It’s no secret that adopting artificial intelligence can be difficult, which discourages some companies from even attempting to use AI in their businesses. However, there might now be a new option for using AI: AI as a service. 

The promise of AI

AI is a computer programming methodology that allows the computer to make decisions for itself. This can be done in a number of ways, but the most common approach is machine learning. In machine learning, a computer is given data and can eventually “learn” how to make decisions based on that data.

“Machines that learn” is an enormous development. Google CEO Sundar Pichai believes artificial intelligence could have more profound implications for humanity than electricity or fire.

When done well, with strategy and execution, customized AI solutions can differentiate a company and create a competitive advantage.

The challenge of AI

However, for all its potential, most companies struggle to adopt AI. Failure rates for AI projects are high at 80 per cent or more. There are a few reasons for this. First, data is often siloed within companies, making it difficult for AI systems to learn from it. 

Second, AI requires specialized talent that is hard to find, and which has to be managed in a different way than most IT personnel. IT teams are used to managing engineers working towards specific goals, such as releasing a feature. By contrast, data scientists are expected to ask and answer questions, such as why something happens. The answers are not known in advance, and the goals are often moving targets.

Third, even when done well, it can take years to realize the benefits. Given this, we can understand why companies sometimes hesitate to initiate these types of projects.

More accessible AI alternatives

There are other options available, promising to deliver you what your company needs cheaper, faster and better that you ever could on your own. This is known as AI as a Service (AIaaS) for niche problems related to: sales, marketing, human resources, finance and IT.

AIaaS is the delivery of artificial intelligence services through the cloud. This means that companies can access AI services without having to increase their headcount, or add to their overhead. There is no need to invest in the training of your own developers. You don’t need to be responsible for owning all the components of your IT stack.

Advantages of AI as a Service

There are many different AI as a service providers, each with their own offerings.  In general, these are the main benefits of working with an AIaaS vendor:

  • Cost. Most AIaaS involve a pay-as-you go subscription model. This means companies only pay for the services they use without the upfront costs of hiring staff, or building their own infrastructure. For smaller organizations, this means that for a fraction of the cost of building something on their own, SMBs can compete with much larger companies. 
  • Simplicity. In most cases there is not a need for advanced technical skills to access the AI. Sometimes there is only a single line of code that your IT team needs to work with in order to access the AI service.
  • Speed. Rather than spending months or even years building your own AI models, with AIaaS, SMBs can be using the AI in a matter of days or weeks.
  • Consistent improvements. As the AI vendor responds to the needs of other customers, the level of service they provide improves year over year without you have to do anything. 
  • Scalability. As your business grows, and you need additional AI services, you can easily increase what you need without having to worry about additional infrastructure. Your AI service can seamlessly grow as your revenue or products grow.

Disadvantages of AI as a Service

Before rushing out to sign up for one of these services, it is important to do your research before selecting a provider, because not all of them are created equal.  More generally, there are some other risks you should consider:

  • Limited Strategic Value.  One of the primary reasons for investing in a custom AI solution is that the AI learns something new about your business.  This is information that no one else has, and can serve as a differentiator.  With AIaaS, however, it is a generic solution.  It is going to be difficult to strategically differentiate yourself with a tool that anyone can use. 
  • Reduced Transparency.  With AIaaS you do not own the system.  You are a tenant in someone else’s system and you are unable to see into the “black box.”  If you need to be able to explain why you are taking certain decisions, you may need the visibility of your own solution.
  • Cost escalation.  You should investigate what the costs will be if you start to scale your usage.  Does something that seems affordable in the beginning turn into much higher-than-anticipated costs in the long run?

Conclusion

Companies face a trade-off when it comes to investments in AI. On the one hand, with custom solutions comes the possibility of differentiation, but the necessary commitment and the risk of failure are high.  On the other hand, it is possible to reduce some of those risks with AIaaS, but the payoff may not be sufficiently high. Whether AIaaS is a good gateway into the world of AI depends on the particular problems that companies are looking to address, and the level of risk they can tolerate.

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Jim Love, Chief Content Officer, IT World Canada
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John Stroud and Jen Schellinck
*** John is a strategic adviser to leaders on linking people with technology.  John is a certified OpenExO consultant in exponential technologies. Prior to launching AI Guides, John served as Vice President, Strategy at a federal crown corporation ($600M budget and 8000+ workforce) with responsibilities for Strategy, Governance, and Human Resources. John obtained his Masters of Philosophy from Oxford, his law degree and Masters of Public Administration from the University of Victoria, and his ICD.D for completing the Director’s Education Program at the Institute of Corporate Directors. ***Jen Schellinck’s goal as an AI Guide is to help organizations understand the value that cutting-edge data technology can bring to their work and success. She uses her knowledge of Artificial Intelligence, Machine Learning and Data Science to help organizations achieve their greater potential. She received her PhD in Cognitive Science in 2009 and has been active in the AI field for ten years. She is currently an adjunct researcher at the Institute of Cognitive Science at Carleton University and continues to be an active researcher in the field.