According to Forbes, as of 2018, only 11 percent of small and medium businesses (SMBs) use AI, and 41 percent feel that it’s too complex for their needs. But AI and machine learning don't need to be complex, and the first wave of SMBs to embrace these technologies will undoubtedly get ahead (in a big way).
Given the unveiling of new (and improved) Dataiku Lite and Free versions, we talked to Clay Kelly, our specialist on putting SMBs on their path to Enterprise AI. Here's what he had to say about the challenges and how SMBs can overcome them to not just survive, but thrive.
Lynn Heidmann (LH): You’ve written before about how SMBs have some advantages when it comes to data science, machine learning, and - ultimately - AI. But I imagine they also have some disadvantages - can you talk about those?
Clay Kelly (CK): The biggest disadvantage I see for SMBs when it comes to getting started (or expanding) their AI program is talent acquisition, or team size. Many larger organizations have the budget to hire droves of data scientists and engineers, but that’s not really the case for most SMBs-- many of whom just want to start with a few team members and plan to grow the AI program over the next few years.
This means they need to do more with less, and that’s where data science platforms (like Dataiku) come in. You don’t need to be a data scientist in order to help create data science pipelines and work on machine learning models; and in fact, by providing the right tools, SMBs can unlock a great way to augment analyst roles to grow skills and move toward a data scientist role (which ultimately can reduce employee churn).
LH: Why is it important for SMBs to get started with AI?
CK: The short answer is: survival. SMBs are already at a huge disadvantage when compared with larger companies in the same vertical or industry, particularly in terms of budget or head count. The good news is that many SMBs and startups already think about how AI can impact their business, and they are often far more agile in terms of introducing an AI program than most larger companies (less red tape, old ways of thinking, etc.)
SMBs need to get started with AI in order to survive.
For example, most small teams I talk to have extensive Python and open-source knowledge, and they understand what a trained, productionalized, operationalized model can do for them in terms of time savings and revenue increase. In order to disrupt and survive, they then need the tooling to help make up for team size and budget allocations yet still give them edge that the larger players may be too slow to implement.
LH: Are there any SMBs that have been successful in getting started on their machine learning journey? If so, how did they do it, and why were they successful?
CK: Marlette Funding/Best Egg comes to mind because they are a great example of a small FinTech/lending company that is using Dataiku to every advantage they can. With just a few people and Dataiku, they’ve been able to tackle larger problems like fraud detection in an efficient way that they weren’t able to before; they’ve brought in business users to projects where they normally may have been excluded because they didn’t code or have deep machine learning knowledge.
Once they saw what was possible with Dataiku, they thought of (and solved) other challenges important to the business. All without increasing headcount dramatically-- and that was just the first year.
LH: What is the best advice you have for SMBs looking to get started in AI?
CK: Reach out to me! Just kidding.
My advice would not to fall into the trap where you think your company is too small, or team not extensive enough, to explore incorporating AI into your business plans. AI is a big term that means many things to many companies, but it is not some wild, expensive frontier that only the big players are getting into.
SMBs: Never too small to make a difference with AI
I’ve brought on customers where the company size (not team size) was 10 people, and they realized value nearly immediately. Dataiku has worked extensively over the past year to make the platform more accessible to SMBs, so it’s worth a conversation to figure out if we can help. It may pay off big down the road.
LH: Anything else to add regarding AI and SMBs that I didn’t ask about?
CK: The need for AI and other predictive technology is only going to grow, and so competition is only going to get more fierce, no matter what industry your SMB is in-- whether you’re trying to steal market share from the larger companies, or fending off close competitors in the SMB world (or, more likely, both).
Don’t wait to begin exploring how tools (like Dataiku) could fit into your plans, because it’s entirely likely your competitors are doing that as you read this. The solution to the AI challenge might be a lot easier than you think, and the benefits may be bigger than you imagine.
Ready to dive in and learn more? See how else data platforms can help even the smallest of teams scale - get the free white paper Data Science Tools: What Are They, And Why Do Data Teams Need Them?