Strategic Rollout Plan for Copilot Adoption within an Organization: Comprehensive Onboarding and Continuous Measurement
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I1P106Keywords:
Copilot Adoption, Organizational AI Strategy, Onboarding, Continuous Measurement, Productivity Analytics, Digital Transformation, Governance, Change ManagementAbstract
The breaking of productivity applications based on Artificial Intelligence (AI) solutions, such as GitHub Copilot, Microsoft Copilot, and others characteristic of generative AI assistants, is altering the direction of the modern organizations, making their work easier, software-development more rapid and decision-making more efficient. The adoption of Copilot, however, can be successful only through well-thought technological implementation, along with an adequate strategy of onboarding and continued measurement. The current paper will recommend the strategic implementation process of Copilot in the company context and especially systematic onboarding, cultural acclimatization, governance structure, and goal-based productivity and employee involvement measurement. We also give a staged process of adoption, to encompass the pre-adoption assessment; pilot adoption; enterprise-wide adoption and cycles of improvement. There is also organizational change management practices, the application of the human-AI collaboration theory, and performance analysis combined in the paper to ensure the value is created in the long-term. A combination of quantitative (KPIs, metrics of productivity, ROI analysis) and qualitative (survey, feedback loops, focus groups) methodology is developed. Literature Survey The relevant resourcing suggested by AI adoption, digital transformation and human-in the-loop AI systems is discussed in the literature survey. A case study is also provided in order to demonstrate the practical impact of planned Copilot adoption. The statistics show that the gradual rollout can be successful and can result in the improvement of the productivity up to 25-40 percent and the onboarding period can be reduced to 30 percent with a two-digit rise in the score of its efficiency. Finally, we introduce a governance system that will ensure that Copilot technologies are applied in the enterprise in an ethical, safe, and sustainable manner
References
[1] Svetlana, N., Anna, N., Svetlana, M., Tatiana, G., & Olga, M. (2022). Artificial intelligence as a driver of business process transformation. Procedia Computer Science, 213, 276-284.
[2] Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The impact of ai on developer productivity: Evidence from github copilot. arXiv preprint arXiv:2302.06590.
[3] Hiatt, J. (2006). ADKAR: a model for change in business, government, and our community. Prosci.
[4] Cullen, K. E., & Taube, J. S. (2017). Our sense of direction: progress, controversies and challenges. Nature neuroscience, 20(11), 1465-1473.
[5] Sholl, M. J. (1988). The relation between sense of direction and mental geographic updating. Intelligence, 12(3), 299-314.
[6] Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116.
[7] Chadwick, I. C., & Raver, J. L. (2015). Motivating organizations to learn: Goal orientation and its influence on organizational learning. Journal of management, 41(3), 957-986.
[8] Hallbert, B. P. (2020). Stakeholder Engagement Review Meeting Summary Report (No. INL/EXT-19-54520-Rev000). Idaho National Lab.(INL), Idaho Falls, ID (United States).
[9] Skiba, R. (2024). Managing recruitment and onboarding. After Midnight Publishing.
[10] Holmström, J. (2022). From AI to digital transformation: The AI readiness framework. Business horizons, 65(3), 329-339.
[11] Mihai, F., Aleca, O. E., & Gheorghe, M. (2023). Digital transformation based on AI technologies in European union organizations. Electronics, 12(11), 2386.
[12] Feroz, K., & Kwak, M. (2024). Digital transformation (DT) and artificial intelligence (AI) convergence in organizations. Journal of Computer Information Systems, 1-17.
[13] Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2024). Methodological approach to assessing the current state of organizations for AI-Based digital transformation. Applied System Innovation, 7(1), 14.
[14] Subramaniam, N. (2023). Digital transformation and artificial intelligence in organizations. Journal of Financial Transformation, 58, 90-97.
[15] Fragiadakis, G., Diou, C., Kousiouris, G., & Nikolaidou, M. (2024). Evaluating human-ai collaboration: A review and methodological framework. arXiv preprint arXiv:2407.19098.
[16] Muller, M., & Weisz, J. (2022, June). Extending a human-ai collaboration framework with dynamism and sociality. In Proceedings of the 1st Annual Meeting of the Symposium on Human-Computer Interaction for Work (pp. 1-12).
[17] Valtiner, D., & Reidl, C. (2021). On change management in the age of artificial intelligence: a sustainable approach to overcome problems in adapting to a disruptive, technological transformation. Journal of Advanced Management Science, 9(3).
[18] Burgess, A. (2018). The executive guide to artificial intelligence. How to identify and implement applications for AI in your organization. London: AJBurgess Ltd, 3.
[19] Rane, N., Choudhary, S. P., & Rane, J. (2024). Acceptance of artificial intelligence: key factors, challenges, and implementation strategies. Journal of Applied Artificial Intelligence, 5(2), 50-70.
[20] Kulkov, I., Kulkova, J., Rohrbeck, R., Menvielle, L., Kaartemo, V., & Makkonen, H. (2024). Artificial intelligence‐driven sustainable development: Examining organizational, technical, and processing approaches to achieving global goals. Sustainable Development, 32(3), 2253-2267.
[21] Rusum, G. P., Pappula, K. K., & Anasuri, S. (2020). Constraint Solving at Scale: Optimizing Performance in Complex Parametric Assemblies. International Journal of Emerging Trends in Computer Science and Information Technology, 1(2), 47-55. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I2P106
[22] Pappula, K. K., & Anasuri, S. (2020). A Domain-Specific Language for Automating Feature-Based Part Creation in Parametric CAD. International Journal of Emerging Research in Engineering and Technology, 1(3), 35-44. https://doi.org/10.63282/3050-922X.IJERET-V1I3P105
[23] Rahul, N. (2020). Optimizing Claims Reserves and Payments with AI: Predictive Models for Financial Accuracy. International Journal of Emerging Trends in Computer Science and Information Technology, 1(3), 46-55. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I3P106
[24] Enjam, G. R. (2020). Ransomware Resilience and Recovery Planning for Insurance Infrastructure. International Journal of AI, BigData, Computational and Management Studies, 1(4), 29-37. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I4P104
[25] Pappula, K. K., Anasuri, S., & Rusum, G. P. (2021). Building Observability into Full-Stack Systems: Metrics That Matter. International Journal of Emerging Research in Engineering and Technology, 2(4), 48-58. https://doi.org/10.63282/3050-922X.IJERET-V2I4P106
[26] Pedda Muntala, P. S. R., & Karri, N. (2021). Leveraging Oracle Fusion ERP’s Embedded AI for Predictive Financial Forecasting. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(3), 74-82. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I3P108
[27] Rahul, N. (2021). Strengthening Fraud Prevention with AI in P&C Insurance: Enhancing Cyber Resilience. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 43-53. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P106
[28] Enjam, G. R. (2021). Data Privacy & Encryption Practices in Cloud-Based Guidewire Deployments. International Journal of AI, BigData, Computational and Management Studies, 2(3), 64-73. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I3P108
[29] Karri, N. (2021). Self-Driving Databases. International Journal of Emerging Trends in Computer Science and Information Technology, 2(1), 74-83. https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P10
[30] Rusum, G. P. (2022). WebAssembly across Platforms: Running Native Apps in the Browser, Cloud, and Edge. International Journal of Emerging Trends in Computer Science and Information Technology, 3(1), 107-115. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I1P112
[31] Pappula, K. K. (2022). Architectural Evolution: Transitioning from Monoliths to Service-Oriented Systems. International Journal of Emerging Research in Engineering and Technology, 3(4), 53-62. https://doi.org/10.63282/3050-922X.IJERET-V3I4P107
[32] Anasuri, S. (2022). Adversarial Attacks and Defenses in Deep Neural Networks. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 77-85. https://doi.org/10.63282/xs971f03
[33] Pedda Muntala, P. S. R. (2022). Anomaly Detection in Expense Management using Oracle AI Services. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 87-94. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P109
[34] Rahul, N. (2022). Automating Claims, Policy, and Billing with AI in Guidewire: Streamlining Insurance Operations. International Journal of Emerging Research in Engineering and Technology, 3(4), 75-83. https://doi.org/10.63282/3050-922X.IJERET-V3I4P109
[35] Enjam, G. R. (2022). Energy-Efficient Load Balancing in Distributed Insurance Systems Using AI-Optimized Switching Techniques. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 68-76. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P108
[36] Karri, N., & Pedda Muntala, P. S. R. (2022). AI in Capacity Planning. International Journal of AI, BigData, Computational and Management Studies, 3(1), 99-108. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I1P111
[37] Tekale, K. M., & Rahul, N. (2022). AI and Predictive Analytics in Underwriting, 2022 Advancements in Machine Learning for Loss Prediction and Customer Segmentation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 95-113. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P111
[38] Rusum, G. P., & Anasuri, S. (2023). Composable Enterprise Architecture: A New Paradigm for Modular Software Design. International Journal of Emerging Research in Engineering and Technology, 4(1), 99-111. https://doi.org/10.63282/3050-922X.IJERET-V4I1P111
[39] Pappula, K. K. (2023). Reinforcement Learning for Intelligent Batching in Production Pipelines. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 76-86. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P109
[40] Anasuri, S. (2023). Secure Software Supply Chains in Open-Source Ecosystems. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 62-74. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P108
[41] Pedda Muntala, P. S. R., & Karri, N. (2023). Leveraging Oracle Digital Assistant (ODA) to Automate ERP Transactions and Improve User Productivity. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 97-104. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P111
[42] Rahul, N. (2023). Transforming Underwriting with AI: Evolving Risk Assessment and Policy Pricing in P&C Insurance. International Journal of AI, BigData, Computational and Management Studies, 4(3), 92-101. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P110
[43] Enjam, G. R. (2023). Modernizing Legacy Insurance Systems with Microservices on Guidewire Cloud Platform. International Journal of Emerging Research in Engineering and Technology, 4(4), 90-100. https://doi.org/10.63282/3050-922X.IJERET-V4I4P109
[44] Tekale, K. M., Enjam, G. R., & Rahul, N. (2023). AI Risk Coverage: Designing New Products to Cover Liability from AI Model Failures or Biased Algorithmic Decisions. International Journal of AI, BigData, Computational and Management Studies, 4(1), 137-146. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I1P114
[45] Karri, N., Jangam, S. K., & Pedda Muntala, P. S. R. (2023). AI-Driven Indexing Strategies. International Journal of AI, BigData, Computational and Management Studies, 4(2), 111-119. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I2P112
[46] Rusum, G. P., & Pappula, K. K. (2024). Platform Engineering: Empowering Developers with Internal Developer Platforms (IDPs). International Journal of AI, BigData, Computational and Management Studies, 5(1), 89-101. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P110
[47] Gowtham Reddy Enjam, Sandeep Channapura Chandragowda, "Decentralized Insured Identity Verification in Cloud Platform using Blockchain-Backed Digital IDs and Biometric Fusion" International Journal of Multidisciplinary on Science and Management, Vol. 1, No. 2, pp. 75-86, 2024.
[48] Pappula, K. K., & Anasuri, S. (2024). Deep Learning for Industrial Barcode Recognition at High Throughput. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 79-91. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P108
[49] Rahul, N. (2024). Improving Policy Integrity with AI: Detecting Fraud in Policy Issuance and Claims. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 117-129. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P111
[50] Pedda Muntala, P. S. R., & Karri, N. (2024). Evaluating the ROI of Embedded AI Capabilities in Oracle Fusion ERP. International Journal of AI, BigData, Computational and Management Studies, 5(1), 114-126. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P112
[51] Anasuri, S., & Pappula, K. K. (2024). Human-AI Co-Creation Systems in Design and Art. International Journal of AI, BigData, Computational and Management Studies, 5(1), 102-113. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P111
[52] Karri, N. (2024). Real-Time Performance Monitoring with AI. International Journal of Emerging Trends in Computer Science and Information Technology, 5(1), 102-111. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I1P111
[53] Tekale, K. M. (2024). AI Governance in Underwriting and Claims: Responding to 2024 Regulations on Generative AI, Bias Detection, and Explainability in Insurance Decisioning. International Journal of AI, BigData, Computational and Management Studies, 5(1), 159-166. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P116
[54] Pappula, K. K., & Rusum, G. P. (2020). Custom CAD Plugin Architecture for Enforcing Industry-Specific Design Standards. International Journal of AI, BigData, Computational and Management Studies, 1(4), 19-28. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I4P103
[55] Rahul, N. (2020). Vehicle and Property Loss Assessment with AI: Automating Damage Estimations in Claims. International Journal of Emerging Research in Engineering and Technology, 1(4), 38-46. https://doi.org/10.63282/3050-922X.IJERET-V1I4P105
[56] Enjam, G. R., & Tekale, K. M. (2020). Transitioning from Monolith to Microservices in Policy Administration. International Journal of Emerging Research in Engineering and Technology, 1(3), 45-52. https://doi.org/10.63282/3050-922X.IJERETV1I3P106
[57] Pappula, K. K., & Rusum, G. P. (2021). Designing Developer-Centric Internal APIs for Rapid Full-Stack Development. International Journal of AI, BigData, Computational and Management Studies, 2(4), 80-88. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I4P108
[58] Pedda Muntala, P. S. R. (2021). Integrating AI with Oracle Fusion ERP for Autonomous Financial Close. International Journal of AI, BigData, Computational and Management Studies, 2(2), 76-86. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I2P109
[59] Rahul, N. (2021). AI-Enhanced API Integrations: Advancing Guidewire Ecosystems with Real-Time Data. International Journal of Emerging Research in Engineering and Technology, 2(1), 57-66. https://doi.org/10.63282/3050-922X.IJERET-V2I1P107
[60] Enjam, G. R., & Chandragowda, S. C. (2021). RESTful API Design for Modular Insurance Platforms. International Journal of Emerging Research in Engineering and Technology, 2(3), 71-78. https://doi.org/10.63282/3050-922X.IJERET-V2I3P108
[61] Karri, N. (2021). AI-Powered Query Optimization. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 63-71. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P108
[62] Rusum, G. P., & Pappula, kiran K. . (2022). Event-Driven Architecture Patterns for Real-Time, Reactive Systems. International Journal of Emerging Research in Engineering and Technology, 3(3), 108-116. https://doi.org/10.63282/3050-922X.IJERET-V3I3P111
[63] Pappula, K. K. (2022). Containerized Zero-Downtime Deployments in Full-Stack Systems. International Journal of AI, BigData, Computational and Management Studies, 3(4), 60-69. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P107
[64] Anasuri, S. (2022). Formal Verification of Autonomous System Software. International Journal of Emerging Research in Engineering and Technology, 3(1), 95-104. https://doi.org/10.63282/3050-922X.IJERET-V3I1P110
[65] Pedda Muntala, P. S. R., & Jangam, S. K. (2022). Predictive Analytics in Oracle Fusion Cloud ERP: Leveraging Historical Data for Business Forecasting. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 86-95. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P110
[66] Rahul, N. (2022). Optimizing Rating Engines through AI and Machine Learning: Revolutionizing Pricing Precision. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), 93-101. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P110
[67] Enjam, G. R. (2022). Secure Data Masking Strategies for Cloud-Native Insurance Systems. International Journal of Emerging Trends in Computer Science and Information Technology, 3(2), 87-94. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I2P109
[68] Karri, N. (2022). Predictive Maintenance for Database Systems. International Journal of Emerging Research in Engineering and Technology, 3(1), 105-115. https://doi.org/10.63282/3050-922X.IJERET-V3I1P111
[69] Tekale, K. M. T., & Enjam, G. reddy . (2022). The Evolving Landscape of Cyber Risk Coverage in P&C Policies. International Journal of Emerging Trends in Computer Science and Information Technology, 3(3), 117-126. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I1P113
[70] Rusum, G. P. (2023). Secure Software Supply Chains: Managing Dependencies in an AI-Augmented Dev World. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(3), 85-97. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P110
[71] Pappula, K. K. (2023). Edge-Deployed Computer Vision for Real-Time Defect Detection. International Journal of AI, BigData, Computational and Management Studies, 4(3), 72-81. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I3P108
[72] Anasuri, S., Rusum, G. P., & Pappula, K. K. (2023). AI-Driven Software Design Patterns: Automation in System Architecture. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(1), 78-88. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P109
[73] Pedda Muntala, P. S. R., & Karri, N. (2023). Managing Machine Learning Lifecycle in Oracle Cloud Infrastructure for ERP-Related Use Cases. International Journal of Emerging Research in Engineering and Technology, 4(3), 87-97. https://doi.org/10.63282/3050-922X.IJERET-V4I3P110
[74] Enjam, G. R., Tekale, K. M., & Chandragowda, S. C. (2023). Zero-Downtime CI/CD Production Deployments for Insurance SaaS Using Blue/Green Deployments. International Journal of Emerging Research in Engineering and Technology, 4(3), 98-106. https://doi.org/10.63282/3050-922X.IJERET-V4I3P111
[75] Tekale , K. M. (2023). AI-Powered Claims Processing: Reducing Cycle Times and Improving Accuracy. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(2), 113-123. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P113
[76] Karri, N., & Pedda Muntala, P. S. R. (2023). Query Optimization Using Machine Learning. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 109-117. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P112
[77] Rusum, G. P. (2024). Trustworthy AI in Software Systems: From Explainability to Regulatory Compliance. International Journal of Emerging Research in Engineering and Technology, 5(1), 71-81. https://doi.org/10.63282/3050-922X.IJERET-V5I1P109
[78] Enjam, G. R. (2024). AI-Powered API Gateways for Adaptive Rate Limiting and Threat Detection. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(4), 117-129. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P112
[79] Pappula, K. K., & Rusum, G. P. (2024). AI-Assisted Address Validation Using Hybrid Rule-Based and ML Models. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(4), 91-104. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P110
[80] Rahul, N. (2024). Revolutionizing Medical Bill Reviews with AI: Enhancing Claims Processing Accuracy and Efficiency. International Journal of AI, BigData, Computational and Management Studies, 5(2), 128-140. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I2P113
[81] Partha Sarathi Reddy Pedda Muntala, "AI-Powered Expense and Procurement Automation in Oracle Fusion Cloud" International Journal of Multidisciplinary on Science and Management, Vol. 1, No. 3, pp. 62-75, 2024.
[82] Anasuri, S., & Rusum, G. P. (2024). Software Supply Chain Security: Policy, Tooling, and Real-World Incidents. International Journal of Emerging Trends in Computer Science and Information Technology, 5(3), 79-89. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I3P108
[83] Karri, N., & Jangam, S. K. (2024). Semantic Search with AI Vector Search. International Journal of AI, BigData, Computational and Management Studies, 5(2), 141-150. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I2P114
[84] Tekale, K. M., & Rahul, N. (2024). AI Bias Mitigation in Insurance Pricing and Claims Decisions. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 138-148. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P113.
