CXOT today Engaged in an exclusive interview with Sunil Sanon, Senior Vice President and Business Head, Data and Analytics, Infosys

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

As with any enterprise-scale strategic initiative, AI-first strategies require planning, prioritization, and developing a roadmap to integrate, targeting low-hanging fruit and delivering immediate business results. Continuous monitoring to ensure performance and value alignment.

While organizations are already in a much better position to implement AI strategies on their digital journey, by taking a strategic approach and leveraging the organization’s unique advantages, every company, regardless of its current digital state Regardless, AI has the ability to integrate successfully. and gain a competitive edge. New technologies like AI, especially generative AI, demand collaboration between enterprise IT and business teams. This synergistic approach is critical to the successful implementation of AI, driving real-world business outcomes that build trust and drive companies toward AI-first strategies and new business models (eg: new AI driven products/services such as predictive maintenance as a service, autonomous insurance claims processing, or even reimagine and implement models for faster and better results).

Integrating AI-first strategies involves a coordinated shift on multiple fronts, such as educational attainment, engagement with emerging technologies, and a vigilant awareness of ethical considerations. While companies often overlook the importance of organizational structure and culture, we insist on extraordinary innovation when driving change. Most importantly, a series of micro changes.

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

Successful AI strategies need to encompass the entire business ecosystem and long-term goals. Preparing enterprises for AI, AI-driven business transformation, and building an AI economy are three key components of the integration of an AI-first strategy with responsible design principles at the core.

Aiming for AI-first strategies New businesses can benefit from establishing a dedicated AI strategy and value office. These in-house or vendor partnered teams empower them to leverage the latest AI for business transformation. By aligning a business-oriented AI strategy with results-based value creation, they can measure success through real results. This approach, aligned with an innovative shareholder value framework, ensures AI integration and adoption maximizes value for all stakeholders.

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

To optimize data for AI with decentralized development, enterprises must tackle a complex set of interrelated challenges. These include developing a clear business strategy for AI adoption, managing a fragmented data landscape through strong governance, building trust in AI systems and their control mechanisms, accelerating the growth and speed of data. Management includes building, and maintaining scalable data infrastructure, developing data and AI skills. Organization, overcoming cultural resistance to AI integration and similar.

Optimizing data for AI is a game changer that helps companies rapidly leverage human capabilities and unlock business value. This will be achieved by unlocking economies of scale, empowering ecosystems, and accelerating growth. In the era of creative AI, connecting to, retrieving, and generating relevant information for AI is a key fundamental requirement for enterprises, primarily with privacy and security in mind. Behind the scenes of AI, there is a critical component that often goes unnoticed: the data for the AI ​​infrastructure.

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

Cloud-based solutions or high-performance computing clusters can provide flexibility and efficiency when it comes to the heavy lifting of AI. In addition, designing a modular AI infrastructure where components can be easily replaced or upgraded provides an advantage for rapidly integrating new AI tools and services. Nearly half of respondents to Infosys’ Gen AI North America report cited data challenges (either privacy and security or usability or context creation) as their biggest obstacle to implementing generative AI.

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 solid foundation for success.

However, this robust infrastructure can come at a price. With cloud infrastructure and computing costs for AI rising and data decentralization, enterprises are struggling to rein in cloud spending. Adoption of FinOps for AI/gen AI practices will help control and optimize data and cloud costs.

Q3. How can organizations stay agile amid the rapid growth of AI?

Being agile during rapid AI development is critical to keeping pace with technology. By fostering a culture of continuous learning and rapid experimentation, businesses can move away from traditional hierarchical models of decision-making and help companies make better decisions to stay ahead in a dynamic market. Implementing AI requires a change in organizational mindset and culture. To foster an agile mindset in various aspects of the business, continuous learning and adaptation should be the primary focus. Businesses need to meet short-term needs and long-term goals simultaneously. In the short term, this means staying up-to-date with AI developments in various business functions. Investing in the right talent and improving the existing workforce is critical to long-term agility. This ensures that the organization has skilled people to implement and adapt to new AI developments.

Another important aspect of being 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 protects information while fostering collaboration, breaking down silos, and fueling cross-functional data sharing. Its impact on better adaptation is profound, fueling agile responses in several important ways. For example, faster decisions become the norm when employees can analyze changing market conditions and client needs through directly accessible data sets.

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

AI and advanced analytics enable businesses to win customer loyalty and gain a competitive edge through personalized experiences, predictive service needs proactively, driving cognitive processes for faster and more accurate decisions, and leveraging data for tailored offers. Empower through informed decisions. It promotes the innovation of new products and services, identifies sales opportunities, optimizes pricing strategies, and manages resources so that businesses can gain a competitive advantage in the marketplace. Businesses must be aware of customer demands and trends to stay competitive, measure trends from social media or customer feedback or behavioral data before they become commonplace, and encourage creativity based on these observations. Foster a doing environment.

Enterprises leveraging AI and advanced analytics compete with their peers and often create new industry value chains and cross industry boundaries. For example, a major transportation service provider is looking to create a logistics hub that allows a customer to bid on available capacity to meet their transportation needs. For this, the company is bringing in an ecosystem of other complimentary transportation providers such as 3PL players and competitors where their networks are lacking. By gathering the intelligence on-demand picture, they can help players in the ecosystem better respond to opportunities.

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

AI initiatives unlock significant value – increased productivity, customer satisfaction, growth, profitability, and innovation. However, responsible AI practices are critical to ensuring ethical compliance, mitigating risks, maintaining trust, and protecting privacy and security. By adopting scalable data and AI governance mechanisms, organizations can become sustainable AI-first businesses. Organizations with highly satisfactory AI outcomes consistently demonstrate reliable, ethical, and responsible data and AI practices.

Models are becoming increasingly complex, especially general AI models, making data governance essential to their operation. Ensuring that training data is unbiased, controlled, and ethically obtained helps produce model outputs that accurately represent the world and minimize societal risks.

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

Enterprises can ensure accountability by design in their data and AI practices by prioritizing these fundamental principles: transparency and fairness for consumers, high-quality data (relevant, complete, and accurate), and oversight. A human perspective 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 growth.

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