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Vinay Chandran
Partner at QuantumBlack, AI by McKinsey
Vinay Chandran is a partner based in Dubai specializing in advanced analytics-led business transformations.
As co-leader of QuantumBlack in the Middle East, he focuses on transforming business processes for clients through data and analytical techniques.
With over 15 years of experience, Vinay has served institutions across sectors like telecommunications, technology, oil and gas, metals manufacturing, and consumer retail.
His educational background includes executive programs in Machine Learning in Business from MIT Sloan, an MBA in General Management from Cornell Johnson, an MBA in Business Administration from London Business School, a Masters in Business Systems from Monash University, and a Bachelors in Engineering from RMIT University.
Vinay's professional journey includes roles at McKinsey & Company as a Principal, Associate Principal, Engagement Manager, and Associate.
He also has prior experience as a Summer Intern at Visa International, an Information Analyst at EDS Australia, and a Software Engineer at Motorola Australia.
Highlights
What is the right operating model as institutions look to scale the impact of GenAI? The choice of operating model is one of the 7 elements (strategic direction, talent strategy, operating model, data strategy, technology strategy, adoption and scaling approach, responsible AI strategy) that is important to understand define as institutions scale AI
Generally there are four archetypes that enterprises consider in these discussions:
- Highly centralized: Establishing an AI Factory (similar to the Digital Factory concept) which is responsible for developing the AI strategy for all BUs and the enterprise, building AI solutions, defining standards, creating a central data platform etc
- Centrally led and BU executed: Establishing a Center of Excellence (COE) that leads in the AI strategy development across all BUs, defining standards, creating a central data platform. The execution however is led by the BU leaders with support from the COE
- BU led and centrally supported: BUs lead in formulating how AI solutions will deliver business impact. A central team will support BUs in providing relevant platform capabilities and also specialists in product/platform teams. These are typically manifested in product & platform oriented operating models.
- Highly decentralized: BUs lead in executing AI strategies with no central team established
There isn't a "magic" operating model archetype that works. However I believe the following 3 truths hold for institutions to successfully move forward:
- Start from business strategy: Before deciding on the right operating model, it is critical to understand the business value to be created and what needs to happen to achieve that vision
- Agility: Institutions evolve their operating models as they evolve in their maturity and adoption of AI
- Get started: Get started and learn from experiences. Through these experiences, institutions are able to make practical adjustment to the operating model
A few of my colleagues (Kevin Buehler, Larry Lerner, Brian Weintraub) looked at this in further detail for banks. A link to the article is below.
A few of my colleagues (Alexander Sukharevsky, Michael Chui, Lareina Yee, Bryce Hall) just published QB's latest perspective on the State of AI in early 2024.
Several interesting insights that I reflected on: -- AI adoption has increased significantly with the increased interest of the business and broader community to GenAI applications In the GCC this is also true - have been 10s of conversations with institutions who have experimented with GenAI in several domains. In a few domains, the region is also at the forefront of exploring what is possible (Picture 1)
-- Be ever vigilant of the challenges As institutions adopt AI, we also found it critical to plan and pro-actively manage the challenges faced in capturing at scale impact. Data, Risk & Responsible AI and Operating model where the top elements. In fact as institutions adopt GenAI and start realizing impact these elements are even more important to focus on. Be ever vigilant! (Picture 2)
-- At scale impact however still remains a journey many are still working on Driving adoption and scale requires re-wiring of business domains and a human centered approach to embedding these technologies. One of the exhibits showed how senior managers and middle managers are barely using GenAI on a regular basis for work (Picture 3)
-- Timelines to implement GenAI support rapid experimentation Like all other digital technologies, experimentation and learning from these experiments is very effective. Our research found that institutions are able to implement GenAI capabilities within months. This timeframe enables experimentation, reflection and adjusting your journey foreward in Quarterly Business Reviews (Picture 4)
For further insights would encourage you to read the article: https://lnkd.in/d7SmfR58
QuantumBlack, AI by McKinsey #GenAI #AI #Rewiring #ResponsibleAI
