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Key Takeaways
- The AI story has moved from “deploy it everywhere” to “prove it works when the stakes are highest,” and public markets are starting to price that distinction in.
- The winners in this next phase will be the companies that treat AI as mission‑critical infrastructure: mapped, hardened, monitored and pressure‑tested like any other core system.
The AI conversation is starting to change. For the past few years, business leaders have been under pressure to move quickly. They have approved new tools, launched pilots and encouraged teams to find ways to automate more of their work. The question was whether a company was adopting AI fast enough.
Public markets are beginning to ask a harder question. Can businesses built around AI meet the expectations surrounding them?
The 2026 IPO market has attracted enormous attention from investors, driven partly by high-profile technology and AI infrastructure offerings. At the same time, the reception has not been universally enthusiastic. Some newly public companies connected to the AI build-out have faced immediate scrutiny over their valuations, capital requirements and ability to generate durable returns.
That is a useful lesson for every business leader, even if taking a company public is nowhere on the agenda.
AI adoption was the easy part. The next phase is proving that the infrastructure, operating model and business outcomes underneath it can hold up.
Over my 25 years of experience helping scale technology companies, I have seen a similar pattern with nearly every major wave of innovation. Leaders move quickly to capture the opportunity. Then the technology becomes important enough that its underlying weaknesses can no longer be ignored. AI is reaching that point now.
Determine what stops working when AI does
When an AI tool is used occasionally to summarize notes or brainstorm ideas, losing access to it is inconvenient. When the same technology is embedded in customer service, sales, scheduling, logistics or financial operations, losing access becomes a business interruption. That distinction matters.
Start by identifying every AI-supported workflow tied directly to revenue, customer experience or employee productivity. Then ask what happens when that workflow is unavailable for five minutes, one hour, or an entire business day.
I have seen this firsthand while working with a children’s toy retailer, when the company relied on an AI-powered voice application that let customers record personalized messages as part of the purchase experience. But an unstable internet prevented stores from using it. The stores stayed open, but without one of the experiences that set the brand apart, they lost a differentiator that customers had come to expect. And that is exactly what happens when AI depends on infrastructure that cannot reliably support it.
Map the infrastructure beneath every AI outcome
AI may appear to employees as one application, but the experience depends on a much longer chain.
The user needs a stable connection. The application may need to reach a cloud platform, retrieve company data, send information to an external model and return the result to another business system. Each step creates another dependency.
When the experience works, that complexity is mostly invisible. When it fails, teams often blame the application because that is the part they can see.
From what I have seen, that diagnosis is frequently incomplete.
An AI application can appear slow because of packet loss, congestion or inconsistent connectivity. A workflow can fail because one integration timed out. An automated process can produce delayed information because the systems feeding it were not designed to exchange data in real time.
AI does not eliminate operational complexity. In many cases, it makes the consequences of that complexity more visible.
Leaders should map the complete path supporting every critical use case. That includes the applications, integrations, data sources, cloud services, connectivity and internal processes involved.
Then look for single points of failure. If one provider, connection or manual approval can stop the entire workflow, the business is not ready to treat that workflow as mission-critical.
This is where companies need to resist the temptation to solve every problem by adding another tool. Adding more technology to an unstable foundation usually creates more places for the process to break down.
Measure whether AI improves the business under pressure
A successful demonstration is not the same as a reliable operating model.
An AI tool may perform well during a controlled pilot with a limited number of users. The real test comes when hundreds of employees access it at once, customer demand spikes or the network is already carrying business-critical traffic.
This is similar to what happens in a growing franchise. A location may operate smoothly during an average afternoon, but its busiest hour reveals whether its systems were actually built to scale.
AI needs the same kind of pressure test.
Instead of measuring only licenses, prompts or time saved, leaders should connect AI performance to operational outcomes. Does the technology shorten customer wait times when demand is highest? Does it help employees complete transactions more consistently?
Leaders should also test how the business responds when an AI-supported workflow fails. A continuity plan stored in a document is not enough. Teams need to test it under realistic conditions.
The companies that benefit most from AI will be the ones that can recognize degradation early, respond automatically where possible and prevent a technology problem from becoming a customer-facing failure.
The AI race is becoming a reliability test
The enormous attention surrounding AI-related investments and public offerings tells business leaders something important. The market believes AI will support a meaningful share of future growth.
It is also beginning to demand evidence.
Your customers, employees and investors will eventually do the same. They will care less about how many AI tools you have introduced and more about whether those tools make the business perform better when it matters.
Before approving the next implementation, examine the systems supporting the tools you already have. Identify the processes that cannot afford to stop. Remove unnecessary complexity, build redundancy around critical operations and test how the business performs under pressure.
Adopting AI may help a company move faster. Building the foundation beneath it determines whether the company can keep moving.
Key Takeaways
- The AI story has moved from “deploy it everywhere” to “prove it works when the stakes are highest,” and public markets are starting to price that distinction in.
- The winners in this next phase will be the companies that treat AI as mission‑critical infrastructure: mapped, hardened, monitored and pressure‑tested like any other core system.
The AI conversation is starting to change. For the past few years, business leaders have been under pressure to move quickly. They have approved new tools, launched pilots and encouraged teams to find ways to automate more of their work. The question was whether a company was adopting AI fast enough.
Public markets are beginning to ask a harder question. Can businesses built around AI meet the expectations surrounding them?
The 2026 IPO market has attracted enormous attention from investors, driven partly by high-profile technology and AI infrastructure offerings. At the same time, the reception has not been universally enthusiastic. Some newly public companies connected to the AI build-out have faced immediate scrutiny over their valuations, capital requirements and ability to generate durable returns.