What It Means to Be an AI Native Company

In the world of tech startups, there's an adage that goes something like this: "If you're not moving forward, you're moving backward." This sentiment has never been truer than in the era of artificial intelligence. Just as cloud computing revolutionized the way we store and process data, AI is poised to redefine how we build, innovate, and even think about technology.

When new technology companies get started today, they're in the cloud by default; when new technology companies get started in 5 years, they'll be built with AI by default.

Yet for all the hype and excitement over AI in business, many companies are still treating it as an add-on or afterthought. The transformation will come when the next round of large enterprises starting today are AI-native from the ground up, with AI fully integrated into their product, data, and operational strategies.

AI Native Company Definition

A company designed around the use of AI, where its products and operations cannot function effectively without the integration of artificial intelligence.

At its core, an AI native company is designed from the ground up around artificial intelligence. Its products leverage AI so intrinsically that they could not properly function without it. Rather than casually experimenting with AI or worse, applying an AI label for marketing, an AI native company embeds AI into its underlying infrastructure and workflows.

Being an AI-native company means AI permeates the business' day-to-day operations, data pipelines, and strategic planning much like the internet and cloud computing does today.

AI in New Product Development

Becoming an AI-native company begins from the very first spark of inspiration for a new product or service. Rather than tacking on AI as an afterthought down the road, next-generation builders infuse intelligence into their ideas from the start.

Even at the early conceptual stage, visionaries now architect completely novel experiences that intrinsically analyze data, adapt to users, automate workflows, and enhance engagement.

Yet this also raises questions on the build vs buy paradigm for AI capabilities. Startups could leverage existing solutions from vendors like OpenAI to accelerate time-to-market. However, over-reliance on external APIs risks commoditization if competitors share the same building blocks. A world of ‘AI wrapper’ startups isn’t a very exciting one. For core differentiated services, engineering teams may prefer crafting proprietary algorithms tailored to their niche needs.

The AI build-vs-buy decision framework:

• Non-core utilities: leverage external AI-as-a-service 
• Technological differentiation & IP: build custom AI from the ground up
• Multi-layered products: combine in-house and third-party AI 
  

The goal of an AI Native company is to create products and services that are seamlessly integrated with AI, offering an experience that would not be possible with AI and one that leverages its full potential.

AI in Company Operations

Beyond products and services, becoming an AI-native company requires weaving intelligence into the fabric of business operations too. Marketing teams tap generative writing to automate content creation. Strategists leverage analytical tools to inform positioning. Sales staff enlist conversational AI to qualify leads.

AI-native companies use artificial intelligence so pervasively that if it were ripped out, the company would no longer be able to function:

• In marketing, instead of just optimizing pay-per-click bids, AI-native teams create omni-channel content flows driven by algorithms - personalized web pages, dynamic sales emails, tailored social posts, contextual in-app messages, and more. 
• Strategy leaders augment their intuition with quantitative neural network models to simulate market scenarios and stress test new directions.
• In sales, machine learning qualifies promising prospects to route to human closers while conversational AI handles common inquiries to scale efficiency. 
• In product development, natural language algorithms generate user stories and prototyping blueprints to accelerate workflow. 
• In engineering, AI pair programmers enhance developer velocity, best practice conformance, and oversight.
  

The common thread is that rather than deployed tactically, AI assumes a strategic role embedded in workflows, data pipelines, and platforms. Leaders don’t just ask “How could AI augment this process?” but “How could we reinvent this process around AI?” Made possible by exponential advances in computer vision, speech synthesis, language mastery, and code generation, this new normal redefines state of the art.

The Future is AI Native

AI isn't just a wave to catch; it's the ocean in which future companies will swim.

In this new technology landscape, the distinction between tech companies and AI companies is blurring. AI in operations isn't just a productivity hack; it's the new baseline. AI enabled products will be the default.

Much like the internet fundamentally rewired business in the 1990s and cloud computing did in the 2000s, artificial intelligence promises to again reshape every sector it touches over the coming decade.

Companies that fail to make AI intrinsic to their product DNA risk extinction. But the path from buzzword compliance to truly reimagining companies around intelligent systems remains unclear to many legacy organizations. 

Welcome to the era of AI Native companies – a world where AI isn't just part of the game; it is the game.