The New Way To Apply AI In Business

Countless companies are getting started with AI and machine learning. There are many discussions taking place everywhere from board meetings to business lunches about the value and ROI of AI/ML initiatives.

However, there are many misconceptions about what is truly needed to apply artificial intelligence successfully in a company. For example,

“We need to hire data scientists with PhDs and many years of experience.”

This is one of the most common limiting beliefs, and it usually stems from a lack of basic understanding about what AI is. I strongly believe that business leaders need to understand AI/ML fundamentals and its business capabilities before getting started with projects (check out my blog post titled “AI Fundamentals For Business Leaders” for more information).

AI, machine learning, and deep learning are no longer “magical” technologies that require PhDs to understand and implement. In fact, it’s getting much easier to understand and implement AI use cases. Once you go beyond the hype and understand the basics of how AI works, it’s no longer rocket science.

We have seen with our clients and other companies how people with minimal formal education and experience are implementing AI projects successfully, often outperforming people with PhDs and ‘years of experience.’ This is because they are positively obsessed with self-education and understanding the business needs, not just the highly technical details.

Currently, data scientists and machine learning engineers are perceived as expensive commodities. They definitely add a lot of value to companies, and we cannot afford to ignore AI in our business.

However, going forward, the majority of practical business AI use cases will not require hardcore data scientists to build everything from scratch with a ton of custom AI programming. In some cases you do need this, especially if you are building your own in-house platform and AI technology, but in most businesses we are looking for practical results and a clear ROI in the shortest time possible.

Fortunately, there are companies who have built end-to-end platforms to help businesses hit the ground running with their AI use case implementations. These platforms allow you to implement AI/ML use cases without needing to rely on world-class machine learning expertise long-term. This is because these platforms take care of the complex AI and machine learning for you behind the scenes (which took years to develop).

And this is perfectly fine. Why? Because we are not looking for fancy AI/ML code – we are looking for business results. If you don’t need to build a platform from scratch using rockstar data scientists, why would you? When you focus on results and ROI, many time and cost-effective opportunities emerge.

I believe data scientists and machine learning engineers will quickly become commodities, especially due to the large amount of online courses/trainings that can quickly teach someone the basics.

Hence, the highest value of AI/ML talent in most businesses will come from their ability to engineer and implement scalable end-to-end platforms and put together components that touch multiple areas of the business. Machine learning is only one component of a solution to a business problem or need. There are many other components that are crucial to use cases, such as database integrations, cloud functions and microservices, deployments, user experience, etc.

There are multiple platforms that can handle different use cases. Specific platforms are beyond the scope of this article, but here are some you can check out:

  • AutoML
  • Dialogflow, IBM Watson, Amazon Lex and Alexa (conversational AI)
  • Amazon SageMaker

There are many others, and each one has its own strengths and weaknesses. Many companies work with AI consultants to help them assess business use cases and the appropriate technologies to implement them.

Suppose you hire AI consultants to help you get started and build initial momentum. You may or may not want to rely on these services 100% long-term. Therefore, having them implement powerful already-developed platforms to accelerate your AI adoption is an intelligent business decision.

This allows you to more easily maintain and extend your AI solutions, while minimizing your dependencies on consultants, expensive AI talent, and maintaining constantly changing custom code. Also, since skill/knowledge transfer should be a part of every AI consulting engagement, it will be much easier and intuitive for your team to learn how to use and maintain the platforms (solutions).

If you need help to accelerate your company’s machine learning efforts, or if you need help getting started with enterprise AI adoption, send me a LinkedIn message or email me at info@carloslaraai.com and I will be happy to help you.

Subscribe to this blog to get the latest tactics and strategies to thrive in this new era of AI and machine learning.

Subscribe to my YouTube channel for business AI video tutorials and technical hands-on tutorials.

Client case studies and testimonials: https://gradientgroup.ai/enterprise-case-studies/

Follow me on LinkedIn for more content: linkedin.com/in/CarlosLaraAI

Leave a Reply

%d bloggers like this: