Artificial intelligence (AI) is reshaping business, offering unprecedented opportunities for growth, innovation, and efficiency. From automating routine tasks to enhancing decision-making with data-driven insights, AI can transform your organization.
According to a McKinsey report, AI could contribute about $13 trillion to the global economy by 2030, highlighting the importance of understanding and using it.
Considering artificial intelligence’s benefits, many organizations are exploring AI-driven business models—strategies that integrate AI into core operations to drive value and maintain a competitive edge. Before getting into how to make your business strategy more AI-driven, here’s what an AI business model is.
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An AI business model uses AI technologies to create, deliver, and capture value innovatively. Unlike traditional business models that rely on manual processes, those driven by AI integrate machine learning, data analytics, and automation to enhance operational efficiency and long-term scalability.
Central to their approach is the AI factory, a systematic framework that continuously processes and refines raw data into valuable insights. It includes interconnected components like data pipelines and machine learning models to automate decision-making.
The AI factory powers AI-focused business models, including:
- AI product as a service (PaaS): AI-enhanced products that provide ongoing services, such as home assistants that learn from user interactions
- AI data monetization: Companies that gather and analyze data to sell insights or predictive analytics
- AI-driven platforms: Digital platforms that use AI to match products or services with customer needs, like ride-sharing apps that optimize routes and pricing in real-time
“The AI factory, as its output, does three things,” says Harvard Business School Professor Karim Lakhani, who co-teaches the online course AI Essentials for Business with HBS Professor Marco Iansiti. “Predictions, pattern recognition, and process automation.”
Predictions allow you to anticipate customer behavior and inventory needs to make decisions proactively. With pattern recognition, you can spot emerging trends and potential risks to adapt to change more easily. Process automation handles repetitive tasks like customer service or medical image analysis to boost efficiency and free up valuable resources.
To stay competitive, your organization must embrace AI. Here are four AI business model characteristics that help drive value creation, growth, and digital transformation.
Related: Listen to Jen Stave, launch director of Harvard’s Digital Data Design (D^3) Institute, discuss AI's transformative impact across industries on The Parlor Room podcast.
4 AI-Driven Business Characteristics
1. Datafication
Data is the cornerstone of AI business models and essential to training and optimizing AI systems.
You can collect it from:
- Customer interactions
- Operational processes
- Market trends
You can then process and analyze it to extract valuable insights to enhance decision-making and strategic planning. AI Essentials for Business refers to this as “datafication.”
For example, when Google acquired Nest in 2014, it transformed a simple thermostat into a smart device that controls temperature and collects data. In AI Essentials for Business, Lakhani highlights the typical functions homeowners manage with traditional thermostats.
“You buy fuel. You set the temperature. You sense temperature. And you turn the furnace on and off,” Lakhani says. “There’s data there, but none of that’s actually being pulled out, right?”
While traditional thermostats’ basic data is useful, Nest takes it further through datafication, turning user activity into actionable insights. By analyzing heating and cooling patterns, Nest helps homeowners optimize energy use and save money—often around eight percent annually on heating and cooling costs.
“This datafication enables you to start to rethink what you can do in terms of value creation and value capture,” Lakhani says in AI Essentials for Business.
2. Algorithm Development
Another characteristic is using information to build algorithms—because AI business models use a lot of data. The process typically starts with collecting data and then training machine learning models to recognize patterns, make predictions, and automate decision-making.
“The data by itself doesn't do anything,” Iansiti says in AI Essentials for Business. “So you actually need to figure out which algorithm you're going to choose. You're going to figure out what type of algorithm you need. You need to figure out what to do with it.”
Selecting the right algorithm begins with understanding your problem and the type of data you have.
For example, Netflix’s recommendation algorithm aligns with its goal of maximizing engagement by continuously analyzing viewer preference data to deliver personalized content. This ensures subscribers are likelier to discover and watch content they enjoy—boosting their satisfaction, viewing times, and loyalty in a competitive market.
Choose an algorithm that aligns with your business goals and objectives, and be ready to adjust as you gain access to new data and market insights.
Related: The Advantages of Data-Driven Decision-Making
3. Increased Automation
Automation is crucial to your AI business model because it enables you to streamline manual, repetitive tasks.
However, it can raise layoff concerns. According to Goldman Sachs, roughly two-thirds of U.S. occupations are exposed to some degree of AI automation. Despite that, research shows that it complements rather than replaces most jobs.
One benefit of AI automation is that it allows you to shift employees' focus to more strategic tasks. For example, before implementing AI, Amazon associates manually tested individual products to optimize packaging. Once Amazon realized that approach was unsustainable at scale, it created the AI-powered Package Decision Engine to automate much of the work and enable employees to concentrate on high-value activities, such as:
- Developing sustainability initiatives
- Improving customer experiences
- Analyzing data to enhance services
If you’re interested in employing AI automation, identify repetitive tasks within your organization. For instance, you could automate inventory management, data entry, or customer support using AI tools like chatbots or inventory tracking systems.
Related: 5 Key Benefits of Integrating AI into Your Business
4. Innovation
In an AI-driven business, innovation isn’t just about developing products but digital transformation. That means empowering your team to explore ideas, test emerging technologies, and iterate on existing processes.
In AI Essentials for Business, Iansiti explains that AI-first innovation goes beyond new technologies or products. While traditional companies can struggle to adapt due to siloed operations and rigid cultures, those that are AI-first can innovate more dynamically and efficiently with digital cores and integrated data platforms.
“It's not a new product. It's not a new service,” Iansiti says. “It's a fundamentally new kind of organization. It's a new kind of company built on a different premise altogether that’s coming at you to essentially serve the same kind of customers—and more—and continue to build and aggregate value in a new way.”
For example, Microsoft transformed into an AI-first company by shifting its focus from traditional software to cloud computing. It broke down silos by unifying its engineering teams under a single data platform. That enabled faster innovation, more agile response to market demands, and new customer value through its Azure cloud platform and AI-driven tools.
To drive similar innovation, assess your digital capabilities and organizational structure. Foster cross-functional collaboration and break down silos to enhance data sharing and agility across departments—setting the stage for more dynamic, impactful innovation.
How to Thrive in the Age of AI
With these characteristics in mind, explore how an AI-driven business model can solve your organization’s challenges. Whether automating repetitive tasks, enhancing decision-making with data insights, or developing new products and services, strategically implementing AI can provide a competitive advantage.
To learn more about AI business models, consider enrolling in AI Essentials for Business to gain the knowledge and tools to make informed decisions and shape your organization’s digital transformation strategy.
Do you want to become an AI-first company? Explore AI Essentials for Business—one of our online digital transformation courses—and download our interactive online learning success guide to discover the benefits of online programs and how to prepare for the next step in your educational journey.
