How AI-Enhanced Analytics Can Optimize Decision Workflows
Remember how the world was taken aback by the sheer power of generative AI when ChatGPT was launched? That was the moment when AI became more than a buzzword—from a thing of the future into our current reality. Since then, tools like ChatGPT have become mainstream, and new AI tools aimed at optimizing efficiency across industries and organizational departments are popping up as we speak. The domain of analytics is no exception. AI-enhanced analytics uses machine learning to quickly turn complex, unstructured business data into clear, actionable insights. Marry AI with analytics, and you gain the ability to efficiently comb through extensive data, spot trends, and refine processes. This marriage leads to better, quicker decisions across various business operations, from financial strategies and supply chain management to customer service. Keep reading to explore three concrete examples of AI-enhanced analytics in action.
Improving Business Decision Modeling
In the context of business analysis, decision modeling is the process of creating a structured, typically visual representation of how decisions are made in a company. A decision model allows you to divide the most complex decisions into manageable data-driven subsets that are understandable to higher-ups as well as technical teams without being too detailed. AI-enhanced analytics tools let you create smart visualizations and dynamic data-driven dashboards. They can convert raw data collected from various sources (your website, app, social media, CRM, and so on) into easy-to-grasp-and-act-upon visual insights. Such visualizations highlight trends, patterns, and anomalies. They enable executives to make more informed decisions faster. Pyramid Analytics offers a comprehensive platform that enhances business and decision modeling across various functions. It allows for no-code analysis, enabling users without deep technical expertise to leverage complex data sets and derive actionable business intelligence through natural language querying. Unlike other AI-enhanced BI platforms, Pyramid integrates with multiple LLMs, so you can create decision workflows optimized for the situation at hand.
Enhancing Financial Decision Workflows
Financial planning, forecasting, risk management—these are some of the areas where AI is making a dent. Think more efficiently and faster, data-backed decisions. Small businesses and even individuals are starting to embrace new finance-focused AI tools. Intuit, for instance, recently launched Intuit Assist. It is an AI-based tool that helps entrepreneurs and your average Joe make smarter financial choices. It works across various Intuit products like QuickBooks and Mailchimp. It uses data from business transactions, consumer accounts, and tax filings to provide personalized financial insights. Intuit Assist automates complicated tasks and offers custom advice. It connects users with experts if needed, making financial management easier for those without a finance background. AI is changing how we make financial forecasts and conduct risk assessments. Banks and financial firms are upgrading their tech stack to use AI models for analyzing ginormous data sets. This helps them better understand credit risks and respond to market changes. Citibank, for example, uses AI to check financial statements during the loan approval process, improving its assessment of credit risks. ZestFinance uses AI to analyze non-traditional data, helping people with little credit history get better credit scores. Major investment firms like Citadel use machine learning to assess and adjust investment risk strategies. BlackRock uses AI to analyze data for trading and risk management, and has even set up a special team, “AI Labs,” to dig into more cool stuff AI can do. This increasing AI-driven approach in firms across the board hints at how financial decision workflows are leveling up for better precision.
Supply Chain and Logistics Optimization
AI’s ability for logical thinking is also changing the logistics industry. As you can guess, giants like Amazon are leading the way.
Amazon has built proprietary AI models to predict demand and fine-tune logistics. The model studies past data, what’s trending right now, and then makes predictions to manage inventory and improve delivery routes efficiently. What this means is chopping down both delivery times and costs, boosting efficiency across their extensive network.
AI also helps Amazon adapt to any supply chain disruptions at the drop of a hat. For instance, if there is an unexpected demand surge, its AI adjusts routes and manages inventory in real-time. It helps ensure deliveries remain on schedule and inventory levels are appropriate, avoiding shortages or excess.
This adaptability minimizes disruption risks, helping maintain resilient and cost-effective supply chains. By using AI, companies can react faster to changes and enhance their customer service.
