Hello fellow humans! How are we thinking? How are we thinking about thinking? To properly wrangle AI processes, we have to think deeply about our objectives, our cognitive processes, and how to train both professionals and students to acquire and sustain those skills for the future.

Dig in, and dig deep. Today, I also have a feature piece with practical tips on leveraging AI as a force multiplier at small firms.

Enjoy!

The emergence of agentic AI systems that can plan, reason, and act autonomously represents a major shift in AI capabilities. Stories consistently discuss how these systems require new organizational structures, governance frameworks, and leadership approaches. The focus is on moving from task-based automation to outcome-oriented AI systems that can coordinate across departments and functions.

The authors for Harvard Business Review share an agentic workflow:

We have to break down our own cognitive processes to understand them at a granular level so that we can instruct agents on what they need to do, and even create a separate agent for each cognitive step in the process. That requires an unusually high level of meta-cognition.

This also highlights the fragility of current systems because “every exception has to be hard-coded and the automation breaks when systems or processes change. Agentic AI, in contrast, is designed to execute decisions autonomously to achieve overarching objectives or outcomes.”

Agentic AI marks a turning point in the evolution of artificial intelligence — shifting from passive tools that execute predefined tasks to autonomous systems that plan, reason, and act toward goals. Harvard Business Review emphasizes that a successful deployment needs to be understood as a complete system of many tools that also address human intent, transparency, and accountability. Similarly, the European Institute for Management Technology (EIMT) analysis of 2025 AI trends highlights that generative systems are becoming embedded across business functions, demanding cross-functional coordination and ethical governance to ensure they amplify human creativity rather than replace it.

But AI’s adaptability is a double-edged sword: some adaptations may be exactly what we need, while other adaptations might take us off-course. Human-in-the-loop ecosystems can protect against unwelcome actions, and designing those human-computer interaction systems will be the next challenge for organizations using AI to scale up their work.

Organizations that adapt structurally and cognitively to AI will be the ones best positioned for new opportunities that will be possible over the next 5-10 years and that we can’t even imagine yet. There are technical challenges to be sure, but the bigger challenge is human. Leaders need to develop meta-skills like systems thinking, discernment, and cognitive flexibility to steer these autonomous systems toward shared outcomes, not fragmented automations. Adaptation, not adoption, defines the next leap.

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Educational institutions built around memorization and content-delivery are struggling to find a strategy for handling AI tools. Educators are caught between the Scylla of failing to prepare students for their future work environments and the Charybdis of students delivering AI slop. In considering how to navigate this future, Jamillah Moore Ed.D. writing for Psychology Today argues that higher education must shift how to think about learning, advising, and career preparation. Students are increasingly asking how AI tools will shape their future work, and institutions built around memorisation and content-delivery are struggling to keep up. Moore highlights that employers still value technical skills, but the differentiators in an AI-rich future are human capabilities: emotional intelligence, collaboration, resilience and ethical reasoning. On campuses, she sees cultural and structural shifts: career services must evolve into coaching for lifelong learning; AI literacy must be integrated across disciplines; and higher education must build flexible credentials, employer partnerships, and psychological readiness for disruption. The article also calls attention to the mental-health dimension: students sense instability, must adapt to change rapidly, and need supportive frameworks to reclaim agency in their careers.

Everyone in knowledge work, students and professionals alike need to adapt to prioritize soft skills and systems-thinking over raw technical skill. As AI erodes the differentiator of “knowing the answer”, the value moves to asking better questions, collaborating across domains, adapting in real time, and ethically choosing how we use tools. Your focus on critical thinking, emotional intelligence and systems literacy finds a clear ally in this article. For your audience (educators, learners, professionals), this signals that building resilience and generative human-capability is more urgent than ever. In short: the responsibility is shifting from “learn this content” to “develop this adaptability and discernment”.

AI isn’t for the giants. Complex workflows and intricate regulatory guidelines present a great opportunity for AI implementations, but they have a lot of up-front costs that conservative enterprises may not want to engage. Amazon, a famously manual process organization that has not adopted AI in their processes is a high profile case study.

But a small firm with a narrow customer base and straightforward processes might be able to implement automated AI processes over a long weekend.

The opportunity isn’t in replacing people — it’s in extending them. The real leverage comes when AI amplifies what a small, focused team already does well.

Here are five ways AI can help small firms punch above their weight — and three practical steps to make each one work.