We've restructured our AI bootcamps around one simple idea: participants should leave with something real they built and the training + capability to keep building.

Starting now, every bootcamp includes a capstone project that participants build progressively through homework across all six sessions. By the final day, each person has created a tangible deliverable for their actual work. And for the decision-makers who invest in their team's development, we deliver a summary report showing exactly what your team accomplished. It's the difference between "we trained them" and "here's what they built."

Interested in learning more? Reply “Bootcamp” or visit kiingo.com.

• Anthropic launched Cowork, a desktop AI agent that reads and modifies files—essentially Claude Code for people who've never touched a terminal. Built in roughly a week and a half using Claude Code itself, the tool is available to Max subscribers on macOS. (TechCrunch, Anthropic)

• Google rolled out AI image generation, a persistent Gemini side panel, and "auto browse" capabilities to Chrome, letting AI Pro and Ultra subscribers command the browser to log into websites, shop for merchandise, and draft social media posts. (CNBC, AP News)

• CIOs and CHROs are increasingly partnering on AI initiatives, with Fortune reporting that the most successful AI implementations come from collaboration between technology and people functions. The gap between AI winners and laggards may have more to do with org design than technical capability. (Fortune)

• Half of CEOs surveyed by BCG believe their job stability depends on getting AI right in 2026, with AI innovation now a top-three strategic priority for two-thirds of CEOs. (BCG, World Economic Forum)

• Anthropic CEO Dario Amodei published "The Adolescence of Technology," a 15,000-word essay mapping out five categories of AI risk—from autonomy concerns to labor displacement—while cautioning against both doomerism and dismissiveness, essentially treating powerful AI like a civilizational rite of passage we need to navigate with clear eyes. (Dario Amodei)

• Google DeepMind released AlphaGenome, an AI model that predicts how mutations affect gene expression in the 98% of DNA that doesn't code for proteins. It’s the "dark matter" of the genome that scientists have struggled to decode for decades. Researchers are already using it to narrow down genetic drivers of rare diseases. (Scientific American, Nature)

• OpenClaw—née Clawdbot, then Moltbot—has become the most talked-about AI agent on the internet, an open-source personal assistant that can manage your email, browse the web, and shop on your behalf while security researchers warn it's "a disaster waiting to happen" since it requires root access to your system. Its AI-only social network, Moltbook, already has 770,000 agents posting manifestos and trading cryptocurrency. (CNBC, Fortune)

• Anthropic launched interactive tools in Claude, letting users open Asana timelines, draft Slack messages, and build Figma diagrams without leaving the conversation—essentially turning Claude into a control center for your work apps rather than just an assistant that talks about them. (Claude, Help Net Security)

Gallup's Q4 2025 workforce study reveals a telling pattern: 69% of leaders use AI at work, compared to 55% of managers and just 40% of individual contributors. Frequent AI use (at least a few times weekly) has risen from 17% to 44% among leaders since Q2 2023, while individual contributors have only moved from 9% to 23%. The gap isn't just about access; Gallup finds that lack of clear use cases is the most common barrier to individual adoption. Leaders see obvious applications for their work. Everyone else is still figuring it out.

Translation: The productivity gains leaders are capturing don't automatically flow downstream. If you want AI adoption across your organization, you need to make the "why use this" question obvious at every level—not just the C-suite.

Sources: Gallup Workplace AI Adoption (Jan 2026)

Your project just wrapped. Everyone's relieved, the Slack channel is quiet, and the lessons learned are already evaporating. Two weeks from now, nobody will remember what actually went wrong, or right, or why.

AI finds patterns across retrospectives that humans lose in the relief of finishing. Try this with: post-mortem documents, project Slack channels, email threads, meeting notes, or timeline data from the past 3-6 months.

Try both as a single comprehensive prompt and by running each part separately. You'll often get better results if you start with the retrospective summary first, then gradually build to pattern recognition and systemic fixes.

Role: "Act as an experienced program manager analyzing project retrospectives for a [your business type] company that runs [typical project type] lasting [typical duration]."

Task: "Analyze these retrospective materials to identify recurring patterns across projects and develop actionable improvements that address root causes, not symptoms."

Context: "We're seeing [problem: missed deadlines, scope creep, stakeholder surprises, resource conflicts]. Teams are typically [X people] working on [project types]. Projects run [X weeks/months]. Stakeholders include [roles/titles]."

Format: Deliver:

PROJECT SUMMARY | Projects analyzed: [X]; Time period: [X]; Team sizes: [X]; Types: [client, internal, etc.]

PATTERN ANALYSIS | Table: Issue Category | Frequency Across Projects | Project Phase | Business Impact | Upstream Cause

ROOT CAUSE MAPPING | For top 3 recurring issues: What teams say happened (surface complaint); What actually caused it (process gap, communication failure, resource issue); Where in the project lifecycle the root cause originates; Which role had the visibility to catch it early

ACTION FRAMEWORK | For each root cause: Prevention mechanism (what to put in place); Detection checkpoint (when to check for it); Escalation trigger (what signals intervention); Owner role (who's accountable)

TEAM PATTERN INSIGHTS | Strengths: What this team consistently does well; Blind spots: Where they repeatedly struggle; Cultural factors: Unspoken norms that help or hurt

Constraints: Separate process issues from people issues; Distinguish between "this project was unique" and "this is how we always fail"; Identify issues that required cross-team coordination vs. issues within a single team's control; Be honest about fixes that require budget or leadership decisions.

Tip: Run this quarterly. Teams that systematically extract patterns from retrospectives complete projects 25% faster than teams that treat each project as a one-off.

Decision Fatigue Spotter (ChatGPT or Claude)
Review my last week of emails, messages, and meeting notes. Find the decisions I made that didn't need to be mine: things I approved that someone else could have owned, questions I answered that had obvious answers, choices I weighed in on out of habit rather than necessity. For each one, tell me who should have decided and what would need to change for me to let go. Return as: Decisions I should stop making + who should own them instead + one process tweak to make delegation stick.

Context Switch Counter (ChatGPT or Claude)
Look at my calendar and task list from the past week. Count how many times I switched between unrelated projects or topics in a single day. Identify the days where I bounced between the most things and estimate how much focus time I actually had versus time lost to reorienting. Return as: Total context switches per day + my worst day and why + three scheduling changes that would give me longer uninterrupted blocks.

Assumption Surfacer (ChatGPT or Claude)
Read this project plan or proposal. List every assumption baked into it that nobody has explicitly stated or validated. For each assumption, tell me what happens if it turns out to be wrong, how confident we should be that it's true, and who on the team would know. Return as: Hidden assumptions ranked by risk + the three we should validate before moving forward + one question to ask at the next meeting.

See More AI Wins For Your Team

I've spent the last couple weeks getting deeper into Claude Code, and I keep running into the same realization: this isn't just a coding tool. It's a way to build things that exist for exactly as long as you need them, and then they vanish.

We've been calling it ephemeral software: tools that solve a specific problem for a specific moment, without the overhead of building something permanent. Need to quickly transform a CSV into a formatted report? Build it, run it, done. Need a one-time script to clean up a messy dataset before a presentation? Same thing.

The mental shift is subtle but significant: you stop asking "is there a tool for this?" and start asking "can I describe what I need?" The barrier between having a problem and having a solution just becomes the right conversation.

I'm still learning where the edges are: what works, what breaks, what takes longer to explain than to just do manually. But I keep finding myself reaching for it now, which is usually the sign that something has actually changed how I work.

Kiingo is an AI consultancy & advisory firm that helps companies unlock real business value with artificial intelligence. From hands-on training to strategic planning and tailored implementation, we partner with growth-minded organizations to build AI fluency, generate more value per team member, reduce inefficiencies, and create lasting competitive advantage. We believe in humans, amplified by AI. Whether you're exploring AI for the first time or ready to scale your efforts, we'll meet you where you are and guide you forward—with clarity, confidence, and results.

Want to talk more? Let's schedule a time.