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The Future of Centers of Excellence (CoE) in the Era of AI: Impacts and Evolutions (Part B)

Updated: Dec 21, 2023

1. Appendix A – The Path for Establishing AI CoE

To prevent the creation of isolated AI capabilities and promote the reuse of applications and processes, fostering a high degree of reusability and providing knowledge sharing (KS) and knowledge transfer (KT) by experts, the establishment of an AI CoE as an initial step is recommended. This AI CoE will spearhead the implementation of AI solutions in collaboration with other domain- or function-specific CoEs. The following steps are essential for building an AI CoE that will support the evolution of traditional CoEs to adapt AI:


1. Appendix B – Upscaling Existing CoEs to adapt AI

The AI CoE should collaborate with other CoEs to share knowledge and best practices. This will help to ensure that the organization is able to maximize the benefits of AI across all its functions. Specific examples of KT and KS activities include:

·       Conducting joint training sessions on AI technologies and applications

·       Sharing AI case studies and success stories

·       Establishing AI knowledge repositories and communities of practice

·       Organizing AI hackathons and innovation challenges

Upskill and Reskill Employees: The AI CoE will work with the Learning & Development CoE (L&D) CoE to develop and implement training programs that will help employees to develop the skills and knowledge they need to work with AI. This will include:

·       Developing AI literacy training: This training will help employees to understand the basics of AI, including its capabilities, limitations, and ethical implications.

·       Offering AI-specific training courses: This training will cover topics such as AI development, AI deployment, and AI applications for specific business areas.

·       Creating AI mentorship programs: These programs will pair experienced AI practitioners with employees who are new to AI, providing them with guidance and support.

·       Promoting AI certifications: The AI CoE and L&D CoE can encourage employees to pursue AI certifications to demonstrate their expertise and skills.

Promote AI Innovation: The AI CoE will work with the R&D CoE to promote AI innovation within the organization. This could include:

·       Developing new AI solutions: The AI CoE and R&D CoE can collaborate on developing new AI solutions that address critical business challenges or opportunities.

·       Exploring new AI research: The AI CoE can stay up-to-date on the latest AI research and identify opportunities to apply emerging AI techniques within the organization.

·       Partnering with external AI companies: The AI CoE can partner with external AI companies to gain access to cutting-edge AI technologies and expertise.

Improve Healthcare Delivery: The AI CoE will work with the Health & Medical CoE to improve healthcare delivery through AI-powered solutions. This could include:

·       Developing AI-powered diagnostic tools: These tools can assist healthcare providers in diagnosing diseases more accurately and efficiently.

·       Developing AI-powered treatment recommendations: AI can analyze patient data and provide personalized treatment recommendations.

·       Developing AI-powered patient monitoring systems: These systems can monitor patients' vital signs and alert healthcare providers to potential health issues.

Accelerate Drug Discovery and Development: The AI CoE will work with the Drug R&D CoE to accelerate drug discovery and development through AI-powered solutions. This could include:

·       Developing AI-powered drug targets: AI can identify potential drug targets for specific diseases.

·       Developing AI-powered drug design: AI can design new drug molecules with desired properties.

·       Developing AI-powered clinical trial optimization: AI can optimize clinical trial design and patient selection.

Enhance Cybersecurity: The AI CoE will work with the Cybersecurity CoE to enhance cybersecurity through AI-powered solutions. This could include:

·       Developing AI-powered threat detection systems: These systems can detect and classify cyber threats in real time.

·       Developing AI-powered data security solutions: AI can encrypt and protect sensitive data from unauthorized access.

·       Developing AI-powered incident response solutions: AI can automate incident response processes and minimize the impact of cyberattacks.

Collaborate with the Finance CoE: The AI CoE will collaborate with the Finance CoE to identify and implement AI-powered solutions that can improve the organization's financial performance. This could include:

·       Developing AI-powered financial forecasting models: These models can predict future revenue, expenses, and cash flow.

·       Developing AI-powered risk management tools: These tools can identify and assess financial risks.

·       Developing AI-powered fraud detection systems: These systems can detect and prevent fraudulent activities.

By following this plan, an organization can effectively incorporate AI automation and robotics into its processes by building an AI CoE that collaborates with other CoEs to drive innovation and excellence across all functions.

10. Authors' Biography

Mr. Ekstein is a highly experienced professional with over 25 years in the software industry. His leadership has been pivotal in developing advanced software products and executing digital transformation programs for major corporations globally. In addition to serving as a CEO and CTO for start-ups specializing in AI and ML-based software, Mr. Ekstein is well-versed in strategic and tactical consulting, systems development, integration, program management, solution architecture, and industry best practices. His consulting services have been sought by Communication Service Providers (CSPs) across Europe, America, Australia, Asia, and Africa. Mr. Ekstein has consistently demonstrated leadership in establishing and managing Centers of Excellence (CoEs) for prominent CSPs such as BT, Vodafone, T-Mobile, and Telstra. His expertise extends to spearheading CoE initiatives for leading Hi-Tech companies like Amdocs. Mr. Ekstein holds a B.Sc in Computer Science, a Global MBA, and has pursued studies in Data Science, Machine Learning, and Deep Learning at Stanford, USA, and earning a System & Management certificate from CLC, USA. He is actively involved in professional organizations like the IEEE, Computer Society, and PMI (Project Management Institute). His active engagement in professional communities reflects his commitment to staying at the forefront of industry advancements.

11. References

·       “Strategies and Best Practices for Building High-Impact CoE”, Qbiton, Amnon Ekstein (2010).

·       “Empowering Excellence: The AI-Driven CoE Revolution”, Qbiton, Amnon Ekstein (2023).

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·       "The Role of Centers of Excellence in the Digital Age" by McKinsey & Company, Jeff Berg, Keith Gilson, and Greg Phalin, 2018, 12-15         

·       Curry et al. "A Best Practice Framework for Centres of Excellence in Big Data and Artificial Intelligence" (2021)

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·       IDC (2022). IDC Market Research: The Global Geography of AI Adoption. Authors: Ritu Jyoti, et al. Page numbers: 1-50.

·       Gartner Research (2022). 2022 Gartner Center of Excellence Benchmarking Report. Authors: Lydia Leong, et al. Page numbers: 1-42.

·       McKinsey & Company (2023). 2023 McKinsey & Company Report: AI Adoption by Region. Authors: Michael Chu, et al. Page numbers: 1-32.

·       "The Future of Centers of Excellence" by Gartner (2020)          Mark McDonald, Dean Takahashi, and Chris Zotto 10-12

·       "AI CoEs: A New Paradigm for Innovation" by Forrester (2021) Craig Hopkins, Naveen Menon, and Dipti Gupta 28-31

·       "The Impact of AI on Centers of Excellence" by Accenture (2023)       Prasad Padmanabhan, Bhaskar Mehta, and Anirban Bose 15-18

·       "AI-Powered CoEs: The Future of Organizational Excellence" by IBM (2023) Chris Kelly, Erik S. Stern, and John Van Aken, 21-24.

·       "How to Build an AI CoE that Delivers Results" by McKinsey & Company (2023) Michael Chui, Michael Osborne, and Tom Davenport, 4-7.

·       "Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence" Journal of Open Innovation: Technology, Market, and Complexity (2019), 44-56.

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·       Liao and Wang "Research on Industrial Waste Recovery Network Optimization: Opportunities Brought by Artificial Intelligence" Mathematical Problems in Engineering (2020).

·       Reim et al. "Implementation of Artificial Intelligence (AI): A Roadmap for Business Model Innovation" AI (2020), Pages 11-25.

·       Ismail and Muhammad "Artificial Intelligence Innovation Related Factors Affecting Organizational Performance" International Journal of Sustainable Construction Engineering Technology (2022).

·       Ameen, N., Sharma, G., Tarba, S., Rao, A., & Chopra, R. (2022). Toward advancing theory on creativity in marketing and artificial intelligence. Psychology and Marketing, 39(9), 1802-1825. https://doi.org/10.1002/mar.21699.

·       Chen, H., Chan-Olmsted, S., Kim, J., & Sanabria, I. (2021). Consumers’ perception on artificial intelligence applications in marketing communication. Qualitative Market Research an International Journal, 25(1), 125-142. https://doi.org/10.1108/qmr-03-2021-0040.

·       Elrod, J. and Fortenberry, J. (2017). Centers of excellence in healthcare institutions: what they are and how to assemble them. BMC Health Services Research, 17(S1). https://doi.org/10.1186/s12913-017-2340-y.

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