What is Artificial Intelligence as a Service (AIaaS)?

Imagine, being able to harness the power of ai immediately, without the need to build your internal team from scratch and have them take the months or years to gel. Immediate results, with minimal investment is the promise that AIaaS makes, and there is evidence to back up these claims (Fogerty & Bell, 2014), so long as the right relationship is formed with the vendor. 

Artificial Intelligence as a service (AIaaS) is a business model that democratizes access to machine learning and provides insights through the advanced analysis of data sources by a group of data scientists outside of your company. Some call it "user-friendly analytics programs that surface analytics data to business users within their current workflows” (Wareham, 2015). 

The right AIaaS vendor has a team in place; data scientists, engineers, visualization analysts, and advisors who harness their multi sector experience, analytics applications and algorithms, and advanced computational power to analyze your data to provide hindsights (past), insights (present), and foresights (future). 

In the same way that a marketing agency or law firm leverage their past experience promoting similar products, AIaaS firms leverage their past experience to get you to insights faster. This can mean reusing code, modifying existing algorithms, understanding the schema of a data source, infrastructure and tools, and experience working on similar projects. AIaaS favours open-source tools like Hadoop and Spark and open-source languages like Python, R, and Scala, which are all provided for free. The magic is the combination of human skill, computational power, open-source tools, and your data. 

There is a land grab happening right now with new vendors offering data platforms popping up every other day. These new products may be useful, but rarely without a heavy lift from skilled practitioners who eat, sleep, and breath data science, and rarely until you’re looking to optimize your existing internal data practice. My recommendation is to save your money and invest in the right team using readily available, open-source technology.

Why should you care?

We have a super computer lovingly named “The Beast” which has the equivalent of 10,000 laptops computational power

"So what, why should I care?," as my Organizational Behaviour professor used to ask. Because...

• Insights from data lead to better decision making
• You’ve had it on your “to do” list for the last four years and haven’t made progress
• There are over 8500 job postings for data scientists right now on LinkedIn so hiring is going to be very difficult
• Many data scientists are drawn to the variety that AIaaS provides as they get to work on many different projects and have a team of people and tools to support their work
• Research has shown that firms who are data driven in their decision making are 6% more productive than those who are not categorized as data driven 
• It is likely that your competition is investigating data science as a source of competitive advantage

How to start?

The company I work with provide AIaaS. We have experience that can help many companies leverage the following tools:

• Cloud computing - Microsoft Azure or Amazon Web Service
• Tableau and PowerBI data visualization
• Open source tools like Python, R and Scala
• Data lake reference architectures 
• Hadoop & Spark ecosystem
• Machine Learning, Artificial Intelligence
• Predictive Analytics
• We have a super computer lovingly named “The Beast” which has the equivalent of 10,000 laptops computational power

We may be a fit, we may not be. We may be able to help, we may not. Maybe you’re not ready to invest in this type of service as your organization isn’t ready for it. 

You’re not going to know until you reach out. A conversation costs you nothing and may just lead to incredible results for your company. 

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Jonny Stevens works in a data consultancy and is using his research on advanced analytics and decision making to help his clients implement data science solutions that drive business value.