AI

We Asked Experts About the Most Responsible Ways to Use AI Tools – Here’s What They Said

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Three years after ChatGPT's release, AI use divides people into those who refuse it and those who use it daily. Experts advise using AI as a brainstorming partner, research assistant, and organizer while maintaining personal judgment, cautioning against overreliance and emphasizing the need to verify AI-generated information with credible sources.

https://www.theguardian.com/lifeandstyle/ng-interactive/2026/mar/18/how-to-use-ai-tools-expert-guide

Shadow AI Has Already Moved Into Your Organization

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The article explains that “shadow AI” is already widespread in organizations, as employees use public or unapproved AI tools to speed up work without going through IT or security review. Because these tools can be accessed instantly in a browser, blocking them is often ineffective, resulting in lost visibility into how company data is used. The article concludes that organizations must shift from trying to prohibit AI use to creating governance frameworks, approved tools, and clear policies that enable productivity while maintaining security and compliance. 

https://www.forbes.com/sites/tonybradley/2026/03/19/shadow-ai-has-already-moved-into-your-organization/

What It Takes to Level up Your Org’s AI Maturity

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In an interview with AI transformation practitioners Afshean Talasaz and Zar Toolan, key insights are shared on how organizations can advance their AI maturity from initial adoption to driving significant business impact. They emphasize the importance of a combined innovator-operator leadership mindset, detailed preparation, and aligning AI investments with long-term business strategies, supported by strong C-suite and CEO commitment. This approach helps companies move beyond treating AI as an operational tool to embedding it as a strategic asset that delivers measurable value and competitive advantage.

https://www.cio.com/article/4146645/what-it-takes-to-level-up-your-orgs-ai-maturity.html

AI Without Sovereignty Is Just Outsourced Intelligence

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In his opinion piece, Floyd DCosta argues that enterprises adopting AI often gain capability but lack sovereignty—control over how AI models and data are used—creating long-term risks and dependencies on third-party vendors. He emphasizes AI sovereignty as essential, encompassing governance, transparency, data and model control, operational autonomy, and strategic independence, warning that without it, organizations may inadvertently cede their competitive intelligence and face regulatory and operational challenges.

https://www.cio.com/article/4147102/ai-without-sovereignty-is-just-outsourced-intelligence.html

Deterministic AI: What It Is and When to Use It

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Deterministic AI refers to systems that produce the same output every time they receive the same input, combining AI’s ability to interpret data with deterministic workflows that ensure consistency and control. This hybrid approach uses probabilistic AI models to analyze and classify inputs while embedding their outputs in rule-based automation that executes reliably, making it ideal for enterprise workflows needing predictable, repeatable results. Zapier exemplifies this by orchestrating AI-powered workflows that maintain deterministic execution, blending AI’s flexibility in understanding complexity with automation’s dependability.

https://zapier.com/blog/deterministic-ai/

Shadow AI Risk: How SaaS Apps Are Quietly Enabling Massive Breaches

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A report from Grip Security reveals that all analyzed companies operate SaaS environments embedded with AI, with a 490% year-over-year increase in public SaaS attacks, 80% involving sensitive data. The article highlights how “shadow AI”—agentic AI within SaaS apps often implemented without IT oversight—enables attackers to use stolen OAuth tokens to cascade breaches across multiple organizations, exemplified by the widespread 2025 Salesloft Drift breach, emphasizing the urgent need for better visibility, continuous governance, and risk-based controls of AI in SaaS to prevent massive cascading cybersecurity incidents.

https://www.securityweek.com/the-shadow-ai-problem-how-saas-apps-are-quietly-enabling-massive-breaches/

Companies Say the Risks of ‘Open’ Artificial Intelligence Models Are Worth It

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The article reports that many companies are adopting open or partially open AI models despite security and governance concerns, because they offer lower cost, greater customization, and more control than proprietary systems. Firms say smaller, adaptable models are often better suited for business-specific tasks, and most organizations use a mix of open and closed models depending on the use case. The article concludes that while open models introduce risks such as supply-chain vulnerabilities and potential backdoors, companies believe the flexibility and performance benefits make those risks manageable.

https://www.wsj.com/cio-journal/companies-say-the-risks-of-open-artificial-intelligence-models-are-worth-it-0d3ee664

AI Still Doesn’t Work Very Well, Businesses Are Faking It, and a Reckoning Is Coming

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Experts from AI advisory firm Codestrap warn that enterprise AI applications often fail to deliver expected benefits due to underlying model limitations and lack of proper metrics to assess AI-generated code quality and business content. They predict a reckoning in 8-9 months as AI misuse leads to failures, lawsuits, pricing pressures, and insurance challenges, urging businesses to adopt clearer strategies, measure true outcomes, and address the hype around AI capabilities.

https://www.theregister.com/2026/03/17/ai_businesses_faking_it_reckoning_coming_codestrap/

Every Layer of Review Makes You 10x Slower

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The article argues that each additional layer of review in a process slows progress by a factor of ten, primarily due to waiting time rather than effort, and this bottleneck is not alleviated by AI coding tools. While reviews are necessary to maintain quality and reduce costly mistakes as organizations grow, excessive layers can degrade efficiency and mask root causes of problems, leading to a culture that values checks over genuine quality improvement. The author suggests adopting a Deming-inspired approach emphasizing trust, continuous systemic improvements, and modular small teams that produce high-quality components to reduce reliance on slow review cycles and create a more effective, scalable engineering culture.

https://apenwarr.ca/log/20260316

We Are All AI Philosophers Now

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The article emphasizes that AI systems inherently carry the biases and values of their creators through design choices, data, and policy decisions, meaning AI is never truly neutral. It calls on IT leaders to recognize that adopting AI is a governance decision that requires disciplined oversight, transparency, and accountability to manage risks and ensure AI-driven decisions align with organizational and societal values.

https://www.cio.com/article/4145026/we-are-all-ai-philosophers-now.html

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