Build vs Buy AI: A Comprehensive Guide

The AI Dilemma

In today’s business world, artificial intelligence (AI) is no longer science fiction but a key to innovation, efficiency, and competitive advantage. From automating mundane tasks to data-driven decision-making, the potential of AI systems is huge. But as organizations look to leverage AI they have a big decision to make: should they build AI in-house or buy off-the-shelf AI from vendors?

This article goes into the details of this decision, the factors you need to consider, the pros and cons of each approach, and the possibility of a hybrid approach.

1. Business Context


Before we get into the build vs buy debate, you need to understand your business context. AI is not a one-size-fits-all solution; the right approach depends on several key factors:

1.1. Business Goals and Strategic Alignment with Business Strategy

The first step in any AI strategy is to align it with your overall business strategy and objectives. What problems do you want AI to solve? Are you looking to improve customer experience, optimize operations, or create new revenue streams? Clearly define your goals and that will help you determine if you need a custom in-house solution or if an existing product will do the job.

1.2. Industry and Competition

Think about the specific challenges in your industry and how competitors are using AI. In highly competitive spaces, having a unique AI solution that aligns with your core business can be a big differentiator. In industries where AI is still in its infancy an off-the-shelf solution may be enough.

1.3. Organizational Readiness

Assess your organization’s readiness for AI, including the maturity of your data engineering infrastructure, the availability of skilled people, and the openness of your culture to change. The decision to build or buy should be influenced by your current capabilities and the resources you are willing to invest in AI development.

2. Understanding Your AI Needs

2.1. Identify Business Goals and Objectives

To determine whether to buy or build an AI solution, it’s essential to identify your business goals and objectives. What problems do you want to solve with AI? Are you aiming to improve operational efficiency, enhance customer experience, or gain a competitive advantage? Clearly defining your goals will help you understand the type of AI solution you need. For instance, if your primary objective is to streamline operations, you might look for AI tools that automate routine tasks. On the other hand, if you want to enhance customer interactions, a custom solution tailored to your specific needs might be more appropriate. By aligning your AI strategy with your business goals, you can ensure that your investment delivers the desired outcomes.

2.2. Determine the Type of AI Solution Required

Once you have identified your business goals, the next step is to determine the type of AI solution required. Do you need a pre-built solution that can be quickly deployed, or do you require a custom-built solution tailored to your unique business needs? Consider whether you need a generative AI model for tasks like content creation or a traditional machine learning model for predictive analytics. Assess your data requirements: do you have sufficient data to train an AI model, or will you need to invest in data collection and preprocessing? Answering these questions will help you choose the AI solution that best fits your business needs and objectives.

2.3. Assess Current Capabilities and Resources

Before deciding to buy or build an AI solution, it’s crucial to assess your current capabilities and resources. Do you have the necessary technical expertise, including data scientists and AI engineers, to build and maintain an AI system? Evaluate whether your team has the skills and experience required to develop and manage AI models. Additionally, consider the development tools and infrastructure you have in place. Do you have the necessary hardware and software to support AI development? Budget constraints are another critical factor—building an AI solution in-house can be resource-intensive, requiring significant investment in both time and money. Finally, consider the support of your business leaders. Are they committed to investing in AI, and do they understand the potential benefits and challenges? By carefully evaluating these factors, you can make an informed decision about whether to build or buy an AI solution that aligns with your business needs and strategic goals.

3. The Economics of AI: Cost

Cost is often the biggest factor in the build vs buy decision. However, the financial implications go beyond the initial cost.


3.1. Upfront

Building an AI solution in-house requires a big upfront investment. This means hiring or upskilling a team of data scientists, engineers, and other specialists and investing in the necessary hardware and software infrastructure. Buying an AI solution often means a lower upfront cost but this can vary depending on the complexity and scope of the solution.

3.2. Ongoing Maintenance and Support

The cost of maintaining and updating an AI system can be high especially if you build it in-house. You’ll need to monitor the system, update models, and ensure compliance with changing regulations. Purchased solutions often include support and updates as part of the vendor agreement but this may come with recurring fees.

3.3. Hidden Costs

Both build and buy have hidden costs. For in-house solutions, these might be development delays, additional hires, or the cost of scaling the solution. For purchased solutions, the hidden costs might be integration with existing systems, customization fees, or the cost of switching vendors if the solution doesn’t meet expectations.

4. Time to Market: Speed vs Customization

In today’s fast-paced business environment, time to market is a key consideration.

4.1. Build: The Long Road

Building custom built solutions is a time-consuming process. It involves not only developing the technology but also training it on your data, testing it, and refining it based on real-world performance. This gives you a solution tailored to your needs but requires patience and a long-term commitment.

4.2. Buy: Fast Deployment with Pre Built Solutions

Buying an AI solution means faster deployment. Many vendors offer pre-trained models that can be plugged in quickly. This is especially useful if you need to address an immediate business need or if time to market is a competitive factor. But you may have to compromise on flexibility and customization compared to a build solution.

5. Talent and Expertise: The Human Element

AI is as much about people as it is about technology. The decision to build or buy is about the talent within your organization.


5.1. Building Requires a Specialized Team of Data Scientists

Building custom solutions from scratch requires a highly specialized team. This includes data scientists, machine learning engineers, software developers and domain experts who can translate business needs into technical requirements. Attracting and retaining this talent can be tough, especially in a competitive job market. Managing an AI team also requires leadership that understands both the technical and strategic aspects of AI.

5.2. Buying Leverages External Expertise

Buying an AI solution means you’re relying on the expertise of external vendors. This is useful if your organization doesn’t have the in-house talent to develop AI. Vendors often provide end-to-end solutions from deployment to ongoing support so your team can focus on other strategic areas. However, this approach can lead to dependency on external providers and limit your ability to innovate independently.

6. Scalability and Flexibility: Future Proofing


AI solutions need to be scalable to grow with your business and flexible to adapt to changing needs.

6.1. Scalability of In-House Solutions

Building an AI solution in-house means you can design for scalability from the start. You can create an architecture that grows with your data, users, and computational requirements. However, designing for scalability requires a lot of foresight and technical expertise. Any mistakes in the design phase will cost you dearly down the line.

6.2. Flexibility of Bought Solutions

Purchased AI solutions offer varying degrees of scalability and flexibility. Many vendors provide cloud-based solutions that scale effortlessly as your business grows. However, these solutions may not offer the same level of customization as a build solution. If your business needs change dramatically you may find yourself constrained by the limitations of a bought solution.

7. Intellectual Property and Competitive Advantage

Intellectual property (IP) is a big consideration if AI is core to your competitive strategy.


7.1. IP in Building

When you build an AI solution in-house you retain full ownership of the intellectual property. This is a big advantage if the AI provides a unique value proposition that sets you apart from competitors. Owning the IP means you have control over how the technology is used, modified, and commercialized in the future.

7.2. IP in Buying

When you buy an AI solution the IP usually stays with the vendor. This means you’ll be using the same technology as your competitors and potentially losing your competitive advantage. However, some vendors offer co-development agreements or licensing arrangements that give you more flexibility over IP rights.

8. Hybrid Approach: The Best of Both Worlds?


For many organizations the best approach will be a hybrid approach, combining elements of both building and buying AI.

8.1. Build the Core, Buy the Rest

One hybrid approach is to build the core AI capabilities that are core to your business and buy the rest. For instance, you might leverage a large language model for natural language processing tasks while building proprietary algorithms for other core functions. For example, you might build a proprietary machine-learning algorithm in-house and buy data management tools from a vendor.

8.2. Hybrid Case Studies

Many organizations have implemented hybrid approaches. For example, a financial services firm might build custom algorithms for trading and buy an AI platform for customer service automation. This allows them to maintain a competitive edge in their core area and benefit from pre-built solutions in other areas.

9. Decision-Making Framework: A Strategic Approach

Making the right decision requires a structured approach. Here’s a framework to help you decide:

9.1. Organization Readiness

• Infrastructure: Do you have the data infrastructure?

• Talent: Do you have the necessary skills?

• Culture: Is your organization open to new technology?

9.2. Strategic Importance

• Core Competency: Is AI core to your competitive advantage?

• Long-Term Vision: How will AI fit into your long-term strategy?

9.3. Costs and Benefits

• Total Cost of Ownership: Consider both upfront and ongoing costs.

• Risk Assessment: What are the risks of each approach and how can they be mitigated?

9.4. Pilot Programs

• Test and Learn: Consider running a pilot to test both build and buy. Use this as an opportunity to gather data and insights to inform your decision.

Conclusion: Make It Your Own

The build or buy decision is not a straightforward one. It requires consideration of your organization’s objectives, resources, and the broader competitive landscape. Building gives you a bespoke solution that aligns with your business needs but comes with higher costs and risks. Buying gives you speed and access to expertise but limits customization and scalability.

In the end, it’s up to you. Follow the framework above and consider a hybrid approach and you’ll make a strategic decision for your organization to thrive in the AI world.