Leading generative AI trends and top questions we're answering

Generative AI Trends

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.

Current Impacts of Gen AI and How Organizations Can Scale Gen AI Investments.

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?

High adoption: Generative AI use cases

Take a sneak peek at our early research findings on gen AI, as enterprise leaders debate if gen AI is going beyond the pilot phase, and explore use cases and adoption across enterprises.

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Generative AI trends and risk

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.

Generative AI sourcing and pricing

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?

Generative AI partners

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?

Generative AI providers

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

Recent generative AI reports

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The AI LLM assessment

Large Language Models (LLMs) have revolutionized the field of Artificial Intelligence (AI), driving remarkable achievements across diverse language-related tasks. However, the market is flooded with LLMs, ranging from proprietary off-the-shelf to open-source options. What’s missing is a single, consolidated source of detailed information on these models and a fair and practical framework to assess them.

To address this challenge, we have assessed the most popular LLMs available today so organizations can identify the best one for their needs. For this assessment, we have analyzed 13 popular proprietary and open-source LLMs to help enterprises and service providers across all industries and geographies compare and assess key LLM providers’ offerings.

Our assessment framework evaluates LLMs and rates them according to their ease of adoption and capabilities.

AI LLM Assessment - An Evaluation of Generative AI Large Language Models

Please select the LLMs you would like to compare.

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.

  • Risks
    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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