Introduction: A New Technology Stack
Over the last four decades, the information technology market has grown exponentially to more than $2 trillion. During this time, the IT industry has experienced the transition from mainframe computing to minicomputers, to personal computing right at our homes, to internet computing, and handheld computing. The software industry has transitioned from custom applications based on mainframe standards to applications developed on a relational database to enterprise application software, to mobile apps, and now to the AI-enabled enterprise.
Today it is unimaginable that any major corporation would close its intelligent forum without an enterprise resource planning system. The IT industry is now undergoing another major shift, where its business can’t solely be based on mainframe computers. A new era of 21st-century technologies – including flexible cloud computing, the internet of things, and artificial intelligence – is driving digital execution across enterprise, commerce, and management globally. Online transformation presents several special requirements that create the void for an entirely new software technology deployment. The requirements are numerous.
This article describes the requirements of the new digital transformation software with Prescriptive Insight and the current approach to Simulation Transparent AI Model Execution – i.e., using Model Performance to build applications by batch Deployment, Real-time Deployment, Model Monitoring, Model Management components and cloud services.
Finally, to describe how the C3 AI Suite, AI Fairness, AI Trust and AI Ethics with its unique model-driven architecture fully addresses the requirements for the digital movement, providing a low-code/no-code AI and IoT platform that accelerates software expansion and reduces cost and risk, delivering future-proof applications.
Artificial intelligence is redefining the very importance of intelligent modelling and execution in an enterprise. The rapidly progressing Machine learning capability is on its way to revolutionizing every aspect of an enterprise. The ability to access and process data online has levelled the playing field and brought every enterprise a unique opportunity for progress. Here’s what enterprise AI can do to help Model performance and provide Data insights.
Purpose of Enterprise AI
Enterprises across the world are experiencing a shift in the relative adoption of AI. These applications will present individual enterprises as many opportunities as it does challenges when the transfer of data towards the transition to AI hosting is to be accomplished. While access to AI, data monitoring, and Prescriptive Insight is common to all enterprises, what is not common is how each enterprise utilises that knowledge—and on what grounds. It is crucial to understand the levels that will define their individual and collective success in putting AI to good use.
There are many complexities in each enterprise system that will determine whether an enterprise will be able to quickly use the data and information from its existing talent to develop AI, automate, and deploy to succeed.
As the conditions of AI deployment accelerates, it is difficult to capture that staying competitive means being more intelligent in day-to-day tasks as well as noteworthy decisions for an enterprise’s survival. It is overlooked how enterprises across nations are expected to face tremendous challenges and changes in the coming years, with automation compelled growth as the only workforce to lead during those changes. As a result, it is essential to understand what AI-driven growth means for enterprises.
The emerging movements in AI-driven automation mirror momentous shifts of players and actions in the AI ecosystem that reveal the realisation of ideas, interests, influence, and investments in the AI discipline of enterprise adoption and transformation. Enterprises have started to understand the overall effects of the automated statistical learning-driven platforms far beyond narrow artificial intelligence, crossing economic, commerce, education, governance, and trade supply chains. While the relationship between enterprises and automation is complex, and at times weary, the energy and pace of AI-driven automation change along with anticipated challenges and opportunities for its: products, services, processes, operations, and supply chains all come with huge benefits. From what it appears, the AI applications of the future will be composed of hybrid systems with several components and reliant on many different data sets, methodologies, and models.
The ever-growing cyberspace is connecting humans and machines across the world. It is not only the human users and interface-based applications that are getting connected but the growing number of internet of things (IoT) devices are also getting activated and operational with the rollout of new smart technologies. Individually and collectively, the ever-complex connectivity of man and machines is producing enormous amounts of data and is driving the rapid evolution of AI across enterprises. However, there never seemed to be enough power and speed behind AI for enterprises to implement ideal techniques and strategies. While AI-driven automation emerged many years ago, it is only now evolving as real-time computing, and as massively parallel processing systems advance AI performance even further. As a result, AI brought automation is now moving further as a fundamental trend.
Many functional parts of enterprises are already benefiting from the AI movement. From R&D tasks, customer assistance, finance, accounting, and IT, there are rapid transformations from experimental to settled AI technology across enterprises. There is no doubt all enterprises will benefit from intelligent decision making to simplified supply chains, customer relations to recruitment techniques.
As the Enterprise AI market rises, so does the demand for AI-as-a-service. Moreover, AI-driven automation, Transparent AI, and low-code platforms are merging as the competitive landscape. New organizational capabilities are becoming critical, and so is the necessity for effective management of the growing security risks of AI.
Now, common sense tasks have become more comprehensible for computers to process, AI-driven intelligent applications and robots will become extremely useful in business operations and supply chains. Without a complete understanding of use cases — the problems can be solved using AI, where to apply AI, what data sets to use, how to get credible data and skilled resources — still slows down AI adoption, company culture also plays an important role in AI adoption strategies and has seemingly been a barrier to AI adoption.
Enterprise Digital Data Infrastructure
While enterprises are taking advantage of global, regional, local explanation and decision analytics AI brings, web platforms are beginning to employ these technologies and benefits. The AI is shaped by several variables and external factors, many of which are influenced by data choices made at the enterprises. So, how will availability, affordability, accessibility, and security of data impact potential AI growth for enterprises?
As seen, many enterprises lack the required digital data infrastructure. The lack of digital support, in turn, makes it harder for opportunities and innovations in AI, making it challenging to be equipped to the enterprise needs adequately — leaving the company with outdated data, information, and ecosystem. Moreover, the trustworthiness of the data sets also is an emerging concern. That directs us to these important questions: how are enterprises handling digital data infrastructure challenges? What are the various data classifications that are vital for enterprises?
While enterprises have already employed AI in analytics, many meaningful data partnerships are emerging. The emerging integrated structured data and text, when available to train AI systems, will bring much-needed progress in enterprise AI. It will be interesting to see how this new data-driven world brings each enterprise across industries, both opportunities, and risks.
The possibility of Enterprise AI to transform the enterprise ecosystem in many ways. From decision making to supply chain intelligence and tracking capabilities to the automation of business processes, AI can change the entire enterprise ecosystem across spaces.
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