When discussing topics including Machine Learning and Artificial intelligence, it’s best to define them since many people use the terms interchangeably. I like the definition based on Dave Robinson’s publications to best articulate the meaning:

  • Data Science produces insights based on data and is performed either manually or automatically.
  • Machine Learning (ML) builds on Data Science to produce predictions.
  • Artificial Intelligence (AI) builds on Machine Learning and produces actions based on the predictions based on the insights.

Most people use these concepts every day and don’t know it! For example, when new emails arrive, AI is automatically used to decide whether or not the email is “good” and sent to the inbox, or “spam” and sent to the junk folder. Most people today can’t imagine filtering all the email they get by hand and with good reason. AI handles this for you and does so very well.

Pitfalls for Deploying AI

So when looking to deploy AI, John Edwards article in CIO on “7 Reasons Your AI Strategy isn’t Working”, is a great list of the common pitfalls that new Artificial Intelligence adopters are falling into. The article captures how Dito approaches AI amazingly well:

  • You have to prepare people to use AI.
  • You need responsible, ethical AI implementations with a clear development process and understanding of methods that can and cannot be used.
  • You should explore all the use cases for AI.  If data is the new oil, then AI is the new oil drill.
  • Embrace AI in your business process.  Once you let AI sort your spam email, you don’t go back.  Adapt your processes for the new era.
  • Monitor your AI.  AI must be constantly appraised for performance, efficacy, and inherent bias with a repeatable result that a human, albeit slowly, can recreate.  AI shouldn’t be a black box, unquestionable decision making oracle.
  • Get leadership onboard.  Executive buy-in is imperative to success with any IT project.  Demonstrate the impact, explore the pros & cons, and visualize the benefits of developing an AI strategy.
  • Finally, apportion funds for technology AND implementation so users can fully utilize the new AI.  

At Dito, we help customers embrace AI, stay abreast of the constant advancements, and leverage Google technologies for BigQuery, optical character recognition (OCR), data extraction, security monitoring/reaction, data classification, model training, sentiment analysis, and even predictive fraud.

Dito’s team is certified and trained including staff with the extraordinarily rare Machine Learning certification with Google.  We help develop and deploy some of the largest scaled AI projects but whether you’re looking to augment an enterprise system, dip your toes into the world of AI,  or dive right in, Dito’s expertise can lead your endeavor to success.

Kevin is the principal evangelist for Dito helping companies migrate, modernize & scale with Google Cloud.  He specializes in  network, e-mail, and business privacy and security.  As a cloud specialist, he is a Google Workspace Top Contributor, Google Workspace Developer Experts, and Google Workspace Ambassador.  He is also a member expert of the U.S. Marine Corps Cyber Auxiliary.

View KAM’s LinkedIn & CV here.

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