The recent wave of new opportunities to transform enterprise productivity, innovation and success leveraging artificial intelligence (AI) as part of digital transformation is driven by the tremendous advances in our ability to collect, operate on and extract knowledge from extremely large volumes of data. If not used with adequate care and caution this rapidly evolving opportunity currently comes with catastrophic implications for the enterprise that will be hard to course correct by throwing resources at.
In the first of this two part post on adoption of artificial intelligence in enterprises 'AI for Enterprises. Part 1: Where are we in tackling the popular adage GIGO?' posted in the MassTLC website and the extended version at ReSurfX website, we dealt with various aspects of data quality effects on digital transformation in the context of the popular adage garbage-in-garbage-out (GIGO). In this second installment (Part 2) of this post ‘AI for Enterprises: Metrics for Matriculates?’ also posted in the MassTLC website the theme centers on the power and pitfalls in the use of metrics from and for AI and machine learning (ML) based solutions. This is an extended form of the second part of the post and includes a powerful use case in the healthcare sector using ReSurfX solutions and the enterprise SaaS product ReSurfX::vysen. I highlight common problems in use of metrics and possible solutions that span beyond technical aspects to include enterprise architecture, culture and education appropriate to different organizational divisions and roles in leveraging these multi-disciplinary advances.
The recent AI wave driven by tremendous ability to utilize data helps gain more knowledge from data, make valuable predictions, create novel applications (e.g., driverless cars) and improve nearly all sectors of business as well as our everyday life. This great stride is brought about by a confluence of progress in IT, and other technologies that enable generation and or collection of data, storage, handling and compute with concomitant attempts to improve sophisticated approaches to process data. In this post we explore the status of our ability to tackle the popular adage “Garbage In, Garbage Out (GIGO)” to maximize the opportunities and enable progress. This is the first of a two-part post on adopting AI at scale. Centered on the theme of GIGO, we will also explore early successes of (true and pseudo) AI-based solutions, implications for subsequent progress, preparing businesses for reaping value from these solutions and setting appropriate strategies for digital and AI transformation. We take this opportunity to highlight advantages of the approach the outcomes intelligence company ReSurfX is taking with excellent success.
This is a longer version of the post with same title at Massachusetts Technology Leadership Council (MassTLC) - published on June 20, 2022 that was also spotlighted in MassTLC Newsletter of June 22, 2022.
Once in a rare while, unexpected events leave a major impact on society such as the current COVID-19 pandemic, necessitating additional urgency to create, innovate and be more efficient. That is true for established enterprises and emerging ones alike.
ReSurfX is an ‘outcomes intelligence’ company focused on improving innovation, outcomes, and ROI of enterprises from data-intensive activities, with primary focus on healthcare (Pharma, Biotech and patient care). ReSurfX provides value through significant improvements in accuracy, robustness, novel insights, and advance prediction of outcome directions by leveraging a data-source agnostic novel machine learning (ML) approach we invented – Adaptive Hypersurface Technology (AHT). ReSurfX::vysen is an enterprise software product that delivers these functionalities.
Here we demonstrate the predictive power of AHT based functionalities through an extremely powerful SyRTOP configuration of ReSurfX::vysen product and outline several applications. The rare form of validations provided here are in the form of FDA actions based on Real World Evidence and Post Market Surveillance that are difficult to achieve even with expensive targeted studies.