What is all the fuss about AI and ML? 

What is it?

Artificial Intelligence, simply put, is the ability of a computer to do tasks usually done by humans.  Think: Chatbots or Alexa.

Machine Learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Think: Face Recognition for your phone, or Voice Recognition on Alexa.

Since the age of IoT (internet of things) in 2000, and the great AI movement in 2010, automation has become a driving force in business. The top 3 Uses of AI are Reduced Cost, Automation, and Customer Experience. 

Surprisingly, ONLY 45% of all businesses with 100-10K+ employees are using it. 

For companies of 10K-1000 employees, the priority is to Reduce costs, Automate, Customer Experience. 

For companies of 100 employees, reverse the order. Customer experience is #1.

With all this Automation Upgrade, there are shortages of qualified talent. Fewer companies can implement these levels of technical advances. 

There are 4 common mistakes when it comes to finding the right Talent in an EXCLUSIVE market:

1. There is not a well-defined reason for automation.  Do you have the Data Stack that needs a Data Scientist, or might a good data engineer work for you?
2. Is your Job Description a grocery list?  “Must have 5 years, and a PhD, and Scala, and Python, must have built Neural networks, Must have experience, with Pytorch, and TensorFlow; Must have experience with Kubernetes, and Snowflake.”  REALLY?  Do you need developing, deep learning, or modeling – or do you just need to organize your Data?
3. Do you have a reason for a Data Scientist to join your firm?  Do you have a large enough Data Stack to excite them?  Do you need Development, Analytics, or Modeling?  Do you have a need for both in the same person? 
4. Do you have the budget?  A data scientist costs $200K plus. 

What if you were more creative and inviting? 

What if you found an Electrical Engineer, or a Computer Science degreed individual, that got a Master, or a Data Science certificate, and has been using those skills in the market for a year or two?  Is their Forest Modeling different from a PHD?  Is their year of Python programming different from a PHD? Especially if they are interested in your company, ready to help solve your problems?

What might that strategy look like for you?  

You don’t have to search alone.  We can help, we think differently, with your company in mind.