Description

 

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 Business Intelligence (BI) tools with even fewer using machine learning (ML) models or Artificial intelligence (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, ML 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, ML 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 ML models for use in automatic real-time scoring and analysis
  • Real-time monitoring of operational 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
  • Rule engines and AI Agents to make automatic decisions and take automatic actions
  • Using prescriptive ML models for automated alerts
  • Using prescriptive ML 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 course 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 optimization, 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.

Why attend

You will learn how to:

  • Justify, architect, and integrate AI Agents, classic ML models and business intelligence into operational business processes and applications as part of a coordinated program to improve business performance
  • 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
  • Create intelligent apps and how to use AI Agents as a digital workforce to automate tasks
  • Use real-time data integration, on-demand decision services, prescriptive ML 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 everyday business operations 

Who should attend

This course 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.

Related Content

Why use Embedded Analytics, Intelligent Apps and AI Automation?

Mike Ferguson explains how you can embed Business Intelligence, Machine Learining and Artificial intelligence automation into applications and processes to make your company data and AI-driven.

 

What is the role of Process Mining in AI Automation?

Code: EA2025
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Outline

 
 
 
 
 
 
 
 
 
 

An Introduction to Data And AI-Driven Business Optimization

This module looks at how embedded analytics, classic machine learning (ML) and Artificial intelligence (AI) automation can help companies improve efficiency and effectiveness of decision making in operational business processes. It looks at the business benefits of embedded analytics, intelligent apps and AI automation, where businesses want to utilize these capabilities in core applications and processes at what is needed to make it possible.

  • What do we mean by embedded analytics and intelligent apps?
  • What is AI automation and what can AI-Agents do?
  • Business benefits – Why use embedded analytics, event-driven prescriptive ML models, and AI-automation?
  • How can it help optimize business and improve effectiveness?
  • Examples and case studies of using embedded analytics, intelligent apps and AI automation in practice, e.g. IoT analytics, fraud detection, personalizing the customer experience, supply chain optimization, customer service chatbot, email generation
  • What's needed to get started?
  • Key mistakes and how to avoid them

Technologies and Tools for the Data-Driven Intelligent Enterprise

This module looks at the technology components to consider when implementing embedded analytics, intelligent apps, and Artificial intelligence (AI) automation.

  • Classic Business Intelligence (BI) and conversational analytics as a service for on-demand insights
  • Predictive machine learning (ML) model services for on-demand scoring
  • Prescriptive ML model services for on-demand recommendations
  • MLOps for ML model management
  • Generative AI and AI Automation tools
    • Process mining
    • Robotic Process Automation
    • Intelligent document processing
    • Retrieval Augmented Generation
    • Tools for building AI-Agents
    • AI-driven orchestration, e.g., IBM Watson Orchestrate
  • Using ML models and Generative AI-Agent LLMs embedded in databases
  • Augmented analysis for assisted rapid problem identification
  • Embedding ML models, BI services and AI Agents into operational applications to create intelligent apps
  • Scalable messaging e.g., Kafka, AWS Kinesis, Azure Event Hubs, Solace
  • Streaming analytics pipelines on real-time data
    • Event driven data integration
    • Event-driven prescriptive ML and AI-Agents for automated actions
  • Using real-time data and ML for situational awareness dashboards
  • Rules engines and AI Agents for automated decision management
  • Automated Action services and workflows including multi-Agent action
  • Pushing alerts and recommendations to mobile workers
  • The role of business integration technologies such as REST, GraphQL, Enterprise Service Bus and Business process management
  • The role of business integration or BI and AI using as REST, GraphQL APIs, Enterprise Service Bus and Business process management
  • Low code / no-code automation using ML and tools like Microsoft PowerAutomate

Architectures and Methodologies for Creating the AI-Driven Intelligent Enterprise

This module looks at the architecture options for integrating analytics, machine learning (ML) and Artificial intelligence (AI) Agents into applications and implementing event processing for decision automation. It also looks at the pros and cons of these options and at methodologies for doing it.

  • Embedding analytics and ML – why a single approach is not enough
  • A methodology to successfully implement embedded analytics and ML
  • Understanding user communities, roles and the applications they use
  • Understanding business processes, process events and external events
  • Right-time operational analytics requirements - who needs what Business Intelligence (BI), ML and AI automation and when?
  • Integration options for internal and external exploitation of right-time intelligence, prescriptive analytics, AI Agents and AI automation
    • Integrating real-time insights into dashboards and planning
    • Using on-demand AI automated decision services in a SOA
    • Integrating BI, ML and AI automation with business process management (BPM) software
    • Automatic decision services
    • AI-driven orchestration and actions
    • Edge computing – real-time analytics at the edge
  • The implications of right-time embedded analytics, ML and AI on existing analytical systems
  • Pros and cons of different options
  • Identifying the best architecture option for role-based business optimization

Embedding Analytics and AI Into Operational Applications and Processes

This module looks at why embedding analytics and Artificial intelligence (AI) in applications and processes and introducing automation is becoming mission critical to reducing costs and improving efficiency. It looks at how to create intelligent apps and processes by invoking analytical machine learning (ML) and AI Agent services from applications and business process management software. It focuses on how to use orchestration to leverage analytics, ML, Generative AI and AI automation and how to monitor cost and efficiency of business processes.

  • Integrating Business Intelligence (BI), ML and AI into operational applications and business processes using on-demand web services
  • Augmenting and automating human tasks using robotic process automation, intelligent document processing and AI-Agents
  • The power of human and AI-driven orchestration
  • Automation building blocks
  • The AI-driven automation process
  • Human and AI-driven decision and action automation
  • Monitoring operational business processes
    • Change Data Capture
    • Using event-driven data integration and in-memory data
    • Streaming analytics
    • Using predictive models for automated analysis, scoring and pattern detection
    • Using rules engines and AI Planning Agents for automated decision management
    • Data streaming technologies - Amazon Kinesis, Azure Event Hubs and IoT Hubs, Google Cloud Pub/Sub, Kafka, Solace
    • Streaming analytics technologies - Google Cloud DataFlow, Informatica IDMC, Kafka Streams, SAS Stream Processing, Spark Streaming, Striim, Ververica Apache Flink, VoltDB
  • Deploying ML models at the edge to monitor IoT devices and optimize processes
  • Optimizing operational processes using prescriptive analytics and live recommendations

Using Embedded Analytics, Intelligent Apps and AI Automation in CRM and Supply Chain Operations

This module looks at how to create intelligent apps and processes in front-office and back-office business operations. It discusses how right-time analytics, machine learning (ML) and Artificial intelligence (AI) can be leveraged across all customer touchpoints for targeted and personalized customer marketing, sales, and service and for improving customer retention and satisfaction while lowering cost. It also looks at how to optimize supply chain operations using prescriptive analytics and AI Agents for alerting and automated actions.

  • Building a customer data platform (CDP) for single view of the customer
  • The customer intelligent front office - using embedded Business Intelligence (BI), ML and AI Automation to improve marketing, sales and service
  • Leveraging automated analysis for alerting and recommendations to guide front-office operations
  • Integrating analytics and ML with multi-channel campaign management systems
  • Using operational BI, ML and decision automation to support a mobile sales force
  • AI-driven chatbots in customer service
  • Acting on insights from mobile devices
  • Using real-time and prescriptive analytics in fraud detection
  • Continuous monitoring of supply chain performance and operational cost
  • Building a Supply Chain Control Tower and Command Centre
  • Automating supply chain optimization using demand intelligence
  • Right-time alerting in supply chain operations

Active Dynamic Planning and Management for Continuous Optimization

This module shows how business integration software can be used to integrate Business Intelligence (BI), machine learning (ML) and Artificial intelligence (AI) Assistants and AI automation services with business planning to dynamically manage business performance at strategic and operational levels.

  • Integrating prescriptive analytics and on-demand operational BI into planning scorecards with KPIs for live alerts and operational performance monitoring
  • AI-assisted dynamic planning - taking action to solve operational business problems as they happen to keep your business optimized

Instructor

Mike Ferguson

 

Mike Ferguson is the Managing Director of Intelligent Business Strategies Limited. As an independent IT industry analyst and consultant, he specializes in BI/Analytics and data management. With over 40 years of IT experience, Mike has consulted for dozens of companies on BI/analytics, data strategy, technology selection, data architecture and data management.

Mike is also conference chairman of Big Data LDN, the fastest-growing data and analytics conference in Europe and a member of the EDM Council CDMC Executive Advisory Board. He has spoken at events all over the world and written numerous articles.

Formerly he was a principal and co-founder of Codd and Date Europe Limited – the inventors of the Relational Model, a Chief Architect at Teradata on the Teradata DBMS.

He teaches popular master classes in Data Warehouse Modernization, Big Data Architecture & Technology, How to Govern Data Across a Distributed Data Landscape, Practical Guidelines for Implementing a Data Mesh (Data Catalog, Data Fabric, Data Products, Data Marketplace), Real-Time Analytics, Embedded Analytics, Intelligent Apps & AI Automation, Migrating your Data Warehouse to the Cloud, Modern Data Architecture and Data Virtualisation & the Logical Data Warehouse.

Dates

This course is only available as Customer Specific Training, whereby we can deliver private courses arranged at both a location (or virtual) and time to suit you, covering the right content to address your specific learning needs. Contact us by e-mail at info@q4k.com.

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