Artificial Intelligence has tremendous potential to benefit businesses and society as a whole. It can analyze massive sets of data with astonishing speed and provide executives with actionable insights. Already, it’s being used to improve customer experience, automate routine tasks, and reduce errors. In the future, AI will be applied to challenging situations ranging from making medical diagnoses to fighting crime.
However, with all this potential, AI currently has limitations when it comes to practical real-world implementation. Machine-learning expert Edward Scott, the CEO of ElectrifAi LLC, is dedicated to tackling this problem and making the promise of AI a reality.
Defining the Problem
Artificial intelligence has a last-mile problem: how do you take AI from a testing environment into a practical business environment? Businesses face difficulties with:
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Measuring the performance of AI in a meaningful way
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Enabling AI to adapt to changes in the organization or the data
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Dealing with unexpected consequences when AI moves from a controlled testing environment into the complexities of real life
How ElectrifAi LLC Is Solving AI’s Last-Mile Problem
Many businesses that use AI don’t track its performance – largely because they don’t know how. This creates serious problems because developing AI is an iterative process that involves repeatedly going back to the drawing board. Without a good tracking system, you’re not going to be able to do this and will run right into the last-mile problem.
Scott, the CEO of ElectrifAi, recommends that businesses use three approaches to solve the last-mile problem:
Use clear metrics of success. Before you even start using AI for a particular application, clearly define the problem you are trying to solve. That will help you know what data you’re going to need. Next, create a list of quantifiable measures of success, so that you will know if you are on track.
Know how to use models. Companies may fail to use modeling effectively because they don’t have the expertise they need in data science, data engineering, and machine learning. That expertise is needed to effectively develop, run, and manage models.
Use pre-built machine learning solutions. When companies lack the expertise and internal resources to deploy AI effectively, they waste time and money trying to do it on their own and fall short of achieving the results that they want. This is where pre-built machine learning solutions, like the ones provided by ElectrifAi, come in. By outsourcing their AI development needs, companies can avoid having to hire large numbers of technical people to provide the expertise they would need to develop AI internally. Instead, they can leverage the deep knowledge of experts who provide machine learning in the cloud, on the company’s premises, or as a service.
The benefits are substantial. ElectrifAi can help your company unlock the value in its data. You’ll start seeing results in just 6-8 weeks. Pre-built solutions can help you:
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Increase your revenue by improving your ability to acquire and retain customers
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Optimize your inventory and supply chain
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Use analytics to reduce costs and risks
Edward Scott and the ElectrifAi LLC team work to solve the last-mile problem and release the potential of artificial intelligence to businesses, organizations, and the world.
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