Director's Guide to AI Technology!

Do you know the difference between AI types and when and where to use them?

Generative AI

Predictive AI

Agentic AI

Autonomous AI

Reinforcement Learning AI

Understanding AI Technologies

Overview of AI Types

Machine Learning described in simple words, can happen in 3 ways:

1. Computers watch and observe what others do, then copy that action.

Machine learning gives computers and machines access to data (information), so they can then learn for themselves without a human having to program, type in or speak a command.

2. Computers watch and observe and then use logic to make their own decision based on previous experiences with “data”.

3. Computers learn from their previous mistakes.

◦ Deep Learning

Deep learning isa method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human

brain Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.

Natural Language Processing

Natural language processing (NLP) is a computer science and artificial intelligence (AI) field that uses machine learning to allow computers to understand and communicate with human language. NLP can analyze and generate

text and speech, and is used in many everyday applications.

Automation and Robotics

Intelligent automation (IA)

The combination of AI, robotic process automation (RPA), and business process management (BPM) to create end-to-end processes and orchestrate work.

AI can be integrated into robotics to improve decision-making and enhance a robot's capabilities. For example, AI robots can perform repetitive tasks like checking inventory and alerting staff to out-of-stock items in retail environments.

AI and automation are often used together, but they have distinct characteristics and applications. AI is a more advanced form of automation because it introduces an element of intelligence, allowing it to adapt and perform a broader range of tasks.

The AI robotics market is expected to grow to over $35 billion by 2026, up from $6.9 billion in 2021.

What companies are creating AI platforms and what are they designed for?

Comprehensive AI Models Research & Pricing Guide 2025

Executive Summary

This research provides a complete overview of major AI platforms and models available as of October 2025,

organized by type: Writing & Text, General-Purpose Assistants, Documentation & Productivity, Analytics & Data, Design & Creation, Video Generation, and Audio & Voice. Each entry includes company information, model type, primary use cases, and current pricing.

KEY RESEARCH FINDINGS

Major Trends & Insights in the AI Landscape (October 2025)

1. Pricing Evolution

Canva Teams pricing saw a 300% increase in 2025 due to AI feature integration, with teams of five previously paying $120/year now facing $500 annual bills
Claude Max pricing introduced two tiers: $100/month (5x usage) and $200/month (20x usage), positioning Claude directly against OpenAI's $200 ChatGPT Pro
OpenAI introduced ChatGPT Pro at $200/month to offer scaled access to premium models

2. AI Integration Across Platforms

Starting January 15, 2025, all Google Workspace Business and Enterprise plans now include Gemini AI features, with Gemini no longer offered as a separate product
Canva AI (Magic Studio) is a suite of 25+ AI tools integrated directly into the Canva editor rather than offered as separate applications

3. Model Capabilities

Sonnet 4 supports up to a 1M-token context window, enabling users to ingest entire codebases or large document sets in a single request
Gemini AI Pro includes Veo video generation access and priority processing for multimodal tasks

4. Enterprise & Team Features

Claude Enterprise plan reportedly costs $60 per seat for a minimum of 70 users with a 12-month contract, resulting in minimal pricing around $50,000
Canva Teams adds collaboration and admin features starting at $10/user/month or $100/year per person with a 3-user minimum

Gaps & Controversies

Pricing Transparency Issues: Many users view Canva's forced AI price hike as lacking justification, with small businesses forced to pay premium prices.

Notable Big Tech Companies creating Platforms integrating AI:

IBM Watson

IBM Watson is a computer system capable of answering questions posed in natural language.

It was developed as a part of IBM's DeepQA project by a research team, led by principal investigator

David Ferrucci. Watson was named after IBM's founder and first CEO, industrialist Thomas J. Watson.

Date introduced: 2010

Year:2011

Architecture:2,880 POWER7 processor threads

Location:Thomas J. Watson Research Center,New York, USA

Google AI

Google AI is a division of Google dedicated to artificial intelligence. It was announced at Google I/O 2017 by CEO Sundar Pichai. This division has expanded its reach with research facilities in various parts of the world such as Zurich, Paris, Israel, and Beijing.

Founded:2017

Microsoft Azure AI

Azure AI Language is a cloud-based service that provides Natural Language Processing (NLP) features for understanding and analyzing text. Use this service to help build intelligent applications using the web-based Language Studio, REST APIs, and client libraries.

Google Cloud and Microsoft Azure are both cloud computing platforms that offer a range of services for building applications:

Services

Both platforms offer compute, storage, database, and networking services. Azure also offers specialized services for big data, analytics, game and mobile app development, and data warehousing. Google Cloud offers strong support for containers and serverless.

Global reach

Azure has a broader global infrastructure than Google Cloud, with more regions and region pairs.

Specialties

Azure excels in enterprise-focused infrastructure and platform services. Google Cloud prioritizes cloud-native approaches and has an edge in advanced machine learning.

Storage

Azure uses ID drives for transient capacity, and Page Blobs VM-based volumes are stored in Block Storage. Google's Cloud Platform offers both brief stockpiling and constant circles

Amazon: AWS AI Services

Scale the next wave of innovation in AI by leveraging more than 25 years of pioneering AI experience from Amazon. AWS makes AI accessible to more people – from builders and data scientists to business analysts and students. With the most comprehensive set of AI services, tools, and resources, AWS brings deep expertise to over 100,000 customers to meet the demands of their business and unlock the value of their data. Security, privacy, and responsible AI have never been more critical. Customers can build and scale with AWS on a foundation of privacy, end-to-end security, and AI governance to transform at an unprecedented rate.

Cisco Systems:AI Network Analytics

Cisco AI Network Analytics is an application within Cisco DNA Center that leverages the power of Machine Learning and Machine Reasoning to provide accurate insights that are specific to your network deployment, which allows you to quickly troubleshoot issues.

Cisco AI Network Analytics consists of the following:

Overview

A worldwide cloud-based data platform where Machine Learning models are built and analyzed for your specific network environment.

A Machine Reasoning inference engine that automates human expertise and captures the workflows in a knowledge base repository. Machine Learning Cisco AI Network Analytics leverages advanced Machine Learning (ML) techniques and an advanced cloud learning platform with de-identified network event data, to identify critical issues in your network, and provide a rich set of information so that you can quickly troubleshoot issues, know their root causes, identify trends and insights, and obtain relevant comparative perspectives. Cisco AI Network Analytics provides this value using a simple, intuitive, and powerful user interface within Cisco DNA Center that is fully integrated with Cisco DNA Assurance.

Proven AI Use Cases

Machine Learning:

Vendor: Cisco

Use Case: Predictive Network Maintenance

Deep Learning:

Vendor: IBM Watson

Use Case: Threat Detection in Cybersecurity

Natural Language Processing:

Vendor: Microsoft Azure

Use Case: Automated Help Desks

Automation:

Vendor: Google AI

Use Case: Automated Incident Response

Robotics

Vendor: AWS

Use Case: Drones for Network Inspection

Drivers and Decision-Making Factors

Choosing the Right AI Model

Business Goals Alignment

My Boss is telling me to start using AI in our company but not how or where to start.

Specific Departmental use case (ex: Automate visibility for management and all employees for sales funnel and forecasting – CRm system integration with Salesforce.com)

Differentiators for AI Vendors

Unique Features of Each Vendor

Customer Support and SLA

Integration Capabilities

Vendor Support and Reliability

Data Availability and Quality

Scalability and Integration

Implementation Strategies

Timeline and Phased Approach

Integration with Existing Systems

Change Management and Training

Cloud vs. On-premise Considerations

Support and Maintenance

Ongoing Monitoring

Change Management and Training

Regular Updates and Upgrades

User Support and Training

Business Cost Factors | Cost-Benefit Analysis

Initial Investment

Cost = TCO-Cost Savings

Total Cost of Ownership

· per user +

· Infrastructure +

· Power and Cooling+

· integration+

· testing+

Operational Costs

· ongoing orchestration +

· management+

· and maintenance +

· training (tech engineers, non tech users)

Cost Savings

Hard Cost

· Potential reduced number of employees to perform tasks now done by AI

· Less infrastructure in cloud and hybrid designed systems (Servers are cloud based and move from Capex to Opex.

a. Capex (capital expenditure), price spent on long-term assets like buildings or equipment.

i. provides future benefits and is depreciated over time

b. Opex (operational expenditure) is the ongoing, day-to-day spending required to run the business.

i. provides short-term benefits and is expensed in the year it's incurred.

Soft cost

· Productivity Gain

a. Employees work smarter not harder

b. Faster

c. provide greater output

d. increased morale

e. increased sense of accomplishment and self worth

f. happier demeanor

g. better customer facing representation of self and company

h. a re positive presentation of personal and company band

Pros and Cons of AI Technologies

Machine Learning:

Pros: Data-driven insights, automation

Cons: Requires large datasets, complexity

Deep Learning:

Pros: High accuracy, ability to learn from non-structured data

Cons: Resource-intensive, less transparent

Natural Language Processing:

Pros: Improved user interaction, automation

Cons: Language complexities, context understanding challenges

Conclusion

· Future of AI in IT : Importance of AI in Networking and Cyber-security

AI plays a crucial role in networking and cyber-security by enhancing the ability to detect, respond, and mitigate threats.

Threat Detection and Prevention: AI can analyze vast amounts of data in real time to identify unusual patterns or anomalies that may indicate a security threat, such as malware or phishing attacks.

Automated Response: AI systems can automate responses to detected threats, reducing the time it takes to mitigate attacks. This is especially important for preventing data breaches and minimizing damage.

Behavioral Analysis: By learning the normal behavior of users and systems, AI can help identify insider threats or compromised accounts by flagging deviations from established patterns.

Vulnerability Management: AI can assist in scanning networks for vulnerabilities, prioritizing them based on risk, and suggesting remediation strategies, helping organizations stay ahead of potential exploits.

Predictive Analytics: By analyzing historical data, AI can predict potential security incidents, allowing organizations to proactively strengthen their defenses.

Network Traffic Analysis: AI tools can analyze network traffic to identify malicious activity, optimize performance, and enhance overall network security.

User Authentication: AI can improve user authentication processes through advanced techniques like biometric analysis and behavioral biometrics, making it harder for unauthorized users to gain access.

Phishing Detection: AI algorithms can analyze email content and metadata to identify potential phishing attempts, helping to safeguard users from falling victim to such attacks.

Incident Response: AI can streamline incident response processes, providing security teams with actionable insights and recommended responses based on historical data and current threats.\Cost Efficiency: Automating routine security tasks with AI can free up IT staff to focus on more strategic initiatives, ultimately reducing operational costs and improving security posture.