Findability hopes to make AI adoption easier for enterprises
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Today ‘big data’ just isn’t enough. To provide meaningful insights and valuable analytics for optimal decision making, companies need to adopt the concept of wide data.
While big data focuses on the so-called “three Vs” – volume, speed and value – broad data houses are focused on value, according to Anand Mahurkar, founder and CEO of the leading enterprise AI company findability sciences. That is, it’s not just a mass of data for the sake of data, or data derived from a few sources. It links data from a wide variety of sometimes seemingly disparate sources to enable deeper, more targeted analysis.
“Broad data doesn’t just mean the data in my organization; it goes beyond the boundaries of my organization and combines external data, internal data, structured and unstructured data,” explains Mahurkar to VentureBeat. “If you want to know what’s going to happen and what to do, you need ‘wide data’ and not just ‘big data’.”
The Evolution of Enterprise AI
This is a fundamental concept in enterprise AI, embedding artificial intelligence methodologies into an organization’s data strategy. The software category is experiencing rapid growth as more companies across all industries undergo digital transformation. Global Industry Analysts Inc. predicts that the global enterprise AI market will reach $15.9 billion by 2026. That’s an increase of $1.8 billion in 2020.
Findability Sciences is working to differentiate itself in a market with dominant players such as C3 AI, Abacus.ai, Microsoft and Snowflake. Specifically, the Boston-headquartered company focuses on what Mahurkar called traditional businesses — such as those in the manufacturing and retail sectors — that still use legacy software products. This remains a sizeable market: there are more than 60,000 companies worldwide with revenues of $200 million or more.
Especially after the pandemic, these enterprises are beginning to understand the need for digital transformation and AI, but they are struggling with adoption and implementation, Mahurkar said. Running a custom build to embed AI into existing infrastructure can be a crippling proposition, and outsourcing can be costly and unnecessarily time consuming.
Embedding AI technology
To help companies tackle – and ideally manage – the transition, Findability today launched its new white-label suite Findability.Inside. Rapidly deployable and repeatable, it enables companies to embed AI technology into their pre-existing hardware and software, enhancing features and functionality and generating new insights and efficiencies. The suite leverages advanced capabilities, including computer vision, machine learning, and natural language processing to aid in forecasting and forecasting, pricing optimization, market targeting and segmentation, sales prospecting, online advertising, and customer service.
A unique feature is the NLP-driven automatic summary of video conferencing and industrial-scale document scanning. With intelligent document processing, Mahurkar explained, embedded AI can analyze keywords and context in text-heavy documents and create automatic summaries that save valuable human reading time.
It’s no secret that many outside factors affect any given business, he noted. But in the past, such factors have been difficult to predict or prepare for – which is where enterprise AI can be so valuable.
For example, suppose you are an air conditioning unit manufacturer that gets your coils from China. Economists have suggested market distortions and fluctuations that could affect supply chain, costs and time-to-market; these compelling details can help you fill in any gaps. Likewise, being aware of forecasted weather patterns in your key sales areas may prompt you to market more there or move to other areas.
Mahurkar gave another example of a Silicon Valley-based digital signal processing company that used the Findability platform to track click propensity. In studying that, it made adjustments to online advertising and real-time bid optimization to offer more competitive rates when it comes to cost-per-impression and cost-per-mille.
For companies that trade in physical products, Mahurkar adds, enterprise AI can help by tracking and predicting inventory, supply chain issues, market conditions and price fluctuations. “Most software will tell you what happened, they’ll tell you about the past,” such as revenue and profit, he said. “But customers are looking for the ability to know what’s going to happen and what to do. They want leading indicators.”
He described Findability.Inside as a low-code, inexpensive, easy-to-use suite that integrates quickly. Businesses see end results in improved legacy products, improved customer service, increased revenue, and customer satisfaction and retention. They can drive digital transformation without having to develop code or invest significant human talent that is critical elsewhere.
Mahurkar, a first-generation immigrant and entrepreneur who came to the US 20 years ago, founded Findability in 2018 with an initial investment from SoftBank Group. In just a short time, the company has gathered high-profile clients such as IBM, Snowflake and Red Hat, and its products have been used in conversational computing, as well as to analyze ad effectiveness, assess propensity to pay, predict apartment rentals and occupancy rates. and optimizing supply chains.
As Mahurkar explained, he wanted to build a technology that connects internal, external, structured and unstructured data to “improve a company’s ability to find information” and enable them to realize the potential of data and that apply to business improvement.
Rolling out a suite like Findability’s, he added, “Now their products can be enabled with AI in just a few months, and it will tell their end customers that this will happen to your business. And here’s what you should do.” to do.'”
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