This seems like a very strong selling point for B2B AI providers versus in-house enterprise builds of AI. > First, industry stakeholders often misunderstand —

This seems like a very strong selling point for B2B AI providers versus in-house enterprise builds of AI. > First, industry stakeholders often misunderstand — or miscommunicate — what problem needs to be solved using AI. The provider at least partially validates that this is a problem space that AI can improve which lowers the risk for the enterprise client. > Second, many AI projects fail because the organization lacks the necessary data to adequately train an effective AI model. The provider leverages it's own proprietary data and/or pre-trained models which lowers the risk for the enterprise client. They also have the cross-client knowledge to best leverage and verify client data. > Third, in some cases, AI projects fail because the organization focuses more on using the latest and greatest technology than on solving real problems for their intended users. Provider, especially startups, will lie about using the latest tech while doing something boring under the hood. This, amusingly, mitigates this risk. > Fourth, organizations might not have adequate infrastructure to manage their data and deploy completed AI models, which increases the likelihood of project failure. The provider manages this unless it's on-prem although in the latter it can provide support on deployments. > Finally, in some cases, AI projects fail because the technology is applied to problems that are too difficult for AI to solve. Still a risk but a VC or big tech budgets covers that so another win.