July 15, 2024

Association Salers

Solidarity in Success

Integrating and Optimizing for Success

6 min read

CXOToday has engaged in an exclusive interview with Sunil Senan, Senior Vice President and Business Head, Data and Analytics, Infosys 


Q1. How can organizations integrate AI-first strategies into their business models?  

Like with any strategic initiative done at an enterprise scale, integrating AI first strategies requires planning, prioritization, and developing a roadmap, delivering quick business results through targeting the low-hanging fruit and continuous monitoring to ensure alignment to value.

While the organizations already on their digital journey are in a much better position to implement AI strategies, by following a strategic approach and leveraging the unique advantages of the organization, every company, regardless of its current digital state, has the potential to successfully integrate AI and gain a competitive edge. Newer technologies like AI, particularly generative AI, demand a collaborative effort between Enterprise IT and Business teams. This synergistic approach is critical for successful AI implementation, driving real-world business results that build confidence and propel companies towards AI-first strategies and new business models (example: new AI-driven products/services such as predictive maintenance as a service, autonomous insurance claims processing, or even process reimagination and models for faster and better results).

Integrating AI-first strategies involves a synchronized shift across multiple fronts, like- educational pursuits, engaging with emerging technologies, and a vigilant awareness of ethical considerations. While companies often overlook the importance of organizational structure & culture, we insist on ambidextrous innovation when driving transformations. Most importantly, a series of micro transformations.

To measure ROI comprehensively, enterprises must capture both direct and indirect benefits of AI such as increased human bandwidth freed up by AI/gen AI.

Successful AI strategies need to be holistic, encompassing the entire business ecosystem and long-term goals. Getting enterprises ready for AI, AI-powered business transformation, and building an AI economy are the three key components of AI-first strategy integration with the Responsible by Design principle at the core.

New businesses aiming for AI-first strategies can benefit from setting up a dedicated AI Strategy and Value office. These in-house or vendor-partnered teams empower them to leverage cutting-edge AI for business transformation. By harmonizing a business-centric AI strategy with outcome-driven value creation, they can measure success through real results. This approach, aligned with a modern shareholder value framework, ensures AI integration and adoption maximizes value for all stakeholders.


Q2. What are the best methods to optimize data for AI, especially with decentralized growth? How can organizations ensure their data analytics infrastructure supports this?  

To optimize data for AI with decentralized growth, enterprises must tackle a complex set of interconnected challenges. These include developing a clear business strategy for AI adoption, organizing a fragmented data landscape through robust governance, building trust in AI systems and their control mechanisms, building, and maintaining scalable data infrastructure to handle data growth and velocity, fostering Data and AI skills within the organization, overcoming cultural resistance to AI integration and so on.

Optimizing data for AI is a game-changer that helps companies rapidly amplify human potential and uncover business value. This will be achieved by unlocking efficiencies at scale, empowering the ecosystem, and accelerating growth.  In the age of generative AI, connecting, harvesting, and correlating information from all data with privacy and security at core is one of the critical foundational needs for enterprises to get their data ready for AI. Behind the scenes of AI, there’s a critical component that often goes unnoticed: Data for AI infrastructure.

AI applications often deal with massive datasets.  The infrastructure needs to be scalable to handle these growing data volumes efficiently. It should seamlessly integrate data from various sources covering both structured and unstructured data. This allows for a more holistic view of AI analysis. To ensure agility and scalability, organizations need to consider a multi-pronged approach.

When it comes to the heavy lifting of AI, cloud-based solutions or high-performance computing clusters can provide elasticity and efficiency. Also, designing a modular AI infrastructure where components can be easily swapped or upgraded provides an advantage to integrate new AI tools and services faster as they emerge. Nearly half of the respondents in the Infosys’ Gen AI North America report cited data challenges (either privacy and security or usability or context generation) as their biggest obstacle to generative AI implementation.

By establishing a robust data infrastructure that integrates diverse data sources, prioritizes data quality and governance, and leverages AI/automation, organizations can empower their AI initiatives with a strong foundation for success.

However, this robust infrastructure can also come at a cost. With the cost of cloud infrastructure and computing for AI going up and with decentralized data growth, enterprises are struggling to rein in cloud costs. Adopting FinOps for AI/gen AI practices will help control and optimize data and cloud costs.


Q3. How can organizations remain agile amidst rapid AI advancements?  

Remaining agile during the rapid AI advancements is critical to keep up the pace with the technology. By fostering a culture of continuous learning and rapid experimentation, businesses can shift away from the traditional hierarchical model of decision-making and help companies make better decisions to stay ahead in a dynamic market. Implementing AI requires a shift in organizational mindset and culture. To cultivate an agile mindset across various aspects of the business, continuous learning and adaptability should be the primary focus. Businesses need to address both short-term needs and long-term goals simultaneously. In the short run, this means staying current with AI advancements in various business functions.  Investing in the right talent and upskilling the existing workforce is crucial for long-term agility. This ensures the organization has the skilled personnel to implement and adapt to new AI developments.

Another critical aspect of staying agile is data democratization. It breaks down data silos and encourages collaboration across departments, leading to better decision-making and innovation. A secure data collaboration framework safeguards information while fostering collaboration, breaking down silos, and fueling cross-functional data sharing. Its impact on enhanced adaptability is profound, fueling agile responses in several key ways. For example, faster decisions become the norm when employees can analyze changing market conditions and client needs directly through accessible data sets.


Q4. In what ways can businesses leverage AI and advanced analytics to enhance customer and stay competitive?  

AI and advanced analytics empower businesses to win customer loyalty and achieve a competitive edge through personalized experiences, predicting needs for proactive service, running cognitive operations for faster and more accurate resolutions, and data-driven decisions for tailored offerings. It fuels innovation of new products and services, identifies sales opportunities, optimizes pricing strategies, and manages resources so businesses can achieve a competitive advantage in the marketplace. Businesses must remain aware of customer demands and propensities to stay competitive, measuring trends from social media or customer feedback or behavior data before they become common practice and nurturing an atmosphere that motivates creativity based on these observations.

Enterprises leveraging AI and Advanced Analytics outcompete their peers and often create new industry value chains and transcend industry boundaries. For example, a large transportation service provider has been embarking on an effort to create a logistic hub that allows a consumer to bid on the available capacity to satisfy their transportation needs. For this, the company is bringing together an ecosystem of other complimentary transportation providers like the 3PL players and also competitors where their networks are lacking. By aggregating the intelligence on-demand picture, they can help the players in the ecosystem respond to opportunities better.


Q5. Given the rapid expansion of data, how can organizations benefit from a “Responsible by Design” approach?  

AI initiatives unlock significant value – increased productivity, customer satisfaction, growth, profitability, and innovation. However, responsible AI practices are crucial to ensure ethical compliance, mitigate risks, maintain trust, and safeguard privacy and security. By adopting a scalable data & AI governance approach, organizations can become sustainable AI-first businesses. Enterprises with highly satisfactory AI outcomes consistently demonstrate trustworthy, ethical, and responsible data and AI practices.

Models are becoming increasingly complex, particularly gen AI models, making data governance essential for their operation.  Ensuring training data is unbiased, controlled, and ethically sourced helps produce model outputs that accurately depict the world and minimize societal risks.

The current landscape of AI development – with diverse creators, technologies, and processes – necessitates clear standards and accountability frameworks, especially as regulations/compliance needs are still evolving. Until AI models consistently deliver trustworthy and reliable outputs, robust monitoring with defined KPIs and metrics is crucial.

Enterprises can ensure Responsible by Design in their data and AI practices by prioritizing these core principles: transparency and fairness for consumers, high-quality data (relevant, complete, and accurate), and a human-in-the-loop approach with oversight and intervention capabilities. Additionally, embedding Privacy-by-Design and Security-by-Design principles protects data, AI, and systems, while considering the sustainability of AI solutions and understanding both front-end and back-end processes promotes responsible development throughout.


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