Preparation is key to AI success in 2022

Hear from CIOs, CTOs, and other executives and senior executives on data and AI strategies at the Future of Work Summit on January 12, 2022. Learn more


Artificial intelligence differs from previous technological innovations in a crucial way: It is not just another platform to deploy, but a fundamental change in the way data is used. As such, it requires a substantial overhaul of the way the business collects, processes, and ultimately deploys data to meet business and operational goals.

So while it may be tempting to bring AI into legacy environments as quickly as possible, a wiser course of action would be to take a more careful and thoughtful approach. One thing to keep in mind is that AI is only as good as the data it can access, so strengthening both the infrastructure and the data management and readiness processes will play an important role in this. the success or failure of future AI-based initiatives.

Quality and quantity

According to Open Data Science, the need to foster large amounts of high-quality data is paramount for AI to deliver positive results. In order to deliver valuable insights and enable intelligent algorithms to continuously learn, AI needs to connect with the right data from the start. Organizations not only need to develop high-quality data sources before investing in AI, but they also need to reorient their entire culture so that everyone from data scientists to business knowledge workers understands. the data needs of AI and how the results can be influenced. by the type and quality of data entered into the system.

In this way, AI is not just a technological development, but a cultural change within the organization. By taking on many repetitive and repetitive tasks that tend to slow down processes, AI is changing the nature of human work to encompass more creative and strategic endeavors, thereby increasing the value of data, systems and people to the business model. global. To achieve this, however, AI must be deployed strategically, not haphazardly.

Before investing in AI, technology consultancy New Line Info recommends a thorough analysis of all processes to see where intelligence can have the most impact. Part of that review should include the myriad ways AI may require new methods of communicating data and the development of whole new frameworks for effective modeling and forecasting. The goal here is not to produce sporadic gains or one-off initiatives, but to drive a more holistic transformation of data operations and user experiences.

By its very nature, this transformation will be evolutionary and not revolutionary. There is no hard line between today’s business and a smart futuristic business, so every organization will have to make their way through the woods. On Inside Big Data recently, solutions architect Provectus Rinat Gareev identified seven stages for AI adoption, starting with figuring out exactly what you hope to do with it. AI can be adapted to almost any environment and optimized for any task, so having a way to measure its success from the start is crucial.

Chart the way forward for AI

Additionally, organizations should identify priority use cases and establish development roadmaps for each based on technical feasibility, ROI, and other factors. Only then should you move to a general basis for large-scale implementation and rapid organization-wide scaling, not to someday complete this transformation, but to perpetually create a more efficient and effective data ecosystem.

Perhaps the most important thing to keep in mind about AI, however, is that it isn’t a silver bullet for everything that plagues the business. As CIO Dive’s Roberto Torres recently pointed out, there is currently a gap between what is possible and what is expected of AI, and this disconnection is hampering implementation. Sometimes the limits lie in the AI ​​itself, as people come to believe that an intelligence based on algorithms is capable of harnessing far more than it can actually accomplish. But issues can also arise within the supporting infrastructure, in data preparation as mentioned above, or sometimes just applying a given AI model to the wrong process.

The point is, the company has only taken the very first steps on a long journey towards a new cultural paradigm, and there will undoubtedly be many missteps, wrong turns and about-faces along the way. So, while it’s important to get your hands dirty with AI sooner rather than later, you also need to take a break and figure out what you need to do to prepare for this change and what you hope to get out of it.

VentureBeat

VentureBeat’s mission is to be a digital public place for technical decision-makers to learn about transformative technology and conduct transactions. Our site provides essential information on data technologies and strategies to guide you in managing your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the topics that interest you
  • our newsletters
  • Closed thought leader content and discounted access to our popular events, such as Transform 2021: Learn more
  • networking features, and more

Become a member


Source link

Comments are closed.