Bookmark: From proof of concept to production: Embracing systems thinking
Transform your AI strategy with a systems-thinking approach, ensuring seamless transition from proof of concept to impactful production deployment.
Here’s a notable quote from the article: “AI at scale is a significant organizational change that must be managed and starts with ongoing investments in AI literacy and workforce readiness.” This underscores the transformative impact of AI on business operations.
From Proof Of Concept To Production: Embracing Systems Thinking
The article, “Flexible Work Can’t Replace The Office—But Here’s How To Make It Work,” discusses the challenges enterprises face in fully implementing generative AI (GenAI) beyond the proof-of-concept phase. Despite its transformative potential, many projects stall due to poor data quality, inadequate risk controls, rising costs, and unclear business value. To advance AI from conception to production, a systems-thinking approach is critical, viewing AI as a fundamental shift akin to enterprise resource planning systems. This involves strategy, secure AI applications, a robust data supply chain, well-defined AI operations, and a product-thinking mindset. Key considerations include establishing ethical and compliant AI strategies, securing data control, ensuring ongoing compliance, and integrating AI into core business functions. Successful AI deployment demands significant resource investment, focused on data quality, security, and infrastructure. Viewing AI as an evolving business element rather than a standalone technology is essential for sustained success. Through systemic thinking and continuous adaptation, organizations can leverage AI’s full potential as a cornerstone of their operational framework.
The agent-shaped org chart
Every real org has the same topology: principal, role-holder, specialists. Staff AI maps onto it, node for node, and the cost collapse shows up in the deliverables that were always just human-handoff overhead.
AI as staff, not software
Two frames for what AI is doing to work. The tool frame makes tools smarter. The staff frame makes roles unnecessary. Those aren't the same product, the same company, or the same industry.
Knowledge work was never work
Knowledge work was always coordination between humans who couldn't share state directly. The artifacts were never the work. They were the overhead — and AI just made the overhead optional.
The work of being available now
A book on AI, judgment, and staying human at work.
The practice of work in progress
Practical essays on how work actually gets done.
The file I almost made twice
A small operational footgun that runs everywhere — building a parallel system when the one you have is fine.
The actor doesn't get to be the verifier
The worker isn't lying. The worker is reporting what it thought it did, which is always one step removed from what the world actually shows. The fix isn't more self-honesty. The fix is a different pair of eyes.
Shopping is the last mile
Every meal planning app treats cooking as the hard problem and shopping as a logistics detail. They have it backwards. Cooking is mostly solved. Shopping is the last mile.
Bookmark: I’m not convinced ethical generative AI currently exists
Explore the ethical challenges of generative AI, from data acquisition to environmental impact, and why true ethical solutions remain elusive.
Embracing AI in education: Balancing integrity with future workforce demands
Explore how educators can balance academic integrity with the need for AI literacy, preparing students for a future-driven workforce.
Bookmark: The next wave of automation: Will AI disrupt more high-skill jobs?
Explore how AI is reshaping high-skill jobs, driving the need for new skills and offering opportunities in a rapidly evolving job market.