The foundations of AI: Build deep before you build tall

Every leadership stakeholder I meet, either within my organization or at any leadership conference or event, is very excited about artificial intelligence (AI). The conversations swing between predictive analytics, creative content and automation at scale.

Though the ambition is admirable, the reality is that most companies are trying to build skyscrapers on sand, and it is common sense that without strong foundations, even the tallest towers fall. AI is no different. You can buy the most advanced algorithms, hire an army of data scientists or run a dozen pilots, but if your fundamentals are not solid, the structure will not stand. 

I have seen this repeatedly play out in global organizations. AI projects do not fail because the models are flawed. They fail because the foundations are not ready. Readiness has less to do with technology and far more to do with culture, data and execution discipline. Before you build up, you must build deep, and that’s the only way progress lasts.

Here are six key foundation stones that determine whether your AI skyscraper will rise or collapse.

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1. Culture: The bedrock few want to talk about

Every skyscraper needs a bedrock. For AI, that bedrock is culture. Without it, everything else wobbles. AI adoption often stumbles not because of technical complexity, but because employees don’t trust it. In practice, culture comes down to a few things. You need champions who can translate between business and tech. You need to celebrate small wins, so people see the value quickly. People need to hear, again and again, that AI is here to elevate their roles and not to replace them.

I can cite an example of a widely reported case in the advertising industry that highlights this. A global ad firm introduced AI-driven audience targeting and the models worked flawlessly. However, planners ignored them until leadership spotlighted early adopters, rewarding curiosity and creativity. Lo and behold! The narrative changed from “job loss” to “job elevation” and the organization saw a significant increase in adoption of digital solutions.

2. Data discipline: Pouring the concrete

If culture is the bedrock, data is the concrete. Strong, clean and connected data keeps everything stable. Weak data cracks under pressure. Most companies do not lack data; they lack usable data. Duplicates, silos and outdated systems create instability. At the end of the day, it’s not about how much data you have, but it is whether that data is concrete or quicksand.

Industry case studies often point to insurers. In one example, a global insurer’s fraud detection system generated endless false alarms. The real problem wasn’t the model but it was that customer data was scattered across eight systems, none aligned. Once they standardized, the false alarms simply vanished.

3. Infrastructure: The steel framework that keeps it standing

Every skyscraper needs steel to stand tall. For AI, infrastructure is that framework that includes scalable cloud, secure systems and transparent cost models. Without it, even the smartest algorithms buckle.

One case frequently discussed in healthcare circles involves a hospital network that developed AI algorithms to flag patient risks in real time. Legacy servers could not process electronic health records fast enough, turning “immediate alerts” into delays that frustrated clinicians. Those alerts finally became useful at the point of care only when they shifted to scalable infrastructure.

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4. Strategy: Do not skip the blueprint

Imagine pouring concrete before drawing a floor plan. Would you ever do that? Of course not, since you would end up wasting money and time, and the skyscraper might collapse halfway through. That’s exactly what happens when companies rush into AI without a strategy. Start small, solve real pain points and scale with confidence.

One widely cited example in the retail sector shows how starting small with chatbots delivered big customer satisfaction wins. A global retailer aimed to bring AI into customer service. Rather than trying to automate everything at once, they mapped customer pain points and piloted an AI chatbot in just one product category. After refining and proving success, they scaled it across product lines. Response times improved, customers noticed and satisfaction scores went up.

5. Risk management: Guardrails that protect and do not restrict

Every skyscraper has safety codes. AI needs the same. Bias, data misuse and reputational hits are not theoretical risks. They are costly, public and painful ones.

At a recent industry conference, a payments provider shared how its AI fraud detection rollout backfired without proper access controls. A contractor misused sensitive customer data, triggering regulatory scrutiny. Without guardrails, what looked like innovation quickly turned into liability.

6. Tools: Choosing the right bricks and not the flashiest ones

Thousands of AI tools have flooded the market, each promising transformation. You don’t build skyscrapers by piling random bricks. You choose what fits the design.

One case study presented at an AI summit showed how a FinTech startup learned this when evaluating natural language tools for customer service. The most advanced option dazzled with features but confused its customers. The team chose a simpler, more intuitive tool that customers trusted and customer satisfaction scores shot up.

The bottom line: Build to last

AI is not about height but about strong foundations. The skyscraper metaphor is simple, but the point is real. AI success is not about who builds the tallest or fastest. It is about who has built a strong foundation based on which the skyscraper will not only rise, but it will also last.

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