“Data Silos and a Lack of Skilled Talent are Among the Biggest Challenges Organizations Face Today," Anant Adya, Executive Vice President & Service Offering Head, Infosys

One of the biggest blind spots enterprises face is overlooking the importance of getting enterprises data ready for AI, integration, and leveraging the cloud. Many organizations underestimate the complexities involved in ensuring clean, structured, and accessible data that aligns with AI objectives. The cloud plays a crucial role by providing scalable infrastructure, enabling real-time data access, and supporting seamless integration across platforms. Additionally, a lack of collaboration between business and technology teams often hinders the alignment of AI strategies with core business goals. Security and compliance gaps, especially in regulated industries, further complicate scaling AI initiatives. By utilizing the cloud alongside a robust data governance framework and fostering cross-functional collaboration, organizations can effectively overcome these barriers.

The convergence of cloud computing, artificial intelligence, and industry-specific data is transforming how businesses adapt to rapid digital changes and meet rising customer expectations. Together, these technologies enable hyper-personalization, operational efficiency, and predictive decision-making – tools that are essential in today's environment.

In healthcare, cloud-hosted AI insights are creating personalized treatment plans that improve patient outcomes. In retail, AI-powered dynamic pricing helps businesses respond to shifting market demands. Across industries, the ability to analyze and act on vast amounts of data in real time has become a critical advantage.

As data volumes grow and competition intensifies, the synergy between cloud, AI, and data equips organizations to deliver actionable insights at unparalleled speed and scale. 

Edge computing is becoming essential for real-time data processing as businesses increasingly adopt IoT and AI technologies. By processing data closer to its source, it minimizes latency and supports faster, more agile decision-making. Industries like manufacturing and transportation benefit from this by enabling predictive maintenance and optimizing logistics almost instantly. By integrating edge computing with cloud systems through hybrid architectures, businesses can balance local processing with centralized insights, driving adaptive and informed operations. 

Generative AI is driving meaningful change across industries with practical applications. In marketing, it crafts personalized email campaigns and compelling ad copy. In software development, it accelerates coding with precise AI-generated snippets. Manufacturing benefits from AI-driven prototypes, speeding up product design and reducing time-to-market. Retail uses AI-powered chatbots for tailored shopping recommendations, while healthcare applies it to create patient-specific treatment plans and summarize medical reports. In finance, generative AI enhances fraud detection and risk assessment, helping institutions make accurate, timely decisions. In transportation, it optimizes route planning and reduces fuel consumption.

By 2025, AI-first cloud architectures will embed artificial intelligence at every level, transforming how organizations handle data, streamline operations, and deliver value. These systems will offer scalable, AI-powered services that efficiently manage complex tasks and large datasets. They will also democratize access to advanced machine learning tools, empowering businesses of all sizes, while incorporating strong governance to ensure security, compliance, and ethical AI use.

Industries like finance and healthcare, already leaders in AI adoption, will play a key role in driving these advancements. In finance, AI-first clouds will improve fraud detection, personalized banking, and real-time risk management. In healthcare, they will enhance diagnostics, predictive analytics, and patient care, while ensuring compliance with strict regulations.

Key performance indicators (KPIs) are essential for measuring the success of cloud modernization. They cover areas like operational efficiency, innovation speed, cost optimization, and enhanced customer experience, offering clear insights into the impact of modernization efforts on business growth. Metrics such as lower IT overhead, faster project delivery, improved system uptime, and increased application reliability highlight measurable returns on investment.

Customer-focused KPIs, including retention rates, Net Promoter Scores (NPS), and satisfaction levels, demonstrate how modernization can elevate user experience and build loyalty. Operational scalability – showing how systems handle growing workloads – is another critical measure of success.

To stay competitive, businesses must also prioritize adaptability as a strategic KPI. This reflects their ability to respond to changing customer needs, emerging trends, or new opportunities quickly. By integrating these KPIs into evaluation processes, organizations can create a structured framework to track the effectiveness of their cloud modernization strategies.

Data silos and a lack of skilled talent are among the biggest challenges organizations face today. Despite the potential of cloud ecosystems, many struggle to integrate legacy systems with modern platforms, causing inefficiencies and disrupting data flow. These silos not only slow decision-making but also limit innovation by restricting insights.

At the same time, the shortage of AI-literate talent is a growing obstacle. Without teams skilled in implementing and leveraging cloud and AI technologies, organizations risk falling behind in a competitive market. Addressing these issues requires a clear focus on platform interoperability for seamless system integration and investing in robust upskilling programs. These programs should equip teams with essential cloud and AI skills, enabling them to navigate complex systems, harness data effectively, and drive impactful results.

Emerging risks in the digital landscape, such as data sovereignty, regulatory fragmentation, and AI bias, are reshaping how businesses operate. With increasing data localization requirements, organizations must adapt their storage and processing strategies to comply with diverse regulations or risk legal and financial consequences. The lack of unified AI regulations globally creates further challenges, leaving businesses navigating a complex and shifting compliance landscape. Additionally, unchecked bias in AI algorithms raises ethical concerns and potential reputational damage. To address these challenges, companies need agile compliance frameworks and ethical AI practices that emphasize transparency, fairness, and accountability. 

The future of AI observability will focus on proactive monitoring, explainability, and bias detection to ensure AI operates reliably and ethically. Advanced tools will go beyond basic metrics, providing real-time insights into algorithm behavior, decision-making patterns, and anomalies. This will help businesses address issues quickly, reducing downtime and improving efficiency. 

AI governance will emphasize fairness, accountability, and transparency, embedding ethical principles into every stage of the AI lifecycle—from data collection and model training to deployment and monitoring. This approach aims to reduce risks like bias, unintended outcomes, and mistrust. 

Together, observability and governance will form the foundation for building trust in complex, automated cloud environments. By combining technical innovation with ethical oversight, businesses can ensure their AI systems are consistent, fair, and transparent.

Infosys Cobalt is a comprehensive suite of solutions designed to help enterprises seamlessly navigate their cloud and AI journeys. With a focus on scalability and regulatory compliance – key priorities in today’s business landscape – Cobalt leverages advanced digital foundations, data flows, and AI capabilities. This enables organizations to efficiently scale AI models across hybrid and multi-cloud environments while optimizing performance and costs.

The Responsible AI Suite, part of Infosys Topaz, is crafted to help businesses balance innovation with ethical considerations. It focuses on preventing bias, safeguarding data privacy, and ensuring strong returns on AI investments. Structured around the Scan, Shield, and Steer framework, the suite includes over 10 offerings to monitor, protect, and guide AI systems, fostering responsible AI adoption with robust governance and security.

By tackling the complexities of technical, policy, and governance challenges, the Responsible AI Suite supports enterprises in embedding ethical AI practices across their operations. Reflecting Infosys’ commitment to being an AI-first company, these solutions demonstrate how innovation can drive impact while upholding the highest standards of responsibility.

Infosys collaborates closely with clients in sectors like financial services, manufacturing, and healthcare to co-create cloud and AI frameworks that tackle their unique challenges. For financial services, for instance, we design platforms that enhance fraud detection, improve credit risk assessments, and elevate customer engagement through predictive analytics. 

Similarly, in manufacturing, our AI-driven solutions optimize supply chain management, provide predictive maintenance capabilities, and integrate Industry 4.0 principles for smarter factories. For the healthcare vertical, Infosys develops intelligent frameworks that improve care delivery through predictive diagnostics, operational efficiencies, and enhanced patient experiences, while adhering to stringent data privacy standards like HIPAA.

Our approach involves immersing ourselves in our clients' industries and challenges. By applying AI strategically, underpinned by Infosys Cobalt’s robust cloud capabilities, we co-create solutions that are tailored to accelerate value creation and business outcomes.

Operating globally, Infosys understands the importance of adapting to the unique regulatory and cultural landscapes of each region. For example, in Europe, where GDPR enforces strict data privacy standards, we design AI solutions with privacy-by-design principles and prioritize secure cloud storage practices. 

In North America and APAC, where rapid innovation drives digitization, we focus on hyper-scalable AI solutions tailored to industries that value speed and agility. Cultural nuances further shape our strategies, such as ensuring inclusive AI designs to support diverse user groups in multicultural regions.

This localized approach, combined with a consistent global vision, allows Infosys to deliver solutions that align with regional needs while maintaining our commitment to innovation and client success.

In 2020, we launched Infosys Cobalt with a focus on technology. Since then, Cobalt’s narrative has shifted from cost-saving and operational optimization to driving growth and transformation. Initially, we focused on migrating data centers, modernizing infrastructure, and transferring workloads. This foundation enabled us to develop industry-specific solutions, marking a major strategic shift.

We’ve introduced industry cloud offerings like Infosys Cobalt Airline Cloud, Infosys FS Cloud, and Infosys Agri Cloud. Using platforms such as Edge, Equinox, and Finacle, we drive innovation and efficiency for our clients. With Infosys Topaz, we’ve pioneered the integration of artificial intelligence (AI) and cloud computing, highlighting their interconnection.

AI-driven efficiency emphasizes the importance of migrating infrastructure, modernizing applications, and platform engineering – key areas of client investment. Business transformation, including enterprise applications and SaaS, remains vital. Security and sustainability are integral to our solutions, with security built into designs and sustainability ensuring long-term impact.

Our vision is to create a Fluid and Timeless Enterprise that maximizes AI’s value. This requires interconnected applications generating large data volumes, supported by a robust hybrid cloud across edge, core, and cloud functions. A digital, composable, and autonomous enterprise minimizes migration disruptions, allowing organizations to focus on delivering strong business and technology outcomes.

The dynamic landscape of cloud and AI calls for a workforce that blends technical skills with domain expertise. Key areas of focus include:

• AI Engineering: Developing, deploying, and scaling machine learning models within compliance-driven environments. 

• Cloud Architecture: Creating scalable, multi-cloud solutions to support AI ecosystems. 

• Data Governance: Ensuring data security, compliance, and ethical AI practices in a globally regulated world. 

•  Cross-functional Collaboration: Bridging technical and business teams to deliver AI solutions aligned with business goals. 

While progress has been made, a skills gap persists, particularly in merging technical expertise with ethical considerations in AI. Infosys is addressing this through Digital Reskilling Initiatives, equipping employees and clients with future-ready capabilities. We believe bridging this gap requires collaboration between academia, corporations, and governments, fostering the responsible adoption of cloud and AI solutions. Together, we can navigate what’s next.

Scaling responsible AI in large enterprises presents several challenges. One major issue is balancing innovation with compliance. While enterprises are eager to adopt AI across business units, doing so responsibly requires clear governance models, ethical frameworks, and compliance mechanisms aligned with local and global regulations. Another challenge is addressing bias in AI models. Enterprises rely on vast datasets from diverse geographic and cultural contexts, which can unintentionally introduce bias. Cultural transformation is also key to scaling AI. Leadership must encourage collaboration between technology and business units, boost AI literacy across the organization, and commit to ethical AI adoption. 

The Responsible AI Suite, part of Infosys Topaz, helps enterprises balance innovation with ethics, addressing issues like bias and privacy while maximizing ROI. Built around the Scan, Shield, and Steer framework, it includes over 10 offerings designed to monitor and protect AI systems while fostering responsible AI adoption, governance, ethics, and security.