Future historians will have much to study as they look back on the COVID-19 pandemic and its aftermath. Not only the epidemiology of the coronavirus – the mechanisms by which it spreads and how it infects its hosts – but also the range of responses to the pandemic by governments, business, and society.

Clearly, the pandemic has tested the ability of national, state, and local governments everywhere to respond to a once-in-a-century health crisis. It has called into question policies ranging from pandemic preparedness to individual privacy and the structure of healthcare systems. Those debates will continue for decades to come.

For businesses, the pandemic has also been a test. It has been a pressure test of organizational resilience, agility, and adaptability. For many enterprises, the pandemic and the resulting economic shutdown have become a test of survival. And it has clearly intensified the urgency around digital transformation.

Consider, for instance, the oil and gas industry. For many companies, survival will depend on their ability to predict, quickly interpret and respond to price fluctuations based upon rapidly changing market dynamics. Step-function improvements in operational efficiencies – through AI-enabled capabilities such as predictive maintenance and production yield optimization – will be imperative. It is that simple.

The pandemic has exposed the brittleness and inflexibility of existing supply chains for many global manufacturers. These companies are looking to AI to transform and optimize their supply chain operations, reconfigure their distribution networks, and predict and mitigate resource risks.

At C3.ai, in our conversations with organizations across industries globally – conversations that, despite the economic shutdown, have increased in both volume and urgency over the last few months – we are seeing a heightened focus on the need to accelerate digital transformation. The gist of those conversations reduces to two basic questions that organizations ask us: How do we scale AI across our enterprise, and how do we do it rapidly?

Over the next several years, virtually all enterprise application software will become AI-enabled. The vast majority of these enterprise AI applications will run on elastic cloud computing infrastructure from leading vendors such as AWS and Microsoft Azure. Building, deploying, and scaling enterprise AI applications on these public clouds will be a top priority.

The challenges of building enterprise AI applications are significant. Elastic clouds offered by AWS, Azure, and others provide immensely powerful capabilities that are essential to the operation of enterprise AI applications. But building applications by attempting to stitch together dozens of native AWS or Azure services and other required components is exceedingly complex. With this approach, developers end up devoting the vast majority of their effort – 80 percent or more – on writing code to make these components interoperate seamlessly and ensure all the “plumbing” works.

This resembles the state of application development prior to the introduction of relational database management system (RDBMS) technology in the 1980s. Before RDBMS technology, application developers had to understand and address the low-level complexities of storing and retrieving data in order to develop a transactional application. RDBMS technology removed all those complexities for developers, allowing them to concentrate on the functionality of the application rather than the plumbing.

Today, organizations face a similar situation as they seek to deploy enterprise AI applications at scale across their operations, not just as one-off projects in a few limited areas. Building this new class of cloud-based enterprise AI applications involves significantly greater complexity than previous generations of enterprise software, including numerous requirements around data integration and normalization, machine learning modeling, and cloud infrastructure orchestration.

Just as RDBMS technology removed the complexities of storing and retrieving data for developers of previous generations of enterprise software, what’s needed today is a platform that removes the complexities for developing enterprise AI applications. Such a platform is what the C3 AI™ Suite delivers. And that is why organizations that want to deploy enterprise AI at scale engage with C3.ai.

Recently we released a pair of studies that we commissioned from a premier independent systems integrator that specializes in building enterprise applications on AWS and Azure for Fortune 1000 companies. These studies provide a detailed analysis of the time and cost savings of using C3.ai’s technology, in combination with AWS or Azure, to build enterprise AI applications running on each of these clouds.

As reported by the independent systems integrator, the use of C3.ai combination with AWS or Azure resulted in:

  • 25X faster development time
  • 10X to 50X fewer lines of code to write and maintain
  • 1/10 the time to deploy