Section 1:

Decoding AI: Understanding Key Terms and Technologies Shaping Business Solutions

Section 1

Decoding AI: Understanding Key Terms and Technologies Shaping Business Solutions

AI encompasses a range of technologies, each with unique capabilities and applications. Understanding the different types of AI technologies is foundational to any AI sales strategy.

The Different “Flavors” of AI
AI comes in many forms, from traditional models that generate text to advanced models designed for real-time interactions and task execution.
Traditional language models	…Predict next words using statistical patterns.
Conversational language models	…Follow instructions and hold coherent conversations.
Reasoning language models	…Perform multi-step logic and analysis.
Agentic language models	…Plan and execute tasks on behalf of users.

Check out these foundational AI terms→

Glossary

Section 1

Generative AI vs Non-Generative AI: A Brief Overview

There are two broad categories of AI. Most organizations today use both types of AI to solve specific business challenges.

Generative AI creates new outputs like data, images, text and other types of content.
Non-generative AI analyzes and understands existing data to make classifications, predictions, and/or decisions.

Generative AI learns patterns and structures from large datasets during a training phase, typically using deep learning capabilities like neural networks. Once trained, the AI model can generate new content, such as text, images, or music, by sampling from the learned patterns. Generative AI can take data or seed information and use its internal knowledge to produce coherent and contextually relevant output. Feedback from users can also be used to improve the model's output quality over time, making it a versatile tool for various creative and practical business applications.

In contrast, non-generative AI focuses on analyzing existing data using statistical models and algorithms. Companies deploy non-generative AI to identify patterns within datasets, make predictions, and classify information. Common use cases include fraud detection, customer segmentation, and predictive maintenance. Non-generative AI is especially useful for automating decision-making processes and improving operational efficiency.

According to the International Data Corporation (IDC):⁴

The top leaders using generative AI are realizing an ROI of $10x.
43% of respondents said that productivity use cases have provided the strongest ROI.
AI projects are taking less than eight months on average, with most organizations realizing value within 13 months.
The top barrier when implementing AI is a lack of technical and day-to-day AI skills.

Generative AI is having a significant impact on all business areas, including CX, cybersecurity, cloud, and IoT — making it a must-know technology for business leaders.

Section 1

GenAI Market Leaders in 2025
Category	Model Family
Text	GPT-4o (OpenAI) Claude 3.5 Sonnett (Anthropic) DeepSeek R-1/V3 Gemini (Google)
Image Generation	FLUX (Black Forest Labs) Imagen3 (Google) DALL-E 3
Video	Runway Veo-2 (Google) Kling-Pro

Model hosting platform Poe, which hosts more than 100 models, recently released a report providing a breakdown of real-world usage patterns across AI technologies in voice, video, and image generation.⁵

Poe revealed that organizations like OpenAI and Anthropic continue to dominate text generation, while new entrants like DeepSeek and Black Forest Labs are quickly gaining market share.

Unsurprisingly, the report found that new model releases tend to overtake older versions, with users typically migrating to the newest offerings when they become available.

As the AI landscape continues to evolve, we’ll see new technologies and advancements emerge that we never thought possible. Laundry-folding robots could become a household item in the very near future. But for now, let’s explore the known and accessible opportunities you can introduce to your customers today.