Suppose your organization has started taking an AI-first approach by integrating artificial intelligence into specific tasks and processes. You removed initial barriers, increased AI adoption among employees, and began experiencing meaningful results. The next challenge is scaling that AI transformation across teams and business functions so its capabilities are embedded throughout your entire organization.
The six-part framework below can help guide your approach and improve your likelihood of scaling AI responsibly and effectively.
A Framework to Scale AI: Step-by-Step
1. Establish a Strong Foundation
Before expanding your AI footprint, ensure you have the structure, resources, and alignment to support growth. This includes securing stakeholder buy-in and demonstrating AI’s value through at least one or two early pilots.
In the Harvard Business School Online course AI for Leaders, HBS Professor Iavor Bojinov recommends focusing on adoption rates in these initial pilots. Strong adoption enables employees to adapt and provides early evidence of AI’s return on investment (ROI)—both essential for scaling.
Ask yourself:
- Do you have the data and technical infrastructure required to extend your AI solution beyond its pilot? This includes the computing power needed to run AI tools, secure and scalable places to store your data, and the software that enables data processing, machine learning, and overall system management.
- Have you secured the necessary resources—capital, talent, and training capacity—to expand?
- Does leadership have the bandwidth to support an expansion now, or should other initiatives conclude first?
2. Define Your Ambitions
Scaling AI requires clear goals. These objectives serve as your strategic guide as you introduce AI into new areas of the business.
Consider:
- What specific outcomes do you want to achieve as you scale?
- What benefits do you expect? Greater efficiency, lower costs, improved customer experience?
- Which organizational priorities will your AI strategy support?
Collaborate with department heads to understand how AI can advance their goals and establish enterprise-wide alignment.
3. Determine Your Approach to Centralization
A critical decision when scaling AI is whether to adopt a centralized, decentralized, or hybrid model for AI development and governance.
According to Bojinov in AI for Leaders, there’s no single “right” model, only the model that best supports your organization’s priorities.
Centralized models offer:
- Greater consistency
- Reduced duplication
- Stronger organizational oversight
Decentralized models offer:
- Flexibility
- Faster experimentation
- Tailored solutions for each business unit
“Most organizations try to find a balance,” Bojinov explains in AI for Leaders. “How they set that boundary—and how they manage the relationship between the center and the periphery—affects how the organization functions and how well it can scale.”
4. Design Your AI Governance Policy
While many organizations focus on infrastructure, data, and model development, governance measures are equally, if not more, important. Without a clear plan, risks, such as shadow AI use, bias, or privacy issues, can undermine your scaling efforts.
In AI for Leaders, AI governance is defined as the “processes, structures, and policies that guide how an organization uses AI responsibly and effectively.”
Your governance plan should outline:
- Data use: What data can train your models? How is proprietary or sensitive data protected?
- Access: Who can use AI tools, and which datasets can they access?
- Oversight: How will your organization monitor models for effectiveness, ethics, and bias?
- Accountability: Who’s responsible for ensuring compliance? What audit processes are in place?
“People need clarity on how the system is governed,” Bojinov explains in AI for Leaders. “What happens when the model gets it wrong? Who’s accountable? Can they override it? Trust increases when people know there are protections in place.”
5. Build an AI Factory
In the HBS Online course AI Essentials for Business, the “AI factory” is described as a system for building and deploying AI solutions at scale through efficient, repeatable processes—much like an assembly line.
Core components include:
- Data pipeline: How you gather, clean, manage, and secure data
- Algorithm development: Designing and training models, often reusing methods to speed up deployment
- Software infrastructure: The hardware, network, and systems supporting your solutions
- Experimentation platform: A secure space to test and validate models before launch
Together, these elements streamline development, reduce duplication, and support continuous improvement. Implementing an AI factory enables your organization to forecast events, identify trends, and automate routine tasks.
For example, an organization building an AI factory might standardize how customer data flows into a unified pipeline, reuse a proven forecasting model across multiple product lines, and test new models in a shared experimentation environment before deployment. This repeatable process accelerates development while reducing errors and duplicated work.
6. Monitor Your Efforts
Whether you use a centralized or decentralized structure, monitoring performance is essential. Tracking the right key performance indicators (KPIs) helps you understand where AI delivers value and where refinement is needed.
Scalability metrics:
- Number of AI use cases you have in production
- Time to value
- Cost per model
- Overall ROI
Model performance metrics:
- Accuracy, precision, and recall
- Error rates
- Throughput and latency
- Model drift
Governance metrics:
- Bias and fairness scores
- Hallucination rates
- Compliance and security incidents
Another meaningful KPI is upskilling rates: how many employees have developed foundational AI knowledge each month, quarter, or year? Higher upskilling rates indicate a workforce better equipped to use AI tools more effectively.
Scaling Is an Ongoing Journey
Scaling AI isn’t a one-time effort. It’s a continuous cycle that requires ongoing evaluation, refinement, and improvement. Each new deployment creates opportunities to strengthen your processes and avoid past mistakes.
As discussed in AI Essentials for Business, early adopters learned lessons that have paved the way for today’s organizations. While scaling AI can be complex, it’s essential for staying competitive in an evolving digital landscape.
If you’re ready to scale your organization’s AI solutions and efforts, explore HBS Online’s two AI-focused courses: AI for Leaders equips you with the skills to guide AI transformation, while AI Essentials for Business provides a foundational understanding of key concepts. You can also dive deeper by completing three courses in the Digital Transformation and AI Learning Track to earn an advanced Certificate of Specialization. Not sure where to start? Download our free course flowchart to find the right fit.
