You can then scale up to other facilities and ESG factors.Ī holistic approach offers a particular advantage for more complex simulations, such as forecasting market conditions and addressing supply chain challenges. A holistic approach doesn’t mean “everything at the same time.” An effective way to use AI in highly complex decisions, such as ESG, is to start with a specific element, such as a single facility’s carbon footprint. Whether with documents, images or video, AI can wade through this ocean of data, extract exactly what decision-makers need and put this data - and only this data - in front of the right people at the right time. ![]() Many companies are drowning in unstructured, “messy” data. Once you’ve identified the data you need, let AI help you find and use it. Consider which decision-makers will use the model to achieve this outcome, where the model will fit within the decision-making process, how it will integrate with the cloud, and how you will monitor, scale, improve and eventually retire it. Instead, start with the business outcome you seek, then look for the data and analytics to back it up. As you consider new AI models in decision-making, don’t start with the data you have. An estimate of an investment’s carbon footprint may be far more accurate, for example, if AI models project future energy supplies, weather patterns and second-order impacts on your supply chain. Opportunities for AI to support ESG (environmental, social and governance) decisions also abound. AI analysis has much to offer M&A, for example, potentially automating parts of due diligence, predicting likely regulatory responses and projecting a combined company’s results under various conditions. Some complex business decisions are still being made without much use of AI, but that may soon change. ![]() Within this framework, many AI leaders plan to focus on five key priorities, all related to one theme: delivering valuable, real-world business outcomes. Thirty-six percent of companies with a holistic approach to AI are planning to use AI this year to help create a data fabric: an action-ready, 360-degree view of all data that touches their organizations. That can help connect data to AI in ways that benefit as many lines of business as possible. This unified approach to AI aligns well with a unified approach to data: making a person (such as a chief data officer) or a centralized team responsible for data sharing and data governance. An approach to AI initiatives that encompasses business, technology and decision-making priorities helps data, AI and cloud work together smoothly, end-to-end. These systems, in turn, need cloud-based computing power that can scale up and down to help meet demands. AI can deliver more value at scale when it’s embedded in application systems that work nonstop, analyzing and acting on data from inside and outside the organization. This holistic approach supports a critical ingredient of AI success: investing in and managing data, AI and cloud as a unified whole. By including business experience, it helps align outcomes with business priorities, leading to organizational buy-in and to projects that deliver a real impact at a reasonable cost. It brings together AI specialists with analytics teams, software engineers and data scientists. By bringing so many leaders together from across the organization, a holistic approach facilitates scale and data sharing. ![]() They’re roughly twice as likely to report substantial value from AI initiatives to improve productivity, decision-making, customer experience, product and service innovation, employee experience and more. Compared to companies that approach AI in a piecemeal manner, these leaders (just over a third in our survey) are far more likely (36% versus 20%) to report widespread AI adoption.
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