Articles

Business Goal Optimized AI
A key goal of AI Engineering is to drive value from AI solutions. When Gartner says “By 2025, the 10% of enterprises who establish AI Engineering best practices will generate at least three times more value with their AI efforts than the 90% of enterprises who do not,” they mean that organizations with robust AI Engineering capabilities will have tools, processes and people empowered to meet more ambitious business goals from their AI applications. . . .
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AI-Powered Personalization
Personalization is a force multiplier—and business necessity—one that more than 70% of consumers now consider a basic expectation. Furthermore, research shows that personalization most often drives 10 to 15% revenue lift (with company-specific lift spanning 5 to 25%, driven by sector and ability to execute). The more skillful a company becomes in applying data to grow customer knowledge and intimacy, the greater the returns. For digitally native companies that forge a data-backed, direct-to-consumer model, personalization isn’t just how they market, it’s how they operate. . . .
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Cures Act Compliance with CognitiveScale Accelerates Hyper-Personalized Member & Patient Insights
The 21st Century Cures Act (“Cures”) requires that federally regulated payers must be able to exchange patient health information directly with other payers, providers, and the patients themselves. . . .
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New Patents Issued - August 2022
Check out the latest patent issued in the month of August 2022 - Robustness score for an opaque model. A method, system and computer-readable storage medium for performing a cognitive information processing operation. . . .
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AI Composition
At CognitiveScale, we talk about the key capabilities of our Cortex Enterprise AI platform including AI-powered Personalization, Business Goal Optimized AI, and AI Trust & Governance. But the foundational capability that these offerings are based on is AI Composition: the ability to build and deploy scalable, trusted AI applications. . . .
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Improving Healthcare Delivery with AI
The United States Healthcare system has challenges with access, delivery, costs, quality, and more. Artificial intelligence (AI) is seen as one of several key technologies to help improve healthcare, and there are numerous clinical, operational and compliance use cases for AI that are driving improvements. The delivery of higher-quality, affordable healthcare is a goal of healthcare where AI is not often highlighted as a key enabler of industry transformation. . . .
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AI in Banking: Personalization Use Cases
Banks have the information necessary to understand individual clients, but they often fail to surface the insights that could lead to more meaningful and personalized engagement. AI in banking and financial services can close the gap. . . .
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AI in Healthcare Series: Care Optimization & Other Use Cases
This is the first in a new series of articles on AI in Healthcare, especially as it relates to what we at CognitiveScale are doing with clients like Elevance Health (formerly Anthem), CVS Health, United Healthcare, and Kaiser Permanente. Initially, we will describe the key areas where are working with these clients . . .
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New Patents Issued - July 2022
In July 2022 CognitiveScale has been awarded five new patents specific to AI. That brings the total to 109 granted patents, with dozens more applied for and in the process of approval. . . .
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AI-Powered Personalization Drives Improved Customer Engagement
According to McKinsey research, shoppers have a strong point of view on personalization. 72% said they expect the businesses they buy from to recognize them as individuals and know their interests. Furthermore, over 76% of consumers said that receiving personalized communications was a key factor in prompting their consideration of a brand, and 78% said such content made them more likely to repurchase. Personalization is especially effective at driving repeat engagement and loyalty over time. . . .
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Accelerate Customer Acquisition with AI-Powered Personalization
A 2020 Forrester report commissioned by IBM stated: Firms’ personalization strategies do not translate to customer reality. 90% of firms say personalization is imperative to their overall business strategies, but they are missing the mark when it comes to relating to consumers. More than half of consumers say they feel brands are trying to get to know them, but they don’t see their shopping experiences improving because of it. Firms see budding success with flawed personalization strategies, but potential is endless if it’s done right. Even with immature personalization strategies, firms see benefits. From a commerce perspective, they lead to an almost 6% increase in sales revenue and 33% increase in customer loyalty and engagement. For marketing, it’s an 11% decrease in marketing costs. 1 . . .
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AI-Powered Personalization is Transforming Customer Service
According to Deloitte’s 2021 Global Contact Center Survey, companies are increasingly viewing contact centers as revenue generators rather than mere cost centers, which is driving investment strategies for many businesses. In fact, the number of respondents who have revenue generation as their No. 1 strategic priority is projected to double over the next 24 months. Furthermore, nearly 80% of service leaders say they plan to invest in AI technologies over the next two years, the survey finds. . . .
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AI-Powered Personalization Series of Articles: Personalization Use Cases
Personalization is not only a crucial capability, it’s one that punches above its weight, no matter whether the company is a digital native, a brick-and-mortar player, or a behind-the-scenes producer or supplier. Furthermore, McKinsey’s research found that companies that excel at personalization generate 40% more revenue from those activities than average players. Across US industries, shifting to top-quartile performance in personalization would generate over $1 trillion in value. Players who are leaders in personalization achieve outcomes by tailoring offerings and outreach to the right individual at the right moment with the right experiences. . . .
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Data as a Product
A recent article in Harvard Business Review (HBR), “A Better Way to Put Your Data to Work,” by Veeral Desai, Tim Fountaine, and Kayvaun Rowshankish (July-August 2022 edition) argues that in order to realize value from data, organizations need to treat data more like a product. The authors, from McKinsey, offer some specific examples of the challenges organizations face as well as recommendations for how to achieve business goals from data initiatives. McKinsey has also published a companion article on this topic with some additional visuals called “How to unlock the full value of data? Manage it like a product,” by the same authors (June 14, 2022). . . .
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Cortex AI Engineering: Paving the Way to Client-centric AI
CognitiveScale’s Cortex AI Engineering platform is build on three pillars: AI Application Development (Composite AI), Personalization and Trust and Governance. As AI evolves from the MLOps era to the era of Client-centric AI, these foundational pillars become more important and impactful . . .
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AI Engineering: Playbook for Scalable Enterprise AI
CognitiveScale’s Cortex Enterprise AI Platform enables the AI Engineering playbook required to accelerate and scale AI within your enterprise. Cortex is an industry-leading Enterprise AI platform that helps organizations productionalize and operationalize AI systems providing an answer to pressing questions that enterprises have . . .
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Using CognitiveScale’s Learn Center
CognitiveScale announces a new Learn Center, designed to allow visitors to explore our Award Winning AI Engineering Platform in multiple ways. The new Learn Center is filled with Articles, White Papers, Case studies, Industry Reports, Videos and more. Below is a brief description of all the ways visitors can use the Learn Center and an outline of some of the new features soon to come online . . .
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AI Application Development is a Team Sport
Enterprise AI is a team sport that requires various roles and functions to collaborate in order to deliver resilient, repeatable AI-powered solutions at scale. . . .
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CognitiveScale Announces TrustStar
CognitiveScale Announces TrustStar to Deliver Immediate, Actionable Intelligence for the Mortgage Industry. AI Platform Enables Mortgage Lending Institutions to Broaden Market Reach and Win More Business . . .
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The Rise of AI Engineering
AI Engineering is on the rise. According to recent research performed by Gartner “By 2025, the 10% of enterprises who establish AI engineering best practices will generate at least three times more value with their AI efforts than the 90% of enterprises who do not.” Time is of the essence and now is the time to get started. . . .
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CognitiveScale Wins Red Dot Award
CognitiveScale has won a Red Dot Award for 2021 for Cortex 6 and its user interface. Red Dot is an internationally recognized award for good design across several product categories and disciplines . . .
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Survey Results: What’s Driving AI Adoption?
Artificial intelligence (AI) is now a central part of our daily lives. From a consumer perspective, we think nothing of seeing personalized product recommendations while shopping online or having a new series recommended based on what you’ve previously watched. Adoption of AI in the enterprise has been more measured. While AI can boost operational efficiency and drive personalized service experience when done well, too often the intelligent systems businesses rely on to deliver these outcomes are ineffective and include unreliable data, hidden biases and unexplainable results. . . .
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Intelligent Automation Radio Episode: Matt Sanchez on Responsible AI
My name is Guy Nadivi and I'm the host of Intelligent Automation Radio. Our guest on today's episode is Matt Sanchez, Founder and Chief Technology Officer of CognitiveScale, an enterprise AI software company . . .
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Systemic Bias in Financial Services_ Fixing a Growing AI/ML Problem
As computational analytics, including Artificial Intelligence and Machine Learning (AI/ML), gain greater traction and an almost essential status in the processing of massive volumes of data, their ubiquity and often inherent predilection for incorrect outcomes have brought both skepticism and distrust to their algorithms . . .
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