Despite a surge in generative AI use across workplaces, most companies are seeing little measurable ROI. One possible reason is because AI tools are being used to produce “workslop”—content that appears polished but lacks real substance, offloading cognitive labor onto coworkers. Research from BetterUp Labs and Stanford found that 41% of workers have encountered such AI-generated output, costing nearly two hours of rework per instance and creating downstream productivity, trust, and collaboration issues. Leaders need to consider how they may be encouraging indiscriminate organizational mandates and offering too little guidance on quality standards. Read the full Harvard Business Review Article on archive.today

The LinkedIn Guide to Future-Proofing Your Career emphasizes that in the rapidly evolving job market, professionals must adopt a proactive strategy centered on continuous learning to acquire both technical expertise and critical soft skills like leadership, communication, and conflict resolution. Success is built on adaptability and a flexible mindset, which allows professionals to embrace ongoing shifts, remain open to new opportunities, and make necessary mid-career pivots. Furthermore, the article stresses the importance of actively strengthening your personal brand and consistently expanding your professional network on platforms like LinkedIn to maintain visibility, gain industry insights, and tap into the 85% of jobs filled through connections, ultimately cultivating a resilient mindset to navigate technological disruptions and remain relevant. Read the article here

I am learning new things about AI every day and I am not an expert, but I think that AI has a long way to go in becoming more user friendly. But I enjoy little hacks like this from David Birss on how to get AI to write in your tone. See the full video on LinkedIn

AI can only do so much in the hiring process. It ranks your resume and a few other tasks, but eventually a person takes over the hiring process. The way around AI is to have conversations with people. Who can you reach out to today?

The AI job market is flooded with confusing and overlapping titles, and this article offers a “decoder ring” to make sense of them. Many titles are created by combining a few recurring modifiers (like Applied or Forward Deployed), domains (AI, ML, or Gen AI), and suffixes (Engineer, Scientist, Researcher). The distinctions matter: Applied AI Engineers build products using AI models, Forward Deployed Engineers work directly with customers to implement solutions, and “Researcher” or “Scientist” titles often blur between academic-style exploration and product-driven work. The piece also notes that “Generative AI” is becoming less useful as a term as LLMs are used for more than text or image creation, and that “AI Engineer” is emerging as a broad umbrella for those applying AI in real-world systems. Overall, the guide helps professionals and job seekers navigate the shifting jargon and better understand what these evolving roles really mean. Read the full article.