Enterprise cloud adoption has been accelerating for years, as more companies have utilized software-as-a-service models to process increasingly large data sets.
Now, leading businesses are learning that cloud can do more than process data; it can drive business and revenue growth.
In particular, ongoing advancements in artificial intelligence and machine learning are creating new opportunities for businesses to harness the power of their data, with cloud providing the tools necessary to seize them.
Overcoming Early Struggles
As early as 2015, many companies built large on-premises data lakes in their initial attempt to realize the promise of “big data.” These centralized repositories of information, stored in a variety of formats, often became data graveyards, as many companies lacked the computing resources that early AI technologies needed to derive meaningful insights. For example, graph processing for images alone was prohibitively expensive.
In those days, cloud AI platforms hadn’t yet matured enough to motivate businesses to move data-intensive ML projects to a cloud environment. The well-documented potential of big data seemed frozen in time. Fortunately, this was only temporary.
Expanding What’s Possible
More recently, the advent of cloud-native data warehouses like snowflakes, knowledge graphs, and other technologies have allowed enterprises to model data structures that are scalable in terms of both storage and performance.
Major cloud computing providers now offer suites of products that include model development, hosting, and machine learning operationalizations (MLOps), such as Amazon’s AWS SageMaker, released in 2017.
Additionally, cloud vendors have also provided APIs for NLP (e.g., Textract), prediction (Amazon Forecast), and computer vision (Rekognition) that are pre-trained and can be easily integrated into modern applications.
Follow the Leader
Research from Wipro FullStride Cloud Services shows that cloud leaders will continue to expand their computing power during the next several years, with a focus on 5G, edge computing, and grid computing technologies. Amid these investments, leaders are pairing key technologies with cloud, most notably AI. There are many reasons for this strategic decision.
The ever-expanding universe of cloud AI tools has allowed product teams to dramatically reduce development costs and time to market, creating new possibilities for innovative companies.
Adoption of these technologies shouldn’t be undertaken haphazardly.
At Wipro, we’ve found that companies seeking to migrate AI projects to cloud environments can adhere to several best practices to improve their odds of achieving optimal outcomes.
Bringing AI to the Cloud
Among other approaches, Wipro relies on E-IQ (enterprise intelligence quotient), a framework that assigns an intelligence quotient to a given business process, revealing possible AI use cases in the context of five pillars: sense, decide, act, interact, and adapt.
This benchmarking exercise can also help companies establish a road map for preparing projects for the cloud using an agile AI delivery model and reference architecture.
Once the use cases and supporting technical artifacts are identified, a bring-your-own-model approach can accelerate model migration into the optimal compute for endpoints on AWS SageMaker and other associated APIs.
Twice as Nice
To ensure that models don’t show “staleness” or “drift,” a robust MLOps framework guides onboarding and governance, allowing for compute optimization and the periodic recalibration of models during labeling when using AWS Ground Truth.
AI can be particularly helpful in highly regulated industries like financial services, which increasingly rely on complex models to inform their decision-making as regulators impose ever-more-stringent validation requirements.
Utilizing a smart approach to model testing and validation can ensure that internal model-validation teams can effectively inventory their models, saving time and ensuring regulatory compliance in the process.
A Glimpse of the Future
These cloud investments are illuminating many impactful use cases for combining AI with cloud. By leveraging a dynamic duo of AI and cloud, enterprises are equipping themselves to achieve a multitude of objectives, including:
- New revenue streams: One healthcare institution that moved data associated with ML models to the cloud was able to not only optimize costs, but also monetize model predictions. In this case, customers included research institutions who were able to bypass the data collection and aggregation processes needed to build their own models and instead purchase the outcomes directly from the data source to expedite their research. The fees they paid covered the model development costs incurred by the healthcare institution.
- Enhanced customer experiences: Cloud-based AI technologies can drive better customer experiences for all kinds of companies, from cab services to e-commerce stores. In the case of the former, a cab car display equipped with an AI-powered recommendation engine can show passengers personalized offers based on their destinations or movie recommendations constructed via cloud knowledge graphs.
- Shaping strategic outcomes: With the help of AI in the cloud, a CFO can infuse intelligence sourced from both internal and external data into the financial planning process to recommend initiatives for increasing revenue. Similarly, a CMO can identify strategies for optimizing marketing spend across a range of product categories to maximize ROI.
For executives relying solely on data sourced from general ledger/enterprise resource planning systems, this level of insight simply isn’t possible. - Hyperautomation: Cloud AI platforms can enable smart automation to dramatically improve efficiencies related to any number of internal business processes. For instance, a mobile app that makes API calls to Textract can extract information from documentation stored in the cloud to transform HR onboarding and can reduce the time it takes to complete administrative tasks from days to minutes.
Enterprises that are deploying AI in the cloud have already realized all of the above outcomes and many others.
As advancements in cloud computing and AI/ML continue to unfold, the synergistic combination of these two technologies will continue to yield significant competitive advantages for innovative companies.
These competitive advantages will increasingly separate category leaders from the rest of the field.
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