ElectrifAi Shares 3 Use Cases for Consequential AI

While data generation, collection, and organization was once a pipe dream, some of the world’s largest companies now find themselves awash in information overload. Companies have no problem collecting data — but they can face an uphill battle trying to leverage it and turn it into consequential business value to drive their enterprise.

The true goal is to turn data into a strategic weapon, but that’s a new horizon that even the world’s most sophisticated organizations struggle to meet. According to Edward Scott, CEO of ElectrifAi, a global leader in business-ready machine learning solutions, companies need to focus on driving consequential business value. The focus should not be on tech  but optimizing operations and driving top line growth. The technology exists simply to serve the business objective. With consequential AI, businesses can mobilize their data to drive real, measurable business results in 6-8 weeks.

ElectrifAi Shares 3 Use Cases for Consequential AI

What Is Consequential AI?

In Edward Scott’s view, too many companies focus far too much on data plumbing and infrastructure like warehouses and the cloud. Elastic compute and storage have occupied far too much of the conversation according to Scott. To the C-suite this is all mumbo jumbo. It is critical plumbing – but still enabling infrastructure nonetheless. But plumbing and infrastructure don’t automatically lead to business outcomes and unlocking critical business value. Missing data, inaccurate data, outliers, and multiple sources can make it nearly impossible for companies to see value.

Consequential AI is ElectrifAi’s unique approach to helping large and medium size enterprises to quickly unlock business value from all of their structured and unstructured data. ElectrifAi understands that data is tough and that deriving business value from the data is even tougher. The key innovation behind ElectrifAi are pre-built machine learning and NLP solutions. These solutions contain all of the domain expertise required to solve a problem as well as the pre-built data pipelines necessary to clean and transform the source data. All of this pre-built capability is dropped into a docker image which can be deployed on any cloud, platform and even on premise. “We have done the heavy lifting for companies to enable them to quickly solve a business problem with data in 6-8 weeks,” says Scott.  “We serve a core number of industries and solve a core set of problems that we call consequential AI. That depth and that repeatability is the heart and soul of our scalability across the world,” Edward Scott explains.

ElectrifAi finds its clients’ most pressing issues and solves them with a combination of just six to seven data points. “We tell our clients exactly what data we need to solve a particular business problem. We typically don’t need a lot of data and we can take that data raw without having to be cleaned,” Scott explains. Scott’s team plugs this data into the prebuilt machine learning solutions that allow for customization, which has resulted in a stunning 95% success rate.

“We have created something here that does not exist, which is the ability to help clients turn their data into a strategic weapon to drive their business, and to drive their business quickly with what we call Consequential AI. It’s needle moving,” Scott explains. “That’s really the mission of ElectrifAi, which is to create that value quickly through data by leveraging these prebuilt machine learning and NLP solutions.”

ElectrifAi’s Consequential AI: Three Use Cases

Scott posits that AI is only useful if it leads to measurable business outcomes. While it’s just the tip of the iceberg, these three use cases demonstrate how consequential AI can be a game changer for mid-market companies.

Remedying Data Quality and Risk Issues

One of the largest banks in the world tapped ElectrifAi to remedy its data quality issues. The bank’s investment group needed help designing the best risk-adjusted portfolios — but found that its market and risk data from third party vendors were often inaccurate. To add to the problem, 20 terabytes of data came in at different times throughout the day, leading to incomplete and out-of-sync data.

The bank was unaware that its data was inaccruate creating compliance and regulatory risk. ElectrifAi used Consequential AI to help the bank automatically detect quality issues. The prebuilt solutions identified quality problems, notified the provider, and made it possible to fix problems ASAP.

The end result was that the bank’s portfolio managers received better, cleaner data that improved their portfolio management and overall assessment of risk. Today, the bank has a more realistic view of the risks in its portfolio, which led to significant gains.

Optimizing the Supply Chain Network and Inventory Levels

COVID-19 upended historical data models, which made it nearly impossible for companies to remain agile in such a tricky environment. Customer demand changes almost constantly, inflation is high, and supply chain issues mean prices of commodities such as fuel are soaring. “Why does it matter? A: Drivers are expensive. B: Diesel’s 6 bucks a gallon. That’s why it matters,” Edward Scott explains.

Supply chain data has noticeable gaps that make supply chain management fraught with uncertainty. Fortunately, Consequential AI is helping companies optimize their supply chain and inventory.

One of the largest hospitality chains partnered with ElectrifAi to optimize its supply chain costs. The chain needed to minimize the charges for delivery as well as delivery timelines from its many  distribution centers. This would not only reduce its expenditures, but also ensure that ingredients stayed fresh during transit.

ElectrifAi created a prebuilt machine learning solution using the chain’s data. This solution was able to identify which distribution centers should deliver to which units, locate new sites for additional distribution centers, and analyze supply and demand, helping the restaurant cut its delivery costs.

ElectrifAi also used a tiered approach to optimize the restaurant’s inventory in real time leveraging demand forecasting solutions and showing the chain how to reduce its investment in inventory safety stock – thus releasing working capital.

Managing Unstructured Data and Creating New Revenue Streams

“Unstructured data is crushing companies. It’s like a tsunami,” Edward Scott says. “This is particularly true with video and images. Media companies, retailers and fashion houses have massive amounts of images and unstructured data. The question is how to turn that unstructured data into some usable data that can be monetized and put to business use,” asks Scott.

Consequential AI solutions can significantly minimize the effort required to add structure to this data. For example, ElectrifAi’s pre built solutions can automatically annotate and catalog unstructured information like images, making them easily searchable.

Consequential AI: The Ultimate Competitive Advantage

Even the most reputable companies struggle to make sense of their data. In an increasingly volatile and challenging environment, all companies need to optimize costs and operations — and quickly. Consequential AI helps middle-market companies compete against larger, better-funded competitors that can afford internal data teams.

Whether it’s used to improve information quality, optimize the supply chain, or manage unstructured data, Consequential AI has the power to turn data into an invaluable asset. “The solutions that we’re talking about today drive your top-line revenue, optimize your operations, and reduce costs. That’s what consequential AI really is,” Edward Scott concludes.