AI

AI Isn’t Failing, People Are Failing With AI

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The article emphasizes that AI failures stem from improper application rather than from the technology itself, highlighting the importance of domain expertise and understanding model operations. It distinguishes between the effectiveness of models like BERT and GPT, advocating for a risk-based framework in deploying AI to manage industry-specific challenges and data utilization. Successful AI transformation relies on organizational fluency with technology and strategic planning.

https://www.cio.com/article/4135361/ai-isnt-failing-people-are-failing-with-ai.html

Open-Weight AI Models Fail the Jailbreak Test

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Cisco’s State of AI Security report found that open-weight AI models are highly vulnerable to multi-turn jailbreak attacks, with a 92.78% success rate. These attacks, which use iterative prompts to bypass content filters, highlight the need for improved AI security measures. The report also emphasizes the risks associated with excessive agency in AI systems, particularly when they are granted broad autonomous authority over tools and data.

https://www.databreachtoday.com/open-weight-ai-models-fail-jailbreak-test-a-30823

The Biggest AI Fails of 2025: Lessons From Billions in Losses

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2025 saw significant AI failures despite high global spending. Major examples include Volkswagen’s Cariad, which incurred $7.5 billion in losses from a rushed transformation and poor integration, and Taco Bell's AI, which faced public ridicule and operational chaos due to edge case mishandling. Other notable failures include Google’s AI producing false information, a $25 million deepfake scam at Arup, and issues leading to class-action lawsuits against UnitedHealth. Common lessons highlight the importance of starting small, ensuring human oversight, and auditing AI vendors for security. Businesses need to learn from these mistakes to implement AI effectively without repeat failures.

https://www.ninetwothree.co/blog/ai-fails

Building Pro-worker AI

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Brookings identifies AI's potential to enhance worker capabilities through pro-worker technologies, categorizing them into five types: labor-augmenting, capital-augmenting, automating, expertise-leveling, and new task-creating technologies. While new task-creating tech is clearly beneficial for workers, automating tech is not. Pro-worker AI is underdeveloped due to firms prioritizing automation for economic returns. To promote pro-worker AI, policies should focus on health care and education, foster competition, encourage worker input, and create a supportive legal environment for worker ownership of skills.

https://www.brookings.edu/articles/building-pro-worker-ai/

How CIOs Are Strengthening Data And AI Foundations

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CIOs focus on data and AI initiatives, emphasizing shared data platforms, adopting data as a product, and creating AI enablement programs. Organizations cultivate data backbone systems for analytics, enhance governance, and ensure safe AI deployment with internal guidelines, aiming to address trust and compliance concerns while promoting a culture of data literacy. Future discussions will explore operational models and team structures tied to these transformations.

https://www.forrester.com/blogs/how-cios-are-strengthening-data-and-ai-foundations/

Managing the New Blend of Human and Virtual “Co-Workers”

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HR leaders must adapt to a workplace increasingly comprising human and AI collaboration. Key trends identified by Gartner for 2026 include challenges like layoffs due to anticipated AI productivity that hasn’t been realized yet, the need to protect employee mental well-being in the AI landscape, and managing “workslop” caused by poor-quality AI outputs. Additionally, there’s a focus on improving recruiting methods to combat candidate fraud, addressing insider threats amid AI advancements, supporting transitions to trades from tech roles, and ensuring processes are optimized by creative thinkers, not just tech experts. Lastly, employees may demand compensation for training AI counterparts modeled after themselves.

https://www.latimes.com/b2b/human-resources/story/2026-02-22/2026-future-of-work-trends-hr-leaders

Half the AI Agent Market Is One Category the Rest Is Wide Open

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Software engineering comprises nearly 50% of AI agent tool usage, while healthcare, legal, and other sectors each hold less than 5%, indicating vast untapped opportunities. Despite AI's capability to perform efficiently, user trust limits its deployment. Founders should focus on vertical-specific AI solutions, capitalizing on unique workflows and driving change management to unlock growth potential. There are approximately 300 vertical AI unicorns waiting to be created across various industries.

https://garryslist.org/posts/half-the-ai-agent-market-is-one-category-the-rest-is-wide-open

Stop Thinking of AI as a Coworker. It’s an Exoskeleton.

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AI should be viewed as an exoskeleton that enhances human capabilities, rather than as an autonomous agent. Companies that use AI to amplify human work achieve better results than those that expect autonomy. Exoskeleton examples demonstrate significant benefits across manufacturing, the military, and healthcare by reducing injuries and improving efficiency. In product development, AI tools like Kasava provide depth of analysis while keeping human judgment central. The future of AI lies in systems that integrate closely with human workflows, amplifying productivity rather than operating independently.

https://www.kasava.dev/blog/ai-as-exoskeleton

The Work Moved: What the AI Coding Debate Actually Agrees On

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AI coding has increased productivity (98% more PRs) but prolonged review times (91% longer), shifting work from coding to review processes. Various perspectives agree on data yet disagree on implications. Challenges include comprehension debt and the need for robust infrastructure. Strategies vary from spec-driven development to autopilot modes, focusing on context management and oversight. Risks involve reliance on AI without proper guardrails leading to misunderstandings and accountability issues. Ultimately, it's crucial to understand where complexity resides and ensure humans remain engaged in essential tasks.

https://leadership.garden/ai-the-work-moved/

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