AI-native businesses are coming—here’s five ways to prepare

A disruption is brewing, and traditional businesses need to pay heed. AI-native businesses, powered by swarms of AI-driven agents and a revamped tech stack, will soon emerge, getting work done more productively-and unexpectedly-than anything seen before.

This disruption comes on the heels of the digital-native disruption that gave rise to big-tech behemoths like Google, which forever changed internet search with a simple interface backed by a powerful search engine. What's different this time is the sheer speed and agility with which AI-native businesses could impact industries and overturn entrenched business models.

Imagine an adaptive engine that crafts personalized learning paths for students and employees, challenging the current one-size-fits-all model. Or a fashion house that cuts waste using AI-powered design assistants to create made-to-order clothing with sustainably sourced materials.

Unburdened by legacy constraints, these newcomers will make bold moves where others defer to conventional wisdom. With AI as a fundamental building block, they will build entirely new business models and superfast processes that will be a game-changer for businesses and consumers alike.

AI-native disruption coming soon

The question for businesses is when this disruption will begin-and what to do about it. In our recent New Work New World study, conducted in partnership with Oxford Economics, we mapped out a likely adoption curve for generative AI-a key driver for AI-native businesses. According to our analysis, it's in the period of confident adoption, between 2026-2030, that we'll see generative AI's impact on jobs and how work gets done.

This is why the time is now for established businesses to develop their strategies for competing in an AI-native business landscape, from building out a robust technological infrastructure, refining their business model and creating a culture of continuous innovation. In all cases, there's much to learn by understanding how AI-native businesses will operate.

In our newly published report, "How to think-and act-like an AI-native business," we describe what this new landscape will look like, the three main types of AI-native businesses that will emerge, the new AI-driven tech stack and strategic considerations for partnering or competing with AI natives.

How to think like an AI-native business

From our analysis, here are five actions established businesses can take now to be ready when AI-native businesses emerge:

1. Rethink the nuts and bolts of how work gets done. AI-native businesses are retiring the traditional user interface and replacing it with prompt-based interactions with intelligent machines. Advanced algorithms will process natural-language prompts that range from the simple (e.g., fetching data for a new project) to the complex (e.g., assessing the implications of a newly introduced regulation).

   Established businesses should start experimenting with new UI designs by identifying areas ripe for prompt-based streamlining. The goal is to create an experience that is engaging and human controlled.

2. Create an ecosystem of AI models. When it comes to AI models, AI-native businesses won't rely on a single model to do it all. Instead, they'll deploy multiple models optimized for specific tasks, resulting in an ecosystem of AI techniques. This allows them to utilize the diversity of data types and sources to create competitive advantage.

   This approach has precedent in enterprise software systems, which are often a confluence of specialized software from a variety of suppliers that work together to execute processes. Similarly, traditional businesses should understand the strengths of each model type and select the right tool for the job, even if this means investing in the expertise needed to determine what purpose these models genuinely serve and how they can be best deployed.

3. Cut through the data fog. Generating value from data is a seemingly never-ending quest for today's businesses. AI-native businesses will have no such constraints. Not only will they use a diverse set of data from a vast array of sources, but they will also use advanced techniques to visualize complex connections within these datasets to turn it into knowledge.

   Traditional businesses need to start thinking about what data they already have and what data they'll need to acquire to feed their AI models. To overcome data limitations, they can turn to techniques like synthetic data generation or semi-supervised learning. In all cases, they need to be transparent about the data they use and where it comes from.

4. Prioritize computational needs. Generative AI models can be extraordinarily demanding from a computational power perspective, especially during training and fine-tuning. Meeting the compute performance needs of AI will require established businesses to rethink their technology infrastructure to ensure scalability and cost-effectiveness. This calls for a proactive approach toward planning for future computational needs based on the planned use cases and the AI models they will deploy.
5. Use MLOps to evolve quickly and continuously. To enable experimentation, fast deployment and continuous learning, be agile and responsive, AI-native businesses will center their innovation on machine learning operations (MLOps). For them, AI is not a one-time project but a dynamic system that evolves to meet new needs.

   Established businesses need to adopt MLOps principles, themselves. Doing so will enable rapid experimentation, shorten the feedback loop between real-world results and model improvement, and enable them to continuously evolve to match the dynamic market landscape.

Preparing for an AI-native business world

The AI landscape is evolving rapidly, giving businesses a small window of time to create their own AI strategy. By actively studying the innovative approaches of AI-native businesses, established companies can start to think-and even act-like the emerging businesses that put AI at the heart of all they do.

To learn more, read our report "How to think-and act-like an AI native business."

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