Simple machine learning frameworks offer valuable opportunities to test AI’s efficacy at streamlining simple business processes. But for most department heads, buying an “AI-in-a-box” solution has hidden costs lurking behind every customization (some even doubt any future for out-of-the-box AI). And for any real deep learning application, customization is key.
Until industry has more general proficiency around AI models and a better understanding of what to do with proprietary data, an off-the-shelf model is a non-starter – and it’s expensive to find that out the hard way. Yet approaching AI from this angle makes a good case for starting small. Developing a reasonable project scope can set expectations and provide the foundational proof required to get executive or board-level buy-in.
There’s also the question of the day-to-day applications of your AI systems. You can’t call a hotline for maintenance and until there’s better machine learning education and training for general technology practitioners, consistent on-hand guidance is required. This is where we come in. Thanks to the early success of AI-enabled marketing and recommender tools, it’s easy to see why public perception supports the view that machine learning algorithms will make a smooth transition into production. But anyone seriously engaging with AI, whether as a start-up, a researcher, or a large enterprise, can tell you that the road to streamlined operations is long and filled with unanticipated troubleshooting.
We’re currently working on a major data exploration project with an enterprise finance client that couldn’t quite do the deep analytics on their own. So beyond helping them figure out how to grow that particular model, we set it up in a partnership approach where we help them learn how to do the work themselves, while always remaining on hand to tackle new challenges, identify patterns and provide the depth of experience that comes with our combined two decades in the field.
The key to our success is a combination of technical expertise and business know-how, which allows us to partner closely with department heads in finance, information office, or product. Despite the newness of the AI industry, we have successfully deployed internal and external products driven by machine learning. No matter how many PhDs line the office walls, this is arguably the most important factor in an AI business partner.
At Synaptiq, we have the scope and skills to see projects through from conception to build-out to implementation and deployment. With no one-size-fits all, the quickest way to get on board is to have a consultation and discuss a proof of concept.
Contact us to set up an appointment and, no matter what you’re thinking about, we can unearth completely new potential in your data. Learn more about Synaptiq here or download our corporate white paper!