The business world is quickly speeding towards AI adoption with the AI software market alone set to be worth $62.5 billion in 2022, jumping an impressive 21.3% from 2021. Yet, despite dreams of cutting-edge efficiency pulling businesses towards AI, many organizations are being faced with a completely different reality as they unbox their new AI tools and look to integrate them.
From optimizing financial operations to creating a better customer experience, AI has emerged as a proven force in helping organizations meet their next-level operational and strategic goals. However, despite the well-intentioned push among the business world for rapid AI adoption, many businesses are realizing that not only do they lack the proper support infrastructure around their AI tools, but they don’t even know where to start in terms of building it.
AI is an incredibly powerful tool. But to unlock its potential it needs to have a robust complementary network of tools and applications to help businesses thrive. This includes everything from tools that can help with feature extraction, process management, analysis, and machine resource management. Unfortunately, many companies either don’t have any real data infrastructure or what they have is too outdated to really power their AI/ML code in any meaningful way.
With that in mind, here are a few hot topics that have resulted in the need for more sophisticated data estates that architects and businesses should keep in mind so that they can unlock their AI potential.
Migration from On-Premise to the Cloud
Even prior to the outbreak of the COVID-19 pandemic, organizations across the business world were beginning full – or partial – shifts to the cloud. However, with the digital transformation surge that was precipitated by the pandemic, cloud migration efforts have increased significantly. Thus, not only are companies having to cope with the disruption of switching to the cloud but they are also having to make a difficult choice in deciding which technology is best suited to help them power their necessary business functions. This means that companies need to quickly get to grips with the ideal cloud delivery models, cloud technologies, draft in specialized talent and more all while trying to simultaneously integrate AI.
Unfortunately, this is often a recipe for disaster and results in even further disruption in terms of both cloud migration and AI integration. Therefore, businesses need to gameplan their cloud migrations in a way that allows them to lay the framework for future AI adoption before jumping into early. This will give architecture teams the ability to effectively build their data estates ahead of time, thus paving the way for AI adoption and ultimately success.
The Shift to Self-Service Insights
As the business world continues to speed up, businesses have naturally grown more and more frustrated with bottlenecks that prevent them from monitoring the activated decisions and simulate and activate new decisions based on real-time data signals. And this has subsequently given rise to the dawn of “self-service” analytics. Insights are arguably never more effective than in real-time. Therefore, businesses have increasingly looked for ways to make their data easily synthesizable and accessible without having to involve analysts. But to make this happen, companies have had to adopt new components – Decision augmentation and automation systems that integrate AI/BI insights, Real-time data signals with Decision engines for monitoring, simulations and activations – which is an unchartered territory for many businesses and thus can lead to delays and stagnating success on broader tech and business intelligence priorities.
Data Governance
As data has become more central to business operations it has also come under greater governance requirements. Meaning, businesses now need a comprehensive suite of tools that can allow them to easily deliver oversight and reporting across their business, technical, and operational metadata. Many companies already have some sort of data governance related suite in place. However, because of the importance of explainability in governing AI, existing tools do not have the capacity to deliver the amount of insights needed to comply with modern governance needs. In addition, as AI and data science learns within an organization and becomes more intelligent, businesses will need more and more scalability and agility from their data estates and thus,need to embrace more modern tools to help them keep up.
Modern data operations is both incredibly exciting and complex. And in order to ensure that companies are able to hit all of the marks in their AI and data science journeys they need to have the internal framework to support it. And by keeping these few trends and potential stumbling blocks in mind, businesses can build the data estates they need to drive success both today and in the future.
Authors
Shashidhar Ramakrishnaiah
