Before You Deploy That Big AI Installation,Take A Few Minutes and Ask Yourself These Questions

Softvision’s AI & Machine Learning Guild Master, Rares Ivan Featured in MarTech Advisor

July 2, 2018 – Softvision’s AI & Machine Learning Guild Master, Rares Ivan is a featured guest author on MarTechAdvisor.com.

In his article, Rares reveals four important questions you should ask yourself before incorporating AI into your business. According to Rares, it’s important to take the right approach and the answers to these questions should help shed some light on whether you should move ahead with an AI strategy.

  • What problem am I trying to solve?
    When you’re able to identify the business or technology problem, you may be surprised to learn that an AI solution may not be the right answer.

    Enlist a small team to review where you are today, vs. where you want to be twelve to eighteen months from now. Sometimes, while you may think you want AI as part of your solution, you can spend a great deal of money trying to solve a small problem, when a slight adjustment to legacy systems can be just as beneficial.
  • How good is your data?
    This is the most important question you can ask yourself, because as good and as effective as AI and Machine Learning can be, in the end, they’ll only be as good as your data.
  • Does your internal team or technology partner really understand AI?
    While most IT departments are tech smart, that doesn’t mean they get the broader business implications for what AI can do now and in the future. If you don’t have the right internal team, all the more reason to make sure you get the right external partner.
  • So, where do I start?
    At Softvision, scale is everything, and we try to customize solutions that directly address a problem and add benefits to an overall technology solution. Rather than going big right away, we usually recommend a ‘proof of concept’ project to start. Proof of concepts typically represent one to three months of activity, mostly depending on data availability; a burst of work that can quickly determine data validity and the viability of a broader AI implementation.

To learn more and read the full article, click here.

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