As artificial intelligence (AI) moved from research labs into everyday operations, organizations swiftly began integrating its capabilities into their workflows.
According to a McKinsey report, 69 percent of companies began investing in AI before 2024, and 92 percent plan to increase those investments by 2029. Yet, a growing number are being built from the ground up with AI at their core: AI-native businesses.
This guide defines what it means to be AI-native, how it differs from integrating AI into existing systems, and the foundational pillars of an AI-native approach.
What Does “AI-Native” Mean?
In Harvard Business School Online’s AI for Leaders course, an AI-native business is defined as one built from the ground up to leverage AI for value creation and problem-solving. AI is embedded in every stage of the organization, from research and development to marketing, customer engagement, and human resources.
In this way, AI-native businesses are similar to digital-native companies, which grew alongside the internet by prioritizing digital infrastructure and customer experience.
AI-First vs. AI-Native
Some might use “AI-native” and “AI-first” interchangeably. Yet, while similar, they’re distinct concepts. AI-first companies incorporate AI as a core capability that enhances products, services, and operations. AI-native organizations go further by structuring the entire business model and value proposition around AI.
To illustrate the difference, consider the following two businesses:
- Business A: A 30-year-old firm systematically incorporating AI tools across its systems. This is an AI-first company.
- Business B: A startup built within the last year with AI embedded in every process from day one. This is an AI-native company.
AI-Native vs. Embedded AI
Most established businesses begin their AI journeys through embedded AI—adding AI-powered tools to existing workflows. While embedded AI can provide early value, relying only on third-party tools may limit long-term progress. Without moving toward AI-nativity, companies risk plateauing efficiency gains and creating fragmented intelligence systems.
AI-Native vs. Shadow AI
Shadow AI occurs when employees use AI tools without organizational oversight. While it can boost productivity, it introduces serious risks around data security and compliance.
To prevent shadow AI, organizations should:
- Develop clear AI usage policies
- Provide approved tools
- Educate employees on the risks and safeguards
Key Pillars of an AI-Native Architecture
To build or transition toward an AI-native business, focus on these pillars—each of which forms the foundation of an AI-native business architecture.
1. Foundational Data
HBS Professor Karim Lakhani, who co-teaches AI for Leaders alongside HBS Professor Iavor Bojinov, likens an AI system to a factory that transforms data into insights.
“The raw material entering the factory is data—everything from numbers in spreadsheets to text and images,” Lakhani says in AI for Leaders. “The factory processes this data and produces something useful on the other side: often, a prediction. That might be a forecast of future demand, a recommendation for what a user might want next, or a classification like ‘approve’ or ‘reject.’ In other cases, the output might be a discovered pattern, a group of similar customers, or a new piece of content.”
To support AI initiatives, organizations need robust data collection systems and processes to transform raw information into usable formats. Understanding the data life cycle—generation, collection, processing, storage, management, and analysis—is essential.
Without a strong data foundation, AI transformation isn’t possible.
2. Cybersecurity and Data Privacy
AI-native organizations train models on large volumes of sensitive data, including proprietary company information and customer records. Depending on your industry, this may include financial, medical, or biometric data.
To maintain customer trust and meet regulatory standards, establish strong data governance practices that secure information and ensure privacy. Without them, even the most advanced AI systems can expose your organization to compliance risks and reputational harm.
3. Machine Learning Ecosystem
Every AI-native business relies on a machine learning ecosystem, composed of:
- Algorithms: The logic that processes the data
- Machine learning models: Trained systems that generate predictions or identify patterns
- An orchestration layer: The interface employees use to manage AI-powered tasks
In AI for Leaders, Lakhani describes it through the AI factory metaphor: “Inside this factory, algorithms act as the machinery—sets of instructions that process the incoming data. These algorithms help construct machine learning models, which are the tools that actually generate predictions or identify patterns.”
Most organizations begin by adapting open-source algorithms to their proprietary data. As they mature, they evolve toward building custom models that support their differentiated value proposition.
4. Guardrails, Safeguards, and Feedback Loops
Machine learning models can naturally degrade over time as real-world data shifts. To maintain accuracy, AI-native organizations must implement guardrails, safeguards, and feedback loops—three mechanisms outlined in AI for Leaders:
- Guardrails set boundaries during development to ensure models are used ethically.
- Safeguards monitor deployed models in real time, triggering alerts or human review when the output seems off.
- Feedback loops track performance over time, enabling models to learn from new data and improve accuracy.
Together, these mechanisms support reliability, fairness, and trustworthiness.
AI-Native Business Model Example: A Case Study
Dental AI company VideaHealth exemplifies an AI-native organization built around AI-driven diagnostic tools for the health care industry.
In AI for Leaders, VideaHealth CEO and Co-founder Florian Hillen describes how challenging dental diagnoses can be. Dentists often work under time constraints and review images on small screens, which can lead to missed issues. Simultaneously, patients might question diagnoses due to limited visibility into the process.
“In dentistry, there’s unfortunately a very high mistrust of patients towards the dentists, more so than in any other health care domain,” Hillen explains in AI for Leaders. “There [can be] a little bit of suspicion on the patient side.”
VideaHealth addresses both challenges by building its business around AI-enabled diagnostics. The company trains AI models to analyze dental X-rays with a high degree of accuracy, giving clinicians an objective “second opinion.” The result is improved diagnostic confidence and greater transparency for patients.
With more than 30 diagnostic models, the company’s VideaAI platform now supports an estimated 50,000 dental professionals worldwide. Its success illustrates the value that can be created when an organization embraces an AI-native strategy.
A New Way of Thinking About AI
Most organizations add AI to existing systems. AI-native businesses take a different approach by embedding AI into the core of their strategy, operations, and value creation.
Just as digital-native companies transformed industries through the internet, AI-native organizations are poised to do the same with AI—reshaping how businesses create value inside and out.
If you’re ready to embrace an AI-native framework for your organization, explore HBS Online’s AI for Leaders course, or dive into our Digital Transformation and AI Learning Track, which allows you to complete three courses in the subject area to earn a Certificate of Specialization. To explore all our digital transformation and AI programs, download our free digital transformation flowchart to find the right fit.
