Digital Economy Dispatch #255 -- Fragile AI and How Not to Fail the Resilience Test

Earlier this week, we experienced the fragility at the heart of our hyper-connected digital world. There I sat, all set to kick off an online webinar, when it was clear something had gone wrong. Just before going live, I realized that Zoom was not working. I just couldn’t connect. My phone started pinging with WhatsApp messages and emails: “Is the link broken?”, “Are you having trouble too?”, “How do I connect?”.

The effect of a relatively minor cloud infrastructure outage was rippling across online platforms, taking out not just video conferencing but access to cloud files, shared workspaces, and supporting apps. Several hours of chaos followed as we all juggled alternative communication channels, tried to find explanations, or just went to find another cup of coffee and waited for things to get back to normal. Was this a one-off fluke, or is it a taste of what we can expect in our deeply interconnected, AI-dependent world? And perhaps more importantly, is widespread adoption of AI capabilities likely to make this better or worse?

The Race for AI Adoption

We are all seeing AI spreading through enterprises at breakneck speed, seemingly much faster than governance frameworks can keep up. The 2025 McKinsey State of AI survey reported that 75% of organizations now use AI in at least one business function, yet only 28% have clear executive accountability for governance or oversight. Similarly, the EY Global Responsible AI Pulse Survey found that while 72% of companies have scaled AI extensively, fewer than one in three have formal governance policies in place.

The scale of AI adoption is staggering. According to Netguru’s 2025 AI Adoption Statistics, 78% of firms now apply AI across core operations, up sharply from 55% in 2024, and usage of generative AI nearly doubled in the same period. Yet nearly half of C-suite executives admit their organizations are “tearing apart” under the strain of unmanaged adoption, citing frictions between IT and business units and a lack of coherent strategy.

This mismatch between rapid adoption and weak governance has made many enterprises more dependent and more exposed than ever. It’s against this backdrop of speed and fragility that the recent AWS cloud outage serves as a blunt reminder of how thin the line is between flexible growth and unbounded vulnerability.

When One Line of Code Breaks the World

According to reports, this week’s failure was not a sophisticated breakdown. Just a simple DNS configuration error inside Amazon Web Services triggered a cascading failure that silenced half the internet for several hours. Banks, airlines, government systems, and even AI model hosting platforms were disrupted. The cause was mundane. A single mismanaged update that exposed deep dependencies that few organizations truly understood.​

It's interesting that cloud computing promised resilience through redundancy, yet this event revealed that redundancy is not the same as invulnerability. According to multiple analyses, the outage showed how overly centralized infrastructures magnify fragility. Machine learning pipelines, analytics platforms, and digital services that rely on AWS’s backbone all fell silent. We found out once more that the cloud is not an unbreakable safety net.​

It also reminds us that resilience, in this light, is not just technological; it is strategic. As one commentator observed, “Assuming tech giants are too big to fail is itself a failure of imagination”.​

When Disruption Becomes Deliberate

Of course, not all failures are accidental. Just days before the AWS incident, we saw more evidence of the fragility of digital technology. Jaguar Land Rover (JLR) revealed that its 2025 ransomware attack, initially downplayed as a “technical issue”, could cost the company as much as £1.9 billion, according to news reports. Production halted, 30,000 employees were sent home, and output stopped across key plants.​

What makes this alarming is not just the magnitude of the loss but the incentive structure behind it. Cybercriminals now operate with the sophistication and capital of legitimate enterprises, exploiting digital dependencies for staggering profit. The attack on JLR underscores that resilience cannot be expected, only prepared for.

Following a stream of these high profile attacks, we now recognise that boards can no longer treat cyber resilience as an IT expense; it’s a business survival issue. Every digitally connected enterprise is a potential target because the rewards for attackers are so rich and the penalties so limited.

When AI Expands the Attack Surface

Into this already volatile environment, AI is introducing new complexity. The Trend Micro State of AI Security Report in 2025 found that 93% of organizations expect daily AI-driven cyberattacks, and two-thirds believe AI will have the largest single influence on enterprise security in the coming year.​

Of course, AI can strengthen defences by detecting anomalies, correlating risk signals, and flagging intrusions before humans can. But it also amplifies threats. Malicious actors now use AI for adaptive phishing, deepfake impersonation, automated vulnerability scanning, and even synthetic data poisoning. The same technology that empowers defenders empowers attackers too, and the arms race is accelerating.

The Double-Edged Sword of AI Acceleration

The lesson for digital leaders is that in deploying AI, it’s essential to be cautious. The frantic rollout of AI tools across enterprises often occurs without clear security or resilience frameworks. Each model, API, or agent adds a node to an expanding dependency web. Just as AWS’s DNS failure caused downstream chaos, a corrupted model, API, or data pipeline failure could have similar ripple effects inside AI-dependent organizations.

Unfortunately, too many firms are deploying generative and predictive models before defining fallback procedures or validation checks. Going faster is only meaningful if the rules of the road are well defined. Speed without structure creates fragility.

Building Resilience into the AI Era

For digital leaders, this is a moment to rethink resilience not as redundancy but as adaptive capacity -- the ability to absorb disruption, reorganize rapidly, and keep critical operations functioning. Achieving that demands re-establishing good digital practice:

• Expose every dependency. Know exactly where your risks lie—from cloud and models to data sources.

• Stress-test your systems. Run real failure drills. Don’t wait for an outage to show your blind spots.

• Design for resilience, not just compliance. Build transparency and accountability into every AI project from the outset.

• Don’t automate for its own sake. Apply AI where it delivers real results, avoiding unnecessary complexity.

• Take responsibility. Outsourcing to the cloud is not outsourcing survival. Plan for failure and own your recovery.

The Human Dimension

The recent failures also remind us that technology alone will not create resilience. What differentiates enduring organizations is leadership that sees technology as an ecosystem that is interconnected, evolving, and occasionally fragile. In the rush toward AI-driven transformation, it’s tempting to move faster than your governance can adapt. But speed without foresight just multiplies the risk.

The AWS outage and JLR breach are not anomalies; they are symptoms of deeper structural fragility. Enterprise resilience now demands that leaders treat every line of code, every connection, and every algorithm as potential points of failure. The challenge is not to slow innovation but to stabilize it. To ensure that as we scale AI, we also secure it. Effective digital leadership lies not in chasing AI’s next capability but in mastering its resilience.