Science fiction has long influenced our perceptions of technologies like artificial intelligence (AI). One of the most prevalent tropes is AI turning against humanity, as depicted in iconic films such as “The Terminator,” and “I, Robot.” These stories usually portray AI as a hostile force, pitting humans against machines.
Luckily, the reality is much less combative. Instead of acting as an adversary, AI is helping humans redefine how they work—boosting productivity, creativity, and business outcomes. In fact, a report by McKinsey shows that GenAI has the potential to automate tedious tasks that currently consume up to 60-70% of employees’ time.
But here’s the catch: For AI to work in your favor, we as humans first need to understand it. And that’s where the concept of augmented intelligence comes into play. By understanding how augmented intelligence works, you can empower your workforce to drive outcomes quickly and effectively.
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Augmented intelligence uses technology such as AI and machine learning (ML) to help humans automate manual tasks, make data-driven decisions, and enhance cognitive performance. Rather than removing humans from the picture, it focuses on creating a partnership between humans and machines so you can solve complex business problems more effectively.
Augmented intelligence and AI are two buzzwords frequently used interchangeably. However, there are some key differences:
1. Autonomous vs collaborative approach
While artificial intelligence is an autonomous system meant to replace human tasks, augmented intelligence is designed to work in conjunction with humans. Augmentation liberates workers from monotonous data tasks, allowing them to focus on more value-added activities such as improving customer experience or offering analytical support.
2. AI as a subset of augmented intelligence
Augmented intelligence involves using any technology to enhance human intelligence, not just AI or ML. In essence, augmented intelligence is the overarching umbrella that covers a wide variety of specific approaches and algorithms. AI sits under that umbrella along with other technologies such as deep learning, robotics, expert systems, and natural language processing. For instance, a manufacturer might use augmented intelligence technologies such as robotics and deep learning to streamline production processes, improve quality control, and predict maintenance needs.
In an episode of The Data Chief, Gilead Sciences Head of Enterprise Data and Analytics Murali Vridhachalam explains: “[W]e have a lot of enterprise data on the cloud, and we will apply AI and machine learning on top of it. It yields special use cases [that were] never before possible because data was always in silos.”
Augmented intelligence can help data scientists convert large datasets into real-time, actionable insights. Unlike traditional data analytics methods that require significant manual data processing and plumping, augmented analytics solutions like ThoughtSpot empower users to create interactive Liveboards and gain personalized insights via SpotIQ. Even better, the powerful self-service analytics capabilities offer real-time views of what’s happening within an organization, allowing business users to easily identify trends, patterns, and anomalies.
Here’s how ThoughtSpot’s SpotIQ delivers personalized insights and even sets automated alerts to help you stay on top of KPI changes:
Let’s look at Austin Capital Bank. Earlier, it had a complex and disconnected data environment that delayed marketing optimization efforts. With ThoughtSpot’s augmented intelligence features, users can access real-time data and create their own Liveboards. With these new self-service capabilities, executives can conduct extensive analyses of their paid search, which enabled the bank to optimize spend by 50% and improve retention by 15%.
When we discuss how augmented intelligence works, the implementation process depends largely on your use case. For example, if your goal is to use data to predict future product demand, then the process for implementing augmented intelligence would look like this:
Creating a problem statement: Clearly articulate the business problem you want to address or goal you want to achieve by analyzing your data. In this scenario, the goal is to know which products will experience increased demand in the next quarter. Writing down a distinct problem statement will help you gather relevant data and scope out the details.
Collecting and processing data: Gather structured and unstructured data relevant to the outcome you are trying to achieve. Here, you will collect data from customer relationship management (CRM) systems, point-of-sale (POS) systems, marketing automation tools, and more. Once you have the relevant data, you can use data observability tools to identify and remove discrepancies such as missing values, duplicate data, or outliers. This process is known as data cleaning and is essential for maintaining results accuracy.
Developing machine learning models: Use ML algorithms and advanced statistical methods to create analytical models for analyzing the collected data. Some of the most common data analytics models include predictive models, prescriptive analytics, or sentiment analysis models.
Addressing security and privacy concerns: AI and ML models have demonstrated well-documented instances of data hallucinations and biases. Additionally, these models are susceptible to adversarial attacks, where outputs can be manipulated by subtly altering the input data. Solutions that incorporate a human-in-the-loop mechanism with robust security controls are essential to effectively addressing these concerns. It also is critical to continuously monitor the model’s performance and run tests to check for new biases.
Employing conversational business intelligence (BI) tools: Conversational BI solutions like ThoughtSpot allow stakeholders to interact with data and find actionable insights. These solutions use natural language processing (NLP) and ML algorithms, which allow users to ask questions about their data, create their own visualizations, run forecasts, and identify important trends and anomalies.
Optimization: Because augmented intelligence adapts quickly and incrementally improves over time, it’s essential to keep collecting user feedback and optimizing model performance. The human-in-the-loop element ensures your results are accurate, precise, and relevant.
The above process strikes the perfect balance between AI and human oversight, empowering you to drive significant business outcomes. However, as you continue exploring and creating new cases, you may collect more data and combine different technologies to achieve your desired objective.
Augmented intelligence can help businesses across a wide range of industries reduce bottlenecks, improve customer experiences, and effectively manage risks. Common use cases include:
Finance: Augmented analytics tools can help financial institutions personalize services. By conducting a thorough analysis of an individual’s history, spending habits, and future objectives, AI systems can provide tailored financial advice.
Healthcare: Using AI-powered predictive models, healthcare providers can anticipate and identify diseases in patients at an early stage, often before symptoms become severe. This proactive approach enables timely interventions and the development of personalized care plans.
Retail: Augmented intelligence is helping retailers analyze customer behavior and market trends to generate accurate demand predictions. Such forecasts help minimize waste, prevent overstocking, and boost sustainability efforts.
Public sector: Defense and law enforcement agencies use augmented intelligence to identify suspicious activities in large data sets, streamline functions, and support warfighters in mission applications and weapons systems.
Energy sector: The mining industry uses fleets of complex and widely distributed machines, tooling inventories, and parts databases. With augmented intelligence, businesses are developing powerful AI assistants for maintenance technicians, helping them streamline maintenance work and increase machine reliability.
As a business leader, finding new ways to accelerate growth is an important — if never-ending — part of your job. With augmented intelligence emerging as the new frontier for growth and innovation, businesses are unlocking actionable insights, capitalizing on opportunities, and planning for the future.
Leading in AI-Powered Analytics, ThoughtSpot lets you analyze, explore, and share insights to drive actual business outcomes. Our augmented analytics solution leverages the power of language learning models (LLMs) to provide an intuitive search experience that allows all users to ask questions about their data, create interactive Liveboards, and gain personalized insights via SpotIQ. With AI-augmented insights, conversational BI, and multi-modal experiences, business users can personalize their data insights, ensuring they’re making the most of their data.
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