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Two cornerstones for success with AI maturity
Two cornerstones for success with AI maturity

September 21, 2022

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With the advancement in artificial intelligence (AI), machines have become smarter with their human-like capabilities. From personalized interactions to object identification, they learn process and personal tasks, convert human physiology and behavioural patterns into scientific algorithms to assist human decisions, and churn large data to offer required insights. The result: it has created a favourable impact across industries - including cost leadership, quicker turnaround time, enhanced customer experience, differentiated product design, efficient supply chain, improved sales and marketing effectiveness, and insightful human resources management.

While AI and data are certainly driving transformation, AI adoption is a journey and never in a final state. Hence the need to measure AI maturity - a representation of the current state and incremental advancement in AI capabilities towards achieving defined business objectives.

Focus on strategy and execution

An organization’s AI maturity index must focus on two equally important dimensions - setting up strategic goals and ensuring execution excellence.

Strategic maturity: A trustworthy AI strategy is foundational to sustainable AI adoption, which is reliable, safe, secure, and built on the following:

  • Vision and inspiration: Aims to achieve business goals for quicker turnaround time, customer centricity, cost leadership, competitive edge, and unique industry positioning.
  • Value and impact: Focuses on value for the end users.
  • Culture: Encourages ideation, experimentation, and innovation as a part of day-to-day operations.
  • AI organization: Transforms talent, reinforces change management, enhances sustainability of initiatives, implements robust processes for higher success rate, and ensures governance, ethics and explainability of AI.
  • Human reinforcement: Sensitizes how AI adoption empowers humans to produce better outcomes across value chains.
  • Risk framework: Establishes a risk scoring mechanism to ensure responsible and reliable AI systems that consider moral and ethical norms.

Execution excellence: With the strategy goals in place, enterprises need to focus next on execution excellence. For this, they need to consider the following:

  • Data ecosystem: Conceptualize and define future information architecture which the AI ecosystem will leverage.
  • Technology landscape: Gain know-how of AI technologies and their maturity.
  • Build Vs Buy: Ensure the right balance in adopting off-the-shelf products or platforms and Custom AI which is crucial to uniquely position the organization’s AI maturity, measuring time to market and success rate along the way.
  • Infrastructure integration: Implement and integrate last-mile infrastructure, and low-code platforms to connect AI with business.
  • Continuous improvement: Ensure uplifting accuracy benchmarks - the most vital element of delivery excellence - with right governance, monitoring, data acquisition and AI model refresh rates.

Scaling with AI maturity in the future

To achieve higher AI maturity, enterprises’ entire ecosystem comprising people, culture and partners play a critical role. This journey involves ideation, planning, assessment, POT, pilot, production at scale and agility. The success quotient: a robust AI strategy framework for significant impact on top and bottom lines.

Furthermore, adopting AI is a structural change beyond a technology consideration. It calls for strategically designed AI adoption frameworks to scale with maturity. It helps achieve people-centric systems which are self-preserved and resilient. In the broader context of organizational initiatives, the AI maturity index assists to measure accomplishment and be retrospective.

About the author:

Arvind is an experienced strategist and specialises in transformational change. Arvind is passionate about technology and its ability to help enterprises achieve business success. His expertise and experience primarily are in leading edge technology service line Incubation, nurturing and scaling. In current role, Arvind leads services focuses on Artificial Intelligence (AI) as part of EGG-Architecture & Technology. Arvind is Computer Science Graduate from Government College of Engineering Aurangabad and Executive MBA from SIBM Pune (Gold Medallist).

LinkedIn: https://www.linkedin.com/mwlite/in/arvind-sangvikar-2b832416


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