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ElectrifAi’s Answer to AI’s Last-Mile Problem

Artificial intelligence has so much promise for so many brands. It can spot patterns humans often miss, parse through an immense number of data points, and spit out actionable insights that corporate executives can use. However, according to ElectrifAi CEO Edward Scott, AI, as we know it, has its limitations. 

AI has “last-mile” issues that make it challenging for businesses to see real results from their investment in AI, which stalls its progress. But machine learning experts like ElectrifAi are closing the gap.

Scott is at the forefront of the AI revolution, and he sees big things ahead.

“We want to take AI and machine learning deeper into areas of social good and benefit,” Scott told Authority online magazine. “For example, educating and growing the next generation of data leaders not at the fancy Ivy League schools but in the community colleges and state universities. Giving young people a facility for data and the power of data. That’s where the next great generation of entrepreneurs will come from. We want to give them the tools to succeed. Other areas of interest include using AI to combat human trafficking. We have the power.”

But first, the last-mile problem must be tackled.

AI’s Last-Mile Problem

“Last mile” is usually a term used in the freight or delivery industry to explain the complex, expensive process of getting goods to a customer’s door. AI co-opted the term to refer to the difficulties of taking AI from a pilot that operates in a test environment to operating in a practical business setting. 

How can organizations create an AI model that does what it’s supposed to do? If brands want to use AI to solve enterprise-wide problems, they currently have very few ways to measure the value they see from AI.

In the process of AI development, teams source clean data and engineer different algorithms to solve a business problem. But that’s an oversimplification. AI experiences several challenges in the last mile, including measuring performance. 

Even if the algorithm performs a task, businesses don’t always measure its success in a meaningful way. Many organizations don’t have the internal knowledge to manage AI performance, so in reality, AI often underperforms.

Context is another challenge. AI might work for a period of time in a business, but organizations change and data changes. As the business changes, AI can often become dysfunctional because its original context doesn’t exist in the same state.

And finally, business performance. How well does AI complete an assigned task? Models don’t often perform well when you take them from theoretical testing to real life. AI can also have effects that you didn’t plan for, and sometimes it’s difficult catching the unexpected consequences of rogue AI.

Solving the Last-Mile Problem With ElectrifAi’s Innovations in Machine Learning

The AI development process isn’t linear. It’s an agile process where teams go back to the drawing board repeatedly. It stands to reason that enterprises should track the performance of an AI after deploying it, but many don’t. Tracking the performance of AI after deployment is the biggest hurdle because organizations don’t always know how to measure AI performance. 

Edward Scott shares that these three approaches will help organizations master AI’s last-mile problem. 

1. Clarify success metrics

So many organizations create AI without an end goal in mind. How will you know whether an AI was successful? Aside from completing a task, what other quantitative metrics can you track to determine an AI’s quality? 

Without success metrics, you risk creating an AI model that can’t perform in a business context. Before you create the AI, sit down with your development team to precisely define the problem to be solved and outline quantifiable measures. This will make it easier to determine whether the model is a success or if the team needs to return to the drawing board. Problem definition is paramount. “When you understand the precise problem then you can identify the critical data required to solve the specific problem. So many companies fail to understand this basic point. Start small and be precise”, Scott says.

2. Become fluent in model deployment

ElectrifAi’s Edward Scott believes that AI’s current struggles come from a lack of internal knowledge. To solve last-mile issues, companies have to become fluent in model development. 

“The challenge is that many companies simply lack the data engineering, data science, and [machine learning ops] expertise required to properly build a model, place it into a production system or environment (cloud or on-[premises]), deploy and run,” Scott explains. ”Companies have to develop fluency in how to deploy models in various environments and how to run those models and how to manage models for drift and related issues.”

3. Go with pre-built machine learning solutions

Businesses often lack the internal resources to build and test their AI models. In this case, Scott says it’s often better to outsource to pre-built machine learning solutions like ElectrifAi. “This doesn’t mean they need to hire legions of technical talent. Rather, they can simply gain the basic organizational competency and fluency and leverage emerging players who shoulder much of this complexity and are offering MLaaS or machine learning as a service,” Scott says.

ElectrifAi is innovating AI’s last-mile issue by combining proven, pre-built machine learning-driven solutions, experts who have deep knowledge of data science and AI modeling, and a solution that runs in the cloud, on-premises, or hosted as-a-service. And it is quick with clients seeing results in 6-8 weeks.

ElectrifAi sifts through an organization’s structured and unstructured data to not only create a clearer view of the business, but to create practical business knowledge based on that data.

“Every business has significant amounts of data, and ElectrifAi unlocks the potential of that data with pre-built machine learning software solutions that quickly help enterprise clients drive revenue (customer acquisition and retention), optimize operations (supply chain network and inventory optimization) as well as cut costs and risk through spend and contract analytics,” Scott adds. 

From Buzzword to Business Solution

The last-mile problem has slowed down AI’s progress, but the technology still holds plenty of potential. “Disruption is a positive when it creates a greater good and unleashes untapped potential,” says Edward Scott. “Think about computer vision, which can automate certain visual cognitive tasks yielding greater accuracy and throughput. When we talk about firms or institutions withstanding the test of time, those are usually the firms that have constantly adapted.”

Industries often treat AI as a buzzword, but in reality, it has systemic issues that innovators like ElectrifAi are working hard to solve. As long as AI solves a high value business problem, organizations will continue to invest in this innovation to create not only better businesses, but a better world. 


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