By Don Boxley, CEO and Co-Founder, DH2i (www.dh2i.com)
AI has been dominated by one question for the last two years: How smart is the model?
As a place to start, it’s understandable. Capabilities that were difficult to imagine just a few years ago have been unlocked by better models. Better reasoning, lower costs, faster inference, or more natural conversations are all now promised by every new release.
But, I think… we’re reaching an interesting turning point. The question is changing as AI moves from pilot projects into everyday business operations. Organizations aren’t asking whether AI can generate a better answer. They’re now asking whether they can depend on it. A very different problem, right?
Every Successful AI Project Eventually Becomes an Operations Project
The first version of an AI application is usually built by developers. The second version is owned by operations. That’s true of almost every technology we’ve adopted over the last thirty years. Building something is one challenge. Running it every day is another.
Once an AI app starts supporting customers, approving transactions, helping clinicians, or assisting employees, reliability becomes part of the product. Users don’t separate the model from the application. They simply expect it to work. If it doesn’t, nobody blames the language model. They blame the business.
That’s why I believe the next phase of AI won’t be defined by model improvements alone. It’ll be defined by how well organizations operate the infrastructure underneath those models.
AI Doesn’t Replace Existing Infrastructure
One assumption that keeps showing up is that AI somehow gives organizations an opportunity to start over. It doesn’t.
Most enterprises are introducing AI into environments they’ve spent years refining. They’re not building greenfield environments. Critical databases already exist. Business applications already exist. Windows servers, Linux systems, Kubernetes clusters, public cloud services, private cloud infrastructure, and edge deployments all exist. AI has to live with all of it.
That means the challenge isn’t replacing existing infrastructure. It is making existing infrastructure work together in ways it wasn’t originally designed to.
The Hardest Problems Aren’t AI Problems
The answers rarely have anything to do with model accuracy, when you ask an operations team what worries them. They worry about downtime. They worry about planned maintenance becoming unplanned outages. They worry about databases staying available. They worry about security. They worry about recovering quickly when something breaks. Those concerns haven’t changed because of AI. However, they have become more important.
The less tolerance there is for operational failure, the more business decisions depend on AI.
If an AI app isn’t available during peak business hours – that isn’t an AI problem. Availability is the problem. If an AI app can’t securely access the data it needs – that isn’t an AI problem. Architecture is the problem.
Architecture tends to outlive individual technologies – and that’s an important distinction. Today’s models will eventually be replaced. Good infrastructure should make that tech replacement almost invisible.
Hybrid Isn’t a Compromise
Hybrid infrastructure was treated like a temporary stop on the way to something else for years. I’m not convinced that’s true anymore. Different workloads have different requirements, and that’s why organizations are choosing hybrid.
Development teams may prefer Kubernetes. Production databases may remain on VMs. Sensitive data may stay on-prem. Inference may run closer to users.
Those aren’t signs that modernization has failed. They’re signs that organizations are making practical decisions instead of ideological ones. Infrastructure should support that flexibility rather than fight it.
The Conversation We Should Be Having
When AI discussions begin with model selection, infrastructure often becomes an afterthought. It should be the opposite.
Organizations should understand how they’ll keep the app available, how they’ll protect the data feeding it, how they’ll automate recovery, and how they’ll support the workload as it inevitably moves between environments–all before deciding which model to deploy.
Of course, those are not glamorous conversations. But, they are the ones that determine whether AI becomes a dependable business capability or another isolated technology project.
What You Can Do Tomorrow
If your organization is planning an AI initiative, don’t begin your next meeting by asking which model to use. Instead, ask your architects and operations teams a few different questions:
- What happens when the database goes offline, especially, if this AI app is business-critical? (The ideal answer: It shouldn’t stay offline. With minimal disruption, critical workloads should automatically fail over to a healthy node. Availability should be built into the architecture. It should not depend on someone manually restoring service.)
- If an app, cluster, or site fails, how quickly can we recover? (The ideal answer: Recovery should be measured in seconds or minutes – not hours. Automated detection and failover should reduce downtime and eliminate manual intervention wherever possible.)
- Without having to do a major redesign, can this workload move between environments? (The ideal answer: Yes. Applications should be portable across physical servers, VMs, Kubernetes, cloud, and edge environments. Without requiring the application itself to be rewritten. Infrastructure should adapt to the workload. Not the other way around.)
- From day one, are we designing security, availability, and automation into the architecture, or are we planning to add them later? (The ideal answer: They must be integrated from the beginning, because retrofitting resilience and security after deployment is more expensive, more complex, and introduces unnecessary risk.)
- Which parts need to evolve? And, which parts of our existing infrastructure already solve these problems/challenges? (The ideal answer: Keep what already works. Modernize only where it creates business value. AI should leverage existing investments whenever possible instead of forcing wholesale infrastructure replacement.)
Again, agreed… those conversations may not generate headlines. But they’ll have a far greater impact on whether your AI initiatives succeed over the next five years.
Technology will continue to change… Models will improve… New platforms will emerge… That’s the easy part.
However, organizations won’t lead their respective markets and create lasting value from AI because they chased every new breakthrough. They’ll be the ones that build operational foundations that let them adopt new technology without disrupting the business.
I think we can all agree, that’s actually what good infrastructure has always done.
AI simply gives us another reason to get it right.
Ransomware attack disrupts systems at Japanese railway operator Keio
Posted in Commentary with tags Hacked on September 29, 2026 by itnerdJapanese railway operator Keio Corporation confirmed (Translation here) that a ransomware attack hit its group servers on September 26, causing system disruptions across some of its businesses.
Keio immediately disconnected portions of its network and is working with police and external experts to investigate the attack. The ransomware disrupted business systems at some Keio Group companies, including hotel and payment services, although railway operations were not affected.
Keio said it has not confirmed that confidential company or customer information was leaked but is continuing to investigate the scope of the incident. The disclosure came the same weekend Tokyo Metro reported a separate cyber incident involving systems containing approximately 59,000 member email addresses. The operators have not indicated that the incidents are connected.
Denis Calderone, CTO, Suzu Labs:
“Keio’s train operations survived this ransomware and all indicators point to network isolation between the rail systems and the corporate network. The hotel reservations, supermarket card payments, bus ticketing, department store loyalty points all went down indicating at least some level of shared infrastructure. We’re intrigued that there were 3 different Japanese transportation related incidents (Tokyo Metro had 59,000 member email addresses compromised through a breached vendor server, and Times Car lost data on 6.6 million accounts including driver’s license images) just days before mandatory cyber incident reporting kicks in for critical infrastructure operators.
“Japan’s National Police Agency reported 123 ransomware incidents in the first half of 2026, the highest six-month count on record. Forescout data shows Japan has gone from the 28th most-attacked country by ransomware groups to 14th in just two years, with attacks rising 39% year over year. VPN appliances were the entry point in roughly 60% of those cases. On October 1, Japan’s Active Cyber Defense law takes effect, requiring 257 designated critical infrastructure operators across 15 sectors, including rail, to report cyber incidents promptly to the government. Keio just became the preview of what that reporting obligation looks like in practice.
“Every critical infrastructure operator should be asking which of their systems would pass the same test if ransomware or some other threats were to hit their corporate or production networks tomorrow. The new reporting law is a step in the right direction, but reporting an incident faster doesn’t prevent one. With VPN appliances as the dominant entry point, the fundamentals matter more than the regulation. You should ensure you patch internet-facing equipment aggressively, segment what actually needs to be isolated, and don’t assume that business systems adjacent to critical operations have earned the same level of protection. As the trends this year have been showing, the best practice right now requires you to reduce the attack surface as much as possible. Reduce what is exposed to ease your defensive efforts.”
Seemant Sehgal, Founder & CEO, BreachLock:
“Keio containing the impact to business systems and keeping railway operations running suggests the segmentation between corporate IT and operational technology held up under real conditions, which is not something every operator in this space can currently claim. The useful question for other transit and logistics companies watching this is whether their own segmentation would perform the same way if tested tomorrow.”
Ransomware can make any business stop dead in its tracks or severely impair it. Thus the best advice is to never let the bad guys in so that you don’t get pwned.
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