Autonomous agents will soon run thousands of enterprise workflows, and only organizations with unified, trusted, context-rich data will prevent chaos and unlock reliable value at scale. AI agents are moving beyond coding assistants and customer service chatbots into the operational core of the enterprise. The ROI is promising, but autonomy without alignment is a recipe for chaos. Business leaders need to lay the essential foundations now. Agents are independently handling end-to-end processes across lead generation, supply chain optimization, customer support, and financial reconciliation. A mid-sized organization could easily run 4,000 agents, each making decisions that affect revenue, compliance, and customer experience.

Overview This document describes a pragmatic approach on how to migrate enterprise computer systems to new systems that could evolve forever, address the whole organisations and that are integrated. Governance aspects are as important, if not more, than purely technical IT aspects: human resources, supply chains, call for tenders, and similar. Migration implies not starting from a green field. Style of this document {Principle} Lie if it helps and restate the obvious. Enterprise IT architecture is a complex field. Efforts have been made to make this document accessible to the widest possible public, including non-IT people. To make concepts more accessible, they might be introduced informally without being technically strict (lie) and sprinkled with bits of tutorials (restate). For the gory details follow the references. It could be anything: from one integrated system to many disconnected systems, from properly developed systems to spreadsheets, from internal developed code to external libraries, etc. The first priority is to ensure the functioning of the current system, imperfect as it might be. Avoid the syndrome of not maintaining the current system because it is a waste of money. It is an error to channel most of the IT resources into the new wonderful system on the way. The first step is to prepare emergency manuals for the current system. The guiding scenario for preparing these manuals is that present IT staff operating/maintaining the current system disappear from one day to the next; unpolished manuals would do. New IT replacement staff without any previous knowledge should have a sporting chance of operating/maintaining the current system with the help of emergency manuals which must be printed and stored in a place easy to find.

Anomaly detection to recognize unusual events in large scale systems in a time sensitive manner is critical in many industries, eg. bank fraud, enterprise systems, medical alerts, etc. Large-scale systems often grow in size and complexity over time, and anomaly detection algorithms need to adapt to changing structures. A hierarchical approach takes advantage of the implicit relationships in complex systems and localized context. The features in complex systems may vary drastically in data distribution, capturing different aspects from multiple data sources, and when put together provide a more complete view of the system. In this paper, two datasets are considered, the 1st comprising of system metrics from machines running on a cloud service, and the 2nd of application metrics from a large-scale distributed software system with inherent hierarchies and interconnections amongst its system nodes. Comparing algorithms, across the changepoint based PELT algorithm, cognitive learning-based Hierarchical Temporal Memory algorithms, Support Vector Machines and Conditional Random Fields provides a basis for proposing a Hierarchical Global-Local Conditional Random Field approach to accurately capture anomalies in complex systems across various features. Hierarchical algorithms can learn both the intricacies of specific features, and utilize these in a global abstracted representation to detect anomalous patterns robustly across multi-source feature data and distributed systems. A graphical network analysis on complex systems can further fine-tune datasets to mine relationships based on available features, which can benefit hierarchical models. Furthermore, hierarchical solutions can adapt well to changes at a localized level, learning on new data and changing environments when parts of a system are over-hauled, and translate these learnings to a global view of the system over time.

Artificial intelligence, or AI, is a simulation of intelligent human behavior. It's a computer or system designed to perceive its environment, understand its behaviors, and take action. Consider self-driving cars: AI-driven systems like these integrate AI algorithms, such as machine learning and deep learning, into complex environments that enable automation. AI is estimated to create $13 trillion in economic value worldwide by 2030, according to a McKinsey forecast. That's because AI is transforming engineering in nearly every industry and application area.

RPA helps deliver superior customer experience while also simplifying workflows handled by the human workforce. Rudimentary contact center tasks such as updating contact information, listening to routine voicemail messages, sending acknowledgment emails, etc. can easily be automated saving human agents a great amount of time. RPA reduces operational costs while upping efficiency and productivity. Front-office bots like chatbots can integrate with various enterprise systems like CRM, helpdesk etc. An RPA bot can interface with multiple enterprise systems within the company that may have UIs but not APIs.

Chatbots (front office bots) converse with customers or employees to send information, complete tasks or capture their requests. Based on the use case, a bot needs to integrate with and access information from different enterprise systems. These systems can be around help desk, intranet, CRM, Business Intelligence, LOB, HR knowledge bases and so on. If these systems have modern APIs, then the chatbot can access the required information independently and without any issue. However, if the systems lack modern APIs, the chatbot may not be able to integrate and retrieve information.

The nature, scale, and diversity of the cybersecurity threats that the modern organization faces means leveraging the power of automated security tools is a necessity. Large enterprises can generate billions of distinct system logs and events each day. Manually poring through such information is impossible. Security software and automated tools make the process of sifting through such security data quick and efficient. Among the different categories of cybersecurity tools an organization could use to enforce their security policies, security analytics software is among the most critical.

As part of Firebrand Talent's #Digitalks event series, Lisa Bouari, Executive Director at OutThought, recently spoke to audiences on the topic'AI Assistants: How to take your business to the next level'. The key areas Lisa focused on are detailed below. I'm not sure about you, but I wasn't surprised to hear the latest stats from Gartner suggesting that 25% of organisations will use Chatbots in their customer service by 2020. It's a large number when we consider the thousands of organisations that are yet to adopt this technology – but the take-up rate and activity in the marketplace would suggest we are well on our way. IBM states that 70% of consumers also prefer to message over calling, for customer support.

Under enormous pressure to generate growth, today's C-suite is adopting technology that spawns new capabilities and applications. But many still struggle to scale innovation company-wide. It's creating what we call the innovation achievement gap--the difference between technology innovation investment and realized value. Value is difficult to capture in part because the conventional IT "stack"--spanning software applications, hardware, telecommunications, facilities and data centers--wasn't built for today's world of analytics, sensors, mobile computing, artificial intelligence applications, the Internet of Things (IoT), and billions of devices. Nor was it designed to adapt to the world of tomorrow, whatever that might be. But it's not the case that digital native companies are closing their value achievement gaps, while legacy companies aren't.

Quest Diagnostics, a provider of diagnostic information services, and hc1, the bioinformatics leader in precision testing has unveiled Quest Lab Stewardship powered by hc1, an innovative new service that employs machine learning to harmonize laboratory testing across health systems in order to help optimize laboratory test utilization. Healthcare system wastes around $765 billion a year, due to factors such as unnecessary or inefficiently delivered services as well as missed prevention opportunities, according to the National Academy of Medicine. Although laboratory testing reflects only about 2-3% of overall health care costs in the United States, ordering lab tests is healthcare's single highest-volume activity.[ii] Under-and overutilization* of lab tests can adversely affect clinical decisions, such as by prompting unnecessary or delayed procedures to address missed diagnoses. Quest Lab Stewardship is the result of a strategic collaboration between Quest and hc1 focused on improving costs and clinical impact of lab testing, in- and out- of hospital settings.