When organizations decide to implement AI solutions, they face an important and sometimes confusing choice: should they build their own system, buy an existing one, or go for a mix of both? This decision is crucial because it impacts how quickly they can get AI up and running,as well as their long-term success, how they use their resources, and their competitive edge in the market.

Your Go-To Framework for Build vs. Buy AI

To help guide this decision, we have created a structured framework, evaluating keyconsiderations such as resources for customization, number of users, cloud support, existing workflows, security policies, and available skills. Each factor is scored on a scale from 1 to 5, allowing organizations to identify the best fit for their needs.

1.ADOPT

It represents a maximum buy approach, perfect for organizations with basic requirements and simple workflows. It suits companies with limited AI expertise, minimal security needs, and a small user base (1-100 users). A good example here is our Data Copilot work for a recent retail client. We implemented an off-the-shelf AI solution, specifically a data co-pilot, to track and gain insights from their data. This solution allowed them to automate data analysis for sales with minimal customizations. This aligns with the early stages of AI maturity, where companies focus on adopting basic tools to automate repetitive tasks without major investments in infrastructure.

2.INTEGRATE
This stage involves some building on your own but focuses mostly on buying solutions. It’s suitable for companies that have some in-house skills and need to connect AI with their existing workflows. This approach is ideal for organizations with moderate cloud support and a user base of 101-1000 people. We recently worked with a manufacturing company that wanted to use AI to optimize its supply chain by calculating safety stock for its products. Minor customizations ensured AI integrated smoothly with their existing systems. This fits the moderate AI maturity stage, where companies look to integrate AI solutions into their core operations for efficiency improvements.

3.ENHANCE
This stage finds a good balance between building and buying. It’s ideal for organizations that already have established data pipelines and a mix of in-house skills and support from partners. This approach supports growing user bases and meets standard compliance needs. For instance, consider a healthcare organization we worked with. They aimed to enhance their patient data management system by customizing AI algorithms to analyze patient outcomes more effectively. They also integrated pre-built AI models to simplify data processing and reporting, streamlining their workflow significantly. This corresponds to a mature adoption stage, where organizations blend in-house development with external solutions for optimized performance.

4.ADAPT

It emphasizes a strong build preference, suitable for companies with substantial in- house expertise, complex workflows, and larger user bases (1000+). An example could be a pharmaceutical manufacturing company developing AI-driven drug discovery platforms. This company might leverage its in-house data scientists to build custom AI models to analyze chemical compounds, while integrating AI with their existing laboratory information systems.This approach aligns with a more advanced AI maturity level, where the organization customizes AI to fit highly specialized and interconnected processes.

5.MASTER
This is the maximum build, minimal buy approach, reserved for organizations with full in-house expertise, highly complex workflows, and large-scale user bases. We are in discussions with a manufacturing giant with an in-house team, and they want to adopt a model that allows them to develop proprietary AI systems to optimize every part of their production, from autonomous machines to supply chains. This will give them complete control and enhance efficiency across the board. This reflects the highest maturity stage, where organizations have mastered AI and fully control its development, deployment, and scaling across the enterprise.

Find Your AI Strategy Fit
Konverge AI’s Build vs. Buy Framework helps companies go through every stage of AI adoption, from off-the-shelf to custom solutions. Our scored approach pinpoints the right strategy for maximum efficiency, guiding you right from the data strategy to advanced AI. Know where your organization fits on the spectrum—from minimal to maximum build.