Alphabet CEO Sundar Pichai has compared the potential impact of artificial intelligence (AI) to the impact of electricity—so it may be no surprise that at Google Cloud, we expect to see increased AI and machine learning (ML) momentum across the spectrum of users and use cases.

Some of the momentum is more foundational, such as the hundreds of academic citations that Google AI researchers earn each year, or products like Google Cloud Vertex AI accelerating ML development and experimentation by 5x, with 80% fewer lines of code required. Some are more concrete, like mortgage servicer Mr. Cooper using Google Cloud Document AI to process documents 75% faster with 40% cost savings; Ford leveraging Google Cloud AI services for predictive maintenance and other manufacturing modernizations; and customers across a wide range of industries deploying ML platforms atop Google Cloud.

Together, these proof points reflect our belief that AI is for everyone, and that it should be easy to harness in workflows of all kinds and for people of all levels of technical expertise. We see our customers’ accomplishments as validation of this philosophy and a sign that we are taking away the right things from our conversations with business leaders. Likewise, we see validation in recognition from analysts, which recently includes Google being named a Leader by

Gartner® in the 2022 Magic Quadrant™ for Cloud AI Developer Services report

Forrester in the Forrester Wave™: AI Infrastructure, Q4 2021 report, the Forrester Wave™: Document-Oriented Text Analytics Platforms, Q2 2022 report, and The Forrester Wave™: People-Oriented Text Analytics Platforms, Q2 2022 report

In June, we talked about four pillars that guide our approach to creating products for MLOps and to accelerate development of ML models and their deployment into product. In this article, we’ll look more broadly at our AI and ML philosophy, and what it means to create “AI for everyone.”

AI should be for everyone

One of the pillars we discussed in June was “meeting users where they are,” and this idea extends far beyond products for data scientists. Technical expertise should not be a barrier to implementing AI—otherwise, use cases where AI can help will languish without modernization, and enterprises without well-developed AI practices will risk falling behind their competitors.

To this end, we focus on creating AI and ML services for all kinds of users, e.g.:

• DocumentAI, Contact Center AI, and other solutions that inject AI and ML into business workflows without imposing heavy technical requirements or retraining on users;
• Pre-trained APIs, ranging from Speech to Fleet Optimization, that let developers leverage pre-trained ML models and free them from having to develop core AI technologies from scratch;
• BigQuery ML to unite data analysis tasks with ML;
• AutoML for abstracted and low-code ML production without requiring ML expertise;
• Vertex AI to speed up ML experimentation and deployment, with every tool you need to build deploy and the lifecycle of ML projects
• AI Infrastructure options for training deep learning and machine learning models cost effectively. Including Deep Learning VMs optimized for data science and machine learning tasks and AI accelerators for every use case, from low-cost inference to high-performance training.

It’s important to provide not only leading tools for advanced AI practitioners, but also leading AI services for users of all kinds. Some of this involves abstracting or automating parts of the ML workflow to meet the needs of the job and technical aptitude of the user. Some of it involves integrating our AI and ML services with our broader range of enterprise products, whether that means smarter language models invisibly integrated into Google Docs or BigQuery making ML easily accessible to data analysts. Regardless of any particular angle, AI is turning into a multi-faceted, pervasive technology for businesses and users the world over, so we feel technology providers should reflect this by building platforms that help users harness the power of AI by meeting them wherever they are.

How we’re powering the next generation of AI

Creating products that help bring AI to everyone requires large research investments, including in areas where the path to productization may not be clear for years. We feel a foundation in research combines with our focus on business needs and users to inform sustainable AI products that are in keeping with our AI principles and encourages responsible use of AI.

Many of our recent updates to our AI and ML platforms began as Google research projects. Just consider how DeepMind’s breakthrough AlphaFold project has led to the ability to run protein prediction models in Vertex AI. Or how research into neural networks helped create Vertex AI NAS, which lets data science teams train models more accurately with lower latency and power requirements.

Research is crucial, but also only one way of validating an AI strategy. Products have to speak for themselves when they reach customers, and customers need to see their feedback reflected as products are iterated and updated. This reinforces the importance of seeing customer adoption and success across a range of industries, use cases, and user types. In this regard, we feel very fortunate to work with so many great customers, and very proud of the work we help them accomplish.

I’ve already mentioned Ford and Mr. Cooper, but those are just a small sampling. For example, Vodafone Commercial’s “AI Booster” platform uses the latest Google technology to enable cutting-edge AI use cases such as optimizing customer experiences, customer loyalty, and product recommendations. Our conversational AI technologies are used by companies ranging from Embodied, whose Moxie robot helps children overcome developmental challenges, to HubSpot connecting meeting notes to CRM data. Across our products and across industries around the world, customer stories grow by the day.

We also see validation in our partner network. As we noted in the pillars discussed in June, partners like Nvidia help us to ensure customers have freedom of choice when building their AI stacks, and partners like Neo4j help our customers to expand our services into areas like graph structures. Partners support our mission to bring AI to everyone, helping more customers use our services for new and expanded use cases.

Accelerating the momentum

Overall, to create products that reflect AI’s potential and likely future ubiquity, we have to take all of the preceding factors, from research to customer and analyst conversations to working with partners, and turn them into products and product updates. We’ve been very active over the last year, from the launch of Call Center AI Platform in March, to the new Speech model we released in May, to a range of announcements at the Google Cloud Applied ML Summit in June. We have much more planned in coming months, and we’re excited to work with customers not just to maintain the pace of AI momentum, but to accelerate it. To learn more about Google Cloud’s AI and ML services, visit this link or browse recent AI and ML articles on the Google Cloud Blog.

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Communication service providers (CSPs) are at an inflection point. From stagnating revenues, to network strain in meeting the demands of 5G, to challenges in delivering innovative customer experiences, there’s enormous pressure on the telecommunications industry to transform.

Over the past few years, CSPs around the globe have turned to artificial intelligence (AI) to address some of these challenges, but the lion’s share of an operator’s operational expenses are still spent on infrastructure and data management. This has limited their ability to capitalize on core data assets and develop differentiated customer experiences that meet individual needs. 

Enter generative AI, a type of machine intelligence that’s received enormous attention as of late. We’ve all marveled over its ability to generate text that reads like it was written by a person, to create new images, and to build even musical scores. It’s a riveting addition to the AI toolset — and one that complements machine learning (ML) and its ability to identify patterns to make predictions, spot efficiencies, or interpret large data sets. 

But while there’s a lot of hype around generative AI, at Google Cloud we view it through a much more practical lens for the telecommunications industry. Generative AI can accelerate the transformation already underway, with its potential to streamline many of the tools and processes that CSPs engage with daily, bringing a new level of natural interaction between people and computers, and enabling machines to be programmed to carry out an action with a spoken request, and respond in natural, interactive ways. 

Generative AI builds on existing Google Cloud data, AI, and ML services. For example, Contact Center AI, with human-like interactions between callers and computers, has been successfully adopted by CSPs for many years, increasing the satisfaction of both customers and call center workers. As we add generative AI to this technology, CSPs and their customers will see even greater capabilities and impact, for example, virtual agents that not only provide helpful information, but also let customers make payments and execute other transactions. With generative AI, CSPs will be able to harness customer call summaries to better understand customer sentiment and identify cross-sell and up-sell opportunities. CSPs could also easily and quickly build and deploy virtual agents informed by customer conversations that enable more innovative and personalized customer interactions. And that’s just the start.

Three key areas

The contact center is just one of the areas where practical and generative AI will help drive new value. When reflecting on the key challenges facing CSPs today, three areas in particular stand out where generative AI may be transformative:

1. Personalized experiences: In addition to further improvements in customer call center interactions, generative AI can deliver improved personalization in ecommerce interactions — a big factor in helping customers sort through their choices of phones and calling plans. Personalization is also important for lowering churn, offering relevant new services, and managing the customer lifecycle. For example, generative AI could enable CSPs to produce marketing campaign content customized for select themes, and target individual customers with customized text and images. 
2. Autonomous networks: Generative AI will also help to pave the way for autonomous networks by connecting multiple complex AI/ML models used across network planning and operations with large language models (LLMs) that can understand network behaviors and create action plans in areas including network capacity planning and performance. For example, generative AI will enable CSPs to train models with customer experience and sentiment data to build better prediction capabilities. Importantly, the customer data sets used to tune these models are not public, but curated internal customer data — significantly enhancing privacy, factuality, and relevance, while protecting intellectual property. In addition, generative AI will be able to help network planning and design, which requires high levels of reporting and analysis.
3. Streamlined operations: Both operations center uptime and field service efficiency are crucial for managing costs and improving customer satisfaction. In particular, applying generative AI to field service devices can speed up diagnostics and analysis, and can even help with installation, parts, and troubleshooting, and minimizing the number of times companies have to send out trucks and improve field-service training. Generative AI will also provide a productivity boost to IT development processes, enabling code generation and troubleshooting to deliver reliable software products and services.

Data security and reliability

An under-discussed area in generative AI is the importance of data quality and data security in building and training the LLMs that power the technology. Many CSPs are rightly concerned about intellectual property leaking both into and out of LLMs, risking the security of their systems and their intellectual property. We’ve long offered industry-leading data security and privacy technologies, and with generative AI integrated with Vertex AI, we can ensure all data is secured within the CSP’s environment.

Meanwhile, to ensure that their LLMs generate accurate information, CSPs are building out scenarios and use cases for training on smaller, controlled amounts of their own data, sometimes accompanied by highly trusted sources from partners and others. Google Cloud also provides tools including Prompt Engineering, Tuning, and Reinforcement Learning from Human Feedback to further ensure data factuality and reliability. This will likely result in the first generative AI applications targeted to smaller, high-impact problems, like optimizing network topologies. 

The human element

Of course, people are critical to the success of generative AI, whether that means solving problems in call centers, field service workers combining AI information with their own knowhow, marketing and creative teams brainstorming with generative AI to make new presentations and marketing materials, or operations engineers augmenting and approving an AI suggestion. We’ve built a lot of amazing technology to help augment what people cannot do: synthesize millions, if not billions, of records and sources to help energize new workflows and productivity.

Telecommunications is a fast-changing industry that is tech savvy and hungry to learn and deploy the best possible new technologies, and that includes generative AI. Each meeting with a CSP inspires new ideas, sparks more use cases, and leads to more industry-changing initiatives. It’s exciting to see this pace of change, and we are only just getting started.

We look forward to showing you more soon as we deep dive into some of the exciting CSP industry use cases in upcoming blog posts. We also invite you to find out more about how Google Cloud is partnering with CSPs around the world to deliver a holistic cloud transformation.

As lead for analytics and AI solutions at Google Cloud, my team works with startups building on Google Cloud. This puts us in the fortunate position to learn from founders and engineers about how early-stage startups’ investments can either constrain them or position them for success, even at the seed level. In this post, I want to share a few of the best practices to keep in mind as you’re building. 

Understand your value proposition before diving into a technology stack

If you’re launching a startup in the cloud, you’re no doubt thinking about a technology stack, but it’s important to step back a bit and think carefully about the major value proposition that your startup offers to your customers. That value proposition is going to fundamentally drive the kind of technology that you should pick.

For example, does your system need processing in real time, or can it be done in a batch mode? Can you rely on once-a-day insights or do the insights have to come in as events happen?

Additionally, what kind of latency will your customers face? That latency makes your value proposition either usable or unusable. Early on in Google’s development, leaders realized that no one was going to wait more than a few hundred milliseconds for a web page to show them their results, and that realization drove the technology decisions that have allowed Google to scale from being a startup in a garage to being a trillion dollar company. Your startup needs to define its value to customers with this level of specificity before it can build a technology stack suited to its needs. 

Focus on customer interactions

A few companies have gracefully pulled off big IT pivots that reshaped their value proposition. Netflix, for example, moved from mostly sending DVDs through the mail to becoming a streaming service and major content producer. That’s a huge shift in the user experience and the technology stack necessary to support it, even if the underlying value proposition (i.e., get content to customers) was broadly the same. But it’s also an outlier. If you’re planning for potential changes of this magnitude, rather than focused on getting your value proposition to users, you probably need to sharpen what that value proposition is.

Specifically, you need a clear vision of how customers will access and interact with your business. Typically, they’ll do so over a website or a mobile app, but there are still so many variables. 

Are customers going to transmit documents? If so, in what format? Is handwriting supported or is input limited to typing? Can they use images for optical character recognition? Will it mostly be forms? Will the data be structured or unstructured? If all that sounds  a little overwhelming, don’t worry, it’ll seem simpler by the end of this article—but also be aware: we’re just getting warmed up.

Imagine that most of your customers will access your business via voice, so you know you’ll want to prioritize conversational workflows. That’s a start—but dig deeper.  Even if we suppose you’re usingDialogflow, a Google Cloud conversational AI platform that lets you build and deploy virtual agents, we’re still not really seeing the value proposition.  How will all this work, from the beginning of a typical full customer interaction to the resolution? How many interactions will have to be facilitated over low-bandwidth connections, for example? When it comes to user interactions, make sure you can see an end-to-end use case.

Another example: you’re building a retail website, and one of your end-to-end use cases involves the customer asking if a certain amount of a given product is in stock, whether it’s one unit of the product, ten or hundreds. If the product is not sufficiently stocked, you want your app to offer similar items that are. Will your technology stack support this end-to-end use case?

These considerations are not an argument for premature optimization. There’s value in moving fast, getting minimum viable products to users, and then iterating. But in the early stages, you only get one chance to start on the right foot—and how you navigate that chance will influence a lot of dollars and effort down the road. You need to make sure you have business use cases, not just an idea, before you can start designing a technology stack.  

Here’s how to get in the right frame of mind. Pick three use cases: two that are “bread and butter” and one that is technologically complex.  Make sure your proposed technology stack can support all three, end to end. 

Default toward higher levels of abstraction

Now that we’re in the right frame of mind, we’re ready to think about the technology stack more directly. 

As a startup, you’ll need to conserve resources, and to do that, you’ll want to build at the highest level of abstraction possible for your value proposition. For example, you probably don’t want your people setting up clusters. You don’t want them configuring things if they can use a fully managed service. You want them focused on building your prototype, not managing infrastructure.

This focus has definitely informed how we create products at Google Cloud, as our canonical data stack—Pub/Sub, Dataflow, BigQuery, and Vertex AI—consists of auto-scaling and serverless products.

But management of infrastructure is not the only place where you should err toward a less-is-more philosophy. 

When it comes to architecture, choose no-code over low-code and low-code over writing custom code. For example, rather than writing ETL pipelines to transform the data you need before you land it into BigQuery, you could use pre-built connectors to directly land the raw data into BigQuery. That’s no code right there. Then, transform the data into the form you need using SQL views directly in the data warehouse. This is called ELT, and it is low code. You will be a lot more agile if you choose an ELT approach over an ETL approach. 

Another place is when you choose your ML modeling framework. Don’t start with custom TensorFlow models. Start with AutoML. That’s no-code. You can invoke AutoML directly from BigQuery, avoiding the need to build complex data and ML pipelines. If necessary, move on to pre-built models from TensorFlow Hub, HuggingFace, etc. That’s low-code. Build your own custom ML models only as a last resort.

Focus on getting your vision to market, not chasing technology hype  

The goal is to pick the right technology stack for bringing your vision to market, generating value for customers, conserving resources, and maintaining flexibility for growth. Early IT investments should usually gravitate toward things that preserve flexibility, such as managed services built on standard protocols or open APIs, but they needn’t always rush to the flashiest technologies.  The answer isn’t always ML, for example. The answer might be heuristics to start, with a path to ML once you have collected enough data. You want to make sure that your intelligence layer has enough abstraction so you can mark it up with simple rules at first, but then replace it with a more robust system as you go along. 

Launch and iterate fast with these principles 

The preceding discussion is a reminder that your most expensive resource is your people—and that you really want them to be focused on building your prototype, minimum viable product or production app  You want to launch fast and iterate fast, and the only way you can do that is by focusing on the things that differentiate you. 

But regardless of the technologies you use, the bottom line is the same: follow these four principles. 

• Figure out your major value proposition and design your tech stack around it. 
• Be very careful about user interactions. User experience is super important; you need to make sure you deliver the kind of experience that your customers have grown to expect.
• When you’re building, pick the highest possible level of abstraction possible—the most fully managed tools and no-code/low-code frameworks that give you the functionality that you need. 
• Instead of choosing new or flashy technologies, consider if you can build a “good enough” minimum viable product quickly and come back to a better implementation later. 

To learn more about why startups are choosing Google Cloud, click here.