Six CIOs/CTOs/CDOs from India, Sri Lanka, Uganda, Nigeria and Ecuador delve into building the teams for Gen AI:
The race to harness the power of AI-ML is on, and a key battleground is talent. Companies are strategically building internal AI-ML competencies through targeted hiring, upskilling, and team expansion. Here’s a look at how leading organizations are approaching this critical challenge.
Investing in In-House Expertise
Bhagvan Kommadi, CIO of Capri Global Capital, emphasizes their focus on developing in-house AI-ML competencies through strategic hiring. Over the past year, they’ve been building expertise in areas like AI-ML model development, predictive analytics, AI-enhanced cybersecurity, AI-driven regulatory compliance, and AI-optimized customer experience.
Capri Global Capital has launched two notable AI-driven products: Data Genie, a tool allowing employees to query data through a simple interface for real-time insights, and Capri AI-Dost, a multilingual AI chatbot available in 18 Indian languages, offering 24/7 loan-related assistance.
Initially focused on data engineering and analytics, Capri Global Capital has significantly expanded its capabilities by adding a dedicated Data Science and AI-ML team. This team now includes experts in natural language processing (NLP), deep learning, and AI ethics – skills critical for developing advanced customer interaction systems, enhancing model precision, and ensuring ethical AI practices. This commitment is further solidified by the inauguration of a state-of-the-art Technology Centre in Noida.
Scaling Up and Focusing on Core Technologies
Carlos Cordova, Chief Digital and Data Officer at a leading insurance company, highlights the rapid growth of their AI-ML team, doubling in size over the last 12 months. They’ve focused on hiring individuals with expertise in Azure and Python, their core technologies for developing and deploying AI-ML solutions. Additionally, they’ve sought professionals with experience in data fabric and data governance to manage and streamline data processes, in light of privacy and data protection.
Cordova notes the challenges of securing resources for this expansion, emphasizing the importance of demonstrating early value and impact to justify further investment. By showcasing tangible results, they’ve been able to attract more resources and continue growing their AI-ML capabilities.
Strategic Expansion and Skill Acquisition
Saranga Wijayarathne, Chief Digital Innovation Officer at Softlogic Life, Sri Lanka, details the strategic growth of their AI-ML team, focusing on both new skill acquisition and strengthening core expertise.
New skill acquisition includes generative AI and LLMs for tools like chatbots and co-pilot agents, AI infrastructure and MLOps for optimizing scalable AI systems and cloud services like Azure OpenAI, and AI ethics and governance to ensure responsible AI use.
Strengthened core expertise includes enhanced data engineering and analytics for preprocessing and feature engineering, computer vision for applications like inspection and fraud detection, and AI personalization for improved recommendation engines.
This strategic growth positions Softlogic Life at the forefront of AI innovation, ensuring their solutions align with technological advancements and deliver maximum business value.
Building the AI Dream Team
These examples demonstrate a common theme: building a successful AI-ML capability requires a strategic approach to talent acquisition and development. Whether it’s focusing on in-house expertise, scaling up existing teams, or acquiring new skills, companies are recognizing that their AI strategy is only as good as the team behind it. By investing in the right talent, organizations are laying the foundation for AI-driven innovation and success.
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