By Bryan Kirschner, Vice President, Strategy at DataStax

In their 2020 book Competing in the Age of AI, Harvard Business School professors Marco Iansiti and Karim Lakhani make some bold predictions about the winning enterprises of the future.

These organizations, which they refer to as “AI factories,” build a “virtuous cycle between user engagement, data collection, algorithm design, prediction, and improvement,” unlocking new paths to growth as software moves to the core of the enterprise.

A little more than two years after the publication of their seminal work, data gathered from IT leaders and practitioners lend a lot of credence to Iansiti and Lakhani’s hypotheses — particularly those regarding the kind of technology architectures and strategies that engender success with AI.

The AI factory

Successful AI companies — think Apple, Netflix, Google, Uber, or FedEx — build innovative applications and, as they scale, start the flywheel of data, growth, and improvement spinning by gathering ever-growing amounts of real-time data, accessing it instantly, and tuning their predictions.

User experiences become more personal and intuitive; key decisions can be made nearly instantaneously; and predictions can occur in real-time, empowering a business to improve outcomes in the moment.

This unlocks new paths to growth: in the authors’ words, as AI factories “accumulate data by increasing scale (or even scope), the algorithms get better and the business creates greater value, something that enables more usage and thus the generation of even more data.”

For more traditional firms to achieve this kind of success requires a host of changes in both their operating models and technology profiles.

Open-source software and AI success

The State of the Data Race 2022 report is based on a survey of over 500 IT leaders and practitioners that delved into their organizations’ data strategies.

For the purpose of this analysis, responses were divided into three groups:

those where both AI and ML are already in widespread deploymentthose where AI and ML are at most in the pilot phase or early daysthose in between these two extremes, characterized as being in “limited deployment”

The study assumed the organizations with AI/ML widely in production provide useful information about the evolving shape of the “AI factory” and looked for differences across the three stages of maturity.

Iansiti and Lakhani wrote that AI factories will evolve “from a focus on proprietary technologies and software to an emphasis on shared development and open source” because the competitive advantage they enjoy comes from data they accumulate — not the software they develop in-house.

The survey data backs this up in spades. A strong majority of each of the three AI/ML groups considers open-source software (OSS) at least “somewhat” important to their organization (73%, 96%, and 97%, respectively, ordered from “early days” to “wide deployment”).

But ratings of “very” important closely track AI/ML maturity: 84% of companies with AI/ML in wide deployment describe OSS this way (22%of “early days” organizations do, and this jumps to 46% of those with AI/ML in limited deployment).

Perhaps even more striking, organizations not using OSS are a tiny minority (1%, 1%, and 7%, ordered from “wide deployment” to “early days”). But a majority of those with AI/ML in wide deployment (55%) join companies like The Home Depot in having a company-wide mandate for use of OSS.

Real-time data and AI

Consider the AI leaders mentioned above. These companies have assembled technology infrastructures that enable instantaneous changes and decisions based on real-time feedback. Relying on day-old data and batch processing to update the routing of a package to ensure on-time delivery just doesn’t cut it at FedEx.

So, it isn’t surprising that Iansiti and Lakhani report that AI factories lean into real time. “The top enterprises … develop tailored customer experiences, mitigate the risk of customer churn, anticipate equipment failure, and enable all kinds of process decisions in real time,” they say.

Much like with OSS, findings from The State of the Data Race point to real-time data (and the technology architecture that enables it) as a matter of core strategy for the AI leaders. The substantial use of this correlates with AI maturity: 81% of companies that have broadly deployed AI/ML say real-time data is a core strategy. Forty-eight percent of organizations with limited AI/ML deployment describe it as a core strategy; the figure was 32% for companies in the early stages of AI/ML.

But among the advanced group, a full 61% say that leveraging real-time data is a strategic focus across their organization (four times that of organizations in the early days, and more than twice that of those with limited deployment). And 96%of today’s AI/ML leaders expect all or most of their apps to be real time within three years.

This makes sense: as an enterprise intentionally rewires its operations to make the most of AI/ML, it becomes especially important to eliminate any arbitrary architectural barriers to new use cases that require “speed at scale” anywhere in the business.

Today’s OSS as-a-service ecosystem makes that possible for everyone, freeing the future organization to make the most of its unique customer interactions and datasets.

Uniphore: A case study in real-time data, AI, and OSS

Uniphore helps its enterprise customers cultivate more fruitful relationships with their customers by applying AI to sales and customer service communications. The company relies on real-time data to quickly analyze and provide feedback to salespeople upon thousands of customer reactions during video calls.

“We have about fourteen different AI models we run in real time to coalesce the data into something meaningful for our clients,” says Saurabh Saxena, Uniphore’s head of technology and VP of engineering. “Any kind of latency is going to have a negative effect on the real time side.”

“Without the ability to process data in real-time, our solution really wouldn’t be possible,” he adds.

To get “the speed they need,” Uniphore relies on open-source Apache Cassandra® delivered as a service via DataStax (my employer) Astra DB. Its performance and reliability are key to ensuring Uniphore’s system is something every salesperson is motivated to rely on in order to be more effective in the moment.

But winning adoption among line staff points to another of Iansiti and Lakhani’s insights on the implications of AI for senior management. As the latter explained in a 2021 interview, “AI is good at predictions” — and predictions are “the guts of an organization.” Senior executives need to constantly ask, “Do I have data now to improve my prediction power — my accuracy, my speed?”

As Uniphore points out, sales forecast accuracy is something most sales leaders are concerned about. As a knock-on effect of using Uniphore’s tools, quantitative data on sentiment and engagement can flow into sales forecasts without the need for more staff time. In addition to the direct uplift that sellers experience, forecasts improve– — management to spend their time on more important things, like investing for growth, with greater confidence.

This closes the loop on Iansiti and Lakhani’s insight that AI factories can unlock a more powerful operating model over and above the benefits of individual use cases and point solutions.

Building an AI factory

Organizations that leaned into the insights in Competing in the Age of AI may have stolen a march on their competition. Judging from our survey data, they’ve been amply rewarded for doing so. The good news is that they’ve proven best practices for success — and the tools you need to accelerate your own progress on the journey to becoming an “AI factory” are ready and waiting.

Learn how DataStax enables AI-powered apps

About Bryan Kirschner:

Bryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.

Artificial Intelligence, IT Leadership

A lawsuit has been filed against 13 current and former IBM executives, including CEO and Chairman Arvind Krishna and former CEO Ginni Rometty, accusing the company of securities fraud — bundling mainframe sales together with those of poorly performing products in order to make them appear more profitable than they actually were.

The lawsuit was filed on January 13 in the U.S. District Court for the Southern District of New York,  and seeks class action status for anyone who purchased IBM shares during the period April 4, 2017, to Oct. 20, 2021.

The complaint alleges that the company and some of its executives “knowingly or recklessly engaged in a device, scheme, or artifice to defraud, engaged in acts, practices, and courses of business conduct designed to deceive investors.”

Essentially, it’s alleged that IBM promoted its less popular cloud, analytics, mobile, social, and security products (CAMSS) products as “growth opportunities,” allowing investors to think they were much in demand when, in fact, they were being tacked onto three- to five-year mainframe Enterprise License Agreements (ELA) that were popular with large banking, healthcare, and insurance company customers.

“Defendants misled the market, engaging in a fraudulent scheme to report billions of dollars in mainframe segment and other non-strategic revenues as Strategic Imperatives and CAMSS [“Cloud,” “Analytics,” “Mobile,” “Security,” and “Social,”] revenues, enabling Defendants to report publicly materially distorted segment information,” the lawsuit states. “Defendants portrayed Strategic Imperatives and CAMSS as growing materially beyond actual growth, materially misrepresenting IBM’s shift away from its stagnant legacy mainframe segment.”

According to IBM, “strategic imperatives” are products and initiatives that provide “differentiation driven growth and value.”

IBM is also alleged to have reallocated revenue from its non-strategic Global Business Services (GBS) segment to the company’s Watson-branded AI products — a strategic imperative included in the CAMSS product portfolio — in an attempt to convince investors that the company was successfully expanding beyond its legacy business. As a result, “IBM securities traded at artificially inflated prices” resulting in financial damage to people purchasing company shares during the period covered by the lawsuit, according to the lawsuit.

In response to a request for comment, IBM emailed a statement that said, “IBM’s long-standing commitment to trust, integrity and responsibility extends across all aspects of our business operations. A similar complaint has already been voluntarily dismissed.” 

 In fact, the same complainant who filed the lawsuit last week — the June E. Adams Irrevocable Trust for the benefit of Edward Robert Adams and others who may join the lawsuit   — filed a similar lawsuit last April, then filed a notice in September moving for voluntary dismissal of the case “without prejudice,” reserving the ability to refile the suit.

The reason behind the move to abandon that case was due to disagreement with the lead law firm at the time about how to handle the case, according to The Register, which first reported on the new case filed last week. The law firm submitting the new lawsuit, The Rosen Law Firm, declined to comment.

The case filed last April alleged that IBM had bolstered its stock price and deceived shareholders by moving revenue from its non-strategic mainframe business to its strategic business segments. This previous lawsuit further alleged that by misrepresenting the true nature of CAMSS revenue, it allowed IBM executives to take home larger bonuses than they otherwise would have received.

While this new lawsuit once again alleges that IBM strategically shifted revenue, it omits the accusation related to executive bonuses.

According to the PACER electronic records system, the new case has been referred to District Judge Vincent L. Briccetti, who will have to decide whether to certify class-action status for the lawsuit.

Briccetti is currently adjudicating another ongoing lawsuit filed against IBM. In that case, filed in March last year, Gerald Hayden, an ex-IBM employee, accuses IBM of theft of trade secrets and intellectual property.  Hayden alleges that, while he worked for IBM, the company unlawfully used his proprietary business method — A2E — that he had developed to streamline enterprise sales.

Hayden’s lawsuit alleges that IBM, after promising it would protect his trade secrets, used A2E on projects that he was not working on, moving some of his clients to new projects in areas of the company including cloud and Watson — essentially transferring clients that he had attracted via the A2E methodology from GBS to newer strategic projects.

“IBM thus used A2E’s value proposition to drive IBM’s claimed reinvention of itself as a leader in the hybrid cloud computing industry and as an invaluable consultant to the financial services,” according to the lawsuit. “To add insult to injury, after stealing Plaintiff Hayden’s proprietary A2E business methodology and stripping him of his client base, IBM shortly thereafter terminated Plaintiff for ‘lack of work.’ “

(Additional reporting by Marc Ferranti.)

IBM, Legal, Technology Industry

Next generation chatbots are now writing poetry and giving math lessons, but these smart applications have a bigger job to do. Advanced chatbots simulate human interaction via complex artificial intelligence (AI) processes, or conversational AI. As business-ready systems, conversational AI is joining mainstream tech to deliver strategic benefits to customers and employees. For companies looking to adopt or expand their use of conversational AI, there’s quite a bit to understand and consider. 

Now that humans and machines are talking to each other, decision-makers will need clarity around the capabilities—especially as they vet various products and platforms. It helps to start by defining some key terms.

Artificial intelligence (AI): A wide-ranging category of technology that allows computers to “strive to mimic human intelligence through experience and learning.”[1] Common AI applications involve analysis of language, imagery, video, and data. Machine learning (ML): In its definition of AI, Gartner cites ML as one of AI’s notable “advanced analysis and logic-based techniques,”[2] whereby computer systems can learn from their experiences without explicit programming. Natural language processing (NLP) focuses on machine reading comprehension through grammar and context, enabling it to determine the intended meaning of a sentence.  Known for applications such as voice-to-text and language translation, NLP uses AI and often ML to enable a computer to understand spoken or written human language. Natural language generation (NLG) focuses on text generation, or the construction of text in English or other languages, by a machine and based on a given dataset.Conversational AI: This advanced application of NLP is what allows people to have a spoken or written conversation with a computer system. At their best, conversational AI systems closely match human conversation—passing a measure called the Turing test.[3] Here’s how it works from a technical perspective: During the automatic speech recognition (ASR) stage, a person may ask a question and the application converts that audio waveform to text. During the NLP phase, the question is interpreted, and the device generates a smart response. Finally, the text is converted back into audio for the user during the text-to-speech (TTS) stage. 

A Quick Rundown of How Conversational AI Works

Asking a smart phone whether it’s going to rain, telling a virtual assistant to play ’90s hip hop, requesting a navigation system give directions to a new sushi restaurant—each are examples of interacting with conversational AI. By speaking in a normal voice, a person can communicate with a device that understands, finds answers, and replies with natural-sounding speech.

Conversational AI may seem simple to the end user. But the technology behind it is intricate, involving multiple steps, a massive amount of computing power, and computations that occur in less than 300 milliseconds. When an application is presented with a question, the audio waveform is converted to text in what’s known as the automatic speech recognition stage. Using NLP, the question is interpreted and a response is generated. At the next step, called text-to-speech, the text response is converted into speech signals to generate audio. 

Why Customers and Employees Prefer Conversational AI
Most people have experienced the frustration of talking to a legacy chatbot, and perhaps even resorted to anger or shouting “Representatitive!”. But once chatbots are enhanced with conversational AI capabilities, research shows customer satisfaction rates to be three times higher, attributed to shorter wait times and more accurate, consistent customer support.[4]

For employees, conversational AI can reduce stress and boost productivity by handling most low-level tasks and easing their day-to-day human-machine interactions. This frees up staff for other valuable and higher-level functions, benefiting customers and increasing morale.

Overall, for companies, the benefits may seem obvious: more productive staff and better customer service leading to increased productivity as well as higher customer satisfaction and retention rates. An additional benefit comes from the learning and training of models that continually improve and enhance employee and customer experiences.

Conversational AI in Action, From Retail to Healthcare to Real Estate

In constant search of competitive advantage, companies are increasing their investments in AI to the tune of a projected $204 billion by 2025.[5] Across industries, the technology promises to deepen customer insights, drive employee efficiency, and accelerate innovation. 

In retail, conversational AI is giving shoppers a streamlined experience with call centers and customer service interactions. As the clunky chatbots of yore are replaced with savvy AI chatbots, customers can quickly get their questions answered, receive product recommendations, find the proper digital channel for their inquiry, or connect with a human service agent. 

In healthcare, applications for conversational AI can support telehealth patient triage to identify potential medical conditions. Systems can also be trained to securely manage patient data—making it easier to access information such as test results or immunization records. And the technology can support patients who are scheduling an appointment, checking on insurance eligibility, or looking for a provider.

In real estate, conversational AI tools are being applied to the time-sensitive lead generation process, automating functions for accuracy and efficiency. Chatbots are also handling initial conversations to assess what a customer is looking to buy or sell. Given AI’s ability to handle thousands of calls per day, a program can be integrated with the customer relationship management system, or CRM, to create more positive experiences.

Five Questions to Ask Before Deploying a Conversational AI System

Once a company is ready to explore a conversational AI project, there will be groundwork. Here are five essential questions—and clues to finding the answers.

What kind of hardware do you need? The answer depends on the application scope and throughput needs. Some implementations rely on ML tools and run best on high-performance computing. Others may be more limited in scope. In any case, Dell Technologies Validated Designs offer tested and proven configurations to fit needs based on specific use cases.Which user interface options will your project support? Whether it’s a text-only chatbot or the more user-friendly voice interface, the decision must be based on what’s best for the customer and the budget.What platforms will be supported? Determine how customers might access the chatbot—via mobile app, web, social media—and think about whether to integrate with popular voice assistants. Will you build your own or rely on a vendor? Doing it in-house requires expertise and time but offers more control. If selecting a vendor, consider whether one vendor or multiple vendors will be needed for the end-to-end system. What kind of infrastructure will you need? This depends on whether the implementation will be hosted in a private or public cloud service. For those hosting in their own data centers, likely for compliance or security reasons, be sure the vendor’s systems are designed specifically to meet the speed and performance for conversational AI. 

As consumers become more familiar with AI, using it to create art and pay bills and plan their workouts, the technology holds greater professional promise. Conversational AI is already supporting a number of essential business functions—a boon for customers, staff, and the bottom line. Executives can set the foundation for their own advanced chatbots and other applications by ensuring their IT systems are ready for innovation. 

Read the Guide to Conversational AI for Financial Services, and explore AI solutions from Dell Technologies and Intel

IT Leadership

What is the future of analytics and AI? And how can organizations thrive in an era of disruption? We asked Bryan Harris, Executive Vice President and Chief Technology Officer of analytics software company SAS, for his perspective.

Q: What is your advice to technology leaders for improving organizational resiliency?

A: Right now, we are all in a race against disruption and data. Customer buying habits have changed. Work-life balance has changed. The financial climate has changed. So, how do you establish a data-driven culture to identify and adapt to change? Or, in other words, what is the learning rate of your organization?

This is why executing a persistent data and analytics strategy is so important. It allows you to create a baseline of the past, identify when change happens, adapt your strategy, and make new, informed decisions. This process is your organization’s learning rate and competitive advantage.

Q: How can AI and analytics help business and technology leaders anticipate and adapt to disruption?

A: We are creating data that is outpacing human capacity. So the question becomes: how do you scale human observation and decision-making? AI is becoming the sensors for data in the world we’re in right now. So, we need analytics, machine learning and artificial intelligence (AI) to help scale decision-making for organizations.

Q: What best practices do you recommend around developing and deploying AI?

A: When we talk to customers, we first show them that the resiliency and agility of the cloud allows them to adapt quickly to the changing data environment.

The second step is lowering the barrier of entry for their workforce to become literate in analytics, modeling and decision-making through AI, so they can scale their decision-making. Everyone has a different maturity spot in that curve, but those who achieve this outcome will thrive – even in the face of disruption.

I recommend the following best practices:

Think about the ModelOps life cycle, or the analytics life cycle, as a strategic capability in your organization. If you can observe the world faster, and make and deploy insights and decisions as part of AI workloads faster, you can see the future ahead of time. This means you have a competitive advantage in the market.Innovate responsibly and be aware of bias. We give capabilities and best practices to our customers that allow them to understand the responsibility they have when scaling their business with AI. And we are taking a practical approach to helping customers adhere to the ethical AI legislative policies that are emerging.Ensure explainability and transparency in models. You won’t have adoption of AI unless there is trust in AI. To have trust in AI, you must have transparency. Transparency is critical to the process.

Q: What does the future hold for AI and analytics?

A: Synthetic data is a big conversation for us right now. One of the challenges with AI is getting data labeled. Right now, someone must label, for example, a picture of a car, a house or a dog to train a computer vision model. And then, you must validate the performance of the model against unlabeled data.

Synthetic data, in contrast, allows us to build synthetic data that is statistically congruent to real data. This advancement represents a huge opportunity to help us create more robust models — models that aren’t even possible today because conventional data labeling is too challenging and expensive. SAS lowers the cost of data acquisition and accelerates the time to a model.

If, because of this innovation, they get insights about the future, companies gain a competitive advantage. But they must do it responsibly, with awareness of the bias that AI may inadvertently introduce. That is why we provide capabilities and best practices to our customers that allow them to understand the responsibility they have when scaling their business with AI.

For more information, download the SAS report – “4 Winning Strategies for Digital Transformation” – here.

Artificial Intelligence

Contact centers are evolving rapidly. The days of single-channel, telephony-based call centers are long gone. This old model has given way to the omnichannel customer experience center.

In legacy call centers, the customer’s pathway through sales or service was relatively linear. Call in, speak to an agent, and (hopefully) resolve the issue. In this system, the manager’s focus was strictly on ensuring there would be enough well-trained staff to handle every call as efficiently as possible.

Nowadays, however, the customer journey is more complex, and the path to successful customer experience (CX) may weave its way through various channels, touching both human and robot agents along the way. Today’s managers must not only build an adequate staff, but they must also choose the right solutions to effectively meld together technological and human elements to deliver a near-flawless CX. 

Although many solutions have proved important for managers seeking to create successful contact centers, none are more important than the cloud and conversational AI. You might think of these as the twin pillars of success for today’s contact centers. However, as we’ll discuss here, they’re not sufficient on their own. There’s a third pillar to consider: quality assurance, or dedication to ensuring a finely tuned customer experience at every stage in the customer journey.

The cloud makes the contact center omnipresent

It looks like we’ve reached the tipping point for cloud adoption in contact centers. Deloitte reports that 75% of contact centers plan to migrate their operations to the cloud by mid-2023, if they haven’t already done so. IDC forecasts that investments in cloud solutions will account for 67% of infrastructure spending by 2025, compared to only 33% for non-cloud solutions. Genesys, a major contact center provider, recently announced that, going forward, it will focus its efforts on its Genesys Cloud CX software rather than its on-premises solutions.  

Considering the cloud’s potential, it’s not surprising to see that it’s taking over. Fundamentally, the cloud allows contact centers to keep pace with the changing expectations of employees and customers simultaneously.

The pandemic quickly changed what both groups were looking for. Employees came to expect more accommodating remote work arrangements, and those expectations have held strong even in 2022. According to research by Gallup, only 6% of workers who can do their jobs remotely actually want to return to a full on-site arrangement. Expectations for CX, meanwhile, have continued to rise to new heights, whether in terms of omnichannel service or personalized experiences.

The cloud makes it much easier for contact centers to meet these expectations. Without the need to rely on legacy, brick-and-mortar infrastructure, remote agents can deliver service to customers from anywhere at any time. Plus, the cloud more effectively facilitates seamless omnichannel service delivery and efficient software updates.

From setup to ongoing execution, the cloud is simply easier to manage. With no telecom hardware to purchase, installation and setup happen more quickly. And contact centers can rapidly scale up and down as needed, and when needed, allowing them to effectively manage costs.

The net effect of these benefits is that the cloud creates a new kind of contact center — one that’s omnipresent to deliver a modern customer experience from anywhere and to anyone.

Conversational AI transforms CX

One of the key benefits of moving to the cloud is the availability of conversational AI that can power self-service solutions. This technology, which is indispensable to chatbots and IVR, enables bots to interact with customers in natural — even human — ways.

Thanks to powerful components of AI, such as natural language processing and machine learning, bots are increasingly able to provide much of the service customers seek. In fact, in today’s self-service economy, conversational AI allows consumers to solve many of their own issues. Even more, the machine learning capabilities of AI allow it to easily and quickly collect customer data and use it to personalize the service experience. Unsurprisingly, organizations that employ conversational AI see a 3.5-fold increase in customer satisfaction rates.

That boost in customer satisfaction stems not only from offering personalized self-service, but also from organizations making the most of their human service. While bots handle many of the simpler requests, they reserve agents’ time for handling more complex matters. Ultimately, companies that deploy them can improve customer service while also cutting costs by between 15% and 70%.

This AI-powered CX transformation is already well underway in many industries. Banks use conversational AI to power customer self-service with simple tasks, like money transfers and balance inquiries. Hotels employ it to offer streamlined booking and concierge services. And retailers put it to work engaging customers in more personalized ways.

These are only a few of the basic benefits that forward-thinking companies can gain from deploying conversational AI. Its more advanced forms will power a new kind of proactive CX in the years ahead, shaped by powerful tools like sentiment analysis. 

True success requires a third pillar: quality assurance

Although critical for today’s contact centers, those two pieces are incomplete without the third pillar of quality assurance.

The expanded service capacities enabled by the cloud and conversational AI add new layers of complexity to a contact center’s CX delivery. Cloud migration, for instance, often involves bringing together many disparate legacy systems and remapping the entire customer journey. It requires extensive testing and mapping to make sure it’s done right. 

And as powerful as conversational AI is, it still requires a lot of human guidance to ensure it’s doing its job correctly. Without the capacity for that guidance, IVR or chatbot solutions may cause more CX problems than they solve. They can also be more costly — defects discovered in the IVR or chatbot production environment are much more expensive to undo than they would be when discovered in design.

The best way to provide cost-effective quality assurance is through a robust set of testing solutions that can work with any cloud, IVR, or chatbot solution that a contact center uses. As a platform-agnostic CX assurance solution, that’s exactly what Cyara is designed to do. 

With a powerful solution like Cyara, businesses can speed up cloud migration, correct voice quality issues, load-test IVRs, and performance-test chatbots, regardless of which solutions they use. They can even run more advanced chatbot tests to see how well they follow natural human conversation flows and recognize various speech patterns.

This kind of quality assurance allows contact centers to jump to the cloud and deploy conversational AI with confidence, knowing that both will push their CX forward. Together, these three pillars provide a firm foundation for contact centers of the future.

Ready to get started? Cyara can provide assurance for your cloud migration so you can start building these pillars. Reach out to get started today.

Digital Transformation

Nvidia used to be just a graphics chip vendor, but CEO Jensen Huang wants you to know that the company is now a full-stack computing service provider, and that he may be an artificial construct.

With such lofty ambitions, Nvidia is moving into the cloud, delivering both hardware and software as-a-service. At the company’s GTC Fall conference last week, Huang showed off a few new toys for gamers, but he spent most of his keynote speech outlining the tools Nvidia offers CIOs to accelerate computing in the enterprise.

There was hardware for industrial designers in the new Ada Lovelace RTX GPU; a chip to steer self-driving vehicles while entertaining passengers; and the IGX edge computing platform for autonomous systems.

But it wasn’t only hardware. Software (for drug discovery, biology research, language processing, and building metaverses for industry) and services including consulting, cybersecurity, and software- and infrastructure-as-a-service in the cloud were there too.

Huang punctuated his keynote with demos of a single processor performing photo-realistic, real-time rendering of scenes with natural-looking lighting effects, an AI that can seamlessly fill in missing frames to smooth and speed up animation, and a way of training large language models for AI that allow them to respond to prompts in context-dependent ways. The quality of those demos made it at least somewhat plausible when, in a videoconference with journalists after the keynote, the on-screen Huang quipped, “Don’t be surprised if I’m an AI.”

Joking aside, CIOs will want to pay serious attention to Nvidia’s new cloud services play, as it could enable them to deliver new capabilities across their organizations without increasing equipment budgets. In an age when hardware costs are likely to climb and the industry’s ability to pack more transistors into a given area of silicon is stalling, challenges still exist for many.

“Moore’s law is dead,” said Huang, referencing Gordon Moore’s 1965 statement that the number of transistors on microchips will double about every two years. “And the idea that a chip is going to go down in cost over time, unfortunately, is a story of the past.”

Many factors are contributing to the troubles of chip makers like Nvidia, including difficulty obtaining vital tooling and the rising cost of raw materials such as neon gas (supplies of which have been affected by the war in Ukraine) and the silicon wafers chips are made from.

“A 12-inch wafer is a lot more expensive today than it was yesterday,” Huang said. “And it’s not a little bit more expensive, it is a ton more expensive.”

Nvidia’s response to those rising costs is to develop software optimized so customers get the most out of its processors, helping redress a price-performance balance. “The future is about accelerated full stack,” he said. “Computing is not a chip problem. Computing is a software and chip problem, a full stack challenge.”

Fine-tuning NeMo

To underline that point, Nvidia announced it’s already busy optimizing its NeMo large language model training software for its new H100 chip, which has just entered full production. The H100 is the first chip based on the Hopper architecture that Nvidia unveiled at its Spring GTC conference in March. Other deep learning frameworks being optimized for the H100 include Microsoft DeepSpeed, Google JAX, PyTorch, TensorFlow, and XLA, Nvidia said.

Nvidia Hopper

NeMo also has the distinction of being one of the first two Nvidia products to be sold as a cloud-based service, the other being Omniverse.

The NeMo Large Language Model Service enables developers to train or tailor the responses of large language models built by Nvidia for processing or predicting responses in human languages and computer code. The related BioNeMo LLM Service does something similar for protein structures, predicting their biomolecular properties.

Nvidia’s latest innovation in this area is to enable enterprises to take a model built from billions of parameters and fine-tune it using a few hundred data points, so a chatbot can provide responses more appropriate to a particular context. For example, if a chatbot asked, “What are the rental options?” it might respond, “You can rent a modem for $5 per month,” if it were tuned for an ISP; “We can offer economy, compact and full-size cars,” for a car rental company; or, “We have units from studios to three bedrooms,” for a property management agency.

Such tuning, Nvidia said, can be performed in hours, whereas training a model from scratch can take months. Tuned models, once created, can also be called up using a “prompt token” combined with the original model. Enterprises can run the models on premises or in the cloud or, starting in October, access them in Nvidia’s cloud through an API.

Omniverse Cloud

Nvidia’s Omniverse platform is the foundation of the other suite of cloud services the company offers.

Huang described the platform as having three key features. One is the ability to ingest and store three-dimensional information about worlds: “It’s a modern database in the cloud,” Huang said. Another is its ability to connect devices, people or software agents to that information and to one another. “And the third gives you a viewport into this new world, another way of saying it’s a simulation engine,” Huang said.

Those simulations can be of the real world, in the case of enterprises creating digital twins of manufacturing facilities or products, or of fictional worlds used to train sensor networks (with Omniverse Replicator), robots (with Isaac Sim), and self-driving vehicles (with Drive Sim) by feeding them simulated sensor data.

There’s also Omniverse Nucleus Cloud, which provides a shared Universal Scene Description store for 3D scenes and data that can be used for online collaboration, and Omniverse Farm, a scale-out tool for rendering scenes and generating synthetic data using Omniverse.

Industrial giant Siemens is already using the Omniverse platform to develop digital twins for manufacturing, and Nvidia said the company is now working on delivering those services to its customers using Omniverse Cloud.

Omniverse Farm, Replicator and Isaac Sim are already available in containers for enterprises to deploy on Amazon Web Services’ compute cloud instances equipped with Nvidia GPUs, but enterprises will have to wait for general availability of the other Omniverse Cloud applications as Nvidia managed services. The company is now taking applications for early access.

Nvidia is also opening up new channels to help enterprises consume its new products and services. Management consulting provider Booz Allen Hamilton offers enterprises a new cybersecurity service it calls Cyber Precog, built on Nvidia Morpheus, an AI cybersecurity processing framework, while Deloitte will offer enterprise services around Nvidia’s Omniverse software suite, the companies announced at GTC.

As Nvidia works with consultants and systems integrators to roll out its SaaS and hardware rental offerings, that doesn’t mean it’s going to stop selling hardware outright. Huang noted that some organizations, typically start-ups or those that only use their infrastructure sporadically, prefer to rent, while large, established enterprises prefer to own their infrastructure.

He likened the process of training AI models to operating a factory. “Nvidia is now in the factory business, the most important factory of the future,” he says. Where today’s factories take in raw materials and put out products, he said, “In the future, factories are going to have data come in, and what comes out is going to be intelligence or models.”

But Nvidia needs to package its hardware and software factories for CIOs in different ways, Huang said: “Just like factories today, some people would rather outsource their factory, and some would rather own it. It just depends on what business model you’re in.”

CIO, Cloud Management

For most enterprises, artificial intelligence efforts are no longer science projects or skunkworks distractions. The technology has matured and companies are finding real value in pragmatic use cases that generate actionable insights or unlock new revenue streams. Success is possible for companies that prepare, invest, and partner with AI-fluent experts.

The opportunities around AI have now expanded significantly. Businesses rely on AI for customer service; to improve clinical outcomes in healthcare scenarios; to better assess financial risk; and predict the need for maintenance on manufacturing equipment to improve uptime.

The keys to these successes are bold IT leadership and collaboration within an AI center of excellence that joins together the infrastructure, data science, and business teams who can bring these applications to life.

“Most IT leaders want to be seen as enablers of business transformation,” says Tony Paikeday, senior director of AI systems at NVIDIA. “AI is the most transformative force taking hold in enterprises. This is a unique place where an IT leader can be seen front and center in transforming a business through platform, infrastructure and process, instead of just being a cost center that reacts to problems.”

While each AI project is unique and different, it helps to look back at previous success stories to inspire companies looking to run similar projects. Here are a few examples of how AI has helped to transform organizations:

Domino’s delivers more than 3 billion pizzas a year. They wanted to leverage massive amounts of data and use AI to improve operational efficiencies and customer experience. Using purpose-built AI systems, they were able to quickly train complicated models — taking into account variables such as how many managers and employees are working, the number and complexity of orders in the pipeline, and current traffic conditions — in less than an hour (it had previously taken three days). By iterating more data, they were able to boost accuracy of their model from 75% to 95% for predictions of when an order will be ready.

Lockheed Martin transformed its AI infrastructure from traditional CPU-based systems to a GPU-accelerated infrastructure and improved accuracy of models that predict asset health, minimizing downtime of fleets. Using natural language processing (NLP), they can analyze millions of maintenance records and determine risk levels around parts or components. Costs have been significantly reduced, as 9 out of 10 records today are classified without human involvement and with 95% accuracy.

The Milwaukee School of Engineering needed additional computational resources and an optimized software stack to meet growing AI workloads. Thanks to the adoption of best-in-class AI infrastructure, today 80% of the institution’s computer science team actively uses the cluster and faculty GPU usage has increased by 10x. Now, students don’t need to worry that the “cloud odometer” is always running and limiting experimentation.

St. Jude Children’s Research Hospital needed to address the growing computing demands of their data scientists and researchers, as well as siloed computing infrastructure — both of which were leading to increased cost of redundancies. They developed an AI Center of Excellence incorporating purpose-built AI systems into their HPC cluster to speed development. This central computing hub today provides them with all the computing resources they need to develop models that enable faster reading of radiology studies, faster genomic analysis, and cutting-edge research.BMW receives almost 10,000 new car orders a day, with 100 different options per car and 2,100 possible combinations. Using purpose-built AI infrastructure to train deep neural networks in a simulated 3D virtual world, BMW uses AI-powered logistics robots across their factory — from transporting materials to organizing parts. By using AI to create highly customizable just-in-time manufacturing, they are able to produce a new car in their factory every 56 seconds.

These examples are just the tip of the iceberg in terms of how AI can digitally transform a business and provide efficiency, optimization, and new revenue streams. With the assistance of NVIDIA’s DGXperts — AI-fluent practitioners who provide guidance and expertise — companies can get even more ideas to help them write their AI story.

So, chances are the time is right for you to start yours.

Uncover how to transform using AI with NVIDIA DGX Systems, powered by DGX A100 Tensor core GPUs and AMD EPYC CPUs.

Artificial Intelligence, IT Leadership