What is augmented intelligence?

Augmented intelligence, also known as intelligence amplification (IA), leverages machines to help humans work more efficiently and effectively. Unlike artificial intelligence, which focuses on replicating human behavior, augmented intelligence takes a collaborative approach. It combines human decision-making with machine capabilities to achieve better outcomes. By uniting human ingenuity with advanced technology, augmented intelligence drives innovation and accelerates business growth.

Data science and augmented intelligence.

One way to look at augmented intelligence is that it assists data scientists. With the existence of big data–large data sets that can reveal patterns and trends related to human behavior–data scientists need help synthesizing and analyzing the data to infer actionable insights. With the support of augmented intelligence, data scientists can mine through large quantities of data, and IA’s deep learning capabilities can pull out helpful patterns that can offer insight into customer preferences and predictive analytics. Thus, augmented intelligence not only accelerates data processing, but it also pinpoints essential patterns and trends that would otherwise be lost to the human eye. That’s what makes it crucial to data analytics and predictive data.

Augmented intelligence and augmented analytics.

Augmented analytics uses artificial intelligence and machine learning to enhance analytics across the phases of the data lifecycle. This includes how data is prepared and analyzed and what insights can be inferred from the data to help inform business decision-making and enhance business outcomes.

Augmented intelligence vs. artificial intelligence.

Augmented intelligence is designed to enhance how humans work, not to replace them. While artificial intelligence is seen as an autonomous system, augmented intelligence employs machine learning and deep learning to equip humans with the insights they need to take action. In other words, augmented intelligence serves to deliver data to users in an understandable way so they can decide how they want to move forward.

For example, a video streaming service can make suggested recommendations, but the end-user gets to decide how they would like to respond. Conversely, artificial intelligence like spam filters and plagiarism detection can operate on their own without any human assistance.

Why is augmented intelligence important?

Augmented intelligence is a way to leverage human intelligence with the support of machines to help minimize human bias and error and pick up on patterns and trends. And while augmented intelligence is a way to create better insights to inform better decision making, it’s a process that still needs human oversight to connect with big-picture business objectives.

And with data scientists working hand-in-hand with augmented intelligence, there is a greater likelihood for improvements in data science.

What can you do with augmented intelligence?

Augmented intelligence is a way to improve decision-making without taking humans out of the equation. IA is able to assist data scientists in analyzing large quantities of data, helping to remove human bias and error to produce actionable insights that can inform decision-making. Together, augmented intelligence and human intelligence produce highly accurate data that can lead to industry improvements, innovation, and better business outcomes.

How does augmented intelligence work?

Augmented intelligence empowers human decision-making by combining the strengths of machine learning, deep learning, and data analytics. It processes large volumes of structured and unstructured data to deliver valuable insights. Unlike artificial intelligence, which strives for autonomous operation, augmented intelligence complements human capabilities, enhancing accuracy, efficiency, and the generation of meaningful insights.

1. Data Collection & Integration
   Augmented intelligence collects data from diverse sources, such as structured databases, unstructured text, IoT devices, and real-time analytics. It seamlessly integrates and organizes this information into a unified system, streamlining it for comprehensive analysis.

2. Machine Learning & Deep Learning Analysis

   • Machine Learning (ML): AI models analyze patterns in historical data and continuously improve as they process new information over time.
   • Deep Learning (DL): Neural networks replicate human cognitive processes, enabling them to identify intricate relationships, detect anomalies, and uncover trends within vast data sets.

3. Natural Language Processing (NLP) & Context Understanding
   Natural Language Processing (NLP) empowers machines to understand and analyze human language, enabling businesses to uncover valuable insights from customer feedback, support tickets, and other text-based data.

4. Pattern Recognition & Predictive Insights
   Augmented intelligence analyzes data to uncover key trends, correlations, and anomalies, empowering businesses to predict outcomes, identify risks, and make informed, proactive decisions with confidence.

5. Decision Support & Human Collaboration
   Augmented intelligence enhances, rather than replaces, human judgment by presenting insights through intuitive formats such as dashboards, visualizations, and actionable recommendations. This empowers business leaders, data scientists, and analysts to make better-informed decisions with confidence and clarity.

6. Automation & Process Optimization
   Augmented intelligence streamlines routine workflows by automating tasks like data processing, anomaly detection, and report generation. This empowers employees to shift their focus to higher-level strategic initiatives, enhancing productivity and efficiency.

Augmented intelligence empowers organizations to work more efficiently, minimize errors, and unlock the full potential of their data through seamless integration of advanced capabilities.

How Do Businesses Use Augmented Intelligence? (Applications & Use Cases)

Augmented intelligence is transforming industries by enhancing decision-making, boosting efficiency, and providing actionable insights. Here are some standout applications across various sectors:

1. Healthcare & Medical Research

• Disease Diagnosis & Treatment Recommendations: AI-driven tools enhance medical diagnostics by identifying diseases like cancer through advanced imaging analysis. For instance, IBM Watson supports doctors by recommending treatment options tailored to patient histories and the latest research.
• Predictive Patient Care: Augmented intelligence empowers healthcare providers to anticipate patient deterioration by analyzing real-time vital signs and medical records, enabling timely interventions.
• Accelerating Medical Research: AI revolutionizes drug discovery and clinical trials by uncovering patterns within vast biomedical datasets, significantly speeding up the research process.

2. Financial Services & Risk Management

• Fraud Detection and Prevention: AI-powered models analyze transaction data in real time to identify and prevent fraudulent activities with precision.
• Personalized Wealth Management: Robo-advisors harness AI to craft tailored investment strategies, aligning with users’ financial goals and risk tolerance.
• Credit Scoring and Risk Assessment: Banks utilize AI to evaluate creditworthiness more accurately, minimizing bias in lending decisions.

3. Retail & E-Commerce

• Hyper-Personalized Shopping Experiences: AI-powered recommendation engines leverage customer preferences and shopping patterns to deliver highly tailored product suggestions.
• Dynamic Pricing and Demand Forecasting: Advanced AI predicts market trends and optimizes pricing strategies in real-time to adapt to demand fluctuations.
• Chatbots and Virtual Assistants: Intelligent customer service agents use AI to provide quick, accurate, and seamless support, enhancing the overall user experience.

4. Manufacturing & Supply Chain Optimization

• Predictive Maintenance: By analyzing sensor data, AI can anticipate equipment failures before they occur, minimizing downtime and keeping operations running smoothly.
• Automated Quality Control: With computer vision, AI identifies defects on production lines with greater precision and consistency than manual inspections.
• Supply Chain Optimization: AI enhances efficiency across the supply chain by streamlining inventory management, improving demand forecasting, and optimizing logistics.

5. Marketing & Customer Insights

• Sentiment Analysis and Market Insights: NLP tools assess customer reviews, social media posts, and surveys to uncover public sentiment and identify emerging market trends.
• Optimized Ad Performance: AI enhances ad targeting strategies in real-time, driving higher conversions and maximizing ROI.

6. Government & Security

• Cybersecurity and Threat Detection: AI-powered security systems continuously monitor network activities, identifying and responding to cyber threats in real time.
• Crime Prediction and Law Enforcement: Predictive policing leverages AI to analyze crime patterns, enabling more efficient resource allocation and proactive crime prevention.

7. Education & Training

• Personalized Learning Systems: AI customizes educational content to match each student’s unique learning pace and comprehension, creating a more tailored and effective learning experience.
• AI-Powered Virtual Tutors: Intelligent systems support students by answering questions, evaluating assignments, and offering personalized study guidance.

Integrating augmented intelligence empowers businesses across industries to boost efficiency, uncover valuable insights, and make smarter decisions. This approach fuels innovation and strengthens competitive advantage, paving the way for sustained success.

What are some best practices for augmented intelligence?

To implement best practices for augmented intelligence, it’s essential to start small, build trust, and strategically apply data insights across your organization.

Begin by focusing on a single, clear business objective that can be achieved using actionable data insights. This targeted approach simplifies the process and builds momentum. Transparency is another key practice—engage more business users in the data journey to foster trust and collaboration. To cultivate a truly data-driven culture, align these efforts with your organization’s broader goals and objectives.

At Domo, we believe the true value of data lies in making it accessible to everyone across an organization. That’s why we’ve enhanced our business intelligence platform within the Domo Business Cloud with new augmented capabilities. These upgrades are designed to transform the way data is connected, visualized, analyzed, and extended through integrated workflows, apps, and data-sharing tools.

By augmenting data access and usability, businesses can accelerate innovation and make faster, more informed decisions. Domo’s new capabilities include advanced machine learning, data science tools, and enhanced data management features. These tools empower both technical and non-technical users by streamlining the journey from data to actionable insights in a unified workflow. With these updates, organizations can more easily leverage large volumes of data to drive innovation and achieve their business goals.

What does the future look like for augmented intelligence?

Industry expertise alone is no longer enough to stay ahead. In today’s fast-paced world, being data-driven is essential—and that’s where augmented intelligence steps in. By combining human intuition with powerful algorithms and predictive insights, augmented intelligence enhances decision-making and equips businesses to focus on growth and the future.

While artificial intelligence continues to advance, human input remains irreplaceable. Augmented intelligence embraces this balance, integrating human oversight rather than operating entirely autonomously. With IA as a partner, businesses can innovate, scale, and grow with greater speed and confidence.