march we had just completed, along with another hundred or so others. 1 We were marching, chanting, etc. to tell the AI company CEOs to say the obvious thing they should be shouting from the rooftops: “A
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products
Why Some AI Feels “Process-Obsessed” While Others Just Ship Code
I ran a simple experiment. Same codebase. One AI rated it 9/10 production-ready . Another rated it 5/10 . At first, it looks like one of them is wrong. But the difference is not accuracy — it’s philosophy. Two Types of AI Behavior 1. Process-Driven (Audit Mindset) Focus: edge cases, failure modes, scalability Conservative scoring Assumes production = survives real-world stress 2. Outcome-Driven (Delivery Mindset) Focus: working solution, completeness Generous scoring Assumes production = can be shipped What’s Actually Happening Both are correct — under different assumptions. One asks: “Will this break in production?” The other asks: “Does this solve the problem?” You’re not comparing quality. You’re comparing evaluation lenses . Failure Modes Process-driven systems Over-analysis Slower shi
The convergence of FinTech and artificial intelligence: Driving efficiency and trust in financial services - cio.economictimes.indiatimes.com
The convergence of FinTech and artificial intelligence: Driving efficiency and trust in financial services cio.economictimes.indiatimes.com
Digital Project Abandonment Crisis: Deadweight Loss in Plain Sight
The most cited figure in startup failure research comes from the U.S. Bureau of Labor Statistics: roughly 20% of businesses fail in their first year, and about 65% within ten years. For technology companies specifically, CB Insights' analysis of over 110 startup post-mortems found that 42% failed because there was no genuine market need for what they built. Running out of cash was second at 29% — but as the report noted, cash problems typically trail the market need problem by months. The technology sector fails at higher rates than the broader business population. Approximately 63% of tech businesses fail within five years. For software-as-a-service companies specifically, the dynamics are similar: roughly 90% of SaaS startups fail to reach sustainable scale. None of these numbers are enc
How to Build a Voice Agent With AssemblyAI
This tutorial shows you how to build a complete voice agent that can have natural conversations with users. You'll create an application that listens to speech, processes it with AI, and responds back with voice—handling the full conversation loop in real-time. Read All
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!