Best of LinkedIn: Artificial Intelligence CW 17/ 18

EU AI Act guidance distilled into clear, role-based processes that streamline resourcing and accountability

IBM released the watsonx.governance Compliance Accelerator, embedding live regulatory content directly into governance consoles to reduce manual legal checks

Anthropic published an enterprise playbook that links policy controls to practical deployment stages, enabling boards to oversee AI risk without stalling innovation

Thought leaders argued for explicit liability frameworks and audit trails to bridge the gap between “responsible AI” principles and day-to-day model management

A five-step roadmap recommends integrating ethics reviews into sprint cycles, ensuring early alignment with corporate values before major investments scale

Posts clarified the hierarchy: ethics sets the values, responsible AI defines measurable standards, and governance enforces them through controls and metrics

Executives reframed AI as a creativity amplifier rather than an automation shortcut, stressing human-centric ROI

Data leaders urged firms to capture fresh, high-resolution customer data now instead of over-analysing legacy sets

An “AI-first” mindset emerged – treat every workflow as a candidate for machine collaboration while demanding transparent recommendations from agents

Kubernetes positioned as the de-facto backbone for scaling generative models reliably and cost-effectively

IBM unveiled modular AI agents at Think 2025 that plug into existing tech stacks, cutting time-to-value

Yann LeCun challenged current large-language-model trajectories, calling for architectures that go beyond predictive text toward grounded reasoning

Senior operators highlighted the shift from solo innovation to ecosystem orchestration, with leaders acting as “collaborative architects”

Clinical-trial executives at COG New England showcased AI-enabled patient recruitment partnerships that compress start-up timelines

IBM underscored open-systems alliances and marketplace integrations as critical levers for enterprise adoption

Research exposed “sycophancy” biases in models, prompting calls for diversified training objectives

A deep dive on existential-risk mitigation argued for proactive safety benchmarks alongside capability gains

Google experts shared a practical guide on prompt engineering, bridging theory and hands-on tuning

IBM’s “Client Zero” example reported USD 3.5 billion saved through internal AI, proving credibility before client roll-outs

Change-management specialists warned that unclear ownership remains the top barrier to scaling pilots into enterprise platforms

Managers noted that complex tasks still hinge on people and communication skills even when AI accelerates execution

Developers were reminded that coding assistants amplify, not replace, strong fundamentals