Introduction

Analytic applications, powered by artificial intelligence (AI), are revolutionizing how organizations derive valuable insights, make predictions, and optimize processes.

This rapid emergence of AI technology has been truly stunning.

But, like all relatively novel technologies, it’s not fully clear how it will be implemented by software firms, or how it will likely be adopted by their customers. This includes business intelligence vendors.

While its value seems apparent in the world of analytics, there are also barriers to adoption that will determine the course of AI.

This article delves into the multifaceted impact of AI on analytic applications, exploring its role in enhancing data analysis, driving innovation, and reshaping business strategies, as well as the considerable challenges that will determine the course of its future.

AI and Visual Analytics – Close Relations 

It’s interesting to note that AI and visual cognition are very much designed to solve the same problem: deriving useful observations and patterns within large and continuously changing data sets. 

In traditional visual analytics, certain assumptions regarding the data to be analyzed are fed into the system. The resulting visualizations then rely on the human visual cortex to recognize patterns within these representations and to derive useful, actionable information. 

Often, an iterative process takes place to refine these assumptions and queries based on the results. This may include sharing the data visualizations and reports with others, not only for the sake of awareness but also to strengthen the analytical process by recruiting the cognitive power of more humans, some of whom may have greater expertise or insight.

AI appears to be poised to improve this process in a few different ways. 

First, AI is capable of looking at more data faster and is better at recognizing patterns within them. Thus, the value of AI will in great part be determined by how limited humans are in performing the above tasks. Thus, the size of the datasets for analysis, as well as the rate of change within those sets will define how much benefit applying AI to the task will be. 

For firms with reasonably small and consistent data, the benefit may be quite small. But for many companies, getting a handle on their sprawling data footprint is already a challenge, and is the primary reason they employ business analytics solutions in the first place.

Categorizing Artificial Intelligence (AI)

Artificial Intelligence (AI) can be categorized in several ways based on its capabilities, applications, techniques, and approaches. Below are the common categorizations:

1. By Capabilities

  • Narrow AI (Weak AI): AI systems designed to handle a specific task or a limited range of tasks (e.g., virtual assistants, recommendation systems).
  • General AI (Strong AI): Hypothetical AI that possesses the ability to understand, learn, and apply intelligence across a broad range of tasks at a human level.
  • Superintelligent AI: A theoretical AI that surpasses human intelligence in all aspects, including creativity, problem-solving, and social intelligence.

2. By Functional Areas

  • Reactive Machines: AI systems that can only react to specific inputs without memory or past experiences (e.g., IBM’s Deep Blue chess-playing computer).
  • Limited Memory: AI that can use past experiences to inform current decisions, but the memory is not persistent (e.g., self-driving cars).
  • Theory of Mind: Advanced AI that understands emotions, beliefs, and thoughts, allowing for more complex interactions.
  • Self-Aware AI: AI with self-consciousness and awareness, understanding its own existence and actions.

3. By Learning Techniques

  • Supervised Learning: AI systems trained on labeled data, where the model learns from input-output pairs (e.g., image classification, speech recognition).
  • Unsupervised Learning: AI systems that identify patterns and relationships in data without labeled outputs (e.g., clustering, dimensionality reduction).
  • Semi-Supervised Learning: Combines both labeled and unlabeled data for training, improving learning efficiency and accuracy.
  • Reinforcement Learning: AI learns by interacting with its environment, receiving rewards or penalties for actions (e.g., game playing, robotics).

4. By Application Areas

  • Natural Language Processing (NLP): AI for understanding and generating human language (e.g., chatbots, language translation).
  • Computer Vision: AI for interpreting and processing visual information from the world (e.g., image recognition, object detection).
  • Speech Recognition: AI for converting spoken language into text (e.g., virtual assistants, transcription services).
  • Robotics: AI for controlling and interacting with physical robots (e.g., industrial robots, autonomous drones).

5. By Algorithms and Approaches

  • Machine Learning: AI systems that learn from data through algorithms (e.g., decision trees, support vector machines).
  • Deep Learning: A subset of machine learning involving neural networks with many layers (e.g., convolutional neural networks, recurrent neural networks).
  • Evolutionary Algorithms: AI inspired by biological evolution, using mechanisms like mutation, selection, and crossover (e.g., genetic algorithms).
  • Fuzzy Logic: AI based on reasoning that is approximate rather than fixed and exact, handling uncertainty (e.g., control systems).

6. By Deployment Models

  • Cloud AI: AI services and tools provided over the cloud, offering scalability and remote access (e.g., AWS AI services, Google AI Platform).
  • Edge AI: AI that processes data on local devices or edge servers, reducing latency and bandwidth usage (e.g., smart cameras, IoT devices).
  • Hybrid AI: Combining both cloud and edge AI to leverage the benefits of both approaches.

7. By Industry Applications

  • Healthcare AI: AI applications in medical diagnosis, treatment planning, and patient care (e.g., predictive analytics for disease outbreaks).
  • Finance AI: AI for fraud detection, algorithmic trading, and customer service (e.g., robo-advisors, credit scoring).
  • Retail AI: AI for personalized shopping experiences, inventory management, and sales forecasting (e.g., recommendation systems).
  • Automotive AI: AI in autonomous vehicles, traffic management, and driver assistance systems (e.g., self-driving cars).

8. By Ethical and Philosophical Considerations

  • Ethical AI: AI designed and deployed with considerations for fairness, transparency, and accountability (e.g., bias mitigation, explainability).
  • Human-Centered AI: AI systems that prioritize human values and social impact, ensuring they enhance human capabilities and well-being.

Categorizing Generative AI

Generative AI can be categorized in several ways as it spans multiple aspects of AI capabilities, applications, and techniques. Here’s how generative AI fits into different AI categories:

1. By Functional Areas

  • Creative AI: Generative AI is often considered a part of creative AI, where the system can create new content such as images, text, music, and more. Examples include text generation (e.g., GPT-4), image synthesis (e.g., DALL-E), and music composition.

2. By Learning Techniques

  • Unsupervised Learning: Generative AI models like GANs (Generative Adversarial Networks) can be trained on unlabeled data to generate new data samples.
  • Semi-Supervised Learning: Some generative models leverage both labeled and unlabeled data to enhance their learning process.
  • Reinforcement Learning: Certain generative models, especially in game development or interactive media, use reinforcement learning to improve their outputs based on feedback from the environment.

3. By Algorithms and Approaches

  • Generative Adversarial Networks (GANs): A class of generative models where two neural networks (a generator and a discriminator) compete to produce realistic data samples.
  • Variational Autoencoders (VAEs): A type of generative model that learns to encode input data into a latent space and then decode it to generate new data samples.
  • Transformers: Advanced neural network architectures like GPT (Generative Pre-trained Transformer) that are used for generating text and other sequential data.

4. By Application Areas

  • Natural Language Processing (NLP): Generative AI is extensively used in NLP for tasks such as text generation, machine translation, and conversational agents.
  • Computer Vision: Generative AI is used for creating and manipulating images and videos, such as generating realistic human faces or synthesizing artistic styles.
  • Speech and Audio Processing: Generative AI can create human-like speech, sound effects, and music composition.

5. By Industry Applications

  • Entertainment and Media: Generative AI is used for creating content such as movies, video games, and virtual reality experiences.
  • Marketing and Advertising: AI-generated content is used for personalized advertising, content creation, and brand storytelling.
  • Healthcare: Generative AI is used for generating synthetic medical data for research, simulating medical scenarios, and creating patient-specific treatment plans.

6. By Ethical and Philosophical Considerations

  • Ethical AI: Generative AI raises significant ethical questions, including the potential for misuse in generating fake content, deepfakes, and the ethical implications of AI-created art.
  • Human-Centered AI: Ensuring generative AI tools are designed and used in ways that enhance human creativity and productivity without replacing human judgment and originality.

Generative AI primarily fits into the categories of Creative AI, Unsupervised and Semi-Supervised Learning, and specific algorithms like GANs and VAEs. Its applications span across various domains such as NLP, computer vision, and entertainment, highlighting its versatile and impactful role in modern AI development.

In analytic applications, people make very real business decisions based on the information being returned, and the possible inclusion of such errors make the application of generative AI problematic for obvious reasons. Until proven to be fully trustworthy, there will be hesitancy among many to employ it in analytics. 

The History of AI in Analytic Applications

The integration of AI into analytic applications represents a major paradigm shift in data analysis. While the roots of AI can be traced back several decades, recent advancements in machine learning, deep learning, and cognitive computing have propelled its adoption in analytics.

Early applications of AI in analytics focused on rule-based systems and expert systems, which relied on predefined rules and logic to analyze data. However, these approaches had limitations in handling unstructured data and adapting to dynamic environments.

The advent of machine learning revolutionized the field by enabling algorithms to learn from data and improve performance over time – Machine Learning. Supervised learning techniques, such as regression and classification, paved the way for predictive analytics, allowing organizations to forecast future outcomes based on historical data.

Deep learning, a subset of machine learning inspired by the structure and function of the human brain, further expanded the power of AI in analytics. Deep neural networks, with their ability to automatically extract features from raw data, have achieved remarkable success in tasks such as image recognition, speech recognition, and natural language processing.

Cognitive computing represents the next frontier in AI data analytics, aiming to simulate human-like intelligence in machines. These systems can understand, reason, and learn from data in ways that go beyond traditional algorithms, opening up new possibilities for complex problem-solving and decision-making. 

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Application of AI in Modern Analytics Applications

Streamlining 

One of the primary benefits of AI in analytic applications is its ability to automate labor-intensive data processing tasks. From data cleaning and transformation to feature engineering and model selection, AI algorithms can streamline the entire analytics workflow, allowing data analysts to focus on data driven decision making.

Predictive Analytics

Predictive analytics is another area where AI excels, enabling organizations to forecast future trends and outcomes with unprecedented accuracy. By analyzing historical data and identifying patterns, AI and machine learning models can make informed predictions about future events, such as customer behavior, market trends, and equipment failures. 

Prior attempts at implementing predictive analytics have been encouraging, but the most recent advancements in neural networks promise to make it commonplace in analytic apps.

Democratization of Analytics

One of the areas many analytic product teams find challenging is creating user interfaces for a variety of different end-users, from novices to those with a deep understanding of data structures and queries. By using NLP to interpret questions posed in ordinary language, and then leveraging the power of machine learning and neural networks, useful information can be returned to even the most basic user. All this can happen without the need to learn anything beyond asking a question. 

In this way, low-code and no-code AI solutions enable user friendly access to advanced analytics by average business users.

Monitoring and Alerts

AI-powered anomaly detection algorithms can automatically identify unusual patterns or outliers in data, such as fraudulent transactions, supply chain problems, network intrusions, or equipment failures. By flagging these anomalies in real-time, organizations can take proactive measures to mitigate risks and prevent potential losses using workflow automation.

Personalization

In consumer-facing industries such as e-commerce and digital media, by analyzing vast amounts of customer data, AI algorithms can deliver customers personalized recommendations, content, and offers tailored to individual preferences and behaviors.

Optimizing Business Processes

AI can automate decision-making and resource allocation in real-time. For example, in supply chain management, AI algorithms can optimize inventory levels, shipping routes, and production schedules based on demand forecasts and market conditions, leading to cost savings and operational efficiencies. 

This type of insight can be implemented in a way that does not automate, but augments human decision-making. Until a high level of trust in AI is achieved, this is likely to be the most common use case. 

This collaborative approach combines the strengths of AI algorithms—such as speed, scalability, and pattern recognition—with human judgment, intuition, and domain expertise.

Challenges

While the benefits of AI-driven analytics are undeniable, they are not without the following challenges:

Privacy and Security

One of the primary concerns is data privacy and security. With AI algorithms relying on vast amounts of data to learn and make predictions, there is a risk of sensitive information being compromised or misused. Organizations must implement robust data protection measures that account for the activity of AI within their data sets, including encryption, access controls, and data anonymization, to safeguard against potential breaches.

Acquired Bias

Bias and fairness in AI algorithms are another significant concern, particularly when it comes to decision-making processes that impact individuals’ lives. Biases inherent in training data can lead to unwanted or discriminatory outcomes. Addressing bias in AI requires careful data curation, algorithmic transparency, and ongoing monitoring to ensure accuracy and equity.

Cost – Power Consumption

At this point, there’s no getting around the fact that AI is computing intensive. For example, both the execution of analytic processes (“inference”) as well as the training of AI models on selected data sets, comprises significant computational investment. Running analytic processes is an understood and accepted cost, but it’s important to keep in mind that training is an ongoing requirement as data expands and changes. 

For example, the GPT-3 175B model necessitated a massive amount of computing power for its training, requiring an estimated 3.14E23 FLOPS. This means that even if we were to utilize the most powerful GPUs available, and the cheapest cloud pricing options available for a reserved period of 3 years, the cost of a single training run would still amount to $4.6 million and require 355 GPU-years to complete. 

Throwing more infrastructure at the problem is certainly an option, but it’s clear that the costs can be staggering.

Source: CSET. Note: The blue line represents growing costs assuming compute per dollar doubles every four years, with error shading representing no change in compute costs or a doubling time as fast as every two years. The red line represents expected GDP at a growth of 3 percent per year from 2019 levels with error shading representing growth between 2 and 5 percent.

Making Business Sense

Clearly, the adoption of AI in analytic applications will have a cost, one which will only be paid when the benefits clearly outweigh them. Right now, as businesses carefully scrutinize the abilities of AI, the firms developing them are more focused on accuracy than on efficiency and speed. 

This makes sense in the context of technology adoption; it’s first and foremost critical to sell the potential market on the efficacy of the product, and it must be shown to be accurate and trustworthy. This means that the particular challenge of accuracy in generative AI is likely to slow its adoption in many analytic processes. 

And while there’s no doubt that AI will drive huge investments in AI chip development, this will be a lengthy and ongoing pursuit if AI is to make business sense. 

Conclusions – The Road Ahead

The integration of AI is reshaping the future of analytic applications, unlocking new levels of efficiency, accuracy, and innovation in data analysis. From enhancing traditional analytics tasks to driving innovation across industries, AI-driven analytics has the potential to revolutionize how organizations operate, compete, and innovate.

However, realizing the full potential of AI requires addressing challenges such as data privacy, bias, and transparency, and perhaps most challenging of all, the tremendous costs of the computational resources that must be brought to bear. While embracing emerging trends such as democratization and decision augmentation are extremely attractive, the ultimate level of adoption has to overcome these considerable hurdles. 

We’ll all be watching closely as these trends continue, and as the market acceptance and valuation balance out against the cost and risks.

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