Selling Machine Learning Tech is Tricky
Whether it's open-source, on-premise, or cloud-native, machine learning's position on the bleeding edge of technology makes having focused conversations difficult. Especially if you don't have years of background in statistics and computer science.
Products Are Complex
ML projects vary wildly by several measures: their lifecycles, data sources, techniques, implementation, and use cases fundamentally change depending on the goals of the organization running the project.
Every machine learning project has thousands of moving parts - and as someone selling machine learning - you must understand precisely which of those parts your solution helps with, and how your solution fits into and improves the general lifecycle of the project.
Not only is this challenging, but you have to understand those same things about your competitors too. Conversations can get very complicated, very quickly.
Technology Changes Fast
To make all of this even more complicated, tactical best practices for ML become outdated at an inconceivable pace as data volume increases, architecture becomes more streamlined, and workloads become more efficient.
Sales Cycles Are Long
Machine learning projects are expensive and usually mission-critical for whatever group is doing them. Because of this, buyers require machine learning firms to jump through multiple hoops of proof-of-concepts and technical validations.
To make all of this even more complicated, tactical best practices for ML become outdated at an inconceivable pace as data volume increases, architecture becomes more streamlined, and workloads become more efficient.
Training Is Difficult
The only way for sales teams to be ML experts is continual enablement delivered at scale by top industry experts.
Align helps machine learning enablement teams accomplish this to drive understanding and growth.
