AI-powered analytics create clear views of business activity that you can use to build resiliency and plan for growth. Data-driven insights enhance customer experience, speed up sales, streamline operations and anticipate cash flow risks.
This article presents eight ways AI-powered analytics help solve business challenges. In each case, visualizations were generated using an AI-powered cloud data warehouse and advanced analytics solution to automate data integrations and simplify analyses — without the need for programming or data science expertise.
We’ll begin with Generative AI — a technology so versatile and user friendly, it’s poised to become an essential resource across the organization. Users of any skill level and role can simply type a question in a text box and leverage generative AI large language models (LLMs) for analytics-driven insights, needed right away. Follow-up interactions help users learn more, with instructions to set filters, apply seasonality or change visualization type.
A busy sales manager may use this tool to quickly view top accounts by region, before deciding on the next hire. Since this manager knows these accounts well and the sales experience required, they can quickly interpret the Gen AI outputs and make an informed hiring decision.
Business applications for Gen AI, as well as the AI and machine learning examples we’ll discuss, have the potential to be transformational, whether you’re an emerging or enterprise organization. It’s important to note that quality, complete data and business understanding are essential for successful applications.
A cost trend analysis covering multiple years helps finance teams spot inefficiencies, rising costs and resource shortages. With regular monitoring, leaders can better understand and respond to change and thus minimize budget deviations and cash flow constrictions.
Forecasting provides added insights to help leaders anticipate near-term costs and make informed decisions on future budgets and resource allocations.
High-volume data analysis like this can strain commonly used tools like spreadsheets and piecemeal business intelligence (BI) systems. A cloud data warehouse with AI-powered automations is instrumental for breaking through silos and legacy systems to integrate and standardize historical and current data for fast access. A paired advanced analytics solution can then be used to build a multi-dimensional trend graph and uncover patterns to generate forecasts that guide decisions.
Repeat customers are great for your profitability, as there’s no high acquisition cost. But essential insights on your customer base and retention drivers are difficult to see when data is siloed in CRM, order management and ecommerce systems.
Centralized access to customer touchpoint data within a cloud data warehouse, paired with cluster analysis using an AI-powered analytics solution, helps segment buyers based on purchase patterns and more. These insights are useful when planning retention strategies like upsells, loyalty programs and personalized experiences.
Consolidated financial reports provide a holistic view of the organization’s financial health, with evidence-based insights for strategic planning. This transparency is essential for stakeholders including investors, lenders and regulators.
However, creating these reports is often a manual and time-consuming undertaking — and that’s why many businesses adopt tools to automate and speed up the process while minimizing errors. A cloud data warehouse not only can centralize access to transactional data and other systems data across subsidiaries, but it also aggregates and optimizes data for analytics. This enables an AI-powered advanced analytics solution to direct more resources towards efficiently performing complex calculations and uncovering patterns that lead to actionable insights.
Late customer payments can compromise financial health, limiting the ability to absorb unexpected expenses and seize emerging opportunities. That’s why the ability to predict which customers are most likely to pay late, and the potential revenue impact, can be so beneficial for planning.
An AI predictive model with access to historical and current customer data, including invoices, can help offset cash flow risk by providing the insights needed to craft a proactive, personalized collections strategy and adjust near-term expenses as needed. However, many businesses don’t have extensive in-house AI expertise.
Fortunately, cloud-based automated models with access to company data can help business users gain a competitive advantage by making it easy to use predictive intelligence based on the patterns and trends found in their own systems data.
Market volatility creates pressure on operations teams to meet demand and avoid lost sales that negatively impact revenue and customer loyalty. AI models working with historical sales data, including customer purchases, lead times and other factors, can generate predictions, like when stockouts are likely to occur. These insights help leaders find optimal inventory levels that reduce costs from emergency reorders and expedited shipping.
Cloud technologies designed with AI-powered automations that bring together relevant business data to train predictive models can be instrumental in generating forward-looking insights (e.g., estimates of revenue impact).
Qualified leads may be plentiful, but sales leaders need to know which leads are most likely to convert and generate a deal size to secure revenue targets. Pipeline analysis provides the necessary visibility into deal progress and rep performance to guide how deals are prioritized and how resources are allocated to close more effectively.
AI-powered analytics create added value with comparative insights drawn from integrated data on customer and opportunity history that might get lost in data noise. For example, it can highlight the revenue potential of active opportunities by top reps, as compared with all others. This enhances planning for those in charge and identifies the reps who might benefit from coaching to increase their win rates.
Unexpected change in tariff policy can have long-term ramifications. Visibility into points of exposure within the supply chain helps leaders respond quickly to mitigate risk and embrace opportunities.
Dashboards visualizations based on centralized data from various business systems and departments improve decision-making with insights such as current versus prior year metrics and item-level margins and costs by supplier.
Scenario analysis provides multidimensional modeling of financial impacts from unexpected economic inputs, like a tariff change. For example, companies can simulate the effects of different tariff rates on a per-item basis to help zero in on when it might be advantageous to shift suppliers.
It’s difficult to grow and forecast revenue when you can’t see the warning signals of churn — after all, customers who stop purchasing can simply fall off the radar. Account managers often lack visibility that could come from a consolidation of customer touchpoints data.
Predictive AI can uncover patterns from historical and current data on customers and their orders.
It can segment customers by risk score, showing those at high risk of churn and those on the cusp. These insights can trigger and inform a personalized retention program to protect that revenue, as well as create a starting point for uncovering the broader reasons customers stop buying from you.
As businesses grow, so does the complexity of their data. Information often lives across multiple systems, making it difficult to create a clear picture of performance. Modern analytics platforms are stepping in to solve this challenge by unifying data, automating preparation and applying AI to surface insights that support better decision-making.
NetSuite Analytics Warehouse is one example of this shift. Built on Oracle’s data and analytics technologies, it brings together transactional, historical and third-party data into a single, accessible hub. AI-powered features help standardize and organize that information, while built-in analytics tools make it easier for teams to identify trends, test scenarios and predict outcomes.
Security and governance remain central to this type of system. Hosting on Oracle Cloud Infrastructure includes safeguards like encryption, multifactor authentication and access controls that are critical for organizations handling sensitive information.
The goal is not simply faster reporting, but smarter reporting — where decision-makers at all levels can use reliable data to improve operations, strengthen customer relationships and prepare for future challenges.
If you’re interested in improving and scaling your business with the help of AI analytics, contact us here or call 410.685.5512.