You’re not an AI company until you’re a full-stack company

From Siri and Alexa becoming household names to Apple recently announcing its new HomePod smart speaker, artificial intelligence finally seems to have made the jump from the world of futuristic science fiction straight into our own homes. The variety of tasks that AI creations are taking on is increasing. AI can vacuum our floors, create expensive works of art, and even debate the meaning of life.

With AI technology permeating the public consciousness, tech companies are eager to ride the hype and emphasize the AI aspects of their products. Everyone is claiming to be an AI company, showcasing their neural networks or their bots. But with a narrow and specialized focus, those companies are often missing the big picture. The companies that will rise to the top and stay there are full-stack AI companies.

To build a meaningful AI system, you have to have a big data practice, a software engineering practice, and a user experience design practice. Only then can your AI evolve into a useful and practical tool that can communicate with other applications. You also need to create a feedback loop to determine whether the decisions that your AI system is making are actually helpful to the people using it.

I saw this for myself when I started my career as a knowledge engineer, focused on creating the backend rules and algorithms that give AI systems the ability to “think.” I quickly realized that in order to deliver results, knowledge engineering isn’t enough: I also needed to understand software engineering, the front-facing aspects of the trade that made our AI systems useful and usable to customers. If I wanted to be effective, I needed mastery over the complete, end-to-end solution.

Companies looking to develop successful AI will also need to master every level of the solution: big data, analytics, and user experience (UX). A bot or an algorithm can’t and shouldn’t just stand on its own.

The market has witnessed enthusiasm for the AI trend before, and we can learn from it. Prior to 1990, venture funding poured into artificial intelligence. As early as 1986, university researchers in Munich successfully tested self-driving vans.

In the 1990s, though, the fickle market grew tired of AI. The bubble for AI products burst. Even mentioning the word in a company’s description would hurt a startup’s valuation. That doesn’t mean companies stopped working on AI advancements — they just stopped calling it that. They replaced the terminology with catchphrases like “big data” or “intelligent algorithms” that were still pieces of the AI stack but didn’t take into consideration the full picture.

This focus on just one facet of the science puts companies at risk of missing AI’s larger potential.