Generative AI in business: a marathon, not a sprint Aug 07, 2023 In this article, Paulo discusses the potential of AI to improve productivity, efficiency, and customer experience and how to be cautious about these gains at scale. Learn more
CI&T has acquired renewable energy certificates (I-RECs) to cover 100% of the electricity consumption of its operations in Brazil. May 30, 2023 To further reduce our environmental impact, starting in 2023, we will also seek to support cleaner and renewable energy. For this reason, all energy consumption from our Brazilian operations has been offset through I-RECs. Learn more
Increasing DEV Power with Augmented Coding Oct 28, 2022 Technological approaches and resources accelerate tasks associated with software development and maintain the quality of actions. This is Augmented Coding. Learn more
CI&T is recognized with the Learning Innovator of the Year Award Mar 20, 2024 CI&T has been awarded the Degreed Awards Visionaries Award in third place in the Learning Innovator of the Year Award category by Degreed, provider of CI&T University platform. Learn more
Solving CPG and Retailer Demand Forecasting Dilemmas Download The retailer-supplier relationship isn’t arranged in the way most conducive to confident demand forecasting. In this new report CI&T examines the demand forecasting relationship between retailers and suppliers. The report highlights pain points and opportunities for both parties, revealing a misalignment of data strategies causing ineffective forecasting. DOWNLOAD THE FULL REPORT Key findings from the report include: Suppliers’ highest ranking challenge related to forecasting demand was visibility and access to data. Retailers’ highest ranking challenge related to demand forecasting was scaling the data platformSuppliers reported they were most likely to break demand forecasting down by geography, while retailers were most likely to report breaking down demand forecasting by channel The majority of suppliers reported looking at sales data from the same month over years prior as their predictive approach, while the majority of retailers reported referencing the previous month’s sales to predict the following monthBoth suppliers and retailers overwhelmingly reported that consumer-level data (gender, age, household size) is the most likely type of data leveraged for demand forecasting