You’re reading The Steady Beat, a weekly round-up of hand-picked articles and resources for people who make software products: designers, engineers, product managers, and organizational leaders. Brought to you by the team at Steady.
📢 Webinar: Async Coordination with AI
Inefficient coordination can slow down even the most talented teams, leading to piled up meetings, missed deadlines, and disengaged team members. Our webinar on Thursday, March 6, 2025, aims to teach teams how to speed up their workflow by blending async coordination techniques, human insight, and AI. Participants will learn how to eliminate performance theater with automatic progress briefs, keep teams aligned without extra meetings or manual updates, and use human input and AI to pre-empt coordination chaos. The session is specifically geared towards product leaders, engineering managers, and async-first teams wanting to unlock AI beyond search and chat.
— Steady, 7m, #teamwork, #ai, #productivity
By the Numbers - Outage Economics
- 99.68% — Slack’s uptime for Q1 2025 so far (Slack)
- 80% — The percentage of data center managers who experienced at least one outage in the last three years, proving that downtime isn’t an “if” but a “when.” (Uptime Institute)
- $1 million — The cost of a single hour of downtime for 44% of mid-sized and large enterprises. (ITIC)
- 42% — The share of organizations that experience daily or weekly application attacks, keeping cybersecurity teams in a perpetual state of whack-a-mole. (Radware)
- 16x — How much higher the costs are for companies with frequent downtime compared to those that keep things running smoothly. (LogicMonitor)
- $1.5 trillion — The estimated annual cost of unplanned downtime for Fortune Global 500 industrial organizations, making it an 11% drag on revenue. (Siemens)
— Trilio, 10m, #downtime
AI Agent Stages
Scott Belsky breaks down the future of agent development into five stages, each providing increased functionality. The first level, “Glorified Personalized Help,” offers personalized solutions akin to an FAQ. “Reactive Recommendations,” the second stage, allows agents to complete tasks based on user requests. The third stage, “Proactive Recommendations,” sees agents suggesting tasks to users based on context and usage patterns. “Proactive Action,” the fourth stage, involves agents performing tasks proactively without user initiation. The final and most advanced stage, “Autonomous Workflows,” sees agents running complete workflows independently, potentially making purchases and negotiating with other agents.
— Implications, 7m, #agents, #ai-development, #product-design
AI and Usability Testing
Dr. Eduard Kuric discusses the role of AI in usability testing, highlighting its potential to streamline the process and overcome challenges related to scaling. Traditional usability research often encounters hurdles due to the need for personal interactions with participants, time zone differences, and the requirement for participants to provide complete answers independently. AI could offer a solution to these issues by automating part of the interaction, allowing researchers to gather more data. Unmoderated usability testing, where participants complete tasks without the presence of a moderator, has become increasingly popular with the help of online UX tools. Although this technique offers flexibility and convenience, it lacks the human touch and real-time adaptability a moderator brings. Kuric suggests that AI could fill this gap, with LLMs potentially capable of leading human-like conversations and enhancing data collection.
— Smashing Magazine, 15m, #usability-testing, #ai, #user-experience-research
Engineering Skills in the AI Era
As AI takes over rote coding tasks, developers and engineers need to adapt by honing skills that AI can’t replicate. While AI can generate lines of code, clear and effective communication remains a crucial skill, especially in cross-functional teams. Code review grows in importance too, as engineers need to ensure AI-generated code meets standards and is secure. A grasp of system observability, particularly with AI-generated code in play, is vital. Understanding legacy codebases is another skill in high demand, as AI struggles with “understanding” the context behind older architectural decisions. Other key skills include deep comprehension of code, data modeling, business acumen, and leadership.
— Level Up Software Engineering, 15m, #ai, #software-engineering, #communication
UX to Product Design
Author Kai Wong explores the crucial step of understanding priorities as UX designers evolve into product designers. Wong argues that for most UX designers, priority is based on usability alone, but teams often prioritize less severe usability issues due to factors like reach. Designers often present usability issues without considering prioritization, creating a gap between informing and communicating with the team. Wong suggests using the funnel concept to quantify different steps of a user’s workflow and identify where problems occur. To estimate the quantitative impact and importance of fixing certain issues, Wong recommends the RICE method, which considers reach, impact, confidence, and effort. This approach can help UX designers effectively prioritize tasks, communicate better with their teams, and evolve into effective product designers.
— Data-Informed Design, 11m, #ux, #product-design, #priority-management
Autonomous Team Coordination
The average knowledge worker loses 23 hours weekly to coordination overhead. Fix that with Steady today.
Steady combines human insight, hard data, and AI coordination agents to help teams deliver better work, 5X faster.
Learn more at steady.space.
