Generative AI marks one of the most significant technological shifts in history.
Its impact on individual and business productivity can be significant, with the potential to rival the advent of the internet or the mobile device. Indeed, among organizations considering or using AI, 82% believe it will either significantly change or transform their industry.
What makes generative AI different from other forms of AI that have come before is its ease of use in helping solve everyday problems in people’s personal or professional lives. Anyone who knows how to ask a question of a search engine can use everyday language to interact with a generative AI chatbot or virtual agent — getting it to answer questions, create content, produce images, summarize documents, and much more.
Even better, a single generative AI platform can deliver solutions for multiple use cases, creating a network effect. As the number of users and applications increases, the model is exposed to more data and becomes increasingly accurate and useful — which in turn encourages more users.
Organizations that use generative AI to speed up, automate, scale, and improve business processes stand to reap big benefits. According to McKinsey & Company, generative AI’s impact on productivity could add between $2.6 trillion and $4.4 trillion USD annually to the global economy.
No technology ever takes away the fundamentals of your value proposition and the core value chain in your industry. At the end of the day in healthcare, for example, you’re still trying to improve patient care.
What can change, though, is how you use this technology to enable your teams to improve core offerings, and how you solve fundamental problems that get in the way of delivering them. Indeed, with the right tools, you could even identify and deliver new points of difference.
Step 1
Identify a specific domain
Choose a domain in your company that could benefit from generative AI, such as customer service, patient intake, corporate actions, or marketing content.
Key questions:
In which areas of the business do employees spend significant time on repetitive tasks?
Which areas of the business offer the lowest-risk environment for initial use cases?
Is there a process or part of a role that is, in effect, already standardized (e.g., having to do a particular step or answer a question every time)?
Do you have a large corpus of data that you want to activate, or make more useful?
Where do employees get stuck in the creative process (e.g., writer’s block or creative block)?
Is there an area of the business where employees consistently have to search for existing information using internal knowledge bases and/or external search engines?
Would an incorrect response or hallucination cause harm?
Step 2
Select a persona
Determine which job category or function within the chosen domain you want to make more productive.
Consider these three factors:
Look at job roles that are hard to retain and hard to hire.
Such roles are often repetitive and offer little career advancement. Automating these tasks can free up employees to focus on more strategic work.
Find opportunities to automate repetitive, tedious tasks that are necessary to generate revenue.
For example, the multi-trillion dollar pre-authorization industry in healthcare is incredibly frustrating for patients. It can take hours or even days to get authorization for a simple MRI or specialist visit, and the process is often paper-based. Investment memorandums are another example in which the same information must be gathered over and over again. Generative AI can help automate these tasks so employees can focus on more strategic work.
Make safety and compliance ambient.
Many industries must meet strict requirements to be compliant. In life sciences, for example, every claim about the efficacy of a medicine must be reviewed by an attorney to ensure the language is compliant. An attorney must then review the claim to ensure certain clauses are included in every instance of the fine print. This can be time consuming and repetitive, but is an essential part of the process for making medicine publicly available. Generative AI can help automate critical tasks that help organizations improve accuracy and reduce risk.
Step 3
Determine the data sources the persona needs to be productive
Your generative AI model will be trained on the data you gather. This data should be honed for the specific business or domainlevel problem it is trying to solve, and accessible through enterprise data sources.
For example, if you select a marketing manager as your first persona, it’s important to understand the specifics of their job role. Say they are responsible for creating digital campaign content like ebooks and reports. These assets are promoted and used to capture leads via online forms. If the individual fills out the form and opts in to receive outreach about related topics, the lead is entered into a marketing automation program and scored based on predetermined criteria set by marketing and sales operations teams. If the lead meets the criteria for qualified sales opportunities, it may also be handed off to the sales team.
This individual job role requires several data sources, including:
- A word processing tool like Google Docs to write, edit, and collaboratively review the copy
- A design tool to lay out and format the final copy
- A web platform to publish the content online
- A marketing automation tool to track and measure marketing engagement, tasks, and workflows
- A CRM like Salesforce to ensure sales, support, and marketing are coordinated across all user touchpoints and customer interactions
By starting off with the right data to feed into and fine-tune models, your organization will be able to:
Mitigate hallucinations
AI models are trained to give users what they want, which means they occasionally spit out made-up answers that sound convincing and can be hard to spot. To avoid this problem, you can ground responses in specific data rather than relying on the LLM alone.
Enhance the explainability of AI
Generative AI models can be complex, and the ‘thinking’ that an algorithm uses to produce an output isn’t always clear. Explainable AI is like a sliding scale — there are degrees to which you can explain or reliably steer an LLM’s behavior.
Step 4
Create a threeperson tiger team
Include people from both business and technology:
Individual from the business
Responsible for detailing the job requirements, workflows, challenges, and needs of the day-to-day tasks executed by the chosen persona.
Prompt engineer
Responsible for translating the business persona’s needs, actions, and output into prompts for the generative AI model(s).
ML operations lead
Responsible for building and operating the application in production.
Step 5
Define your intentions, objectives, and the output you are trying to achieve
Ensure you have a human in the loop to oversee the first use cases and provide oversight.
Keep in mind, the value of a generative AI project can come from a number of sources. There’s direct business value, incremental value of generative AI over legacy systems or traditional AI/ML, and the forecasted value of capabilities once scaled to other use cases. Consider the following outcomes, which other organizations have experienced after adopting AI:
Step 6
Design prompts together with the tiger team
Work collaboratively with the tiger team to design prompts that will guide the generative AI model’s responses.
Your three-person tiger team has expertise in the business need, the AI model(s), tuning, and application integration. Use their skills and refer to prompt samples to move quickly in this step.
Step 7
Build a user experience (UX) and user interface (UI)
Create a user-friendly experience and interface that will run the generative AI model in production for the chosen persona’s use case.
Here are some tips to keep in mind:
Keep the interface and design simple.
Begin with a selection screen which allows users to choose the personality to apply to the generated text, images, or output. For example, options could include ‘formal’, ‘casual’, ‘technical’, or ‘creative’.
Create a logical and intuitive user flow that guides users through the AI model’s functionality. Ensure that the interface design aligns with the expected user journey.
Consider how the new interface fits within the larger ecosystem of existing apps, like email and chat, that might have their own built-in generative AI capabilities.
Ensure the UI/UX is responsive and accessible across different devices and screen sizes, including mobile phones, tablets, and desktops.
Step 8
Expand usage to additional individuals
Once you are getting acceptable results from tuning, invite two or three other individuals within the chosen persona to start using the model.
Continue testing, measuring, and tuning with this group until you are getting consistent, quality outputs, then expand usage to five to 10 more people within the chosen persona and continue fine-tuning the process.
With each new individual, make sure you understand the different ways each user interacts with the generative AI model. To do so, conduct user interviews, surveys, or workshops to gather insights into user preferences, pain points, and desired functionalities for the AI model’s interaction.
Step 9
Build a Language Model (LM) operations plan
Develop a plan for productionizing and monitoring the AI model’s output to ensure it functions effectively and safely.
Key questions to ask your management team:
Can we quickly evaluate and experiment with generative AI?
Do we have cost controls during evaluation and experimentation?
How are we measuring impact? Do we have targeted goals and frequent checkpoints to ensure progress?
Do we have a mechanism for continuous improvement? Are we able to assess, evaluate, and re-engage to go deeper within existing use cases or expand to more use cases?
An LM operations plan should include, but is not limited to:
Infrastructure setup
Prepare the necessary infrastructure for model deployment, including scalable compute resources and storage. Set up a version control system to manage model versions effectively.
Deployment and monitoring
Deploy the model in a controlled environment, such as a staging environment, to monitor its behavior before going live. Implement monitoring tools to track model performance, safety, and resource utilization during production.
Output and quality
Develop a system to capture the AI model’s output and evaluate its quality, so you can measure the effectiveness of the AI-generated responses. Jump to the next section for a list of recommended KPIs that can be used to measure generative AI use cases.
Regular audits and evaluation for expansion
Establish a regular evaluation cadence to assess the quality of the AI-generated output and a plan for further expansion to other areas within the same domain.
Continuous performance improvement and model updates
Performance — which is defined by quality and latency — requires updates to the model to incorporate the latest research advancements and improvements. Conduct A/B testing to evaluate the impact of model updates on safety and effectiveness.
Security and compliance
Ensure the entire system is secure, with appropriate access controls and encryption mechanisms to protect sensitive data. Comply with relevant regulations and responsible AI guidelines.
Human-in-the-loop oversight
Set up a human-in-the-loop process to review and moderate generated content, especially in sensitive or highrisk applications. Develop a feedback loop to continually improve the model’s safety and effectiveness based on human moderation.
Incident response and remediation
Develop an incident response plan to handle potential safety breaches or issues promptly and effectively.
Step 10
Expand usage to additional use cases within the same domain
In the beginning of this chapter, we explained how an organization could start with one generative AI use case and naturally expand into three use cases that all enhance the customer service domain: first, it helped customer service agents answer the question; second, it summarized the frequently asked questions agents received over the phone; and third, it produced answers that could be posted online as written FAQs.
With each use case added to the model, the model itself became more accurate in the domain.
Looking ahead at days 60-90
Once you are ready to extend your use case to external users and/or thirdparty data, use these methods and tactics to scale quickly and safely:
01
Host a hackathon
Harness enthusiasm across your teams by hosting a hackathon, which encourages employees to brainstorm ideas and get hands-on with AI — all within a matter of days.
02
Bring in partners
Bring in help. Google Cloud Consulting alongside partners can consult on business value and technical implementations, provide training, and even work side-by-side with your teams to transfer knowledge as they build out your implementation. Learn more at cloud.google.com/consulting
03
Create a center of excellence
Excitement about new technologies can often lead to widespread use. A center of excellence in models, tuning, and application integration can help to standardize processes, share knowledge, and ultimately drive innovation.
KPIs for generative AI
When evaluating projects, consider the feasibility, actionability, affordability, anticipated business value, and ultimate return on investment of each generative AI project.
Like any technology investment, you need to prove its worth. Embed ROI measures into every use case and project, and establish KPIs to keep a pulse on progress along the way.
Consider using these commonly used generative AI KPIs to measure and report on the value of generative AI to your organization, board members, and stakeholders. These KPIs apply to generative AI use cases across various domains and industries.
Accuracy
Measure the accuracy of the generative AI model in producing relevant and correct outputs. This can be quantified using metrics such as precision, recall, F1 score, or mean squared error, depending on the nature of the use case.
Productivity
Assess the impact of generative AI on the productivity of the target persona or department. This could include metrics like the number of tasks completed per unit of time, response time, or reduction in manual effort required.
Customer satisfaction
If the generative AI use case involves customer-facing applications, use customer satisfaction surveys or feedback to gauge how well the AI system meets customer needs and expectations.
Cost savings
Measure the cost savings achieved through the use of generative AI. This may involve comparing the costs of employing the AI system to the expenses associated with traditional manual processes or outsourcing.
Turnaround time
Evaluate the time taken for the generative AI model to generate responses or outputs compared to traditional methods. Faster turnaround times can lead to increased efficiency and improved customer experience.
Quality of output
Assess the quality of the generative AI outputs against predefined criteria. This can be done through manual review or automated quality checks, depending on the use case.
Error rate
Quantify the rate at which the generative AI model produces incorrect or undesirable outputs. Minimizing error rates is crucial for maintaining accuracy and reliability
Business impact
Identify specific business metrics that are directly impacted by the generative AI use case, such as increased sales, reduced customer complaints, or improved employee retention.
Training time and cost
Measure the time and resources required to train and fine-tune the generative AI model. Efficient training processes can lead to faster implementation and quicker time-to-value.
Human-in-the-loop metrics
If human intervention is involved in the generative AI process, track metrics related to the efficiency and effectiveness of human oversight.
Scalability
Assess how well the generative AI model scales to accommodate increased usage or higher demands. Scalability is essential for long-term success.
Regulatory compliance
For sensitive domains like healthcare or finance, monitor how well the generative AI system adheres to relevant regulatory requirements and data privacy standards.
The value of generative AI in every industry
Generative AI is more than a bright shiny object — it is an entirely new value stream for business leaders. Already, leading companies are using generative AI to solve some of their most common and timeintensive problems.
According to McKinsey & Company, 75% of generative AI’s value will be realized across customer operations, marketing and sales, software engineering, and R&D.4 It’s already happening, with companies applying LLMs to use cases such as conversational AI in marketing and e-commerce.
In this section, learn how industry leaders are applying generative AI to the top use cases in every industry to unlock new value chains, rewrite processes, and do business in faster time frames and at a lower cost.
“Generative AI opens up a new avenue, allowing people to think fundamentally differently about how business works. Whereas AI and ML were more about productivity and efficiency — doing things smarter and faster than before — now, it’s ‘I can do it completely differently than before’.
Carrie Tharp
VP Strategic Industries, Google Cloud
See what your peers are doing
To accelerate your path to value, familiarize yourself with the industry-specific use cases being adopted today. See how the leaders are realizing value in their organizations, and get inspiration for your own.
Retail and CPG
82%
of retail organizations consider customer service automation to be valuable
Priority use cases
Creative assistance
Empower retail creative teams to create bespoke images and creative content for campaigns and editorial placements, and enable 1:1 personalization.
Customer service automation
Streamline customer service with conversation summaries and task automation.
Conversational commerce
Interactively address queries, provide recommendations, and engage with customers in real time to help them make shopping decisions (for example, “Sure, here are some dresses in your size and style you may like, and here are influencer images for style inspiration”).
New product development
Enhance internal consumer research with easy querying, summarization, and insight generation. Create copy concepts and claims for further testing, and visual concepts for product and packaging designs.
Wendy’s® revolutionizes the drive-thru experience
“Wendy’s introduced the first modern pick-up window in the industry more than 50 years ago, and we’re thrilled to continue our work with Google Cloud to bring a new wave of innovation to the drive-thru experience.”
Todd Penegor
President and CEO, The Wendy’s Company
Wendy’s is automating its drive-through service using an artificial intelligence chatbot powered by natural-language software developed by Google and trained to understand the myriad ways customers order off the menu.
With 75 to 80% of Wendy’s customers choosing the drive-thru as their preferred ordering channel, delivering a smooth ordering experience using AI automation can be difficult due to the complexities of menu options, special requests, and ambient noise. For example, because customers can fully customize their orders and food is prepared when ordered at Wendy’s, this presents billions of possible order combinations available on the Wendy’s menu, leaving room for miscommunication or incorrect orders.
Google Cloud’s generative AI capabilities can now bring a new automated ordering experience to the drive-thru that is intended to enhance the experience that customers, employees, and franchisees expect from Wendy’s.
Wendy’s is beta-testing Google Cloud’s AI technology in a Columbus, OH-area, companyoperated restaurant, and will use the learnings to inform future expansions to more Wendy’s drivethrus. The test will include new generative AI offerings, such as Vertex AI Search and Conversation and more, to have conversations with customers, the ability to understand made-to-order requests and generate responses to frequently asked questions.
This will all be powered by Google’s foundational LLMs that have the data from Wendy’s menu, established business rules and logic for conversation guardrails, and integration with restaurant hardware and the Point of Sale system. By leveraging generative AI, Wendy’s seeks to take the complexity out of the ordering process so employees can focus on serving up fast, fresh-made, quality food and providing exceptional service.
“Google Cloud’s generative AI technology creates a huge opportunity for us to deliver a truly differentiated, faster, and frictionless experience for our customers, and allows our employees to continue focusing on making great food and building relationships with fans that keep them coming back time and again.”
Todd Penegor
President and CEO, The Wendy’s Company
Financial services
79%
of financial services organizations consider virtual assistants to be valuable
Priority use cases
Financial document search and synthesis
Help analysts find and understand information that is buried deep in contracts and other unstructured documents.
Regulatory and compliance assistant
Help business and technical teams monitor regulatory changes that impact their business, and ensure that controls and compliance are consistently implemented (in software and business processes).
Enhanced virtual assistants
Help customers get the answers they need with less human intervention.
Personalized financial recommendations
Improve cross-sell and retention with 1:1 messaging. Tailor financial product recommendations with hyper-personalized and conversational language.
Capital markets research
Use as a research assistant to sift through millions of source documents to identify and summarize key information.
ING Bank uses gen AI to reinvent customer conversational and chatbot experiences
ING Bank strives to provide superior customer experiences by bringing new features to its app. To improve the current chatbot experience, ING is currently testing agent and customer chatbots built on Vertex AI Search and Conversation to find new ways to increase deflection rate and NPS, while speeding up its expansion to new markets, in a language agnostic way. The goal is to reach 90% accuracy and cut costs by 30% by 2025.
“Using AI in a regulated industry requires proper risk governance. This means that our gen AI chatbots should use the right tone, reduce hallucinations, and not give investment advice or anything else not allowed. By enhancing the customer experience through personalization and easy communication, ING is empowering people to bank on their terms.”
Peter Goderis
ING Tribe Lead COO, Analytics, ING Bank
Deutsche Bank accelerates financial decision-making
Deutsche Bank is testing Google’s generative AI and LLMs at scale to provide new insights to financial analysts, driving operational efficiencies and execution velocity. There is an opportunity to significantly reduce the time it takes to perform banking operations and financial analysts’ tasks, empowering employees by increasing their productivity.
“Generative AI is transforming how we find, sort and analyze information at scale — helping us to support our client’s global ambitions.”
Bernd Leuker
Chief Technology, Data and Innovation Officer, Deutsche Bank
Moody’s is building gen AI apps for finance professionals
Moody’s Corporation is bringing its expertise in financial analysis together with Google Cloud’s advanced gen AI technologies to help its customers and employees leverage new large language models (LLMs) to glean new financial insights and summarize financial data faster.
“Moody’s deep expertise in understanding financial data, disclosures, and reporting uniquely position us to anchor development of fine-tuned large language models. Google Cloud’s Gen AI will help our customers and employees produce new insights faster than ever before.”
Nick Reed
Chief Product Officer, Moody’s Corporation
Healthcare and life sciences
75%
of healthcare organizations consider digital patient concierges to be valuable
Priority use cases
Digital patient concierge
Easily locate, summarize, and generate health plan responses. Clearly explain plans and benefits to members and potential members.
Expedite Prior Authorization (PA)
Reduce clinicians’ admin time on drafting PA letters for procedures, medication, or medical devices, and accelerate patient care.
Public and private contextual search
Query and extract insights from public and private datasets, and summarize research into plain language.
Clinical trial report generation
Accelerate generation of clinical studies and reports, complete with safety/efficacy claims.
We’re already embedding Google Health’s search and summarization capabilities into our Expanse EHR and have delivered that solution to a customer; work we are collectively very proud of. We will be exploring next how the broader capability with Vertex AI Search can further empower providers and patients,” said Helen Waters, EVP and COO of MEDITECH. “Beyond simply synthesizing information, these capabilities can organize and surface the most important information to help overburdened care teams in their workflow.”
Now that we’ve moved our significant data, applications workload and other IT resources from on-premises to Google Cloud, using gen AI for search over our enterprise data has the potential to dramatically improve the information and insights we can deliver to our clinicians and our other team members,” said Kash Patel, EVP and chief digital information officer for Hackensack Meridian Health. “Data is at the core of how we are modernizing healthcare, and Vertex AI Search for healthcare and life sciences will help us make the most of our data.”
Media and entertainment
87%
of media and entertainment organizations consider media content discovery to be valuable
Priority use cases
Media content discovery
Help users discover new content with personalized, conversational search results based on previous behaviors.
Branded consumer interactions
Use intellectual property on media assets to create unique and personalized audience experiences.
Creative assistance
Make it easier for content creators to repurpose content in different formats, helping accelerate time to value and revenue.
Content summarization and metadata
Seamlessly extract metadata from media to enable personalization, monetization, and insights, and easily summarize long-form content.
Internal document and media search
Enable internal editing and operational teams to find the right content at the right time.
Forbes offers gen AIdriven news content recommendations
Forbes recently announced the beta launch of Adelaide, an online news search tool, offering visitors recommendations and insights from Forbes’ trusted journalism leveraging gen AI. What sets Adelaide apart is its conversational and searchbased approach, making content discovery easier and more intuitive. The tool is trained exclusively on Forbes content, utilizing Google Cloud’s gen AI tools.
“Forbes was an early adopter of AI nearly five years ago – and now AI is a foundational technology for our firstparty data platform, ForbesOne. As we look to the future, we are enabling our audiences to better understand how AI can be a tool for good and enhance their lives by offering a more personalized and insightful experience from start to finish.”
Vadim Supitskiy
Chief Digital and Information Officer, Forbes
TIME wants to build community, not just creativity, with LLMs
With trusted sources and chat resources, TIME wants to do more than deliver headline news — playing a bigger role as a beacon for accuracy. As media companies explore the possibilities of generative AI, the publisher sees an opportunity to strengthen its role as a trusted source and community builder.
For years, it’s been using AI-powered recommendations to build affinity and loyalty with readers. Now, with generative AI, TIME hopes to turn a one-way conversation into a dialogue.
“As publishers, what we’ve done for a hundred years, it’s been a oneway street: we put out content for the consumer and they consume it. With generative AI prompts and chat, we actually become able to understand, and have an interaction with, the consumer — creating experiences that are two-way in many ways. That’s why I actually see generative AI as a powerful tool for building community.”
Burhan Hamid
Senior VP Data, Product, and Engineering, TIME
Canva is solving for AI-powered design, for everyone
Canva is using the latest AI technology to empower their customers and make the design process as frictionless as possible. From enabling users to translate their designs into over 100 languages with just a few clicks, to turning short videos into longer and more compelling clips with Google PaLM technology, they are unlocking the magic of AI with Google Cloud.
“There is a quote that I love from the science fiction writer Arthur C. Clarke, ‘Any sufficiently advanced technology is indistinguishable from magic.’ Canva has always been about removing as much friction from the design process as possible and AI technology allows us to make the design process even easier with even less friction.
We are incredibly excited to be working with Google Cloud as we test and explore ways to bring even more magic to our community. Earlier this year we launched Magic Translate and what that enables you to do with just a couple of clicks turn any of your designs into over a hundred languages. Magic video is a time-saver for marketers and teachers who want to be able to cater to a diverse audience.”
Melanie Perkins
Co-founder & CEO, Canva
Manufacturing
80%
of manufacturing organizations consider machine generated events monitoring to be valuable
Priority use cases
Machine-generated events monitoring
Interpret telemetry from equipment to reduce unplanned downtime, optimize operations, and maximize utilization.
Document search and synthesis
Retain generations of documents throughout the product lifecycle, and use them to generate new content as needed.
Customer service automation
Provide an easy, informative, and value-added customer service experience that automates and accelerates time-to-resolution for common interactions.
Product/content catalog discovery
Efficiently match requirements to the specifications of products purchased.
Supply chain advisor
Optimize fulfillment by recommending best-suited suppliers based on relevant criteria.
GE Appliances helps consumers create personalized recipes from the food in their kitchen with gen AI
Leading appliance manufacturer, GE Appliances, will enhance and personalize consumer experiences with generative AI. Using Vertex AI, GE Appliances’ SmartHQ consumer app will offer users the the ability to generate custom recipes based on the food in their kitchen with its new feature called Flavorly™ AI. Another feature, SmartHQ Assistant, a conversational AI interface, will also use our generative AI to answer questions about the use and care of connected appliances in the home.
“We are transforming our entire business landscape, from product design and manufacturing to customer interaction. Our goal is to be ‘zero distance’ from our consumers, ensuring we always deliver products and experiences that truly resonate.”
Kevin Nolan
President and CEO, GE Appliances
U.S. Steel aims to simplify equipment maintenance with gen AI
United States Steel Corporation (U.S. Steel) is using Google Cloud’s gen AI technology to help drive efficiencies and improve employee experiences in the largest iron ore mine in North America. The first gen AI-driven application that it will launch is called MineMind™, which aims to simplify equipment maintenance by providing optimal solutions for mechanical problems. When fully operational, MineMind™ will help technicians reduce the amount of time to complete a work order by an estimated 20%. For example, the application will assist maintenance crews by guiding them through truck repairs, ordering parts and distilling complex information, providing verified source information to ensure accuracy.employees by increasing their productivity.
“Faster repair times, less down time, and more satisfying work for our techs are only some of the many benefits we expect with gen AI.”
David Burritt
President and CEO, U. S. Steel
General Motors explores gen AI for enhanced in-vehicle experiences
General Motors and Google Cloud have been collaborating to bring conversational AI technology into millions of GM vehicles, helping drivers in a variety of ways. GM’s decision to collaborate with Google Cloud in exploring new, business-wide generative AI applications builds on a journey the two companies began together in 2019 when GM named its first vehicles with Google built-in. Since then, the number of GM vehicles with Google builtin has grown, giving customers easy access to Google Assistant, Google Maps and Google Play, directly from their vehicles’ center displays. With this technology, GM’s OnStar virtual assistant is now handling more than 1 million customer inquiries a month in the U.S. and Canada.
“Gen AI has the potential to revolutionize the buying, ownership, and interaction experience inside the vehicle and beyond, enabling more opportunities to deliver new features and services.”
Mike Abbott
Executive Vice President, Software and Services, General Motors
Leading global airline supplier, GA Telesis, integrates generative AI technology
As a major supplier of essential equipment in the airline industry, where long-term relationships and trust are the bedrock of many business transactions, GA Telesis’ sales staff receive inquiries from global customers requesting quotes for all sorts of commercial aircraft and jet engine replacement parts.
The typical inquiry is not standardized, requiring sales representatives to quickly decipher the relevant aircraft or jet engine model, applicable codes, quantity required, preferred condition and provenance, and often most importantly, where the part is needed and when. Additionally, in order for airlines to meet their on-time performance metrics, inquiries are often urgent and logistics have to be factored into the equation. GA Telesis’ team is expected to accomplish what can resemble an impossible feat in minutes, not hours.
GA Telesis has selected Google Cloud’s Vertex AI Search and Conversation platform, which is designed to help businesses tune and deploy machine learning models, to help it quickly build innovative AI applications. Leveraging a new data extraction solution the GA Telesis technology team built internally, GA Telesis will be able to automatically synthesize purchase orders and quickly provide customers a quote, eliminating the need for sales teams to manually cross-reference emails with their inventory availability.
“In aerospace, GA Telesis will deploy Google Cloud’s generative AI technology to revolutionize the sales and service processes for the parts the company supplies to major global passenger and cargo carriers.”
Abdol Moabery
CEO, GA Telesis
Priority use cases
Customer or employee service automation
Make online customer service more conversational with human-like support and search.
Employee knowledge search
Make it easier and more effective for staff to get things done, with human-like bots providing IT support, self-service, and T2/T3 guidance to field techs.
Network planning and operations
Easily access and understand complex data on network performance, faults, inventory, infrastructure, and anomaly detection.
Test or code script generation
Generate and test experiments using real work experiences.
Advertising and creative content assistance
Generate interactive and relevant content with highly personalized messaging.
Contract analysis and negotiation
Automate contract negotiations with suppliers by analyzing bills, trends, and other supply data.
“As part of our pledge we’ve made around the value that we draw from AI, we’re looking to save millions of pounds by better understanding geographical information, alongside infrastructure and health and safety information, to optimize how our engineers design and deploy our fibre network, one of the largest infrastructure projects in the UK. It’s great the benefits that AI can bring.
Thomas Dücke
Chief Operating Officer, BT Group
When you think about what generative AI can do when it really reaches its full potential you can think of it as every human being having a personal assistant.”
Hesham Fahmy
Chief Information Officer, TELUS
Innovate faster with generative AI for business
When a new technology moves as fast as generative AI does, it can be hard to keep up.
As a strategic partner to our customers, Google Cloud helps leaders chart their path forward with the appropriate frameworks, tools, and governance structures — and ingrain a responsible, consciously cautious approach to AI across your organization.
Google is an AI-first company. Having already built some of the industry’s leading AI capabilities, we continue to focus on making it easy and scalable for everyone to innovate with AI.
We support the needs of generative AI in your organization in a number of ways.
We have the most comprehensive platform now available and ready to-go with strong support from leading organizations — helping you create amazing content, synthesize and organize information, automate processes, and build engaging customer experiences.
Your data is your data. We do not use our customers’ data to train Google’s models. The question we hear most is, “Do I have control of my data, brand, IP risk, and ability to meet regulatory requirements? The answer is “Yes”.
Everyone can be an AI developer. All users with varying levels of expertise can create innovative enterprise search, chat, and vision apps. We enable both business and technology practitioners to be more productive using AI assistants.
We deliver infrastructure that is optimized for AI workloads by giving you access to the latest GPUs and TPUs, a rich choice of deep learning VMs, and the ability to easily build custom AI software.
The Google Cloud AI portfolio supports all stages of your generative AI journey. With a rapidly growing suite of generative AI technologies being made available — along with new educational and consulting programs, blueprints for specific industry use cases, and our growing partner ecosystem — we are ready to get you and your teams learning, building with, and deploying generative AI.
We’re building a capability that allows you to get on the bus for generative AI and go where it’s going. Start building out your own enterprise skill sets and capabilities, such that when you find the right use cases, and the right value levers — you have the capability to do that.”
Carrie Tharp
VP Strategic Industries, Google Cloud
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