List of SAS AI Platform Customers
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Since 2010, our global team of researchers has been studying SAS AI Platform customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased SAS AI Platform for Cognitive Computing from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using SAS AI Platform for Cognitive Computing include: Royal Bank of Scotland (RBS), a United Kingdom based Banking and Financial Services organisation with 79099 employees and revenues of $16.16 billion, Konica Minolta, a Japan based Professional Services organisation with 39755 employees and revenues of $7.37 billion, Daiwa Securities, a Japan based Banking and Financial Services organisation with 14783 employees and revenues of $5.32 billion and many others.
Contact us if you need a completed and verified list of companies using SAS AI Platform, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the Artificial Intelligence software purchases.
The SAS AI Platform customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of Artificial Intelligence software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Daiwa Securities | Banking and Financial Services | 14783 | $5.3B | Japan | SAS Institute | SAS AI Platform | Cognitive Computing | 2016 | x |
In 2016, Daiwa Securities implemented SAS AI Platform. The deployment centralized analytics and machine learning capabilities to support customer analytics and sales planning across branches, contact center and sales representatives, positioning SAS AI Platform within the Cognitive Computing application category.
The implementation concentrated on advanced customer analysis, operationalizing predictive analytics and machine learning workflows to strengthen customer proposals and uncover more opportunities to enhance customer relationships. Daiwa Securities established an Artificial Intelligence Office in its Sales Planning Department to govern model development, prioritize use cases and translate analytic outputs into sales planning guidance.
Operational coverage included sales planning, branch networks and the contact center, with analytic outputs consumed by sales teams and customer-facing staff to inform interactions and proposals. Governance was centralized through the AI Office which managed model lifecycle and rollout sequencing across business functions, aligning SAS AI Platform capabilities with day to day sales and customer support processes.
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Konica Minolta | Professional Services | 39755 | $7.4B | Japan | SAS Institute | SAS AI Platform | Cognitive Computing | 2017 | x |
In 2017, Konica Minolta implemented the SAS AI Platform as a Cognitive Computing solution to operationalize machine learning models across Konica Minolta Japan. The deployment concentrated on embedding predictive analytics into service operations and product engineering workflows, enabling model-driven decision making for maintenance, quality control, and customer service processes.
The SAS AI Platform implementation included model development, training, automated scoring pipelines, and model lifecycle management to support repeatable machine learning workflows. Data preparation, feature engineering and model validation processes were institutionalized within the platform to accelerate model publication into production. These capabilities align with Cognitive Computing functionality for supervised learning, operational scoring, and analytics orchestration.
Governance emphasized standardized model validation and operational controls to align analytics outputs with business decision processes, with rollout focused on Konica Minolta Japan operational units. Konica Minolta used the SAS AI Platform to reduce costs and enhance competitive edge, applying machine learning outputs directly to operational decisioning. The narrative highlights implementation scope, core Cognitive Computing modules, and integration of model-driven analytics into business functions.
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Royal Bank of Scotland (RBS) | Banking and Financial Services | 79099 | $16.2B | United Kingdom | SAS Institute | SAS AI Platform | Cognitive Computing | 2018 | n/a |
In 2018, Royal Bank of Scotland implemented SAS AI Platform to introduce Cognitive Computing capabilities across the organization, initiating an enterprise program to centralize advanced analytics across the bank's data estate. The deployment anchored analytics governance and a common platform approach intended to enable data-driven decisions that transform organizational processes and support improved employee engagement and customer service.
The implementation integrated multiple SAS products as functional modules, including SAS Visual Analytics for interactive reporting and self-service discovery, SAS Enterprise Miner for predictive model development and scoring, SAS Text Miner and SAS Contextual Analysis for unstructured data and natural language processing, and broader SAS AI solutions for model orchestration and operationalization. These components collectively provided visualization, predictive analytics, text analytics, and AI model lifecycle capabilities consistent with Cognitive Computing deployments.
Architecturally the SAS AI Platform was positioned as a centralized analytics layer spanning the whole of the bank's data estate, enabling cross-functional access to models and insights. Operational scope extended across the RBS Group, aligning analytics outputs to business functions such as customer service and employee engagement, and supporting data-driven decision workflows across business units.
Governance and process work focused on unifying analytics ownership under a single vendor to simplify delivery across data domains, while institution-wide rollout emphasized standardized model development, review and deployment processes. The program explicitly aimed to drive transformational change across RBS Group, improve employee engagement and deliver excellent customer service as part of the bank's ambition of earning customers' trust.
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