Categories of AI Tools for Smarter Organizational Choices

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Categories of AI Tools

Organizations have rapidly adopted Artificial Intelligence (AI) in recent years. This adoption, driven by several simultaneous innovations, often causes organizations to make impulsive decisions. In this article, I aim to introduce AI tools in categories to help organizations make more informed choices by having some ideas about the big picture. You may already be using multiple AI-based tools, and when you realize what category they belong to, you can enhance your search to find more accurate solutions.

1. Natural Language Processing (NLP) Tools

NLP tools process information and replicate human language to perform tasks such as translation, conversation (chatbots), and sentiment analysis (IBM Watson Natural Language). Organizations can use NLP tools to streamline and automate customer support, editorial tasks, translation, and content management processes.

2. Computer Vision Tools

Computer vision tools help automate the recognition and processing of non-text-based content such as videos, charts, and images. OpenCV for image recognition and Google Cloud Video for videos are examples of these tools. Computer vision tools are also widely used in surveillance systems to detect suspicious activities and face recognition.

3. Speech Recognition and Generation Tools

Most of us use these in our daily lives. Speech recognition and generation tools are essential parts of voice assistant technology, where users’ voice-based inputs are captured, processed, and responded to. Google Assistant and Amazon Alexa are examples of this technology. Text-to-speech (TTS) converts text into voice and is used to produce videos from text as well as assist people with visual impairments.

4. Machine Learning (ML) and Deep Learning Tools

Organizations generally need expertise to benefit from machine learning and deep learning tools. These tools are powerful for building custom models to enhance data-driven decision-making processes. Machine learning tools mostly help developers and data scientists to build and train models. If an organization lacks the technical expertise to work with these tools, they may use tools like Google AutoML to automate the machine learning process.

5. Robotic Process Automation (RPA) Tools

RPAs can handle tasks with predefined steps. Since the steps are predefined and mostly have specific workflows, RPAs can manage repetitive tasks. Zapier is a good example of an RPA tool. RPAs help businesses minimize human errors and maximize organizational performance, allowing employees to focus more on creative tasks over repetitive ones.

6. Predictive Analytics Tools

Predictive analytics tools work with input from historical data and provide predictions as output. Financial, educational, and cybersecurity organizations widely use these tools.

7. Recommender Systems

Recommender systems analyze user behaviors to make assumptions about their expectations. This process, previously handled with manual coding, plugins, and surveys, is now more precise with AI. In my experience, this category is one of the most beneficial for marketing organizations. Recommender systems, for instance, can greatly assist e-commerce businesses by personalizing the shopping experience for users. For more details, visit my guide on e-commerce marketing strategies.

8. Data Science and Analytics Platforms

These tools assist data scientists by providing faster and more unified access to visualization, modeling, and analysis tools. I believe this is the most critical category in the AI industry as it directly impacts research, implementation, and innovation. DataRobot is an example of these tools.

9. AI Ethics and Fairness Tools

Just like most innovations in history, AI comes with some risks. AI can make mistakes and, worse, be manipulated. AI ethics and fairness tools help minimize unfairness, often due to biases, in AI systems. Microsoft Fairlearn is an example of these tools.

10. Augmented Reality (AR) and Virtual Reality (VR) Tools

AR/VR tools play major roles in gaming, business, and education, where users can participate in events without being physically present. Enhancing, replicating, and creating environments help users engage with the environment and facilitate more efficient communication.

11. AI Security Tools

Although in its early stages, AI security tools can help predict threats and take action against them based on historical knowledge of AI systems. However, one downside of AI security tools is their inability to deal with unknown (zero-day) threats.

In conclusion, these are broad categories of AI tools that provide an overview of what’s available before you decide to sign up for these services. My advice to organizations is not to commit to long-term financial agreements for these services, as innovation in these areas never slows down and better options might be available imminently. Before deciding to use an AI system, especially if you have to pay for it, prioritize your organization’s needs and adopt it gradually. Testing AI systems’ contributions to your organization is another critical step in validating your decisions.

For instance, if you introduce a chatbot to your organization to replace your customer support, do this in phases, such as enabling it for certain hours of the day. Review the conversations and push surveys if possible. Once you have enough data, analyze them against your existing KPIs, such as the percentage of resolved issues and time spent per customer support interaction.

About the Author

Ugur Gulaydin
Visionary Chief Marketing Officer with a profound quantitative background excels in leading transformative marketing strategies across competitive B2B sectors like cybersecurity, managed IT services, home automation, and cloud security. Specializes in assembling and guiding elite teams to pioneer performance marketing techniques, focusing on measurable, scalable outcomes. Follow me on LinkedIn

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