Process Excellence & Cognitive Business Automation – Challenges, Approaches & Projects

• Next Level Accounting: What are hybrid models for intelligent automation and which backend processes should be automate
• Process Mapping: How to avoid automating tasks instead of processes?
• Enhanced Process Mining: How to optimize automation tracking & analysis, automatic process visualization, root cause analysis and KPIs?
• Automating less standardized and smaller volume processes: How they determined which “low volume, less standardized” – or both” processes were cleared for IA, including their business case that won their stakeholders’ approval?
• Process Mining and Process Automation: How to utilize process mining as a first step to process automation optimization?
• RPA CoE: What are the benefits of creating an RPA Center of Excellence and which guidelines for assessment, design, development and deployment of robots are needed?
• Determining your CoE Model: What are CoE model types and the pros & cons of each?

Tools & Technologies

• Advanced Automation: What are RPA techniques like semantic text recognition or Cognitive like NLP to deal with unstructured data?
• The RPA and AI Roadmap: How does my Data Readiness affect my journey?
• Data Governance: How to streamline your Data preparation flow?
• Data management strategies for RPA: How to curate and standardize data as a precursor for RPA and gather data along your process definition?
• Model and data life-cycle at scale; Measuring performance for time-series models and NLP systems and use cases for RPA?
• Managing Bots Workloads & Schedules: How to determine which processes your bots should handle and how to manage cost scenarios of bots?
• Infrastructure Design: How do you ensure your systems are compatible with the technologies your business partners would like to bring into your organization, and how can you set up your organization’s IT infrastructure so to maximize the technologies running through them?
• IA Data Security: What are technological safeguards specifically designed to protect an organizations’ data infrastructure?

Intelligent Automation Project Management & Organization

• Roadmapping & Piloting RPA: What are practical timelines to accomplish your RPA objectives and how to identify and prioritize the processes that are ripe for Intelligent Automation to ensure maximum program success?
• Continuous evaluation of your Intelligent Automation & RPA program: What are realistic IA project management timelines & KPIs to meet and performance measurements to ensure your IA opportunities and benefits meet their maximum potential and what are software tools and workflow dashboards to ensure your IA programs stay on track towards success?
• Realistic IA payback & ROI expectations: How to scale up an IA program and achieve payback sooner vs. later and how to budget financial investments?
• Cost-Budget Management: Where to start and what are the costs of process automation?
• RPA & IA Governance: How to control and steer RPA programs with effective RPA governance strategies to achieve maximum benefit from your virtual workforce
• Small vs, Scale: What are the benefits of starting small and how doing so it will lower risks?
• Mid-sized companies: How to calculate and generate value without scale?
• Sizing up the vendor landscape: Which IA software tools would be the best fit for your functional needs, project/s scope, and budgets?

People & Culture

• Workplace Management: How successful is cognitive automation in the workplace?
• Change management initiatives: How to set up partnerships that encourage successful communication between departments, staff, and stakeholders?
• Talent Management in RPA and intelligent automation: How to recruit top IA talents considering the fierce market demand and high salaries being offered by the competition