Welcome to The GenAI CoE!!
An example GenAI CoE Org Structure
Generative AI Center of Excellence (CoE) Organization Structure:
Executive Sponsors- Board Members & CEO:
Provide strategic guidance for Generative AI initiatives. Align Generative AI projects with overall business objectives. Ensure sufficient budget allocation and resource support.
Key Performance Indicators (KPIs): ·Successful implementation of Generative AI projects. ·Increased business efficiency and innovation due to Generative AI adoption. ·Regular progress reviews and feedback from business units.
CoE Director:
Develop and execute the Generative AI roadmap. ·Oversee project management and resource allocation. ·Foster collaboration with internal and external stakeholders.
Key Performance Indicators (KPIs): ·Timely delivery of Generative AI projects. ·High team collaboration and satisfaction. ·Effective communication and alignment with business units.
Data Science Leader:
Lead the research and development of Generative AI models, foundational models to applications: training data optimization and preprocessing, curation, distillation, contextualization, debiasing. ·Ensure data quality and integrity for training models. ·Stay updated on the latest advancements in Generative AI.
Key Performance Indicators (KPIs): ·Successful deployment of accurate and efficient Generative AI models. ·Continuous improvement of model performance. ·Knowledge sharing within the team and cross-functional collaboration.
Engineering Director:
Develop and maintain the technology and algorithmic infrastructure for Generative AI. ·Implement scalable and reliable solutions. Lead prompt engineering teams and solution architect teams. Deploy talents for domain expertise and business process/ function knowledge for solutions needed by specific business and functional units ·Collaborate with data science and IT teams for seamless integration.
Key Performance Indicators (KPIs): ·High system reliability and performance. ·Efficient integration of Generative AI solutions into existing systems. ·Quick resolution of technical issues and minimal downtime.
Legal & GRC Director:
Develop and enforce ethical guidelines for Generative AI use. ·Ensure compliance with data protection and privacy regulations. ·Conduct regular audits to assess adherence to ethical standards.
Key Performance Indicators (KPIs): ·No ethical violations or privacy breaches reported. ·Successful completion of compliance audits. ·Training and awareness programs for team members.
Talent/ Skills/ Training Leader:
Identify and prioritize business opportunities for Generative AI. ·Assess the impact of Generative AI on business processes. ·Provide insights into potential ROI and benefits.
Key Performance Indicators (KPIs): ·Successful integration of Generative AI into key business processes. ·Positive feedback from business units on the impact of Generative AI. ·Regular reporting on the ROI and business value generated.
Partner Ecosystem Director:
Build, grow, sustain strong technology and talent supply pipelines from tech and talent partner ecosystems, start-up's, TSPs, IT service providers, universities
Key Performance Indicators (KPIs): ·Successful infusion of Generative AI technologies, innovations and talent from partner ecosystems into the enterprise CoE ·Positive feedback from business units on the impact of Generative AI. ·Regular reporting on the ROI and business value generated through partner networks
This organization structure ensures a holistic approach to Generative AI implementation, covering technical, ethical, and business aspects. The defined KRAs and KPIs provide clear performance metrics for each role, fostering accountability and successful project outcomes.
Platformization to Quantization of Business
Digital is done. What's next?
The pivot from the digital red ocean to the early-mover blue ocean, is at Business Platformization. Changing every business from its hard-coded operating models and value propositions to 1- Mixed digital, 2-Digital-first, 3- Digital platforms (as "Services as Software" businesses), is the 123 step platformization.
Platformization then opens up opportunities for legofication- DIY hyper-personalizable business services on intelligent, generative, agentic business platforms. Here, the customer users have the freedom to play and create their own products, within the regulatory rule-bases and frameworks of the industry and the region.
These legolands versions of digital banks to hybrid (man-machine combined) digital health-centers to digital hybrid retails, then take businesses and customers to the metaverses where the customers and systems' agentic digital twins work with their digital-twin equivalents in terms of employees (human + digital twin agent service providers) in a bank, hospital, educational institute, mobility platform.
Ultimately, the most critical levers of all these algorithmic operating models get into quantized business mode, due to the advancements in quantum technologies making current-state data and transactional security systems irrelevant.
All these may come, way faster than we think....
Technology Opportunity Window
From on-prem to multi-cloud to Q-poly clouds...
The AI infrastructure opportunity window is in a state of contiunous high-speed growth and disruption. In current scheme of things, especially with Google, Microsoft, AWS, IBM all joining the Quantum Cloud race with practical testable proofpoints and applications in some cases. The service providers are as usual trying to play the catch-up game.
Quantum is NOT optional
It's coming, and soon as it disrupts the security algorithms on cloud, the era of post-quantum cryptography will dawn upon us.