Since generative AI continues to evolve unabated, professionals are faced with the challenge of adapting their skill sets to stay relevant. Numan Karim of DuxData explores the strategies and skills necessary for professionals to remain competitive and effectively integrate AI into various industries and organisational frameworks.
Karim believes that to remain relevant in the era of generative AI, professionals must prioritise adaptability and continuous learning.
“Keeping up with the latest developments is becoming a full-time job on its own,” said Karim.
Professionals should stay on top of trends, embrace change, and build an intuition for valuable data science content.
“AI is rapidly changing the way we think about generating ROI from data science,” Karim explained.
“We definitely should be excited about the technical possibilities of AI but also stay humble about its limitations and risks,” he added. Key concerns such as hallucination, bias, and quality of outputs must be considered when developing and deploying AI applications.
Karim identified this need and started DuxData, a pioneering course designed to equip data professionals with the necessary leadership, strategy, and communication skills to navigate the complexities of modern data science and AI integration within organisations.
At the heart of DuxData’s curriculum lies the recognition that domain knowledge, communication, and leadership skills are just as crucial as technical proficiency in the data science and AI field. “The mainstream nature of AI means that it’s never been more important for data scientists to have strong business acumen,” Karim emphasised.
The Importance of Soft Skills
Karim highlighted the importance of leadership, communication, influence, strategy, and continuous learning, which have always been the cornerstone for good data professionals.
According to Karim, “These skills are arguably the most important, yet often less emphasised. Change management, data literacy, and culture are widely regarded as the biggest roadblocks to successful data science implementations.”
The modern data scientist or analyst is uniquely positioned to employ an entrepreneurial spirit in their workplace. With the emergence of generative AI, there’s a growing need for data professionals to bridge the gap between business and data science. “The ability to weave technical elements into business operations has been a longstanding challenge for the field,” Karim noted.
Bridging the Gap Between AI and Business Objectives
A curriculum that addresses the critical need to bridge the gap between generative AI advancements and strategic business objectives is essential.
“Strategy and leadership skills are just as crucial as technical proficiency in the data science and AI field. Soft skills are essential now to effectively identify, execute, and embed data products within an organisation,” Karim said.
DuxData, for instance, distinguishes itself with a curriculum that emphasises the integration of data science and AI with business acumen. The topics covered include Data Science Product Development, AI Transformation in Industry, Monetizing Data Science Initiatives, Organisational Data Maturity, and more.
“The rise of generative AI doesn’t change these fundamentals,” Karim said, adding that data professionals should still focus on creating business value through data and AI products embedded into business operations.
Preparing for Generative AI Integration
Professionals need to understand AI’s role in the workplace and frame AI as an extension to current business workflows, not a replacement.
“Leaders must embrace ideas that once sounded ridiculous, products that were previously impossible, and workflows that look very different from the legacy ways of working,” Karim quipped.
Achieving significant productivity and ROI gains will require working in entirely new ways.
Early wins with LLMs can be deceptive. “Getting to 80% iseasy, but getting to the remaining 20% that actually matters is much of the work,” Karim shared. Until a culture of experimentation and exploration takes hold, data and AI cannot deliver their full potential. Intrapreneurial data scientists, who are confident and creative with their technical competencies, will play a crucial role in this transformation.
The Role of Leadership and Strategy
Building skills to leverage LLMs to boost productivity is important, but it’s not enough. “There is a higher standard for technical roles to understand the inherent nuts and bolts of the models being leveraged, their limitations and biases, and to envision a future business process transformed by AI,” Karim asserted.
Leadership, communication, and strategy are vital in navigating the landscape shaped by generative AI. “Missing one or more of these competencies is largely why most ML projects fail to generate any ROI,” he warned.
Data professionals should be comfortable leading from any position in the organisation, thinking critically about what is truly needed and not just what people ask for.
Discernment in AI Applications
Generative AI is extremely shiny right now, reminiscent of Big Data in the early 2010s and ML in the mid-late 2010s. Karim cautions that “the biggest challenge facing data teams today is convincing business leaders that GenAI does not need to be integrated into every process in the organisation”.
Data professionals must ask practical questions to ensure AI solutions are appropriate and add genuine value.
Success in the data profession hinges on delivering what is truly needed, not just what the loudest voices demand. “Help stakeholders, customers, and leaders articulate their needs,” Karim advised. Building domain knowledge, understanding current value creation, and identifying areas for AI to enhance value are more critical than ever.
