One of the major deterrents for any business looking to integrate AI is the price tag. This is not an unfounded assumption… if you’re looking to build an in-house R&D lab, that is. You don’t have to bulk up your own team with six-figure-salary researchers in order to integrate AI into your information infrastructure or products. In fact, even in cases when you could hire your own team of data scientists, outsourcing is often optimal.
Here are five reasons to outsource:
1. There simply aren’t enough data scientists to go around. For technology innovators and Fortune 100 companies, this likely means paying a premium for top (maybe even mediocre) talent. For Chief Information Officers and Product Officers at smaller companies or companies/departments with budgetary constraints, this may mean settling for less than the best.
2. An in-house data scientist may not have diverse enough thinking to come up with the best solution for your business problem/s, particularly if they have focused on a single sector for much of their career. Alternatively, introverted or academic scientists don’t necessarily have the business savvy to produce actionable models. Instead they can get stuck on long term research projects and in analysis paralysis that may not yield results.
3. If they’re really good… your in-house data scientist may not stick around long. We find that they really like working with other data scientists, which creates a risk of them leaving unless you have an established R&D shop.
4. A well-developed machine learning model does not necessarily require constant monitoring and tinkering. Expert data scientists can build a model and train an in house team on how to monitor it without chewing up full time payroll (once the model is built and deployed). Check out this case study to read about a company that took this approach and reaped enormous benefits. Spoiler alert: the team managing this has no formal AI/machine learning training.
5. An AI engagement or proof of concept can – and should – cost you tens of (not hundreds of) thousands of dollars. A good consultant can scope out your project and offer you a customized solution that hits your price point. This can also be a great way to decide if you really need a full time data science resource or not. Approaching AI from this angle makes a good case for starting small and measuring results along the way, something most experts recommend.
If you decide to outsource your AI and machine learning project, here’s a big caution: Beware of “AI in a box” solutions. Until we have more general proficiency around AI models and a better understanding of what to do with our own data, an off-the-shelf model is likely the wrong fit – and it’s expensive to find that out the hard way.
If you are dead set on hiring data scientists in house, this article might help get you thinking in the right direction. A second caution here: If a new hire sounds too good to be true, they probably are. Do a quick search for “become a data scientist” to see how many places offer quick and dirty certifications. These can be great for learning more about the field but aren’t enough to provide a solid foundation of skills upon which to build business critical information or produce machine learning models.
Whichever path you choose, we are here to help you unlock the potential of artificial intelligence and machine learning.