As the business applications of artificial intelligence (AI) continue to rapidly expand, organizations must develop their AI capabilities. Just as no two businesses are quite the same, every company will have to find an approach to AI development that best suits their capabilities and needs. The decision between building AI capabilities in house, outsourcing them to a third party, or opting for a hybrid approach depends on the specific requirements and readiness of every organization. Below, we explore considerations for organizations developing their AI capabilities and where to invest when it comes to buying vs. building AI tools.  

Breaking Down In-house AI Tool Development 

Developing AI tools in house typically involves leveraging APIs (Application Programming Interfaces) from major AI providers like OpenAI (ChatGPT), Google, or Meta. This approach requires substantial coding effort to customize and build a product tailored to specific use cases. 

Pros: 

1. Customization: In-house development allows for extensive customizations, enabling the creation of bespoke solutions that precisely address unique business requirements.
2. Proof of Concept: It is an excellent approach for building proofs of concept, allowing for iterative development and testing.
3. Cost Efficiency: Compared to expensive licensing agreements, developing AI tools in house can be more cost-effective, particularly for smaller-scale projects and when leveraging cost-effective API calls and cloud resources rather than on-prem hosting.

Cons: 

1. Complexity: Developing a full-fledged product powered by AI involves significant coding and technical expertise, making it a complex and resource-intensive process depending on the current resources of an organization.
2. Governance: Building your own AI tools requires major governance considerations and can pose greater liability concerns than third-party options.

What types of organizations should be considering in-house development? 

This approach is suited for companies with a keen interest in AI, that have developers on hand, and those with specific needs that justify the investment in creating customized tools.

 

A Look at Hybrid AI Solutions 

Hybrid AI solutions involve a combination of licensed AI technology and internal customizations. This approach often requires a product owner to manage the use case and oversee the development process. 

Pros: 

1. Versatility: Hybrid solutions allow for specific use cases to be addressed with relatively lower technical barriers, leveraging the user-friendly generative pre-trained transformers (GPT) interface.
2. Speed: These solutions can be quicker to implement compared to building entirely from scratch, facilitating faster deployment and iteration.

Cons: 

1. Cost: Enterprise licensing can be expensive, requiring substantial budget allocations.
2. Organizational Buy-in: Implementing hybrid solutions often necessitates broad organizational support and alignment, which can be challenging to achieve.

What types of organizations should consider a hybrid approach? 

This approach is ideal for organizations with lower technical capacity looking to leverage AI capabilities quickly and effectively, while still having the flexibility to customize solutions to their needs. 

 

Buying AI Tools 

Purchasing AI tools involves acquiring ready-made solutions, such as co-pilot tools from Microsoft, Tableau, or AWS, that integrate with existing systems. 

Pros: 

1. Integration: These tools are often seamlessly integrated into existing platforms, requiring minimal adjustments beyond licensing changes.
2. Simplicity: Ready-made AI tools are easy to deploy and use, offering a straightforward path to leveraging AI without extensive development.
3. Investment Value: They can represent a solid monetary investment, providing advanced capabilities with lower initial development costs.

Cons: 

1. Limited Customization: Purchased tools may lack the specific functionalities needed for particular use cases, offering less flexibility than in-house or hybrid solutions.
2. Support and Updates: There may be less direct support and fewer updates compared to custom-developed solutions, which can impact long-term effectiveness.

What types of organizations should think about investing in AI tools? 

This option is best suited for everyday users and organizations looking for quick, easy-to-use AI solutions without needing extensive customization or technical expertise.  

 

Going Forward 

Regardless of the strategy chosen for developing AI capabilities, governance remains a crucial factor. Utilizing AI tools can be likened to using a calculator that is only 80% accurate; while it adds significant value, it also introduces a layer of risk that must be managed. Therefore, robust governance frameworks are essential to ensure that AI is used responsibly and effectively within the organization. 

For further guidance, Clarkston’s team of digital strategy experts is here to help your organization meet your AI goals.