Google Democratizes AI with Vertex AI

The Democratization of AI refers to making artificial intelligence (AI) technologies and tools broadly accessible to individuals and organizations, regardless of their technical expertise. By simplifying AI development and deployment through user-friendly platforms, automation, and No Code Platforms, the barriers to entry for leveraging AI are significantly reduced. This paradigm empowers a broader range of users—such as citizen developers and analysts—to integrate AI into their workflows, drive innovation, and solve business problems.

Gartner predicts that by 2025, over 65% of applications will be developed using No Code Platform, driven by the need to Democratize AI across enterprises. Forrester Contends AI Democratization is essential for scaling innovation, especially as organizations grapple with a global shortage of AI and data science talent. McKinsey & Company reports that AI democratization enables faster deployment of AI initiatives, leading to operational cost savings and revenue increases of up to 20%.

Google Vertex AI plays a pivotal role in the Democratization of AI by offering tools and platforms that lower the barriers to entry for businesses and individuals looking to harness AI technologies. Here’s a detailed look at how it supports this initiative:

Vertex AI Architecture Overview

Vertex AI combines Google’s expertise in machine learning with its cloud infrastructure to provide an end-to-end solution. Its architecture includes:

Data Integration

• BigQuery Integration: Access and preprocess data directly using SQL. Vertex AI allows “zero-copy” data processing, eliminating the need for redundant storage layers.
• Dataflow: Handles ETL (Extract, Transform, Load) pipelines for streaming or batch data ingestion.
• Dataprep: Offers a UI-driven approach for cleansing and preparing datasets.

Model Training and Fine-Tuning:

• Vertex AI Training: Supports distributed training with frameworks like TensorFlow, PyTorch, and Scikit-learn.
• Pre-trained Models: Incorporates models like Gemini (multimodal and long-context) for tasks requiring advanced reasoning capabilities.
• Custom Model Training: Users can upload custom containers or scripts to train proprietary models.

Deployment and Orchestration:

• Vertex AI Prediction: Provides scalable endpoints with low latency for real-time applications.
• Vertex AI Pipelines: Automates the ML lifecycle, from data preprocessing to deployment, using Kubeflow Pipelines.
• Model Monitoring: Detects data drift, prediction errors, and model degradation.

Specialized AI Tools:

• Dialogflow CX: A conversational AI framework for creating advanced virtual agents.
• AutoML: Enables non-experts to train models on structured and unstructured data.
• Vertex Explainable AI: Provides interpretability insights for predictions, which is crucial for regulated industries like finance and healthcare.

Technical Workflow to Create AI Agents

Step 1: Problem Definition and Data Preparation

• Clearly define the AI Agent’s use case, such as fraud detection, virtual customer service, or inventory optimization.
• Leverage BigQuery or external sources to import data. Use Dataprep for cleaning and preprocessing datasets.

Step 2: Model Training

• Select a pre-trained model or design a custom model using Vertex AI’s training environment. For example:
• Use Gemini 1.5 Pro for text, images, or tabular data tasks.
• Fine-tune with task-specific data using transfer learning in TensorFlow or PyTorch.
• Vertex AI supports distributed training on TPUs (Tensor Processing Units) or GPUs, optimizing performance for large datasets.

Step 3: Deploy the AI Agent

• Deploy the model via Vertex AI Prediction, enabling REST or gRPC endpoints.
• Use Vertex AI Pipelines to automate workflows, ensuring continuous updates as new data becomes available.

Step 4: Integration with Applications

• Integrate AI Agents with enterprise systems using APIs. For instance:
• Use Dialogflow for chatbots integrated with CRMs like Salesforce.
• Implement prediction APIs in e-commerce platforms for real-time recommendations.

Step 5: Monitoring and Iteration

• Enable Model Monitoring to track key performance indicators like accuracy and latency.
• Schedule retraining pipelines based on monitored drift or degradation.

Cost Structure

The cost of using Vertex AI depends on usage components:

• Training Costs: Determined by compute hours for TPU/GPUs.
• Deployment Costs: Based on prediction requests, endpoint uptime, and scaling configurations.
• Storage Costs: For datasets in BigQuery and model artifacts in Google Cloud Storage.

For example, training a mid-sized NLP model may cost $5,000–$10,000 in compute, with additional deployment and storage costs scaling based on usage.

Use Cases for AI Agents

• Customer Support Automation: AI-driven chatbots handling Level 1 queries.
• Healthcare: AI Agents can summarize patient records or assist in diagnoses.
• E-commerce: Personalized product recommendations and inventory management.
• Finance: Fraud detection and automated risk assessment.

Integration Support with GSIs and ISVs

According to an article published by Kvein Ichhpurani on November 20, 2024, entitled, "Build, deploy, and promote AI agents through Google Cloud’s AI agent ecosystem", Google has seen significant momentum from service partners who have used Google Cloud’s technology to help customers successfully build and deploy AI agents. Through this program, our service partners will make their AI agents available to even more customers, including on AI Agent Space in the future. Here are some of their innovative agent solutions:?

• Accenture?is transforming customer support at a major retailer by offering convenient self-service options through virtual assistants, enhancing the overall customer experience.
• Bain?supports SEB’s wealth management division with an AI agent that enhances end-customer conversations with suggested responses and generates call summaries that help increase efficiency by 15%.??
• BCG?provides a sales optimization tool to improve the effectiveness and impact of insurance advisors.?
• Capgemini?optimizes the eCommerce experience by helping retailers accept customer orders through new revenue channels and to accelerate the order-to-cash process for digital stores.
• Cognizant?helps legal teams draft contracts, assigning risk scores and recommendations for how to optimize operational impact.??
• Deloitte?offers a “Care Finder” agent as part of its Agent Fleet, helping care seekers find in-network providers often in less than a minute — significantly faster than the average call time of 5-8 minutes.
• HCLTech?helps predict and eliminate different types of defects on manufacturing products with its manufacturing quality agent, Insight.
• Infosys?optimizes digital marketplaces for a?leading consumer brand manufacturer, providing actionable insights on inventory planning, promotions, and product descriptions.?
• PwC?uses AI agent technology to help oncology clinics streamline administrative work so that doctors can optimize their time with patients.
• TCS?helps build persona-based AI agents contextualized with enterprise knowledge to accelerate software development.
• Wipro?supports a national healthcare provider in using agent technology to develop and adjust contracts, streamlining a complex and time-consuming task while improving accuracy.?

Partners have already given us positive feedback about the support we’ve provided to more effectively scale their agent solutions, including Datatonic, Kyndryl, Quantiphi, and Slalom who plan to bring new agents to market soon. Here’s what partners had to say:

• “Leaders who prioritize and invest in agentic architecture will be at the forefront of their industries, driving future growth with generative AI. For example, Accenture's marketing team is using autonomous agents to streamline campaign creation and execution, reducing manual steps by 25-35%, saving 6% in costs, and speeding up time-to-market by 25-55%.” -?Scott Alfieri, Global Lead, Google Business Group, Accenture
• “BCG continues to see strong business value partnering with Google Cloud to deliver gen AI transformations for our joint clients across industries. Google Cloud's support for a robust ecosystem of AI agents demonstrates its commitment to innovation and democratization of AI.” -?Val Elbert, Managing Director and Senior Partner, BCG
• “By partnering with Google Cloud, we are building AI agents that transform customer experiences and bring efficiency to business processes. Google Cloud's Agent Marketplace empowers Capgemini to continue developing and deploying innovative AI agents, leveraging our deep understanding of our customers.” –?Fernando Alvarez, Chief Strategy and Development Officer and Group Executive Board Member, Capgemini
• “Deloitte has helped some of its largest clients improve how they operate with AI agents built with Google Cloud’s technology. As agentic AI takes off, this initiative can enhance our agent-building and distribution capabilities, thus enabling us to accelerate our clients’ time to business value with AI solutions.” –?Gopal Srinivasan, Alphabet Google Alliance Generative AI Leader, Deloitte Consulting LLP

Offerings from ISV partners

Google indicated their ISV partners are leveraging the power of Google Cloud's AI technology, including Vertex AI and Gemini models, to develop cutting-edge AI agent solutions. Many have already made their offerings available on Google Cloud Marketplace, and we're thrilled that they will be expanding their reach through AI Agent Space to make it even easier for customers to deploy and benefit from these innovative AI agents.?

Here are some examples of their agent capabilities:?

• Bud Financial?uses its "Financial LLM" to provide personalized answers to customer queries and supports automation of banking tasks such as moving money between accounts to avoid overdrafts.
• Dun & Bradstreet?uses its Hoovers SmartSearch AI to help customers quickly build targeted lists of companies and contacts matching specific criteria such as location, industry, and company size, making it easier to identify and action targeted opportunities.
• Elastic?helps SREs and SecOps interpret log messages and errors, optimize code, write reports, and even identify and execute a runbook.?
• Exabeam?enhances cybersecurity with natural language search, visualization, and investigation acceleration, automating threat explanations and next steps for multi-terabyte datasets.
• FullStory?integrates its real-time data capture with Google Cloud's AI to create context-aware conversational agents, enabling faster data discovery and analysis of web and mobile interactions and more intelligent AI responses.
• GrowthLoop?gives marketers tools that automate audience building, suggest optimal targeting, and create custom attributes, optimizing the power of BigQuery data.
• OpenText?enables users to quickly find fast, accurate answers to inquiries that span a broad set of business domains, such as DevOps, customer service, and content management.?
• Quantum Metric?uses its Felix AI agent to help customer service associates quickly summarize and identify important takeaways from consumer engagements, with reporting metrics that help businesses enhance inquiry resolutions.?
• Sprinklr?offers multiple AI agents that can help businesses improve decision-making, resolve service queries, and handle complex tasks end-to-end.?
• Teradata?helps analyze, categorize, and summarize customer inquiries or complaints by using multimodal capabilities that process text and voice data, identifying key trends and actionable insights to enhance customer loyalty.
• ThoughtSpot?uses its Spotter agent to empower customers with autonomous analytics capabilities and a natural-language chat interface that brings deep data analysis and contextual reasoning to any user.?
• Typeface?enables users to automate marketing workloads and across teams with its Arc Agent, which supports marketers with campaign performance, creative content creation updates, and audience optimization.?
• UKG?enhances the workplace experience with Bryte AI, a conversational agent that enables HR administrators and people managers to request information about company policies, business insights, and more.?

Google indicated ISV partners are successfully using their AI platforms to enhance their agent solutions, which they expect to grow through our ecosystem. Here’s what they had to say:?

• “Dun & Bradstreet built Hoovers SmartSearch AI with Google's AI to revolutionize sales prospecting by instantly generating targeted lists of companies and contacts. Through this innovative initiative, customer adoption of our AI agent will be accelerated to help users effortlessly identify ideal customers and accelerate revenue growth.” -?Michael Manos, Chief Technology Officer, Dun & Bradstreet?
• “Elastic AI Assistant uses Vertex AI and Gemini models to empower SREs and SecOps teams to build intelligent agents that interpret log messages, optimize code, automate reports, and even generate runbooks. This is the future of agentic architecture, and it's available now in partnership with Google Cloud.” -?Ken Exner, CPO, Elastic?
• “By leveraging Google's advanced AI capabilities, ThoughtSpot Spotter delivers an autonomous analytics agent that empowers users to extract valuable insights from their data through natural language interactions. We're excited to scale our AI agent to even more customers in partnership with Google Cloud."?-?Sumeet Arora, Chief Development Officer, ThoughtSpot
• “UKG leverages Vertex AI to power UKG Bryte AI, a gen AI sidekick for UKG’s Pro and Ready HCM solutions. Bryte AI is built on UKG’s proprietary people, culture, and work data to enhance insights and decision-making, and to enable more conversational AI experiences” -?Venkat Ramamurthy, Head of Product, AI, and Data, UKG

Google further stated how pleased they have been by how quickly partners have built AI agents to help customers improve their businesses. Additional partners with powerful AI agent capabilities available through Google Cloud include AUI.io, Automation Anywhere, Big SUR AI, BigCommerce, DataStax, Decagon.ai, Dialpad, Elastic, ema.co, Livex.ai, Lyzr.ai, Mojix, Moveo.ai, Regnology, Tamr, UBIX, Tektonic AI, Vijil, VMware, Wisdom AI, and Zeotap.

Competitors

Vertex AI’s primary competitors include:

• Microsoft Azure Machine Learning: Known for integration with Office 365 and Power BI.
• AWS SageMaker: Offers robust model training and deployment but lacks some multimodal capabilities.
• IBM Watson: Focused on explainability and regulated industries.

Future Trends

• Increased Multimodal Support: AI Agents capable of seamlessly handling text, images, and video.
• Enhanced No-Code Tools: Emerging No-Code Platforms such as UBIX empower non-technical Citizen Developers to create and deploy sophisticated AI Agents with little to no requirement for support from data scientists or software engineers.
• Focus on Ethical AI: Building explainable, privacy-preserving AI Agents.

Conclusion

The Democratization of AI offers a transformative opportunity for the Global 500 to unlock the full potential of their workforce and improve operational efficiency. By adopting AI-driven, No-Code platforms companies can reduce costs, increase agility, and drive innovation at scale. With the continued evolution of tools and platforms such as Google Vertex AI along with the innovation of their GSIs and ISV partners, AI Democratization will become an essential strategy for staying competitive in a rapidly changing business environment.