Integrate and optimize for success

AI For Business

CXO Tomday In an exclusive interview Sunil Senan, SVP and Business Head, Data and Analytics, Infosys

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

Like any enterprise-wide strategic initiative, integrating an AI-first strategy requires planning, prioritization, and roadmapping, targeting low-hanging fruit to deliver quick business results, and ongoing monitoring to ensure alignment with value.

Organizations that have already started on their digitalization journey are in a much better position to implement an AI strategy, but by following a strategic approach and leveraging their unique advantages, all businesses can successfully integrate AI and gain competitive advantage, regardless of their current digitalization status. AI, especially new technologies such as generative AI, requires collaboration between enterprise IT and business teams. This synergistic approach is critical to successful AI implementation, driving real business outcomes and building confidence, propelling companies toward AI-first strategies and new business models (e.g., predictive maintenance as a service, automated insurance claims processing, and even rethinking and modeling processes to deliver faster and better results).

Integrating an AI-first strategy involves synchronized shifts across multiple disciplines, including the pursuit of education, engagement with emerging technologies, and careful awareness of ethical considerations. While companies often underestimate the importance of organizational structure and culture, we emphasize ambidextrous innovation in driving change. Most importantly, it's a series of micro-transformations.

To comprehensively measure ROI, companies need to understand both the direct and indirect benefits of AI, such as the increased human bandwidth that AI/gen AI frees up.

A successful AI strategy needs to be holistic, encompassing the entire business ecosystem and long-term goals. AI-ready enterprises, AI-enabled business transformation, and building an AI economy are three key components of an integrated AI-first strategy with the principle of Responsible by Design at its core.

New businesses aiming for an AI-first strategy can benefit from establishing a dedicated AI Strategy and Value Office. These in-house or vendor-partnered teams leverage cutting-edge AI to drive business transformation. By aligning a business-centric AI strategy with outcome-driven value creation, success can be measured through real results. This approach is aligned with modern shareholder value frameworks, ensuring that AI integration and adoption maximizes value for all stakeholders.

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

Optimizing data for AI to keep pace with distributed growth requires enterprises to address complex interrelated challenges, including developing a clear business strategy for AI adoption, organizing fragmented data environments with robust governance, building trust in AI systems and their control mechanisms, creating and maintaining a scalable data infrastructure to keep up with data growth and velocity, cultivating data and AI skills within the organization, and overcoming cultural resistance to AI integration.

Optimizing data for AI is a milestone that helps enterprises rapidly expand human potential and unlock business value. This is achieved through massive efficiency, stronger ecosystems, and accelerated growth. In the era of generative AI, connecting, collecting, and correlating information from all data with privacy and security at the core is one of the key foundational needs for enterprises to prepare their data for AI. Behind the scenes of AI, there is a key component that is often overlooked: data for AI infrastructure.

AI applications often deal with huge datasets. The infrastructure must be scalable to efficiently handle the growing volume of data. Data from various sources, covering both structured and unstructured data, must be seamlessly integrated to provide a more holistic view of AI analytics. To ensure agility and scalability, organizations need to consider a multi-pronged approach.

When it comes to doing the heavy lifting of AI, cloud-based solutions and high-performance computing clusters bring flexibility and efficiency. Additionally, designing a modular AI infrastructure where components can be easily replaced or upgraded has the advantage of allowing for faster integration of new AI tools and services as they emerge. Nearly half of the respondents in Infosys' Gen AI North America report cited data challenges (privacy and security, or usability and context generation) as the 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 power their AI initiatives and lay a strong foundation for success.

However, this robust infrastructure can come at a cost. As cloud infrastructure and computing costs for AI rise and distributed data increases, enterprises struggle to contain cloud costs. Adopting FinOps for your AI/gen AI practice can help you control and optimize your data and cloud costs.

Q3. How can organizations stay agile as AI advances rapidly?

Staying agile amid rapid advances in AI is critical to keep up with the pace of technology. Fostering a culture of continuous learning and rapid experimentation can help companies move away from traditional hierarchical decision-making models and help companies make better decisions to gain an advantage in a dynamic marketplace. Implementing AI requires a shift in organizational mindset and culture. To foster an agile mindset in different aspects of the business, continuous learning and adaptability should be a primary focus. Companies need to simultaneously address both short-term needs and long-term goals. In the short term, this means keeping up with AI advancements across different business functions. Investing in the right talent and upskilling existing employees is critical for long-term agility. This ensures that organizations have skilled personnel to implement and adapt to new AI developments.

Another key aspect of staying agile is the democratization of data, which breaks down data silos and fosters cross-departmental collaboration to improve decision-making and innovation. A secure data collaboration framework protects information while facilitating collaboration, breaking down silos, and facilitating data sharing across departments. Its impact on increased adaptability is significant, facilitating agile responses in several key ways. For example, faster decision-making becomes the norm when employees can analyze changing market conditions and customer needs directly through accessible data sets.

Q4. How can businesses use AI and advanced analytics to empower their customers and stay competitive?

AI and advanced analytics enable businesses to earn customer loyalty and gain competitive advantage through personalized experiences, proactively predicting service needs, performing cognitive operations for faster and more accurate resolutions, and making data-driven decisions for customized service. AI and advanced analytics drive innovation for new products and services, identify sales opportunities, optimize pricing strategies, and manage resources to help businesses gain a competitive advantage in the market. To stay competitive, businesses must stay on top of customer demands and trends, gauge trends from social media and customer feedback and behavioral data to catch them before they become common practice, and foster an atmosphere that inspires creativity based on these observations.

Companies leveraging AI and advanced analytics outperform their peers and often create new industry value chains, crossing industry boundaries. For example, a large transportation service provider is embarking on an initiative to create a logistics hub where consumers can bid on available capacity to fulfill their transportation needs. To do this, the company is integrating with other complementary transportation providers, such as 3PL players, and an ecosystem of competitors that lack networks. By aggregating an on-demand intelligence picture, they can help players in the ecosystem respond appropriately to opportunities.

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

AI initiatives can generate significant value, including increased productivity, customer satisfaction, growth, profitability, and innovation. However, responsible AI practices are essential to ensure ethical compliance, mitigate risk, maintain trust, and protect privacy and security. Adopting a scalable data and AI governance approach can help organizations become sustainable AI-first businesses. Companies with satisfying AI outcomes consistently implement reliable, ethical, and responsible data and AI practices.

As models, especially generational AI models, become increasingly complex, data governance becomes essential to their operation: Ensuring training data is unbiased, controlled, and ethically obtained helps produce model outputs that accurately depict the world and minimize societal risks.

In today's AI development environment with diverse creators, technologies, and processes, clear standards and accountability frameworks are necessary, especially as regulatory and compliance needs are still evolving. Robust monitoring with defined KPIs and metrics is essential until AI models consistently deliver reliable and trustworthy outputs.

Companies can achieve responsible design in their data and AI practices by prioritizing the foundational principles of transparency and fairness to consumers, high-quality data (relevance, completeness, accuracy), and a human-in-the-loop approach with oversight and intervention capabilities. Additionally, incorporating privacy-by-design and security-by-design principles will help protect data, AI, and systems, while considering the sustainability of AI solutions and understanding both front-end and back-end processes will foster responsible development throughout.

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