Digital Innovation & transformation


Gain deeper insights and fuel innovation with cloud AI/ML

Digital Innovation & transformation

Cloud and Machine Learning: They’re better together.

A blog post by Sudi Bhattacharya, managing director, Deloitte Consulting LLP and Ashwin Patil, managing director, Deloitte Consulting LLP

Artificial intelligence (AI) is quickly getting embedded in the core of information technology, and it’s only getting more pervasive. In fact, Gartner recently calculated that in 2021 AI augmentation will create $2.9 trillion of business value and save 6.2 billion man-hours globally.1 Leveraging AI/ML in the cloud can give your organization access to the computing resources and power you need to derive deeper insights, drive innovation, and make your operations more efficient than ever before—and do it cost-effectively.

Digital Innovation & transformation

AI/ML—made better with cloud

A bit of housekeeping first, though. Machine learning is when computer models learn by experience via spotting patterns, correlations, and trends in data. Machine learning models can provide deeper insights into data than humans can alone, and they can be predictive and prescriptive as well. Artificial Intelligence is when computers leverage machine learning to perform tasks and actions that are manual or repetitive—that humans can do—but doing them faster, and sometimes more effectively.

Most organizations dabble with both, and there are many recent AI/ML use cases including upsell/cross-sell recommendations, asset optimization, logistics and distribution efficiency, customized marketing, financial trading, healthcare analysis, data security, and fraud detection. These use cases often mention cost optimization, revenue/margin gain, and risk management as benefits. And the list of examples continues to grow.

But why cloud-fueled AI/ML, and why now? Simply put, cloud providers have matured over the past half-decade and they now can offer you a comprehensive set of compute and storage resources as a service, which means that you can use only what you need, when you need it. Here are some of the ways that cloud can boost AI/ML:

  • Elasticity and scalability. AI models need an elastic cloud infrastructure that can scale up and down depending on workload. Take supply chain and logistics management for example. By leveraging at-scale AI/ML to manage repetitive and manual tasks related to supplier management and product delivery, you can improve your procurement process while freeing up resources in your supply-chain unit to focus on more strategic, value-added activities.
  • Disposability. The disposability of cloud infrastructure is ideal for AI experimentation. For instance, during the development lifecycle for a particular product, cloud-fueled AI/ML can provide the enormous IT resources it takes to drive innovation and agility that can result in lower R&D costs, improved product quality, and faster go-to-market timelines. And, when you don’t need the resources, you can simply spin them down, because they’re provided as a service, and you only use what you need, when you need it.
  • Quick access to large data stores and compute resources. Cloud provides the large-scale data stores and compute resources (GPUs) that can ingest, process, and store high-velocity real-time streaming data, as well as high-volume batch data, needed for AI/ML. And, those resources can be acquired and accessed almost instantaneously, and at any time, once you have a relationship with a cloud provider, without going through a lengthy procurement and footprint allocation process for a costly technical infrastructure. 
  • Pre-built algorithms to help drive faster innovation. The availability of pre-trained AI models are accelerators that can shorten time-to-market. All of the major hyperscalers have pre-trained models that can help you unlock data-driven insights that result in a better experience for your customers –and do it faster and at a lower cost. For example, in many business scenarios, it may be faster and cheaper to use the pre-trained models provided by hyperscalers for projects in areas such as neural language processing, vision enhancement, text and speech processing, to build high value applications infused with AI/ML insight.

How you get there

It’s easy to see how the cloud helps fuel AI/ML to drive insights and innovation. However, it takes planning and insight to get there. Cloud-fueled AI/ML takes vision, a solid foundation, and education coupled with a governance discipline. First up is defining the organization’s AI/ML vision and cloud-enabled strategy that are tied to prioritized business value. One way to quickly demonstrate value to key stakeholders and the C-suite is to conduct a proof of value project in a critical area of need to serve as a pilot use case.

The next step to drive business value across functions and business units is building a strong AI/ML foundation; by enabling models with a robust cloud and data platform to drive insights into business processes. Think of AI/ML as a knowledge and insight accelerator. The models you build (or use through a cloud-provider’s as-a-service delivery) can be powered by the infrastructure and compute resources you need—when you need them, to deliver insights about your business and customers and help you get your products and services to market faster and more efficiently.

With the foundation in place, you can begin to scale, operate, and govern your AI/ML program at the enterprise level. Start by educating and empowering your business users on AI/ML and by transforming business processes. It’s crucial, however, to implement guardrails to scale your AI/ML programs to needs and business goals. As powerful as the Cloud AI/ML capability is, it needs to be well governed to make it sustainable at the enterprise level.

Once you’ve scaled your cloud-AI/ML program, it’s critical to ensure that your insights are accurate and unbiased. In other words, ML models need feedback loops and continual retraining to make sure that the modeling outputs are trustworthy and that the models aren’t biased. This is critical to establishing trust of AI/ML in the organization and in the C-suite.

The potential payoff

If you do it right, the payoff of cloud-fueled AI/ML can be huge. You get the computing infrastructure and power you need, when you need it, to derive insight and drive innovation that helps you differentiate your products and services—and get them to market faster, more efficiently, and while maximizing profitability. In a world where competition is fierce, and competitors seemingly come out of nowhere overnight to challenge you, that power could help you thrive—not just struggle to survive.

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