How AI is Fundamentally Shaping the Future of Business Analytics

As an experienced data analytics consultant, I‘ve seen firsthand how artificial intelligence is rapidly transforming the field. In this comprehensive guide, we’ll unpack how AI is making analytics more efficient, accessible, expansive, and powerful.

Introduction: AI is Revolutionizing Business Analytics

Business analytics has changed immensely since my early days as a consultant relying on Excel spreadsheets. While macros and linked models were powerful then, they pale in comparison to what’s now possible with AI.

In particular, AI is making analytics:

• More automated, saving time and money
• More intuitive for employees to use
• Able to extract insights from more data types
• More accurate and impactful overall

Below we’ll explore real-world examples of each of these emerging trends. With the insights from this guide, you‘ll understand exactly how leaders should leverage AI to transform analytics in their organization.

AI Automation Makes Analytics More Efficient

One major advantage of AI is automating repetitive, manual workflows – and analytics is no exception. Given surging demand and high salaries for data scientists, AI automation provides tremendous efficiency benefits.

AI Systems Can Autonomously Conduct Analysis and Surface Insights

Thanks to machine learning, analytics platforms can now analyze data and discover key insights without human intervention. And based on analytical outcomes, AI can take smart actions or flag findings for humans to review.

For example, an insurance firm trained a deep learning model to monitor claims activity. The AI autonomously analyzes patterns across millions of claims to identify potential fraud. For highly suspicious claims, it automatically sends a notification to the special investigations unit for further review.

This analytical AI identifies more potential fraud than humans manually reviewing claims ever could. And it frees up the investigations team to focus only on the most high-risk incidents rather than waste time on obviously legitimate claims.

According to McKinsey, AI techniques like this reduce fraud losses by up to 25% compared to traditional methods. That’s the powerful impact of AI automation on analytics.

Natural Language Generation Allows AI-Automated Report Writing

Data scientists spend countless hours translating raw analytics into formatted reports, dashboards, and presentations. AI automation is transforming this workflow.

With natural language generation (NLG), AI systems can instantly turn analytical outputs into narratives, reports, and visualizations. Rather than waiting for data scientists to manually build reports, employees get insights quickly through auto-generated content.

In a recent survey, over 50% of data leaders said NLG automation was crucial to making analytics insights more consumable across their business. The time and cost efficiencies of having AI systems produce written reports and narratives are tremendous.

Intuitive AI Makes Analytics More Accessible

Advanced analytics has traditionally required specially trained data scientists. But AI is changing that through intuitive natural language interfaces understandable by anyone.

With natural language search, employees can get answers from data just by typing or voicing plain English questions. They don’t need expertise in complex query languages – they just ask for insights conversationally using AI.

For example, ThoughtSpot uses NLP search to allow business users without technical skills to easily explore data. ThoughtSpot raised $248 million in 2019 to grow this natural language analytics platform.

Brian McCarthy, VP of Research at IDC, says that AI capabilities like ThoughtSpot‘s "allow analytics information and insights to be put directly into the hands of business decision makers." By democratizing access, AI analytics solutions empower widespread adoption across organizations.

AI Unlocks Insights from More Data Types

Legacy business intelligence tools falter when processing unstructured data like text, images, audio, and video. New AI techniques now let companies extract value from these untapped sources.

Natural Language Processing Analyzes Text Data

While Excel handles structured data well, analyzing free-form text is difficult. AI overcomes this through:

• Sentiment analysis – determining emotional tone/intent in text
• Entity recognition – identifying people, places, companies mentioned
• Topic modeling – uncovering key themes in document collections

For example, a manufacturing firm could analyze customer support tickets to detect major pain points. AI tracks frequently mentioned product issues and negative sentiment to uncover problems for engineering to resolve.

According to leading AI analytics provider SAS, their NLP tools can analyze up to 500 billion documents in seconds to extract key insights. Unlocking textual data at this vast scale was impossible before AI.

Converting Speech to Text Enables Audio Analytics

Valuable intel resides in the hours of call center audio recordings from customer service calls. Legacy analytics couldn‘t efficiently process audio…but AI can.

Speech recognition transcribes spoken audio into searchable text transcripts. AI analytics scans these transcripts to surface key phrases, trends, and sentiment signals faster than humans ever could manually.

Transcription AI unlocks a wealth of audio data to improve the customer experience. As Gong.io CEO Amit Bendov notes, "AI gives revenue teams visibility that was previously impossible, accelerating reps’ ramp time and boosting productivity."

Computer Vision Enables Video and Image Analysis

While text and audio analytics are progressing rapidly, AI vision techniques now also unlock insights from visual data.

For example, retailers use image recognition to analyze customer facial expressions reacting to products or in-store displays. Monitoring emotion sentiment helps optimize merchandising tactics.

Meanwhile, manufacturers employ video analytics on high-speed assembly lines. Computer vision AI detects defects or unsafe conditions faster and more accurately than human inspectors.

According to Microsoft, their Custom Vision image recognition service can be trained to categorize images with up to 90% accuracy. Unlocking visual data helps businesses see and understand activity in new ways.

Extracting Value from Documents and Contracts

Semi-structured documents like financial reports, legal contracts, and medical records contain a wealth of locked up data. Manually extracting this data is time-intensive.

Intelligent document processing with AI allows extracting information from documents at scale with much higher accuracy vs humans. For example, law firms use these tools to rapidly extract key clauses from large collections of contracts.

According to research firm Hyperight, over 75% of organizations see document AI as very impactful for unlocking trapped data from documents. This expands the scope of analyzable data considerably.

AI Allows More Data Analytics in a Privacy-Safe Manner

Strict data privacy regulations limit how companies analyze personal customer data. Synthetic data generation techniques allow creating realistic fake customer data that retains statistical properties without exposing personal details.

For instance, banks use synthetic data to model lending risks across customer demographics. Data scientists can perform analytics encompassing age, income, credit history on the fake profiles without compromising on privacy.

Gartner predicts that by 2024, 60% of organizations will use synthetic data for privacy-preserving analytics. AI unlocks more data for analysis while also protecting customer personal information.

Advanced AI Algorithms Make Analytics More Powerful

While traditional analytics relied on simple correlations and regressions, machine learning unlocks new frontiers in analytical sophistication. AI-powered techniques outperform older methods at forecasting, pattern recognition, classification, and more.

More Accurate Forecasting with AI

Predictive analytics depends heavily on the quality of forecasting models. According to a Gartner survey of data leaders, improving forecasting accuracy was a top 5 priority for 47% of organizations. AI empowers major gains through:

• LSTM neural networks – better model time series seasonality and cycles
• Multivariate models – incorporate diverse datasets for context
• Continuous learning – adapt forecasts based on new data

For example, renewable energy firms use AI to predict hourly electricity demand across power grids. Factoring in weather, holidays, and events provides much greater accuracy vs legacy models.

Anomaly Detection Spots More Security Threats

Fraud detection and network security depend on recognizing outlying anomalies from normal behavior. According to research by SAS, AI techniques like deep learning improve fraud prediction by up to 90 percent compared to traditional rules-based systems. Key advantages include:

• Analyzing vastly more data points from across accounts
• Detecting more complex abusive patterns
• Continuously fine-tuning models on emerging threats

With AI, anomalies don’t slip through gaps in pre-defined detection rules. Ongoing machine learning ensures robust protection.

Improved Pattern Recognition for Predictive Maintenance

Spotting early warning signs of equipment failure is crucial for industrial firms to minimize downtime. The volume of sensor data overwhelming for humans to consistently analyze.

AI predictive maintenance examines vast streams of IoT sensor data to detect signals like vibration or temperature changes. By recognizing complex patterns early, issues can be addressed proactively before causing breakdowns.

According to PwC, AI techniques can reduce maintenance costs by 10-40% and cut downtime by up to 50%. The enhanced pattern recognition of AI unlocks major efficiency gains.

Industries Leveraging AI Analytics in Diverse Use Cases

While all sectors stand to gain from AI analytics, adoption is surging across:

Manufacturing

• Predictive maintenance analyzes equipment sensor data to optimize uptime
• Computer vision AI spots defects and quality issues to improve production

Healthcare

• AI assists clinical decisions by surfacing relevant medical records
• Image recognition helps accurately detect cancerous cells in biopsy scans

Retail

• AI tracks micro-customer behavior patterns to optimize pricing
• NLP analyzes customer support messages to enhance ecommerce experiences

Financial Services

• AI analyzes consumer spending activity to offer personalized recommendations
• Cybersecurity analytics models detect credit card transaction fraud in real-time

Human Resources

• AI parses exit interview notes to derive root causes of turnover
• Sentiment analysis on employee survey free text answers uncovers engagement drivers

The use cases are nearly endless. Wherever rich data exists, AI analytics can surface game-changing insights.

Comparing Leading AI Analytics Software Platforms

Many analytics vendors now offer robust AI capabilities. Here we‘ll compare the key strengths of 5 top solutions:

Understanding the unique strengths of each platform helps guide organizations toward the right solution for their needs and challenges. The key is identifying areas where AI augmentation would make the biggest impact based on your strategic analytics priorities.

The Future with AI is Bright for Business Analytics

As we‘ve explored in this guide, artificial intelligence is fundamentally transforming analytics through automation, accessibility, scope, and power. While Excel spreadsheets were a gateway to analytics early in my consulting career, they simply cannot compete with today‘s AI-enabled tools.

Looking ahead, I expect analytics to become even more automated using AI, opening up self-service capabilities to entire organizations. Machine learning will enable tapping into ever more complex and unstructured data types at massive scale. The future is exceedingly bright for data-driven decision making thanks to artificial intelligence.

I hope these insights provide a helpful overview of the tremendous ways AI is shaping the next generation of business analytics. By leveraging these technologies strategically, companies can gain an edge over competitors still stuck using outdated legacy tools. The uptake of AI for analytics is still early – those who adopt these innovations soonest will have a considerable competitive advantage.