Intelligent virtual agents’ (IVA’s) evolving capabilities: Key changes in the IVA market in the last 12-18 months are enabling IVAs to handle much more complex interactions which is driving the ability to serve specialized needs across industries and horizontals
Starting an Automation Center of Excellence – A Roadmap: Map existing assets and capabilities; Build the Initial CoE; Run the initial CoE; Scale up operations
STEP into Change Management: strategize, take the lead, enact, performance analysis
Building and expanding an automation CoE is a closed-loop process: explore and exhibit, educate and execute, empower and expand
5 factors to consider to build the most efficient Smart RPA ecosystem: technology capability, ecosystem fo partners for collaborative technologies, ecosystem of service partners, product training & support, commercial models
Successful Smart RPA: 6 crucial factors
5 Steps to Smart RPA Adoption: understand current state and market capabilities, create a business case and refine target state, if required, determine capability target state, identify all determinants and map path, execute against mapped path
Smart RPA adoption challenges and solutions: process selection, availability of talent, organizational readiness, lack of sufficient data to train AI, transparency in AI decision-making, implementation
About 20% of enterprises that have deployed RPA have implemented AI in conjunction with their RPA deployments*, and the number is rising
Key technologies that form underlying systems for Smart RPA solutions: Key technologies that form underlying systems for Smart RPA solutions: Robotics, Optical Character Recognition (OCR) / computer vision, Process orchestrator / Business Process Management (BPM), analytics, Machine Learning (ML), Natural Language Processing (NLP)
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