Today, AI is the Wild West of IT projects. It started with business intelligence, where data was collected across the enterprise and shipped into a big database.
copyright by searchunifiedcommunications.techtarget.com
Today, AI is the Wild West of IT projects. It started with business intelligence, where data was collected across the enterprise and shipped into a big database. Reports and dashboards were then built on top to help shape our daily decisions.
About a decade ago, the language shifted toward big data and analytics. More recently, the tune has changed to machine learning and deep learning. Marketers took it a step further by calling it AI .
Currently, enterprise adoption of AI includes looking at operational data hiding in every nook and cranny of the enterprise and making that data actionable. Organizations are now focused less on the technology and more on business outcomes and what can be derived from placing learning algorithms on top of the data we have.
People have also become skilled at computer vision and speech analytics to create best practices on converting speech to text and text to speech, as well as identifying objects and actions in images. These advancements, coupled with the idea that machine learning in business processes creates market advantages, have pushed companies toward AI.
When a colleague and I researched the use of AI in real-time communications , we interviewed companies working with AI — from AI-first startups to large enterprises that needed to start AI initiatives. The survey uncovered some challenges that organizations face when adopting machine learning and AI in communications.
1. Finding the right talent
One of the main challenges for AI adoption is finding talent conversant enough in machine learning. The talent pool is relatively small , and large cloud vendors, like Amazon, Google and Facebook, are attracting developers and data scientists with high salaries, bonuses and highly rewarding work.
Enterprises can attract talent in one of three ways:
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- Open offices in smaller technology hubs around the globe. The further away from San Francisco you go, the less of a fight over machine learning talent you will face.
- Compete in the market by offering higher salaries and benefits to lure experienced AI developers.
- Train the existing workforce. One organization we interviewed said all developers were given the opportunity to take online AI courses, which led to 10% of their developers becoming more familiar with AI.
2. Making business sense
Adding machine learning to a healthcare use case is different from adding machine learning to a social network interaction. While both these examples may use similar algorithms and techniques, you’ll need some domain expertise in the market itself.
In contact centers, for example, the dynamics of a sales conversation are quite different than a partnership discussion. In these instances, AI could gauge who speaks when and for how long. Knowing these differences and nuances of the business is just as important as knowing statistics and machine learning.[…]
read more – copyright by searchunifiedcommunications.techtarget.com
Today, AI is the Wild West of IT projects. It started with business intelligence, where data was collected across the enterprise and shipped into a big database.
copyright by searchunifiedcommunications.techtarget.com
About a decade ago, the language shifted toward big data and analytics. More recently, the tune has changed to machine learning and deep learning. Marketers took it a step further by calling it AI .
Currently, enterprise adoption of AI includes looking at operational data hiding in every nook and cranny of the enterprise and making that data actionable. Organizations are now focused less on the technology and more on business outcomes and what can be derived from placing learning algorithms on top of the data we have.
People have also become skilled at computer vision and speech analytics to create best practices on converting speech to text and text to speech, as well as identifying objects and actions in images. These advancements, coupled with the idea that machine learning in business processes creates market advantages, have pushed companies toward AI.
When a colleague and I researched the use of AI in real-time communications , we interviewed companies working with AI — from AI-first startups to large enterprises that needed to start AI initiatives. The survey uncovered some challenges that organizations face when adopting machine learning and AI in communications.
1. Finding the right talent
One of the main challenges for AI adoption is finding talent conversant enough in machine learning. The talent pool is relatively small , and large cloud vendors, like Amazon, Google and Facebook, are attracting developers and data scientists with high salaries, bonuses and highly rewarding work.
Enterprises can attract talent in one of three ways:
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
2. Making business sense
Adding machine learning to a healthcare use case is different from adding machine learning to a social network interaction. While both these examples may use similar algorithms and techniques, you’ll need some domain expertise in the market itself.
In contact centers, for example, the dynamics of a sales conversation are quite different than a partnership discussion. In these instances, AI could gauge who speaks when and for how long. Knowing these differences and nuances of the business is just as important as knowing statistics and machine learning.[…]
read more – copyright by searchunifiedcommunications.techtarget.com
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