Artificial intelligence, machine learning, and natural language processing make it possible for businesses to leverage an incredible amount of data and turn that data into a strategic weapon to drive top line growth and operational optimization. However, many businesses don’t have the expertise or frameworks in place to actually leverage their data. As a result, businesses are awash in information they can’t use.
The solution, according to ElectrifAi CEO Edward Scott, is to solve consequential business problems with prebuilt ML solutions. “What we do at ElectrifAi is help the world’s largest and medium-size corporations turn their data into a strategic weapon to drive top-line revenue growth, customer engagement, and operational optimization,” he told Enterprise Radio.
Scott shares examples of consequential business problems that ElectrifAi’s models solve, as well as his company’s unique process for delivering value on such short timelines.
ElectrifAi’s CEO Edward Scott Explains the Power of Consequential Ai
Many businesses invest in Ai, ML, and NLP, but they often fail to use these innovations to solve real business problems. Edward Scott argues that businesses need consequential AI — that is, Ai that solves the consequential business problems that keep CEOs up at night. “These are needle-moving, very consequential items for the C-suite,” Scott explained.
It’s a unique approach that generates incredible results for ElectrifAi’s customers.
“We’re working with one of the largest restaurant chains in the world. This particular restaurant chain has over 1,000 units. There are about 50 distribution centers that serve those units,” Edward Scott shared. “So they’re moving all kinds of food and materials from the distribution centers out to the restaurants … The client used to actually operate the freight and logistics on an Excel spreadsheet.
“We came in and showed this particular client how to optimize freight and logistics leveraging machine learning. Why does this matter? Because diesel is $5-$6 per gallon and operators are looking to reduce costs, Data and machine learning are the keys. In a world where diesel is $6 a gallon and you’re covering massive amounts of territory from your distribution center to your restaurant units, pennies add up. You need to optimize the freight and logistics, the route, the truck, the materials, to actually drive your costs down to the lowest level to make your operations as efficient as possible,’” he added. As a result of the change, ElectrifAi quickly found a way to save the restaurant chain many millions of dollars.
ElectrifAi’s consequential AI solutions have also revolutionized animal welfare at dairy farms. Dairy farms are using AI and computer vision to prove that they treat their animals humanely — information that they provide to their downstream buyers. “We have come up with computer vision solutions through an 80-camera system on these farms, all processing at the edge,” Edward Scott said. “No going back to the cloud for expensive processing in the cloud, no latency issues. This is edge computing machine learning and computer vision, which is detecting animal welfare in real time and providing video to the farm owner and operator.”
How ElectrifAi Generates Value in Less Than Eight Weeks
The results alone are compelling, but Edward Scott shared that this isn’t ElectrifAi’s true differentiator. What makes the company different is its speed. “Frankly, the revolutionary part of all this is that we have figured out how to do this and how to help the C-suite in six to eight weeks,” Edward Scott stated.
That’s incredibly appealing to leaders who need to see results quickly without dragging a project on for months or years. “No long consulting engagements or activities. No big dollar investments. It’s just turning that data quickly into a strategic weapon to drive the business,” Edward Scott added.
“The biggest limitation is that many businesses do not have data engineers and data scientists,” Scott explained. “The data engineer actually cleans the data, transforms the data, normalizes it, and prepares it for some machine learning, NLP or Computer Visionmodels. Data scientists are the ones who actually write those models and get those models into production.”
The challenge is that many organizations don’t hire for these roles internally. “It’s a paradox. The challenge is that companies are awash in data. But what they’re short on are data engineers and data scientists. Those folks are in very, very short supply and very expensive,” Scott added.
Edward Scott and his team founded ElectrifAi to address this gap in the data modeling market. But even with data scientists and engineers on board, it would normally take an enterprise 18 months or more to build a similar solution. So how does ElectrifAi create time to value in just six to eight weeks?
Through the use of pre-built machine learning and pre-trained natural language processing solutions, ElectrifAi is able to slash development time lines to create time to value in just a few weeks. From there, Edward Scott’s team normalizes, cleans, and organizes the client’s data to fit within the prebuilt models, which they can then customize to the client’s unique situation.
“When they’re prebuilt, what we say to the enterprise customers is: ‘We can help you get your data into operational value in six to eight weeks,’” he explained. “What we’ve done is figured out how to drop the domain expertise to solve a particular problem into machine learning software and to deliver that very, very quickly.”
FAQs related with Artificial Intelligence
Artificial intelligence (AI) is an emerging technology that is rapidly changing the way we live and work. As more businesses and individuals begin to integrate AI into their operations, there are many questions that arise. Here are some frequently asked questions related to artificial intelligence, along with their answers:
Artificial intelligence (AI) refers to the ability of machines to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
There are three main types of AI technologies: rule-based systems, machine learning, and deep learning. Rule-based systems rely on a set of predefined rules to make decisions, while machine learning algorithms learn from data to make predictions. Deep learning is a subset of machine learning that uses neural networks to analyze large amounts of data.
AI has a wide range of practical applications across various industries. Some examples include:
-Chatbots for customer service and support
-Image recognition for detecting and identifying objects in photos and videos
-Predictive analytics for forecasting customer behavior and trends
-Natural language processing for text analysis and translation
-Autonomous vehicles for transportation and logistics
As AI becomes more advanced and integrated into society, there are ethical considerations that need to be addressed. Some of the main concerns include the potential for AI to reinforce biases and inequalities, the impact on jobs and employment, and the risk of AI being used for malicious purposes.
There are several challenges that businesses and individuals may face when implementing AI, including:
ElectrifAi: Making the World Better, Faster, and Safer With AI Solutions
The C-suite needs to see results, and that’s why so many businesses are looking for data solutions. Enterprises crave data, but Edward Scott argues that consequential AI solutions are what they truly need to see value from that data.
With solutions like ElectrifAi, there’s no need to scale up internally, which is both more expensive and time-consuming. Solutions like ElectrifAi are faster and remove the need to hire internal data engineers or scientists. The result? Smarter businesses, improved operations, and a safer world.
