In business, there are three different stages of AI scaling… First comes “proof of concept”, then comes “strategic scaling”, and finally, if all goes according to plan, you will be “industrialized for growth”. surveyed 1,500 CxOs across industries around the world to identify the success factors that have driven AI to scale. We found that growth-driven industrial companies have made several significant cultural shifts towards AI: one of the biggest? Ensure that data, analytics and AI are democratized for all employees and are closely aligned with business growth priorities. Aligning data strategy with business strategy To become an AI-powered organization, you need to start with data. Through increased use of valuable data and AI assets across their organization, 80 percent of their well-planned scaling initiatives are successful. Here are three things a company must do to achieve sustainable industrial growth. Manage "deliberate" AI.

This means setting realistic expectations. With a clearly defined strategy and operating model, timeline, AI structure and governance, and business goals are aligned and progress is possible. Turn off data noise. Over 90 percent of Earth data has been created in the last ten years. Be careful what internal and external data you choose and determine what is critical to the business, because, as the saying goes, "garbage in, garbage out." Treat AI like a team sport. 92 percent of companies that successfully achieved industrial growth used cross-platform multidisciplinary teams.

There are AI advocates everywhere, and the transformation is taking place throughout the organization; The introduction of AI is not the prerogative of a lone champion. Rethinking how data and AI initiatives are used in line with business strategy can lead to a quick return on your investment. This effect is driving the shift from AI pilots to enterprise-wide business transformation. By focusing on the 5-10% of your data that makes up 90% or more of your business value, you ensure that the data is used in your analytics. By investing in your database—data quality, data governance, data governance models for the cloud, demarcation between data creation and consumption, and clear operational models—you will contribute to cleaner data, which in turn promotes smarter AI. If you focus on the 5-10 percent of your data that makes up 90 percent or more of your business value, you ensure that the data is used in your analytics. Your data analytics ultimately feed your AI models, allowing you to extract business-critical insights quickly and at scale to drive better results.

Drive industrial growth by scaling AI The drive to find new growth in the beer industry is growing and competition from other beverages is fierce. One global brewer has transitioned to industrial growth by using machine learning to solve data integrity issues and build more accurate predictive models. Their analytics scaled across hundreds of global datasets, from sales data and forecasts to social media, weather, and more. The company's decision makers gained access to commercial information and valuable information in record time. In the first year, the payback was four times the initial investment. Data Focus and Growth Why are so few companies on the path to AI achieving industrial growth? There seems to be a gap. In our report, we asked, “To what extent do you expect analytics to play a role in your organization’s AI?” Responses varied widely, with 61% of all executives surveyed saying analytics play either a moderate, minor, or no role at all in the development of AI in their organization.

Only 14% of companies that have not advanced on their journey to AI believe that data and analytics are vital to successful AI adoption. And 79% of companies that have had real success with AI technologies say that analytics play an important role in AI. 75% CEOs believe they risk going bankrupt in 5 years if they don't scale AI 79% successful companies believe that analytics play an important role in AI 84% CEOs believe they won't reach their growth goals if they don't scale AI 92% industrial growth companies used cross-platform interdisciplinary teams To implement the connection between data and AI, best practices are needed: Strategy and goals Your data and analytics strategy must be aligned with business goals. 360 view Data discovery and augmentation should use internal  and external data to provide a comprehensive view and create quality predictive analytics. Control Data management requires planning, management, monetization, and compliance. culture Companies must embrace a data-driven culture and the democratization of AI and data. If these milestones are done well, data will become a competitive asset and the ultimate differentiator.

By scaling AI with the cloud, organizations can reposition their offerings, empower and mature data and AI to create new sources of value and ultimately drive sustainable growth. .