The modern world runs on data. Yet, while data is valuable, many companies struggle to turn it into useful insights. Traditional analytics and business intelligence tools tell us "What happened?" but they can't handle huge, complex, real-time data to predict the future. They are often limited in terms of complexity.
Artificial intelligence (AI) analytics (also known as augmented analytics) goes beyond just describing the past to explain "Why it happened?", predict "What will happen?" and recommend "What should we do?". AI analytics automates analysis, understands complex data, finds deep insights and makes accurate predictions or suggestions.
Many tasks can be automated by AI analytics. These include tasks such as data preparation, insight generation and pattern and trend identification. Most often working at a much faster speed and higher accuracy than humans, these systems can enable real-time insights.
AI analytics have become essential for staying competitive and turning data from a burden into a powerful asset. It enables businesses to make better data-driven decisions that reduce costs and drive business outcomes.
The four pillars of AI analytics
AI can be used to enhance all four main types of data analytics. It is also important to note that continuous monitoring and maintenance of AI analytics models are crucial to their continued success.
Descriptive analytics: Understanding "what happened?"
The goal of descriptive analytics is to provide a clear, comprehensive and accurate understanding of past performance and historical events. AI significantly augments descriptive analytics by leveraging machine learning algorithms and Natural Language Processing (NLP) to rapidly sift through and process vast volumes of both structured and unstructured data.
For example, Visual Comfort & Co. leveraged Microsoft Dynamics 365 and consolidated fragmented data across 56 showrooms and its online store. This led to real-time inventory visibility, a 28 percent reduction in processing days, improved order accuracy and streamlined operations.
Diagnostic analytics: Uncovering "why it happened?"
Diagnostic analytics aims to identify the underlying causes, contributing factors and causal correlations within complex datasets, moving beyond "what" to "why." AI improves diagnostic analytics by applying sophisticated algorithms to analyze data, pinpointing root causes and correlations with greater accuracy and speed than traditional analytical methods. This capability is crucial for deep dives into anomalies or unexpected outcomes identified through descriptive analytics.
Predictive analytics: Forecasting "what might happen next?"
Predictive analytics uses advanced algorithms to analyze historical data and identify patterns, enabling more accurate forecasts of future trends, behaviors and outcomes. AI significantly enhances predictive analytics by allowing models to analyze vast and diverse datasets, effectively capture complex nonlinear relationships and manage intricate features with greater precision. This leads to more robust and accurate forecasts across various domains.
Prescriptive analytics: Guiding "what should we do next?"
Prescriptive analytics goes beyond prediction to provide actionable recommendations, suggesting the best course of action for various scenarios to achieve desired outcomes. AI systems elevate prescriptive analytics by not only analyzing data but also simulating the potential outcomes of different proposed actions.
The benefits of AI analytics for organizations
Transforming decision-making
AI impacts strategic planning in two ways: better data-driven decisions and strong predictive insights. AI helps gather and analyze huge amounts of data, which is key for planning based on facts. Also, AI's predictive analytics and simulations offer actionable insights into future scenarios. This helps businesses to create flexible and forward-looking strategies!
AI redefines business strategy for the digital age by simplifying data collection. Traditional planning often starts with slow data gathering. AI automation speeds this up by quickly scanning the web and company data, turning weeks of work into days.
Enhancing operational efficiency and cost reduction
Cutting costs is another major benefit that AI analytics can cause. Repetitive and time-consuming tasks can be automated, which frees up employees to focus on more complex and nuanced tasks that a human would be preferred.
Automation is not the limitation. AI analytics improves accuracy and reduces errors common in manual data processing. AI algorithms can quickly analyze huge amounts of data, finding patterns and trends that humans might miss. This precision leads to more reliable data and better insights for decisions. AI's ability to find and fix errors in data also ensures data quality.
Moreover, AI optimizes how resources are used and reduces waste. For example, in supply chains, AI tools can predict demand and manage inventory, leading to big savings. AI can also streamline tasks like document processing and energy management by analyzing usage patterns. AI analytics platforms continuously monitor data, helping businesses spot problems early, improve efficiency and gain new insights, leading to proactive problem-solving and lower costs.
AI analytics in action
The following examples show how businesses use AI to solve tough problems, improve operations and get measurable results.
How Barclays use AI analytics to detect fraud: Fraud is a big challenge in finance, needing real-time monitoring of transactions. British bank Barclays uses advanced AI systems to monitor transactions live. This AI solution identifies and blocks fraud before it happens, making the bank more efficient and protecting against cyberattacks.
How Capital One and Bank of America use AI analytics to provide personalized customer service: 24/7 personalized banking support is key to customer satisfaction. Capital One launched "Eno," the first natural language SMS assistant from a U.S. bank and Bank of America introduced its chatbot "Erica" in 2018. These AI assistants have helped millions, understood many questions and offered personalized advice. They have also managed account and payment scheduling, greatly improving customer convenience.
How Atomwise speed up drug discovery and vaccine research with AI analytics: Traditional drug discovery is slow and costly, often involving long lab experiments. Atomwise, an AI leader in healthcare, uses AI for virtual screening of molecular compounds for drug discovery.
How Walmart use AI analytics for demand forecasting: A constant challenge for big retailers like Walmart is managing inventory and forecasting demand efficiently to avoid empty shelves or too much stock. Walmart uses AI to predict demand, analyzing past sales, market data and new shopping trends. They also use computer vision and machine learning to track inventory in real-time.
How Amazon uses AI analytics for price optimization: Setting prices dynamically to maximize profit while staying competitive is complex in fast-paced retail. Amazon uses AI to analyze many factors: competitor prices, customer demand, local demographics and ad impact. This allows for dynamic pricing strategies that boost profits. The system adjusts prices in real-time, preventing prices from being too high (scaring customers) or too low (cutting into profits).
How to successfully implement AI analytics
To get the most out of AI analytics, a structured approach and best practices are needed to ensure successful deployment and manage risks. Successfully adding AI analytics to an organization involves several careful steps for a strong and impactful rollout.
First, a thorough needs analysis is vital. Companies must clearly define their business goals and problems AI can solve, setting measurable objectives. This guides the whole project. Rupert Morrison, founder or Orgvue, explained at OPEX Week 2025 that most organizations don’t have a clear strategy when it comes to this. “Organizational design and workforce planning are fundamental for successful execution,” he said.
Second, data procurement and preparation are crucial. High-quality data is essential for AI models. Relevant data must be collected from internal and external sources, then carefully cleaned and organized for training AI models. Good input means good output.
Third, technology selection means choosing the right tools and platforms, like cloud services or ML frameworks. The tech should match the goals and existing systems.
Fourth, model development is where the AI model is built using the prepared data. This involves picking algorithms and training the model to meet company needs, often fine-tuning it for accuracy.
Fifth, testing and validation are critical to ensure the model works as expected. It must be tested on separate data to check performance and find any biases or errors. This builds trust in the model.
Sixth, implementation and integration involves putting the validated AI model into existing company systems. This often means creating interfaces for smooth communication between the AI and other software.
Seventh, monitoring and maintenance are ongoing. The AI model's performance must be continuously tracked, with regular updates to keep it effective as data and needs change.
Finally, feedback and iteration complete the cycle. Using feedback from users helps continuously improve the AI model. Iterative development allows for quick adaptation to changing company needs.
Challenges surrounding AI analytics
While AI analytics offers big benefits, organizations must also handle challenges for successful implementation and lasting value. No form of AI is 100 percent foolproof. However, you can significantly mitigate the risks by following the solutions outlined below.
One of the most pressing issues concerns security and privacy due to the large amounts of data AI handles. How AI uses personally identifiable information (PII), internally or externally, is important to consider and strong measures are needed. Solutions include prioritizing AI cybersecurity with risk assessments and AI threat detection. Developing AI-specific incident response protocols and using zero-trust architectures are essential for data security and detecting AI attacks.
Similarly, issues of bias need to be taken seriously. Age, nationality and gender, amongst other factors, can be intentionally or unintentionally reflected in how the AI is trained.
Scaling issues can also happen when AI projects don't integrate well across the business, staying in silos. This often comes from AI initiatives not aligning with overall strategy. Solutions involve focusing on integrated solutions compatible with existing systems and building a culture that embraces AI widely.
Want to learn more?
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