Large enterprises have a data problem. Organizations often treat data like it's gold, but data isn't actionable by itself. Brands might be obsessed with collecting and storing data, but they often don't know how to put it to use. Instead, they hold it in the hopes of using it someday, leaving it languishing in storage.

"Corporations are awash in data - both structured and unstructured. The question is how to harness that data to drive business value quickly," explained ElectrifAi CEO Edward Scott to digital platform Authority Magazine. The data problem is something that the ElectrifAi team is working tirelessly to fix. "The C-suite doesn't care about cloud, kubernetes, Elastic Compute. They need to drive revenue, reduce costs, and optimize operations. And data is the key. It is the last untapped asset on the balance - paradoxically both an opportunity and a challenge," explains Scott. Further Scott asserts, "Companies that fail to turn their data into a competitive weapon quickly will face an existential moment."

Instead of requiring businesses to create their own internal machine learning and natural language processing solutions, ElectrifAi creates prebuilt machine learning solutions for many industries including financial services, retail, hospitality, manufacturing, health care, and telecom. Scott's team knows what it means to take disparate data and transform it into practical business knowledge.

Here, Edward Scott shares why AI is so beneficial to businesses and how the ElectrifAi team is solving data problems to leverage the power of AI.

Why AI Is the Answer

"Machine learning is quite practical. It can help companies to save costs quickly, find and retain customers, and optimize operations to maximize cash flow," Scott said. In today's economy, businesses can't afford to invest in data without using it.

Companies face a difficult challenge, particularly in this economic environment. Normally, if an enterprise wanted to build a data science organization internally, that would require a significant investment in talent, tools, platform, and data warehouses. The net result is that before a company sees any benefit, it would likely spend $4 million to $5 million and 12 to 18 months. This assumes that the company is able to successfully recruit and retain talent. Instead of paying millions to develop their own AI solutions over the course of several years, brands can mobilize their data in eight weeks with prebuilt machine learning models. While their competition stalls in development, AI-powered companies see value more quickly. It's a classic time to value opportunity.

Another benefit is growth. AI plugs business data into practical applications that grow the organization. It makes data-driven decision-making a reality, which, unsurprisingly, leads to better business outcomes. AI makes the difference between lip service and becoming a data-driven brand.

And then there are holistic solutions. The right machine learning or natural language processing (NLP) solution can benefit everyone from accounting to marketing. It isn't just something that helps one team, but the entire organization. Instead of siloing data in one department, AI solutions make that data (and its solutions) available to the whole enterprise. That's a game changer.

How Innovators Like ElectrifAi Mobilize Data With AI

"The challenge for companies is that the 'data burden' keeps growing," ElectrifAi's Edward Scott said. "Let's define the data burden as massively growing amounts of structured and unstructured data." While organizations struggle to make sense of their structured and unstructured data, it isn't an impossible gap to bridge.

Customer Engagement

Customer engagement is a must for any business, but it's challenging for enterprises that need to boost engagement at scale. AI collects customer data, cleans and enriches it, and uses it to draw actionable conclusions about the customer experience. The strategic aspect of that motion cannot be underestimated. In today's economy, companies must acquire and leverage machine learning to segment those customers and create personalized promotions in order to drive retention and product cross-sell and upsell. "Our machine learning sits on top of existing [customer relationship management] systems and turbocharges them," Edward Scott explained.

For example, ElectrifAi's solution can automatically segment audiences. It mitigates churn by sending marketing messages to customers who display signs that they will churn soon. The solution also uses historical customer data to offer cross-sells and upsells at the right time.

Dynamic Pricing

Pricing is a complex part of e-commerce, but AI solutions make it possible to dynamically price products in real time. AI pulls in data on market share, profitability, revenue, inventory, and demand to keep organizations profitable. It can even keep prices within a certain threshold to meet consumers' expectations - all while increasing the company's top line and bottom line.

Customer Service

More than ever, customer service is a critical differentiator, which is why call centers rely on natural language processing to understand the sentiment in a particular call and to make recommendations to the call center rep to quickly close a call - and in doing so, improve the customer experience. ElectrifAi clients use machine learning to improve agent productivity and onboarding.

The solution also uses smart chatbots to reduce call volume. It routes calls to the most appropriate person to boost first-call resolutions, too. "We are helping telcos inject machine learning into their call center operations to predict who will churn and how to prevent using a combination of voice-to-text and [natural language processing] technologies," Edward Scott added.

Cash Flow Projections

Machine learning leverages internal financial data and external market data to estimate how much cash will flow through a business over a period of time. In other words which customers will pay and when - and why. This helps organizations allocate their capital to the best place at the best time. With rumblings of a recession in 2023, AI-powered cash flow projections help businesses prepare for adverse market conditions.

Spend Analytics

Where is the company's money going? Oftentimes, even the largest companies have no idea. They lack adequate companywide spend taxonomies and categorization and classification to see with whom and exactly where their spend is going - and, just as important, identify the pockets of immediate savings. ElectrifAi machine learning solutions can categorize as much as 98% of spend to help organizations find cost-saving opportunities. "We are helping retailers, manufacturers, energy and chemical companies drive substantial cost savings through comprehensive spend and contract analytics, leveraging machine learning-based vendor and spend categorization, classification, and compression," Edward Scott said.

Inventory and Supply Chain Optimization

Machine learning helps organizations maintain inventory levels without stocking too much or too little. It uses historical company data and market dynamics data to predict customer buying patterns, which affect inventory levels. The net impact of this is to release cash from stranded working capital.

"Take a retailer who has several hundred SKUs and 500 stores. That retailer has a forecast, which has translated into a certain amount of inventory per store," Scott said. "As the year moves forward, store performance is not uniform. Machine learning can take into account SKU performance across all stores and route the right SKUs to the right stores and regions to maximize cash flow and profits. That's being both smart and data-driven."

Improving Agility Through the Power of AI

Organizations that fail to leverage their data are leaving money on the table. "Data is the last untapped asset on the balance sheet. In many organizations, it remains largely underutilized," Edward Scott said. Through innovations in natural language processing, and machine learning, AI solutions from ElectrifAi make it possible for enterprises to see rapid and high ROI from their data.