Posts

Scalable Enterprise AI: Thinking, Architecting and Building (TAB) Intelligent Software Systems

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  Intelligent Software System Artificial intelligence (AI) systems seem to replicate or mimic or augment  human capabilities. There is an increasing interest in adopting such intelligent software systems for automation, decision-making, and recommendations etc.  The Challenge Isolated or individual AI applications, models, proof-of-concepts (PoCs) and protype developments often fail to integrate with or scale at the enterprise level as production ready enterprise systems. This draws our attention to the following key questions:   How would applications and models share data with each other?  How can we ensure that the models have controlled access to the right data based on their permission or need to know basis?  How can we avoid vendor lock-in where enterprises depend on different agents developed or provided by different service providers across the heterogeneous multi-cloud and on premises computing environment?  How can we ensure communicatio...

AI-enabled Intelligent Enterprise: AI Co-pilots

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AI-enabled Intelligent Enterprise  There is an increasing interest in Artificial Intelligence (AI)  systems for decision making, productivity gain, better quality products and services etc. There is a fear or assumption that AI systems will replace human. We should not assume that AI systems will replace humans, rather we can look at them as our agent or  assistants.  Human is the "Principal" who delegates the tasks to the AI system that is called an "Agent". Thus, there is a principal-agent relationship (delegation) between the human and AI system.   AI Co-pilot There are several facets of AI systems, in particular the AI co-pilot system that can assist humans for carrying out tasks on their behalf. These AI co-pilots can be embedded across the enterprise value chain. This article discusses the AI-enabled intelligent enterprise and the applicability of AI co-pilots across the connected core value chain of (1) strategy, (2) architecture, (3) development and...

AI System Architecture and Health Assessment

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AI System AI system receive inputs (data), process them via data processing applications, which can use AI algorithms for further advanced processing of data or intermediate outputs for generating final outputs for desired outcomes. These outputs could be classifications, clustering, contents, decisions, detections, insights, predictions or recommendations. There are several examples of AI systems such as Autonomous Vehicles, Chatbots,  Digital Assistants, Face Recognition and Voice Recognition etc.    A. AI System Architecture AI system architecture consists of at least three interconnected layers and underpinning components: applications, data and algorithms.   Figure 1 . AI System Architecture 1. Application  AI system has AI enabled or augmented or embedded applications that receive initial inputs such as data and apply data processing programs. Based on the processing needs, it makes the data ready for further advanced processing using AI algorithms. A...

Circular Information Lifecycle Management for Enabling Circular Economy

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Circular economy is about recycling, remanufacturing, reusing  products, services and information for continuous value re-co-creation while reducing waste and enhancing positive economic, environmental and social impacts. This requires to re-conceptualize and re-design the economic ecosystems and business models for optimisation and innovation.  Information management is extremally important to provide intelligence & insights for effective decision making  and actions enabling circular economy. This article presents the following circular information lifecycle management for enabling circular economy.  In circular information lifecycle management (CILM), information can be restored and recycled like another product or service in the circular economy.  The CILM is composed of six key stages: discover & collect, classify & secure, prepare & release, access & use, archive & purge, and restore & recycle.  Circular Information Lifecyc...

How to define information or data strategy?

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Information or data strategy is a part of the overall corporate or business strategy. Information (including data) is an important strategic business asset. Thus, information assets should be managed and treated in the similar way as we manage other business assets. An actionable information strategy is required to for the effective handling of information across the enterprise, irrespective of their size, for achieving the business goals and reducing risk including compliance to regulatory requirements and standards.   This post provides and discusses a simple and practical template for defining the information strategy as a part of the business or cooperate strategy. Information strategy can be defined at the ecosystem, enterprise, business function, business area or capability level as appropriate to your context and needs. Information strategy has following key 8 elements (Figure 1): Mission & Vision, Levers & Value Driver, Goals & Objective, Strategies & T...

What is Information? A Theory of Information Trilogy

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A Theory of Information Trilogy for Information Ecosystem by Asif Gill Information is/of Everything (IioE) We hear many definitions of information and related concepts such as data, information itself, knowledge and intelligence. Well, all these definitions seem useful from information probability, quantification, measurement, storage, processing and management perspective. We still wonder what is "it" and how to identity "it", which we call "information". This blog defines and describes nature ecosystem inspired definition of information, which is called here a "Theory of Information Trilogy" (ToIT).  This theory is defined with a view to define and identify information in complex and heterogenous information ecosystems.  This theory defines that the information is (1) matter and (2) energy, which has different (3) states or forms. Matter and energy flow in the natural ecosystem. Further, from natural ecosystem, these 3 matter, energy and their ...

Integrating Agile Planning and Estimation into Traditional Methods for Large Projects

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Agile processes are often considered challenging to adopt in larger and formal project development and management environments. It may not be possible and need for an organisation to adopt a pure agile process for a large project. How can organizations adopt agile planning and estimation best practices within their formal and generic process lifecycle (GPL)? This article presents an agile planning and estimation model that can be used for large projects as a guide. Agile Process The core of the agile process is a collection of sprints or small increments. The agile process can be classified into three key categories or stages: ·        Pre-Sprint – focus on the analysis activities of the GPL ·        Sprint – focus on the planning and development activities of the GPL ·        Post-Sprint – focus on the sprint post-mortem and release activities of the GPL.   The agile process has ...