Why Most AI Startups Don’t Fail Because of the Model (They Fail Because No One Uses It)

Read time: 2.5 minutes

5 Key Challenges That AI Startups Must Address

1️⃣ Investors care much more about growing winding roads versus how well your house is built.
Solution: Use proper signals for measuring traction, such as weekly active users, systems automated via workflow integrations, and decisions influenced by a specific model.

2️⃣ Increasing a model's accuracy does not always mean improving business outcomes.
Solution: Develop ways to convert improved modelling performance into either revenue growth, reduced costs or lower risk.

3️⃣ Customers will not change their workflows just because you have developed an excellent AI product.
Solution: Create an embedded version of your AI product within existing systems (such as Salesforce, Slack, MS 365, ServiceNow) that your customers already use.

4️⃣ Delay no longer to fully distribute your product.
Solution: Start implementing your distribution strategy now by creating growth channels, such as cloud marketplaces and integrated solutions, and by entering into enterprise partnerships.

5️⃣ Building a successful company through technology has far more components than simply developing a better product.
Solution: Develop your company's focus on reducing churn through workflow integrations, creating positive customer experiences, and distributing your products across multiple channels.

💡Key Takeaway: 

For Artificial Intelligence startup companies, the great risk is not an undeliverable model. The substantial risk is that a functional and valid AI model is not used.

Entrepreneurs in a startup environment often underestimate the importance of distribution to their ability to innovate.