Form-Filling on Autopilot: How Generative AI and Amazon Connect are Transforming Agent Workflows
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I2P111Keywords:
Generative AI, Amazon Connect, Real-Time Transcription, AWS Lambda, DynamoDBAbstract
This research aims to combine generative AI systems with Amazon Connect so that form-filling duties in a contact center can be automated. Current contact centers encounter substantial problems in managing manual data entry, which results in higher AHT, variations in documentation, and a high frequency of errors. We propose using real-time speech transcription, AI-driven dynamic form filling, and automated summarization to lower manual work and improve customer service output. The design consists of Amazon Connect for customer communications, Contact Lens for monitoring conversations, AWS Lambda for instant event handling, Amazon DynamoDB for managing structured data, and Amazon Bedrock for integrating generative AI. Tests show that our system leads to a 20% drop in AHT, a 73% drop in data entry errors, and a 78% decrease in documentation time. This research contributes an important step in expanding AI-augmented workflows for customer support and proposes research directions involving knowledge base integration, multilingual solutions, and adaptive learning
Downloads
References
[1] Latif, A., ElBarachi, M., Tsaramirsis, G., & Khan, F. Q. (2024). Generative AI-enabled robotic process automation: A practical case study. In 2024 IEEE 30th International Conference on Parallel and Distributed Systems (ICPADS) (pp. 769-775). IEEE.
[2] Holmström, J., & Carroll, N. (2024). How organisations can innovate with generative AI. Business Horizons, 67(3), 104-112.
[3] Amazon Connect Contact Lens. (2023). Amazon Connect Contact Lens provides generative AI-powered contact summarisation (preview). AWS What's New. Online. https://aws.amazon.com/about-aws/whats-new/2023/11/amazon-connect-contact-lens-ai-powered-summarization-preview/
[4] Dua, I. K., & Patel, P. G. (2024). An introduction to generative AI. In Optimising Generative AI Workloads for Sustainability: Balancing Performance and Environmental Impact in Generative AI (pp. 1-37). Berkeley, CA: Apress.
[5] Xue, X., Lu, Z., Huang, D., Ouyang, W., & Bai, L. (2024). GenAgent: Build collaborative AI systems with automated workflow generation. Case studies are on Comfyui. arXiv preprint arXiv:2409.01392.
[6] Paulose, R., & Neelanath, V. (2024). Generative AI-driven automation of business process reimagining. In 2024 IEEE Recent Advances in Intelligent Computational Systems (RAICS) (pp. 1-6). IEEE.
[7] Al Naqbi, H., Bahroun, Z., & Ahmed, V. (2024). Enhancing work productivity through generative artificial intelligence: A comprehensive literature review. Sustainability, 16(3), 1166.
[8] Ameyo. (2016). How to reduce time spent by call centre agents on data entries. Ameyo Blog. Online. https://www.ameyo.com/blog/how-to-reduce-time-spend-by-call-center-agents-on-data-entries/
[9] Backlund, N. (2020). Implementing Amazon Web Services integration connector with IBM App Connect Enterprise. Master's Thesis, Åbo Akademi University.
[10] Cases, B. U., & Figueiredo, M. (2023). Generative AI with SAP and Amazon Bedrock. SAP Technical Documentation.
[11] Fan, W. (2024). Improving customer service efficiency using generative artificial intelligence. Journal of Service Management, 35(1), 78-96.
[12] Frankish, C., & Noyes, J. (1990). Sources of human error in data entry tasks using speech input. Human Factors, 32(6), 697-716.
[13] Gartner. (2022). Gartner for customer service & support leaders. Gartner Research. https://www.gartner.com/en/customer-service-support
[14] Hill, C., Yates, R., Jones, C., & Kogan, S. L. (2006). Beyond predictable workflows: Enhancing productivity in artful business processes. IBM Systems Journal, 45(4), 663-682.
[15] Jones, R. H. (2021). Data collection and transcription in discourse analysis: A technological history. The Bloomsbury Handbook of Discourse Analysis, 9-28.
[16] Kokala, A. (2024). Harnessing AI for BPM: Streamlining complex workflows and enhancing efficiency. Journal of Business Process Management, 30(2), 215-233.
[17] Lockwood, J. R., & McCaffrey, D. F. (2019). Impact evaluation using analysis of covariance with error-prone covariates that violate surrogacy. Evaluation Review, 43(6), 335-369.
[18] Rodríguez-de-Vera, J. M., Estepa, I. G., Sarasúa, I., Nagarajan, B., & Radeva, P. (2024). Precision at scale: Domain-specific datasets on-demand. arXiv preprint arXiv:2407.03463.
[19] Wiegand, K., Bedewi, M., Mukundakrishnan, K., Tishechkin, D., Ananthan, V., & Kahn, D. (2024). Using generative AI to build a reservoir simulation assistant. In Abu Dhabi International Petroleum Exhibition and Conference (p. D011S020R001). SPE.
[20] Wilkins, M. (2019). Learning Amazon Web Services (AWS): A hands-on guide to the fundamentals of AWS Cloud. Addison-Wesley Professional.
