How generative AI transforms business value

Globally, enterprises are working to keep up with current generative AI trends and identify the benefits and practical use cases while addressing risk. They want to know how to avoid possible threats and, at the same time, move quickly toward generative AI technology adoption to remain competitive.

Everest Group is helping business leaders find the needed answers to their questions, so they can uncover the right opportunities, determine the best price, and ensure safety.

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Key use cases of generative AI

Get a look at our early gen AI research and the top use cases.

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Current impacts of gen AI and how organizations can scale gen AI investments.

Generative AI factors

  • Generative AI potential and accelerated adoption

    Generative AI potential and accelerated adoption

    Enterprises can identify use cases and opportunities to move quickly on generative AI adoption. Everest Group is helping enterprises frame the speed of adoption and effort put in by addressing questions such as:

    • What are the use cases, and where can we apply them?
    • How should we prioritize adoption across use cases?
    • Where are enterprises launching generative AI in business operations today?
    • Which use cases are enterprises adopting for IT?

  • Emerging trends and the risks to watch

    Emerging trends and the risks to watch

    Business leaders need to consider the risks surrounding generative AI, including data security and privacy, bias and ethics, ownership and responsibility, and explainability. The risk questions we’re currently helping enterprises address include:

    • What is the generative AI risk that should be our immediate priority as we plan our adoption strategy?
    • How do we uphold our data security and privacy?
    • How do we know if the information coming from generative AI is reliable?
    • Who has legal rights and legitimate ownership of the content coming from generative AI?
    • What is the best way to ensure the content generated from generative AI is unbiased?

    Take our Generative AI Risk Assessment to gauge the level of risk associated with adoption.

  • How to source smart and price right

    How to source smart and price right

    Utilizing generative AI technology across a wide range of applications can come with a substantial cost. Enterprises have the option to carefully choose specific use cases to ensure a return on investment (RoI). We are helping enterprises with their pricing questions, including:

    • With competition moving rapidly, how do we know what kind of generative AI to invest in for our business needs?
    • How can we determine if we can use specialized models at a lower cost?
    • How do we know if we will achieve RoI, and how can it be measured?
    • How do enterprises know if they are using the right tools for their needs and at the right time?
    • If generative AI does not fit the use case I’m looking for, are there other options?

  • Partnering for generative AI excellence

    Partnering for generative AI excellence

    Service providers can play a significant role in making generative AI adoption more feasible. We’re helping business leaders select the best provider for their needs, answering questions like:

    • Who are the generative AI partners for our specific needs?
    • What’s the difference between providers from a technology perspective and a services perspective?

  • Top providers driving generative AI

    Top providers driving generative AI

    Everest Group is also helping providers investigate questions as they position their services to meet enterprises’ generative AI needs and stay competitive in the market, including:

    • Will generative AI change contracting?
    • Where do you build vs. partner?
    • How do you create moats in services?
    • Who underwrites the risk?
    • Will GPU shortage become a bottleneck to scale?
    • How do you price your generative AI offerings?
    • See the Everest Group AI Top 50™ technology providers

The AI LLM assessment

Large Language Models (LLMs) have transformed AI, enabling breakthroughs across diverse language-related tasks. However, with countless proprietary and open-source options flooding the market, there’s a lack of a clear, practical framework to evaluate them.

To address this, we analyzed the leading LLMs, offering enterprises and service providers a comprehensive assessment to identify the best fit. Our framework rates LLMs based on ease of adoption and capabilities, simplifying decision-making across industries and geographies.

Evaluate generative AI large language models

Compare the capabilities and ease of adoption of current LLMs. Check the boxes next to your chosen LLMs, then click the Compare button. To start over, click Reset.

 

Capability
Measures the basic ability of an LLM to generate effective outputs; measured through four subdimensions.

  • Features
    Distinctive attributes, such as number of parameters and tokens trained on, languages and modalities supported
  • Scalability
    Effort required to scale an LLM for more users
  • Quality of output
    The quality of content generated, including context understanding and reasoning abilities
  • Market perception
    Perception of an LLM and its usefulness in the market

Ease of adoption
This variable evaluates the factors that are crucial for enterprise adoption of an LLM through three subdimensions.

  • Risk
    Susceptibility to common gen AI risks such as data privacy and security, reliability, explainability, biases, and ownership
  • Average cost of usage
    Cost incurred by the user to use the LLM model
  • Market readiness 
    Overall community developed for the LLM to aid users of the mode, including documentation, marketplace, and model availability in multiple modes

Note:

  1. This assessment is applicable as of August 2023
  2. Since Gen AI is a fast-evolving space, we expect quick changes in terms of availability, capabilities, and positions of LLMs on this matrix. Therefore, we plan to publish periodic updates to this assessment

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