AgilePoint

Automation And AI: A Match Made In Heaven?   Anna Covert, founder of Covert Communication, wrote an interesting article on Forbes: Automation vs AI, The Difference Can Make or Break Your Bottom Line.    https://lnkd.in/gegr5x2Q When it comes to investing in Automation vs. AI, Anna's perspective was spot on. But can AI and Automation work fluidly together like a match made in heaven? Understanding the following is the first step: 1)    Functional automation vs. end-to-end business orchestrations Functional automation created using point solutions such as BPA, RPA, etc., generates efficiency and productivity gains. End-to-end orchestrations drive critical business outcomes and KPIs and are highly relevant to dynamic case management, such as enabling individualized customer journey experiences. Yet, most organizations lack end-to-end orchestrations due to the complexity of implementation and the challenges of cross-functional culture and user adoption. AgilePoint's abstraction-enabled platform-agnostic composability simplifies both challenges. 2) Structured automation vs. natively AI-ready hyper-agile automation The majority of automation applications created today are structured automation (i.e., deterministic, predefined) that doesn't handle unexpected exceptions well. For some industries, 20% of exceptions consume 80% of time and resources. Therefore, structured automation cannot take advantage of AI/GenAI's dynamic nature and faces limitations when it comes to operationalizing AI—that is, the use of AI is tactical and limited to task levels via API calls, thereby increasing complexity and hindering business engagement. By contrast, a hyper-agile orchestration enabled by AgilePoint can respond to the unpredictable, probabilistic nature of AI/GenAI and automatically generate AI-ready training data. Further, AgilePoint's AI Control Tower can serve as Agentic AI to monitor, guide, mass-customize, and govern individual executions at the orchestration level. AI Control Tower democratizes AI for business because: ·      It adds no complexity to the process. ·      Domain experts can leverage AI without having a technical background. Hence, AgilePoint facilitates a symbiotic relationship between Automation and AI, paving the way to enable autonomous closed-loop optimizations and future-proof business agility and resiliency. #AgenticAI #BPA #BPM #Workflow #RPA #Low-Code #Automation #Orchestration #End-to-End #Composable #OperationalizeAI #BOAT

Will ‘AI Factory’ replace ‘Software Factory’ as predicted by Nvidia CEO Jensen Huang? The COMPUTEX 2024 took place in Taiwan during the week of 4th June, and I witnessed an extremely interesting phenomenon. The CEOs of three of the leading AI infrastructure enablers, Jensen Huang of Nvidia, Lisa Su of AMD, and Charles Liang of Supermicro, all born in Taiwan, delivered insightful and thought-provoking speeches about how AI, especially generative AI, will disrupt not just the IT industry but across all sectors. Nvidia CEO Jensen Huang delivered the most notable keynote address, predicting an AI Factory will replace the current Software Factory:  a) Software Factory consists of pre-programmed software applications that retrieve defined information. b) Using Nvidia GPU technology, which exponentially scales computation power, generative AI can dynamically generate information and output based on prompts. Therefore, Software Factory will no longer be relevant. c) The Nvidia Inference Microservice (NIM) provides pre-trained models in optimized containers to accelerate the creation of generative AI applications. As a result of Huang's keynote speech, I witnessed an interesting phenomenon. During my subsequent meetings with CIOs of leading high-tech manufacturing companies, many told me that shortly after Huang delivered his keynote address, their CEOs called them with questions about whether their current IT investments, initiatives, operations, resource plans, etc., would be disrupted by generative AI as Huang predicted. In fact, Huang’s view aligned with what we discussed in my prior post – why AI needs a new era of probabilistic adaptability? a) Nearly all of today’s IT systems and software applications are inherently deterministic, i.e., build-to-spec based on well-understood requirements to deliver pre-defined outcomes. b) With AI can continually uncover new findings and generative AI can start with a prompt and dynamically generate information or output, i.e., non-predefined and non-predictable, IT systems and applications must become probabilistic-capable to stay relevant in the era of generative AI. This raises a few questions every business and tech executive should ask. 1) Can we protect our existing IT investment from being disrupted immediately, as Huang predicted, and still cost-effectively practice AI? 2) How can we avoid the need for rip-and-replace by making the current inherently deterministic IT systems smarter, i.e., becoming probabilistic-ready to work with the dynamic nature of generative AI and enabling the gradual build-out of Huang's 'AI Factory' vision? 3) How can we ensure all future applications, automation, and orchestrations are AI and generative AI-ready? 4) How can effective governance be used to ensure boundaries and goals when using generative AI? Let's brainstorm and share perspectives. #AI #GenerativeAI #COMPUTEX #AIFactory #SoftwareFactory #Automation #Orchestration #EnterpriseApplications #AIGovernance #NIM

Why AI Needs a New Era of Probabilistic Adaptability   Much attention these days has been focused on how AI will disrupt the software development paradigm, specifically Gen AI’s ability to generate code and render low-code, no-code tools obsolete. The assumption fails to recognize that AI will change the characteristics of every application we build, disrupting the paradigm of software development as we know it. The reason is simple. Almost all IT systems and software applications developed today are deterministic, i.e., you gather requirements and develop applications accordingly. But in the Age of AI, AI will continually uncover unpredictable new findings and insights, so we must prepare for entering a new era of probabilistic operations. For process, this class of problem can't be easily pre-defined into structured automation and orchestrations that represent the mainstream implementation today. As a result, deterministic computing is no longer adequate for the Age of AI. Technologies that only speed up the development of deterministic applications will not be adequate either. For organizations to thrive and reshape their industries in the Age of AI, they must be able to create probabilistic-ready applications. Therefore, when organizations begin to rethink their application strategies, selecting a next-generation application platform that supports probabilistic use case scenarios is a critical criterion. One such proven architecture is a metadata-abstraction and model-driven composition-enabled codeless composability architecture free of code generation and compilation and without the need for virtual machines. AgilePoint was designed and architected in 2003 to deliver such an architecture that enables probabilistic automation and end-to-end business orchestration. AgilePoint's ultimate vision is to enable the creation of natively AI-ready applications and to provide AI with the ability to dynamically adapt running applications based on continually uncovered real-time findings and insights without modifying code. The architecture naturally supports business process management and low-code use cases, but it is also more scalable, sustainable, and adaptable without incurring technical debt. In 2007, Gartner discussed AgilePoint for the first time in its Cool Vendor in Business Process Management Report: “For processes where the sequence of work is not well understood or that must change dynamically based on the context of the transaction in real-time . . .   This class of process problem is not easily pre-defined into structured automation.   AgilePoint’s internal design enables users to design and deploy processes that can be dynamically changed in real-time.” The discovery implied that AgilePoint was architected in 2003 to be probabilistic-ready for the Age of AI. #AI #GenAI #OperationalizeAI #OperationalAI #LowCode #ProcessAutomation #ProcessOrchestration #Deterministic #Probabilistic #Adaptability #ApplicationInnovation