• Faster Time-to-Market - Can access existing solutions and implement them quickly. • Lower Initial Cost - No need for upfront development costs. • Reduced Risk - Leveraging proven solutions with established track records. • Access to Expertise- Benefitting from the knowledge and experience of providers. • Less Customization- May not perfectly fit specific needs and data. • Less Control - Over the data and intellectual property. • Ongoing Costs-Subscription fees or pay-per-use charges. • Vendor Lock-In- Risk of becoming dependent on a specific vendor. • Organizational Size and Resources - Larger organizations with dedicated AI teams are better suited to building. • Technical Expertise - In-house AI expertise is essential for building and maintaining solutions. • Data Availability and Quality - High-quality data is crucial for training generative AI models effectively. • Urgency and Time Constraints- Buying offers a faster solution if time is of the essence. • Budget and Cost Constraints - Building requires a significant upfront investment, while buying involves recurring costs. • Desired Level of Customization - Building allows for tailored solutions, while buying offers standardized options. • Risk Tolerance - Building carries a higher risk of failure, while buying offers a more proven solution. , CIO, Matterport “It’s the right time to make surgical investments that turn the value-add drivers – operational efficiency and/or increased revenue.”