As per a Gartner report, over 30% of GenAI projects will be dropped by 2026 due to poor data quality, high costs or unclear business value.

Your 4-step blueprint for GenAI success

1. Assess your current data capabilities and needs

Use a data & analytics maturity model to understand your organization’s maturity level – this stock taking is crucial to the success of your data strategy.

• Are you at an aspiration stage, where you will focus on building a strong data foundation and basic analytics capabilities?
• Or have you progressed to having the capability, where you can prioritize building a data science team and advanced analytics skills?
• Or have your team reached a point of competency, where you are ready to start exploring GenAI applications?

A good data and analytics strategy starts with a clear vision. Gartner

In addition to your organization’s maturity level, evaluate your team’s skills and knowledge in AI and machine learning. Identify any skill gaps that need to be addressed through training or hiring. Consider the availability of specialized AI talent within your organization or the potential to partner with external experts. By understanding your team’s capabilities, you can create a roadmap for skill development, allowing you to focus on immediate needs while planning for future expansion plans.

2. Build a robust modern data foundation

One of the first steps in harnessing GenAI is to break down data silos within your organization. Often, crucial data is scattered across multiple platforms, making it difficult to get a unified view. Collecting, integrating and processing this data into a single, cohesive platform is essential for effective AI application.

Think of a retailer with customer data locked in outdated systems. By consolidating this data, they can unlock powerful insights and deliver personalized customer experiences.

Once integrated, it’s vital to ensure the data is clean, well-governed and secure. Poor data quality can lead to flawed AI outputs, while weak governance and security can expose your organization to risks. Building a modern data foundation with these principles in mind will lay the groundwork for reliable and impactful AI applications.

3. Make the right technology and model choices for your GenAI & data needs

Choosing the right technology and AI models is crucial for project success. Consider factors like your team’s expertise, available data and cost. If your team is comfortable with open-source models like Hugging Face or GPT, they offer flexibility but require more maintenance. Managed models like Amazon Bedrock or SageMaker are easier to implement with less upkeep but offer less customization.

Balancing these factors is key to a successful AI implementation. Opt for solutions that your team can manage effectively, that leverage your available data, and that fit within your budget. A well-thought-out choice here can save you time and money in the long run while ensuring robust performance.

Remember, AI performance is not just about speed and processing power; it is about delivering these results without ballooning costs. Proprietary systems can inflate total cost of ownership (TCO) with specialized hardware, licensing fees, and other hidden costs that make scaling more expensive in the long term.

One of the common misconceptions is that all AI workloads must run on expensive GPUs, which, while powerful, are not always the most efficient or necessary option for every type of task.

When selecting a model, evaluate accuracy, cost, latency and scalability. Balancing these factors ensures the chosen solution aligns with your team’s capabilities, available resources, and long-term goals.

The AI universe is constantly evolving, and needs solutions that are not just designed for today, but for tomorrow. Proprietary systems often lock clients into specific technologies, making it costly and difficult to upgrade as AI capabilities advance. Look for open, future-proof AI systems that adapt to new technologies and workloads. This long-term flexibility gives you the confidence that their AI investments will remain viable, no matter how your needs evolve.

4. Use tools and accelerators to advance your AI & GenAI dev lifecycle

Don’t reinvent the wheel – use available cutting-edge tools and platforms to accelerate your GenAI projects. The goal is to streamline the process from data collection to actionable insights. Utilizing accelerators and pre-built tools can help you quickly move from concept to deployment, ensuring that your AI applications deliver value rapidly and efficiently.

If you are working on refining large language models (LLMs), platforms that offer distributed training and fine-tuning can significantly reduce the time from data to insights. These tools not only speed up development but also ensure that your AI models are optimized for performance and cost.

• To optimize AI pipelines, organizations can turn, for example, to the latest Intel Xeon processors with Intel Advanced Matrix Extensions (Intel AMX), a built-in AI accelerator. As part of Intel® AI Engines, Intel AMX was designed to balance inference, the most prominent use case for a CPU in AI applications, with more capabilities for training.
• To optimize Retrieval-Augmented Generation (RAG) pipelines, Langchain is a powerful tool for orchestrating workflows across different data sources. It connects language models with databases, APIs, and cloud storage, enabling seamless chaining of tasks like data retrieval and prompt transformation. This flexibility makes it easy to build scalable RAG systems that can quickly process and augment responses with relevant information.
• For managing embeddings, Amazon Bedrock Knowledgebase can offer a managed vector store solution that integrates with various embedding models. It provides scalable, efficient storage for embeddings, allowing fast retrieval of contextually relevant information from multiple data sources.
• By combining Langchain for orchestration and Bedrock for vector storage, you can build a highly efficient, adaptable RAG pipeline that accelerates insights and enhances AI applications.

By following these steps, you will be well on your way to implementing GenAI in a way that is both strategic and sustainable, driving meaningful outcomes for your organization.

The post Defining Your GenAI Data Strategy: A Roadmap for Enterprise Data Teams appeared first on Cloud Kinetics.

Why attend?

• Networking Opportunities: Engage with fellow CIOs and industry experts to share experiences and best practices.
• Expert Insights: Gain actionable advice on navigating cloud migration and modernization challenges.
• Strategic Planning: Align your IT strategy with the latest cloud technologies to drive innovation and business outcomes.

Agenda highlights

• Benefits of AWS Migration: Explore cost savings, security, scalability, and operational efficiency. Cloud
• Modernization Strategies: Discover how to accelerate digital transformation and optimize IT infrastructure.
• Productivity with Amazon Q: Learn how to automate and streamline migration and modernization processes.
• Customer Success Story: Hear from a leading organization that has successfully leveraged AWS for business growth.

Special offers

Eligible attendees will receive exclusive offers tailored to meet their business needs, ensuring that your participation translates into actionable insights.

The post CXO Roundtable <br> Unlock Business <br> Potential: Leveraging Modernization & Gen AI appeared first on Cloud Kinetics.

According to a McKinsey evaluation, the key to GenAI success is in identifying practical applications that drive real business value rather than getting carried away by exaggerated claims.

Generative AI refers to AI systems that create new content, such as text, images, or music, based on their training data. Key to GenAI are Large Language Models (LLMs) and Foundation Models (FMs), designed to produce human-like text from extensive datasets. Central to GenAI are Large Language Models (LLMs) and Foundation Models (FMs),  which are designed to understand and produce human-like text. These models, trained on vast datasets, enable the generation of coherent and contextually relevant content.

Top 4 business benefits of GenAI applications for enterprises

Gartner forecasts that by 2026, over 80% of enterprises will integrate GenAI APIs or models into their operations, a dramatic increase from less than 5% in 2023.

Business benefits could include:

Enhance decision making

• Scenario planning
• Simulation & hypothesis testing
• Causal inference
• Decision support systems

Optimize business processes

• Document processing
• Data augmentation
• Process optimization
• Real-time decision making

Boost productivity & creativity

• Conversational search
• Summarization
• Content creation
• Code generation

Enhance customer experiences

• Chatbots
• Virtual assistants
• Conversation analytics
• Personalization

Enhance your decision making

Imagine a finance team at a global enterprise that regularly faces high-stakes decisions. Traditionally, analysts would sift through historical data, market trends, and economic indicators to forecast outcomes. Now, with GenAI, this process is not only faster but significantly more accurate.

For instance, the AI model analyses millions of data points from diverse sources in real-time, spotting correlations that even the most seasoned analyst might miss. The team can then leverage these insights to make split-second decisions on investments, risk management, and market strategies, ensuring they stay ahead of the competition.

Optimize business processes

Consider a manufacturing plant that operates 24/7. Equipment failures and unexpected downtime can lead to massive losses. With GenAI, the plant’s maintenance team doesn’t just react to problems — they prevent them.

GenAI systems continuously monitor machinery, analysing patterns and predicting failures before they happen. Picture a team receiving alerts on their tablets, detailing exactly which component is at risk and when it needs attention.

This proactive approach not only prevents costly disruptions but also optimizes resource allocation, as maintenance is performed only when truly necessary, reducing waste and boosting overall efficiency.

Boost productivity and creativity

In entertainment and media, staying relevant requires constant innovation. Picture a content creation team tasked with developing a new series. With GenAI, they’re not starting from scratch.

The AI suggests fresh storylines, generates character arcs, and even predicts audience preferences based on current trends. As the team collaborates with GenAI, they are able to push creative boundaries, producing content that resonates more deeply with audiences.

This blend of human creativity and AI-driven innovation results in ground breaking content that captivates viewers and sets new industry standards.

Enhance customer experience and engagement

Imagine an e-commerce team using GenAI to craft personalized recommendations for each customer based on their behaviour and purchase history. Instead of broad segments, customers receive tailored emails showcasing products they’re likely to love, boosting conversion rates and loyalty.

GenAI enables enterprises to deliver highly personalized and timely customer interactions, from tailored product recommendations in e-commerce to proactive support in retail, while also building emotional connections through personalized messaging. This enhances customer satisfaction, drives loyalty, and fosters long-term business growth.

GenAI use cases for 6 industries

GenAI is transforming various industries by enhancing functionality and efficiency.

Inventory & Sales Analytics

A textile retailer who once struggled with excessive inventory — leading to financial losses and wasted shelf space — can now leverage GenAI to analyse vast datasets like sales history, media trends, and customer preferences. This advanced analysis helps them accurately predict demand, enabling precise ordering for the upcoming season. As a result, they maintain optimal stock levels, reduce waste, free up shelf space, and improve profitability.

Risk Analytics

Retail chains often face challenges like fraudulent transactions and supply chain disruptions. By employing GenAI-driven risk analytics, they can detect anomalies in real time, allowing for intervention before issues escalate. For instance, a sudden spike in returns might be flagged as potential fraud, enabling immediate action and ensuring smooth operations.

Supplier Analytics

Managing multiple suppliers can be daunting, but with GenAI, retailers can assess and manage supplier performance more effectively. By analysing data on reliability, delivery times, and product quality, they can identify underperforming suppliers and make informed decisions, such as seeking alternatives or negotiating better terms, thereby strengthening their supply chain.

Customer Experience

Enhancement Retailers can significantly enhance the customer experience by offering personalized recommendations powered by GenAI. Analysing customer behaviour and preferences allows them to create tailored marketing campaigns and product suggestions, resulting in a more satisfying shopping experience that fosters long-term loyalty.

In the retail industry, information about inventory, sales, and suppliers is now readily available at the click of a button. Businesses can understand risks and make more informed decisions, which was unimaginable just six months ago.

Smart Claims Management

Insurance companies often struggle with slow claims processing, but GenAI offers a solution by automating this process, making it faster and more accurate. When a customer files a claim, the AI can instantly review documentation, verify information, and process the payment, reducing the waiting time from weeks to just days.

Data Enrichment

To improve risk assessment models, insurers can use GenAI to enrich their data by integrating information from various sources, including social media, weather forecasts, and historical claims data. This comprehensive view allows for more accurate risk assessments, leading to better pricing and more effective customer service.

KYC

With GenAI, customer service teams can access a complete view of each policyholder’s profile. The AI compiles data from various touchpoints, ensuring compliance with KYC regulations and enabling more personalized service, such as offering relevant policy updates or efficiently responding to inquiries.

Even in traditional industries like insurance, GenAI is levelling up smart claims management. Companies are beginning to use intelligent document processing to ensure high-quality, consistent service.

Route Optimization

Logistics companies can significantly reduce delivery times and fuel costs with GenAI-driven route planning. By analysing traffic patterns, weather conditions, and delivery schedules, GenAI creates the most efficient routes, leading to faster deliveries and happier customers.

Demand Forecasting

Airlines anticipating surges in bookings, particularly around holidays, can benefit from GenAI’s predictive analytics. This technology forecasts demand, allowing airlines to allocate resources effectively, whether by adding more flights or adjusting prices to maximize revenue.

Supply Chain Optimization

Global shipping companies looking to enhance efficiency can rely on GenAI to optimize supply chain operations. By analysing data on shipping routes, fuel consumption, and port congestion, GenAI helps reduce delays, lower costs, and improve overall service quality.

Government agencies are beginning to use GenAI to make their services much more user-friendly. Citizens can now retrieve information more easily, improving their experience with public services.

Citizen Services

Self-help government services can be made more accessible and user-friendly with AI-powered chatbots. These virtual assistants provide quick and accurate information, such as answering tax filing questions or guiding users through permit applications, enhancing the overall citizen experience.

Intelligent Document Processing and Validation

Government agencies overwhelmed with paperwork can turn to GenAI for automating document processing and validation. Whether it’s passport applications or business licenses, GenAI ensures faster turnaround times and reduces the likelihood of errors.

Risk Analytics

Banks can leverage GenAI to assess and manage financial risks by analysing market trends, credit scores, and transactional data. This allows them to identify potential threats and take preventative measures, minimizing losses and safeguarding their operations.

Collection Analytics

Improving debt collection processes becomes more efficient with GenAI. By analysing customer payment histories and behaviour patterns, the AI can prioritize collections, increasing recovery rates and streamlining operations.

Portfolio Analytics

Investment teams can optimize portfolio performance using GenAI, which provides insights into market movements and potential risks. These insights help the team make informed decisions, maximizing returns for their clients.

Customer Analytics

Banks aiming to offer personalized banking experiences can utilize GenAI to analyse customer data, providing tailored financial advice and recommending relevant products. This personalized approach enhances customer satisfaction and loyalty.

Manufacturing companies are beginning to use GenAI to make high-quality decisions in inventory planning and supply chain management. This technology provides real-time insights that were unimaginable just a short time ago.

Inventory Monitoring

Manufacturers can track and manage inventory in real-time with GenAI. The AI monitors stock levels, predicts demand, and alerts the team to reorder materials before they run out, preventing production delays and reducing costs.

Supplier Analytics

Manufacturing companies can assess supplier performance using GenAI by analysing delivery times, product quality, and pricing. This allows them to choose the best suppliers, negotiate better terms, and maintain a reliable supply chain.

Process Automation

Factories can optimize production processes with GenAI, reducing downtime and increasing efficiency. This automation leads to lower costs and higher output, making operations more streamlined.

Production Planning

Production teams can create efficient schedules using GenAI by analysing data on resource availability, machine capacity, and market demand. This ensures orders are fulfilled on time and within budget.

The post GenAI for Enterprises: Top Benefits, Impact And Industry Use Cases appeared first on Cloud Kinetics.

The highlights section includes a financial profile summing up all the important financial numbers of the report and relevant business information. The customer wanted to utilize GenAI technology to optimize its business report-building process by summarizing the financial profile of every company.

The post GenAI-Powered Data Solution Delivers Tangible Results For A Financial Service Provider appeared first on Cloud Kinetics.

The post A Strong Data Foundation, Analytics & AI Fuel Growth For A Health Tech appeared first on Cloud Kinetics.

Recognizing the growing potential of GenAI, the company now sought to use automation and AI to enhance the speed and efficiency of its customer service. This technology would help ActiUp maintain its high level of service and offer unmatched convenience, even as the number of events and their customer base grew.

The post GenAI Chatbot Offers Seamless 24/7 Customer Support For Event Platform appeared first on Cloud Kinetics.

• Fraudulent transactions across Europe are an estimated €1.8 billion per annum.
• The number of bank frauds in India was up 166% in FY24.
• In the United States 26% of adults surveyed said they had personally experienced bank/credit fraud.

The explosion of online banking, neobanks, fintechs and financial applications has also made it easier for scammers to strike, making it vital to spot anomalies in transactions and strange behaviour to catch fraud early. In this scenario, Artificial intelligence (AI), Generative AI & Machine Learning (ML) are new sentinels for safe and secure business operations and technology, helping banks, financial services, and insurance (BFSI) companies stay one step ahead of fraudsters.

According to one survey, 62% of UK and US based large/mid-sized businesses intend to deploy AI-based solutions to combat the issue.

Power of AI in fraud detection: Traditional security options vs AI-driven solutions

• AI can “learn” from past fraud cases, helping ML algorithms more accurate with time. An AI model flags suspicious data/anomalies in transactional and behavioural data.
• As self-learning models, AI gets smarter over time, reducing the likelihood of repetitive errors and minimizing false positives.
• AI can not only alert the humans overseeing the systems to potential fraud, but also take action by blocking transactions or removing suspicious attached files.

AI and ML can give banks and financial companies a huge advantage with a “two-steps ahead” approach to security and risk management. For instance, for a large multinational bank, fraud detection traditionally involved wading through mountains of data, reading endless reports, and manually checking every suspicious transaction. It was a slow and painful process, often leading to delays in spotting fraud. Sometimes, customers would even have to report the problem themselves, which could mean losing a lot of money before the bank could fix the issue.

The power of AI-backed fraud prevention means that the same bank can now process copious volumes of data in real time, monitor all activity including transactions as they happen. When a possible high risk event begins to occur, it escalates this to the top of the list for review on priority. The bank can now intervene as the fraud is occurring and prevent it from happening or reduce further potential loss. Overall, this can mean better customer satisfaction with fewer losses incurred.

Using AI in fraud detection means fast detection – since AI algorithms act instantly to freeze/block a transaction/account, and offers increased accuracy over traditional methods – since AI applies dynamic rule setting, learning from itself, rather than just predefined rules. Over time, this results in cost optimization as the long-term cost of prevention vs reaction is lower.

Business impact of AI for fraud prevention

AI can be applied in multiple ways to help mitigate the risk of fraud:

• AI-driven analytics platforms can integrate diverse data sources (financial data, market data, customer data) to provide a comprehensive view of risk exposure.
• GenAI for real-time fraud detection identifies suspicious patterns of behaviour through comprehensive data analysis; this helps block and prevent potentially fraudulent activity. 
• AI-powered alert prioritization is used to classify alerts by risk level, ensuring that higher risk cases get assigned for review and intervention first, which means speedy intervention and protection for the business.
• Predictive analytics help determine future risk based on constantly updated data. AI & ML can minimize false positives, making for a seamless customer experience while ensuring security.
• Data-driven operations backed by AI/ML and robust analytics help ensure regulatory compliance and support KYC verification.
• Automation along with a strong GenAI/AI/ML powered business analytics and data engine supports scalability and boosts operational efficiency.

Use Cases for BFSI | How AI helps in fraud prevention

AI-based use cases for fraud prevention in the banking and financial services sectors can take on various forms:

1. Real-Time Anomaly Detection: Systems using GenAI can detect fraud early by learning normal behaviour and spotting unusual activity or deviations that might indicate account takeovers from identity theft or phishing. This improves speed – something that’s crucial when dealing with fraud, where every minute counts.

GenAI-powered behavioural analysis can monitor app usage, banking transactions, payments, or any other financial transaction across channels/touchpoints in real time and flag off potential threats like unusual spending pattern/unauthorized account access, blocking them and preventing fraud.

AI-backed fraud detection enables faster action, better communication and quick resolution. Traditionally, we have relied on programming languages to identify any aberrations. With ML algorithms, statistical analyses and AI, we can implement a framework that easily identifies the current unusual behaviour as well as new behaviour in the future without too many changes in the program and environment. This translates to cost savings, cuts developer time and reduces time to market/time to go live.
Dipti Pasupalak, Data & Analytics Architect, Cloud Kinetics

2. Automated Fraud Reporting and Reduced Manual Reviews: AI and ML allows for automated fraud reporting and reduces the need for manual reviews. GenAI generates suspicious activity reports (SARs) incorporating millions of data points. With a lower burden on analysts, finance and IT teams, their time can be freed up to be used to propel business growth, enhance solutions and drive innovation.

Automation also makes the process of identifying investment fraud, payment fraud, or card fraud faster, more efficient and often more accurate, with lower instances of false positives.

3. Enhanced Authentication with AI: Secure authentication powered by AI can be improved with GenAI and reduce risk in case of forgery or identity theft.

GenAI can help refine algorithms used for recognition and verification, thereby making traditional biometric verification methods more effective and limiting access to only legitimate users. This cuts the risk of unauthorized access/ account takeover fraud.

Use Case | Seamless User onboarding & authentication with AI/ML-powered solutions from AWS: In the online registration process for an account, using ML-powered facial biometrics – with pre-trained facial recognition and embedded analysis capabilities, ID verification, user onboarding and authentication – can be done securely with no need for prior ML expertise in-house.

4. Detecting Variations in Usage Patterns: AI is able to analyse metadata to detect variations from the norm that might be missed in manual reviews by the human eye. As fraudsters begin to use sophisticated methods including AI to commit fraud, the use of AI as a defense against things like deepfakes will be critical.

Take for example a scenario where a customer has been duped into sharing their net banking details with a fraudster. Normally transactions after this would not be flagged since the data compromise has not occurred on the bank’s system. But AI-based risk monitoring software will spot any unusual pattern in the transactions or amounts not in line with their normal transactions, or even things like screen resolution, currency or language used and flag it for manual tele-verification in real time, more swiftly than older methods.

Use Case | Fraud prevention with Snowflake’s scalable multi-cluster shared data architecture and advanced data governance: This can help protect merchants from fraud and risk. Snowflake-powered fraud prevention models are able to identify bad actors, detect attack vectors and block account takeover attempts.

5. Offline Fraud Prevention: AI-powered video analytics can flag off suspicious behaviour at ATMs and branches that may be linked to ATM skimming, usage of stolen cards or cheque forgery.

Use Case | Geospatial analytics and AI for fraud detection from Databricks: Geospatial data, machine learning and a lakehouse architecture from Databricks help FSI clients better understand customer spending behaviours and spot abnormal credit card transaction patterns in real time. This enhances the fraud prevention and detection capabilities of the organization, which in turn reduces losses and helps cement customer trust.

Building an AI-backed fraud detection strategy

Banks and financial institutions aren’t the only ones with an eye on AI. According to Deloitte’s Center for Financial Services, fraud losses in the United States could hit US$40 billion by 2027, on the back of GenAI.

With fraudsters already using AI, industry needs to quickly adopt an AI-backed defense as well. Here’s your roadmap:

• Create a cross-functional fraud management team: Drawn from IT, operations, compliance, legal, data sciences
• Build a multi-layered fraud detection strategy: Use AI in tandem with traditional anomaly detection systems, encryption, multi-factor authentication etc.
• Implement the right environment and tools: These must be compatible with existing infrastructure and scalable and effective. Banks must modernize their infrastructure to effectively leverage AI for fraud prevention.

By migrating to the cloud or adopting a hybrid approach and establishing a robust data platform, banks can ensure timely access to high-quality data. This real-time data empowers AI, ML, and generative AI systems to analyze patterns, identify potential fraud, and enable rapid intervention.

• Follow transparent & ethical data usage: Adhere to customer privacy norms and practise ethical data usage
• Monitor & update regularly: Retrain with new data to stay effective against new types of fraud
• Run simulations: Run controlled realistic fraud attack simulations to check robustness of the systems in place and keep ahead of advanced fraud attacks

Building an effective AI-backed fraud detection strategy into your organization requires an overall commitment to a security-conscious culture. In addition to the AI, ensure every “human firewall” is well armed to respond to fraudulent activity with regular training and a culture that encourages a security-first approach. Dipti Pasupalak, Data & Analytics Architect, Cloud Kinetics

The post How Banks & Financial Services Can Fight Fraud With AI-Driven Analytics appeared first on Cloud Kinetics.

Key Takeaways

• AI-powered chatbots were showcased as a powerful tool for enhancing customer satisfaction. These chatbots can introduce products, provide FAQ support, and streamline customer interactions.
• Attendees saw how AI-driven automation can streamline IT workflows, freeing up valuable time and resources for strategic infrastructure initiatives.
• A live demonstration of AI-powered face recognition technology highlighted its potential applications across various business sectors.

A shoutout to the expert speakers from AWS and Cloud Kinetics who shared their expertise on AI and infrastructure management. They created a 360° view for the audience on how GenAI can transform businesses at a pace and scale that is truly game changing!

The post Unlocking The Business Value Of Generative AI (GenAI) appeared first on Cloud Kinetics.

Attendees delved into how asset management firms had effectively modernized mission-critical applications and deployed Generative AI (GenAI) to build resilience, refine decision-making, and drive customer-centricity across the value chain.

Key insights included:

Enhancing Portfolio Management: Leveraging AI/ML and advanced analytics for sophisticated risk analysis and AI-powered investment decisions.

Accelerating Innovation: Launching new services in portfolio recommendations, digital assets, ESG scoring, and new investable assets.

Navigating Compliance: Using automation strategies to generate alpha, improve credit decision-making, and support KYC verification and fraud detection.

Fueling Hyper-personalized Customer Experiences: Providing frictionless, cross-channel experiences and delivering value-added solutions at the point of need.

Specialists from AWS, Intel, and Cloud Kinetics shared their expertise on emerging trends and challenges in Capital Markets, revealing how innovators are leading the next wave of transformation in the financial services industry.

The post CxO Roundtable Dinner: Redefine The Future Of Finance With Cloud & AI Strategies appeared first on Cloud Kinetics.