The Infrastructure Rush: When AI's Supporting Systems Race Ahead of Readiness

The Infrastructure Rush: When AI’s Supporting Systems Race Ahead of Readiness

South Korea just taught the world an expensive lesson about rushing AI implementation. In March 2025, they launched AI-powered digital textbooks across thousands of schools—a flagship educational initiative costing $850 million. By July, it was effectively dead. Technical failures, inaccurate content, privacy violations, and overwhelmed teachers forced the government to roll it back to optional status. Adoption collapsed from 37% to 19% in just four months (Kwon, 2025; Pivot to AI, 2025).

Here’s what makes this particularly troubling: it’s not just another story about overhyped AI technology. It’s a symptom of something deeper that I’ve been seeing across education, enterprise, and government—what I call the “double beta” problem.

The Double Beta Problem

We talk a lot about AI racing ahead of our ability to understand it. But there’s a second race happening that’s equally dangerous: the infrastructure surrounding AI is also accelerating without proper foundations. Procurement processes, policy frameworks, implementation strategies, and organizational systems are all trying to move at AI speed without building the necessary foundations.

The result? Beta technology deployed through beta processes. Immature AI meets immature implementation systems, and the failures compound.

As I emphasized in my book Artificial Intelligence: A Practical Guide to Understanding AI for Professionals and Students, “the difference between successful and failed AI implementations rarely comes down to technical capabilities. Instead, success depends on clear problem definition, quality data, appropriate integration with existing workflows, and thoughtful change management” (Rissover, 2025, Chapter 11). South Korea’s textbook program failed on all counts simultaneously.

When Speed Kills Success

Look at the timeline. Traditional textbook development in Korea takes 33 months: 18 for development, 9 for review, 6 for preparation. The AI textbooks? Just 18 months total—a 45% compression. When legislator Kang Kyung-sook questioned this rush, she asked the right question: “Why was it rushed? Since they target children, they require careful verification and careful procedures” (Kwon, 2025).

The answer, though never stated officially, is obvious: fear of being left behind in the AI race.

This pattern repeats everywhere. In 2024, 45 U.S. states considered nearly 700 AI bills, but only 20% became law (NAAIA, 2025). Now in 2025, over 1,000 new AI bills are under consideration across different states, while the EU, China, and dozens of other nations craft their own approaches. It’s not productive diversity—it’s regulatory chaos (NAAIA, 2025).

The quality suffers too. As one analysis noted, “decision-makers rely on briefings from consultants who themselves often lack deep technical understanding, resulting in policies that sound sophisticated but crumble under real-world application” (Medium, 2025). We’re getting compliance theater instead of meaningful governance.

The Human Cost of Rushing

The numbers from enterprise AI adoption tell a sobering story. Despite $30-40 billion in investments, 95% of AI projects produce no measurable return. Only 5% of AI pilots make it into production (Fair Observer, 2025). The primary causes? Weak change management and lack of executive sponsorship—not technical problems.

Here’s what really gets me: 38% of AI adoption challenges stem from insufficient training, while 68% of executives report friction between IT and other departments (Gigster, 2025; Appinventiv, 2025). We’re deploying sophisticated technology without teaching people how to use it or integrating it into their workflows.

Meanwhile, a shadow economy has emerged. Only 40% of companies purchase official AI subscriptions, but over 90% of employees use personal ChatGPT or Claude accounts at work (Writer, 2025). When your formal AI initiatives fail but employees innovate independently with consumer tools, that’s a clear signal that top-down rushed implementation isn’t working.

In my book, I emphasized that “technical implementation is often easier than helping people adapt” (Rissover, 2025, Chapter 3). Yet this human dimension consistently gets sacrificed in the rush to deploy.

The Educational Stakes

As someone who teaches computer science at Southern New Hampshire University and Central Texas College, and serves on Ashland University’s AI advisory board, I’m particularly concerned about rushed AI deployment in education.

A recent MIT study should give everyone pause. Researchers found that students who exclusively used ChatGPT-4 for essay writing “demonstrated the least amount of brainwave activity, and cognitive function decreased in key areas of their brains over time” (Kosmyna et al., 2025). Brain connectivity was weakest among AI users compared to students using search engines or working without digital tools.

The MIT team’s conclusion is critical: “these findings support an educational model that delays AI integration until learners have engaged in sufficient self-driven cognitive effort” (Kosmyna et al., 2025). In other words, students need to build foundational cognitive skills before introducing AI assistance.

This directly contradicts the rush to deploy AI throughout educational systems. If AI use without adequate cognitive foundation actually reduces learning capacity, then rushed implementation doesn’t just waste money—it may actively harm students.

As I wrote in my book’s education chapter, “AI in education can reduce or exacerbate inequalities depending on implementation” (Rissover, 2025, Chapter 7). Rushed deployments almost always exacerbate inequality because resources go to technology acquisition instead of equitable access, teacher training, and support systems.

A Better Path Forward

I’m not anti-AI. Far from it. AI represents “a powerful set of tools that, when used thoughtfully, can help us solve problems and create opportunities we never imagined possible” (Rissover, 2025, Introduction). The key word is thoughtfully.

Here’s what thoughtful implementation looks like:

Start with readiness, not technology. Before major AI investments, assess your actual capacity: Do you have quality data? Can your infrastructure support integration? Do you have the expertise for ongoing management? What specific problem are you solving? How will you measure success? (Rissover, 2025, Chapter 3). South Korea’s program rushed past most of these questions.

Match timelines to organizational capacity, not AI hype. In my book, I emphasized that “successful AI implementation requires more than just technology. You need clean, representative data; clear success metrics; stakeholder buy-in; and processes for monitoring and maintaining systems over time” (Rissover, 2025, Chapter 2). These requirements don’t magically appear on compressed timelines. Data cleaning is “often the most time-consuming part of any AI project” (Rissover, 2025, Chapter 2).

Build expertise before mandating adoption. South Korea made textbook use mandatory from day one, only switching to optional after the initiative had failed. Start with voluntary pilots instead. Collect feedback. Iterate on problems. Demonstrate value before expanding. “Position AI as augmenting human capabilities rather than replacing them” (Rissover, 2025, Chapter 3).

Normalize learning from failure. As I wrote, “AI tools rarely work perfectly on the first try. Expect to refine your prompts, adjust your approaches, and learn through experimentation” (Rissover, 2025, Chapter 10). This requires psychological safety—teams need permission to report problems without fear. Enterprises that integrate proper change management are 47% more likely to meet their objectives (Gigster, 2025).

The Irony of Speed

Here’s the paradox: rushing AI adoption likely delays the benefits we’re trying to accelerate. By racing past organizational readiness, we create failures that waste resources, erode trust, and make future AI adoption harder.

South Korea spent $850 million and got four months of dysfunction. How many similar initiatives are in progress right now, headed toward the same outcome? How many people will experience AI’s first impression as frustrating, ineffective technology that promised transformation but delivered disruption?

The lesson isn’t to abandon AI. It’s to recognize that “context and implementation matter more than technology” (Rissover, 2025, Chapter 11). When we build proper foundations—data quality, organizational readiness, training, iterative refinement—we paradoxically move faster toward sustainable AI adoption.

As you evaluate AI opportunities in your organization, ask not just “What can this AI do?” but “Are we ready to deploy it responsibly?” The second question, properly answered, determines whether AI becomes a genuine tool for progress or another expensive lesson in the perils of infrastructure rush.

The choice is ours. We can continue racing ahead with beta technology deployed through beta processes, collecting expensive failures like South Korea’s $850 million lesson. Or we can slow down enough to build the foundations that make AI adoption actually work.

I know which path makes more sense. The question is whether we have the patience to follow it.

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References

Appinventiv. (2025). 11 key AI adoption challenges for enterprises to resolve. https://appinventiv.com/blog/ai-adoption-challenges-enterprise-solutions/

Fair Observer. (2025). Why 95% of enterprise AI projects fail: The pattern we’re not breaking — Part 1. https://www.fairobserver.com/business/technology/why-95-of-enterprise-ai-projects-fail-the-pattern-were-not-breaking-part-1/

Gigster. (2025). 6 change management strategies to avoid enterprise AI adoption pitfalls. https://gigster.com/blog/6-change-management-strategies-to-avoid-enterprise-ai-adoption-pitfalls/

Kosmyna, N., Hauptmann, E., Yuan, Y. T., et al. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. MIT Media Lab. https://www.media.mit.edu/publications/your-brain-on-chatgpt/

Kwon, J. (2025, October 16). South Korea’s AI textbooks fail after rushed rollout. Rest of World. https://restofworld.org/2025/south-korea-ai-textbook/

Medium. (2025). Top 10 AI governance failures exposing leadership gaps in 2025. https://medium.com/@SunDeep11/top-10-ai-governance-failures-exposing-leadership-gaps-in-2025-b3d015e59687

NAAIA. (2025). The 2025 worldwide state of AI regulation. https://naaia.ai/worldwide-state-of-ai-regulation/

Pivot to AI. (2025, October 19). South Korea blows $850m on failed AI school textbooks. https://pivot-to-ai.com/2025/10/19/south-korea-blows-850m-on-failed-ai-school-textbooks/

Rissover, M. N. (2025). Artificial intelligence: A practical guide to understanding AI for professionals and students. Digital Foundations Series.

Writer. (2025). Key findings from our 2025 enterprise AI adoption report. https://writer.com/blog/enterprise-ai-adoption-survey/