I’ve seen many different approaches to AI initiative investment over the past 10 years consulting in this space. Whether you’re an innovator in a small or large business, it’s critical to understand successfully investing in AI is not just about hiring data scientists.
It’s about:
On the smaller side of the scale, our clients tend to initially spend tens of thousands of dollars to “test the waters”.
The client has a use case.
They have a hypothesis of what their data can solve.
And they have a set of data.
In nearly all cases, we’ve found, after multiple rounds of training and tuning models, that the client’s data isn’t as robust as they thought. Upon this realization, clients spend months or even years going back and improving their data on their own before they are ready to actively work on training AI models again.
On the other end of the spectrum, larger companies typically spend hundreds of thousands of dollars to “dip their toes” in AI. While they, too, come to the table with a use case, hypotheses, and data set, they often provide:
Thoughtful insight where they have a problem that could likely be solved by their data.
Engineers that know the data and help prepare the data.
Domain experts that can explain the data.
More times than not, client engineers and domain experts learn through our consulting process how AI really works and are inspired to improve their data along the way. The end result is more production-ready models, sooner.
In the end, those companies that underinvest in AI fail out of the gates and fall behind. And a failed first AI project means months or years of reconsideration while competitors eat their lunch.
Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation.
Contact us if you have a problem to solve, a process to refine, or a question to ask.
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