5 Trends & Takeaways from Google Cloud Next

Categories: CloudTechnology

Executives, decision-makers, technical experts, and Google Cloud partners converged at Google Cloud Next to explore cutting-edge innovations and industry trends. GlobalLogic was there, speaking about modernization strategy and delivering a Cube talk on Intelligently Engineering the Next Gen AI Platform we are building for Hitachi.

Among the buzz at GCN 2024, using GenAI for customer success and process and platform modernization with AI stole the spotlight. Innovative ways companies are evolving from proof of concepts to proof of value were hot topics, too. However, challenges like data integrity and legacy point systems loom large as enterprises shift towards those proof-of-value AI-driven solutions and efficient monetization strategies. Where should you focus now – and what comes next as you develop your innovation roadmap?

Here are five key trends and takeaways from the event that speak to the essential building blocks innovative companies need to lay the groundwork for successful enterprise-grade AI implementations.

1. Applying GenAI for Customer Success

Enterprise-Grade GenAI solutions for customer success are revolutionizing service quality and driving business outcomes. Imagine equipping your frontline staff with GenAI-driven agents, empowering them to ramp up productivity and provide every customer with a personalized, enhanced experience. Built-in multilingual customer support makes GenAI a versatile powerhouse for enterprise teams, catering seamlessly to a global customer base with diverse linguistic preferences. 

This transformative approach to customer success merges advanced technology with human expertise, paving the way for exceptional service delivery and business success in the digital age.

2. Modernizing the Tech Stack & Transforming the SDLC

GenAI is reshaping the software development landscape by empowering developers to drive efficiency and elevate code quality to new heights. This transformative approach extends beyond mere updates—it’s about modernizing the entire stack, from infrastructure to user interface. 

Innovative approaches include automated code generation, building RAG-based applications, enhanced testing and QA, predictive maintenance, and continuous integration and deployment (CI/CD). Leveraging natural language processing (NLP) for documentation, behavioral analysis, automated performance optimization, and real-time monitoring and alerting, GenAI streamlines development processes, improves code quality, and enables proactive decision-making. GenAI empowers developers to drive efficiency, improve security, and elevate software quality to unprecedented heights throughout the SDLC by automating tasks, optimizing performance, and providing actionable insights. 

Through comprehensive refactoring of applications, GenAI is leading the charge towards a future-proofed ecosystem. However, this ambitious undertaking isn’t without its challenges; it demands time, dedication, and a strategic roadmap for success. 

3. Building a Future-Forward Framework for Success

Enterprises face key challenges in unlocking the value of AI, such as ensuring data privacy and security, protecting intellectual property, and managing legal risks. Flexibility is essential to adapt to evolving models and platforms, while effective change management is crucial for successful integration. 

Embracing a 3-tier architecture with composable components over the core platform emerges as the future-forward approach, fostering flexibility and scalability. Having a robust infrastructure and data stack to underpin the GenAI layer is indispensable, forming the bedrock for successful implementation. We refer to this holistic framework as the “platform of platforms,” which not only ensures alignment with business objectives but also facilitates the realization of optimal outcomes in the GenAI journey.

4. Monetizing Applications 

Monetization was a hot topic at Google Cloud Next, and enterprise organizations gravitate towards Google’s own Apigee for several reasons. Apigee’s robust API management platform offers versatile monetization models like pay-per-use and subscriptions, streamlined API productization, customizable developer portals, real-time revenue optimization analytics, seamless billing system integration, and robust security and compliance features. 

For example, we recently designed and built a solution for monetizing an application that uses APIs to access and leverage industry data stored in a cloud-based data lake. This allowed for scalable and serverless architecture, providing reliable and updated information for improved decision-making, identification of new opportunities, and early detection of potential problems. Apigee’s reputation as a trusted and reliable API management platform is backed by Google Cloud’s expertise and infrastructure, further solidifying its appeal to enterprise customers.

5. Evolving the Intelligent Enterprise from POC to Proof of Value

Transitioning from Proof of Concept (POC) to Proof of Value (POV) marks a critical phase in adopting AI technologies, particularly in light of recent challenges. Many POCs implemented in the past year have faltered, and the pressure is on to demonstrate a return on AI investments.

Maturing your AI program from POCs to POV calls for a holistic approach that encompasses not only the capabilities of GenAI but also your foundational architecture, data integrity, and input sources. Maintaining data integrity throughout the AI lifecycle is paramount, as the quality and reliability of inputs significantly impact the efficacy of AI-driven solutions. Equally important is the evaluation and refinement of input sources, ensuring that they provide relevant and accurate data for training and inference purposes. 

Successful GenAI implementations are those that are reliable, responsible, and reusable, cultivating positive user experiences and deriving meaningful value for the enterprise. 

Responsibility means delivering accurate, lawful, and compliant responses that align with internal and external security and governance standards. Reliability shifts the focus to maintaining model integrity over time, combating drift, hallucinations, and emerging security threats with dynamic corrective measures. Finally, reusability emerges as a cornerstone, fostering the adoption of shared mechanisms for data ingestion, preparation, and model training. This comprehensive approach not only curtails costs but also mitigates risks by averting redundant efforts, laying a robust foundation for sustainable AI innovation.

How will you propel your AI strategy beyond ideas and concepts to enterprise-grade, production-ready AI and GenAI solutions? 

Let’s talk about it – get in touch for 30-minute conversation with GlobalLogic’s Generative AI experts.

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