I'll address this from the perspective of a company that's been delivering complete custom data applications for enterprise for the past seven years. More conte

I'll address this from the perspective of a company that's been delivering complete custom data applications for enterprise for the past seven years. More context about problems here[0] and here[1]. The risk is so high that we're building an ML platform to address those. First, the initial conversations take time and depend on the maturity of the organization data wise, and all our clients are huge organizations. You keep exchanging until you find a problem. Sometimes they come to us with a problem in mind, ut it's almost never formulated. We have to extract it and formulate it. Then we have to understand the problem, why is it a problem, what's the desired state. What success looks like. This is not trivial, as many things are qualitative and how do you mesure that? Many people must be involved. You must also address the data question early on. Is there data to work with ? What kind of data? Is it enough ? Do you have to work with several sources, etc. But for now, you want 'some' data. Who has it ? You'll iterate over this multiple time as they send you data you can't work with. Is it sensitive data ? What regulations exist around that ? More people get involved (legal, security, etc). You also have to address ethical questions and what you will and will not do. These are long, long conversations. One aspect not to dismiss is that there may be people who feel threatened by the project. If the project is to reduce menial work, or reduce the skill level required to do something, some people might try to undermine it. Then in the rare cases where you got these right, and the data is good, you'll explore and iterate to train models. This often is the easiest part, but there are challenges for teams on collaboration, tracking, deployment, and including domain expertise in this step which can drag the project further. That's what we're building to solve. Once you have satisfying results, you'll then want a system to make this operational. We built applications to allow clients to use our models under the hood to do something. The deliverable isn't just a model that spits back predictions. This is a software project. You also need to make it possible to adapt: data changes and you have to take that into account (data pipelines, model training and management, concept drift, etc.) All this takes time and money and the effort of many people who are committed to do it, with diverse backgrounds and roles: you need to be able to build relationships and trust and get the help of domain experts. It's an all inclusive or nothing. Frictions, or people moving at different speeds. The ticket price for a data project can be hefty, and there are too many reasons for them to fail. [0]: https://iko.ai/docs/ https://iko.ai/docs/ [1]: https://iko.ai/docs/story/ https://iko.ai/docs/story/