Education

Embedded Analytics, Intelligent Apps, AI Agents & AI Automation

Request information on running this seminar as an Onsite  (can be given as Live Streaming training)

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DATES

19-20 May, 2025 – Live Streaming Training (Agent: Technology Transfer)

OVERVIEW

Although analytics in many organisations is well established, it is still the case that perhaps no more than 25% of employees make use of reports and dashboards from BI tools with even fewer using machine learning models or AI.  There is still a long way to go if companies are to realise the promise of using ML and AI to automatically prevent problems, seize opportunities and continually optimise business processes in everyday business operations.

However, the arrival of Generative AI has had a massive impact.  Businesses have realised the huge productivity benefits, cost reduction and efficiency opportunities that AI brings plus the ability to make timelier and better decisions. Business priorities  have therefore changed to the point where data and analytics are now strategic and demanded in every part of the business. The vision is to maximise use of conversational analytics, classic ML and AI Agents to provide better insights, increase the level of AI automation and deploy AI-Agents to assist and automate tasks and automate more operational decisions.  Executives want everyone in the company to leverage data and analytics to contribute towards improving overall business performance. They want to create an ‘always on’ data and AI-driven intelligent business where conversational analytics,, machine learning models, Generative AI and AI Agents are deployed right across the business so that every person, and every application, in the enterprise is able to leverage the right insights at the right-time in every activity to help them contribute to the overall performance of the business. Therefore, it should be possible to embed BI, conversational analytics, machine learning models and AI Agents into operational business processes to guide and drive decisions and actions in everyday business operations. It should also be possible to automate more using self-learning AI-Agents that can reason, plan, orchestrate, automate and assist.  This would move organisations towards creating intelligent applications, and utilising AI automation for right-time business process optimisation and decision management. This includes embedding analytics and AI into all customer facing applications and websites to enable a personalised customer experience as well as partners and suppliers being guided by explainable BI, alerts, and recommendations, and Generative AI-agents.  The objective is to move towards automated, self-learning, AI-driven business operations.

To make this possible requires:

• Trusted and compliant data
• Analytical web services to integrate classic BI and conversational analytics into operational business processes
• Developing and deploying machine learning models (ML) for use in automatic real-time scoring and analysis
• Real-time monitoring of events to detect exceptions and opportunities as they happen
• On-demand and event driven data integration for real-time analytics
• On-demand and event driven reporting
• Rules engines and AI Agents to make automatic decisions and take automatic actions
• Using prescriptive machine learning models for automated alerts
• Using prescriptive machine learning models for live recommendations
• Reward oriented re-enforcement learning
• AI-Agents to automate tasks and assist people in their natural workflow
• Guided analytics
• Dynamically guided smart processes
• Data governance for trusted data
• Live dashboards and scorecards for situational awareness
• Dynamic, event-driven AI-assisted budgeting and planning

This 2-day seminar shows how you can embed BI, ML AI-Agents and AI-automation into applications and processes to make your company data and AI-driven. The purpose is to achieve ‘always on’ business optimisation, dynamic planning by automating, guiding, and empowering employees, business partners, suppliers, and customers to make better decisions to improve business performance.  It provides a roadmap and methodology to creating the right-time intelligent enterprise by taking an in-depth look at the technologies and methodologies needed to make it happen.

AUDIENCE

This seminar is intended for business and IT professionals responsible for information delivery, business integration and leveraging BI, ML and AI in operational environments. It assumes that you have already built analytical systems and are now looking to leverage insights produced in everyday operations.

LEARNING OBJECTIVES

Attendees will learn how to justify, architect, and integrate AI-Agents, classic machine learning models and business intelligence into operational business processes and applications as part of a coordinated program to improve business performance. They will learn how to use automatic real-time event processing to monitor operational events as they happen to detect problems, identify opportunities, and deploy rule-driven and AI-Agent driven automated decisions  to guide everyday business operations. They will learn how to create intelligent apps and how to use AI-Agents as a digital workforce to automate tasks. Attendees will also understand how to use real-time data integration, on-demand decision services, prescriptive machine learning models as a service, BI web services, queries, real-time decision engines, enterprise alerting and business process automation to put analytics to work in driving every-day business operations.

MODULES

• Module 1: An introduction to data and AI-driven business optimisation
• Module 2: Technologies and tools for the data and AI-driven intelligent enterprise
• Module 3: Architectures and methodologies for creating the AI-driven intelligent enterprise
• Module 4: Embedding analytics and AI into operational applications and processes
• Module 5: Using embedded analytics, intelligent apps and AI automation in CRM and supply chain operation
• Module 6: Active dynamic planning and management for continuous optimisation

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