Council Post: Moving From A Contributor To An AI Leader

Moving From A Contributor To An AI Leader

Once part of popular fiction and television shows, artificial intelligence has moved from the dominion of fantasy and science fiction to the reality of everyday life. AI adoption has been the fastest in enterprises and is poised to bring in digital disruption. For years, enterprises have been actively working towards becoming leaders. But the journey from contributor to leader is difficult and troubled as the AI landscape is constantly changing, and enterprises need to keep pace with this shift.  

The need for effective and efficient decision-making is vital at all levels — strategic, operational, and tactical — so realising a higher ROI becomes a seamless journey from planning to execution. Furthermore, with the ever-evolving technology landscape, storage and computing have helped AI professionals mine big data more quickly, focusing on end-to-end automation. This leads the companies to rethink their AI ecosystem, which could evolve from a service-driven environment to an AI-enabled product environment.

But to emerge as a leader in the ecosystem, enterprises need to transform their workforce or partner with the right AI service provider to create an AGILE workforce. 

The essential components of this framework need to have an integrated view of —

  1. Foundation Layer: Every organisation wants to transform its decision-making process through data and insights but lacks the necessary data architecture. The majority of enterprises have started investing heavily in data transformation projects but lack the unification strategy of aligning their enterprise vision with their data and use-cases. The data layer is unique to an enterprise and cannot have a one size fits all approach. The CXO (CEO, COO, CTO, CIO, CCO, CMO, CDO, CSO, etc.) layer needs to be integrated to drive the enterprise data strategy using a unification lens. 
  2. Experimentation Layer: Problem-solving has evolved over the years considering the business context, math and technology application. The need to develop the best solution (accurate, efficient, and cost-effective) should be driven by investing in cross-functional teams who could work in AGILE setups to come up with quick solutions. Enterprises should be willing to take more risks and endorse a FAIL FAST approach to keep up their pace in their race to innovate. 
  3. Production Layer: Unless a solution is scalable, enterprises will struggle to enable AI solutions as a part of their decision-making process. The need for reusable code architecture along with the end to end automation could help achieve a desirable solution. Technology is going to play a pivotal role in transforming it into a “ModelOps” architecture. An important focus of ModelOps should be to automate the deployment, monitoring, improvement and governance of these AI models running 24/7 within the enterprise. As enterprises become mature, these AI applications will form the bedrock of decision making in various processes.
  4. Presentation Layer: Visual storytelling along with effective articulation can be a very good strategy in disseminating insights and actionable at various levels. The business storytelling needs to vary from an executive to a tactical level, and hence the narrative and the level of detail will vary depending on the audience. This layer becomes the most important driver in adopting the AI-led decision-making and hence needs to be coherent with all the other layers for an effective rollout.

As a company evolves from a service-led organisation to one with AI-enabled product offerings, change management will become its most important driver in reaping benefits. Companies need to create an ecosystem or work with a partner to nurture their workforce on their way to becoming AI leaders. The companies need to be:

  • Agile: Business leaders need to be agile to keep up with change and continue to make critical decisions using the newly available technologies. They must envision a long-term plan but not set it in stone. Business leaders should be agile visionaries who build strategies and roadmaps that adapt to rapid changes.
  • Learn to Unlearn: AI leaders must let go of preconceived notions and seek knowledge in new areas. Creativity along with iterative development can help in innovation.
  • Being Disruptive: The old way of doing things must give way to new – sometimes risky approaches. Willing to take risks and create a culture of outside-of-the-box thinking enabling change management across all layers of leadership.
  • Manage Knowledge – Enterprises should invest in the environment in which knowledge can be created, discovered, shared, transferred, adopted and applied. An enterprise-level knowledge hub could enable the company to move faster rather than being disconnected at different silos. Enabling a culture of sharing and learning from others’ experiences could help in trust and better collaboration.

As the future of modern technology, AI has become more than just a single, independent technology. It is making decisions smarter, data more valuable, and tools more efficient. AI is going to create disruption and new ways of working, as well as facilitate digital transformation. Hence, the need of the hour is to embrace AI and witness the acceleration, innovation, and transformation that it will bring to every walk of life. 

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.

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Biswanath Choudhary
Biswanath is the Global Head of Operations for Evalueserve Analytics LOB, supporting multiple clients across Analytics Advisory, Data Solutions, Advanced Analytics, and Last Mile Solutions. Before Evalueserve, he had worked with Target and Hewlett-Packard in various leadership capacities.

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