Gartner Competitive Landscape Flashcards
Key Findings
Key Findings
■ Hyperscale cloud providers — such as Alibaba Cloud, AWS, Baidu, Google, IBM, Microsoft and Tencent — compete on the speed of AI services adoption by lowering the level of expertise required through low-code/no-code development technologies and custom-made packages.
■ Responsible and explainable AI are increasingly becoming important to gain customers’ trust, with hyperscale cloud providers competing on these as their differentiated value.
■ Hyperscale cloud providers are scaling their influence on cloud-based AI services by expanding their partner ecosystem and improving the access to diverse types of customers.
Opening
Democratization of technologies is extending the cloud-based AI services competition approach from technology-centric to solution-oriented. Technology and service providers must support both professional developers and business users to compete in the AI services market.
Recommendations
Recommendations
Technology and service providers competing in the cloud provider AI services market should:
■ Optimize their AI services product portfolio and improve the speed of business value realization by partnering with specialists who provide industrial/business domain expertise, data and solution.
Strengthen responsible AI by collaborating with vendors that not only provide safety and governance of AI services but also give visibility into how a model arrived at a particular decision.
■ Select hyperscale vendors that have a strong developer community and partner ecosystem by assessing their openness and interoperability support with third-party solutions.
Market Groth 22’-25’
The total AI software market is expected to reach $134.8 billion in 2025, growing at a 29.2% CAGR from 2022 through 2025 (see Measuring the Opportunity in the AI Software Market). According to findings from the 2023 Gartner CIO and Technology Executive Survey, distributed cloud and artificial intelligence/machine learning remain the top technologies that enterprises plan to deploy within the next three years. 1
AI Market Tiers
The market is served by five major types of providers that address either one or multiple types of services, based on their specialty and focus areas. These providers include:
■ Hyperscale cloud providers cover the most comprehensive range of AI services and have the competitive advantages of hyperscale cloud computing infrastructure services specialized in supporting AI services for best performance. The major providers include Amazon Web Services (AWS), Google, IBM and Microsoft for the global market, and Alibaba Cloud, Baidu and Tencent primarily focused on China.
■ DSML platform providers focus on end-to-end DSML platforms that support public, hybrid, private, on-premises or multicloud. Examples of vendors include Databricks, Dataiku, DataRobot, MathWorks, SAS Institute and H2O.ai.
■ AI technology vendors focus on computer vision, natural language technologies or other core AI technologies, such as foundation models and generative AI. Examples include Cognigy, boost.ai, NVIDIA and OpenAI. This category of vendors is dominated by many AI startups, competing with expertise in a niche area or highly integrated solutions for one special domain.
■ Industry and domain platform providers offer AI-applied domain solutions for a specific industry or business. Examples include GE Digital, Hitachi, PTC and Siemens.
■ Enterprise software vendors focus on AI-augmented horizontal software and applications. Examples include Oracle, Salesforce and SAS.
Competitive Situation and Trends
Decrease engineering effort by using low-code/no-code development technologies.
■ Realize business value faster by providing custom-made packed solutions as per
user requirements.
■ Support openness and interoperability with third-party solutions and services.
■ Improve risk and fairness of AI tools by leveraging responsible AI to gain customers’ trust.
■ Build a partner ecosystem and developer community to access diverse types of customers.
Realize Value in Speed and Scale With Democratization and Operationalization of AI
According to the 2023 Gartner CIO and Technology Executive Survey, “growth” and “cost optimization” will be the most common enterprise priorities through 2023. 1 To accelerate solution adoption and business value realization, hyperscale cloud providers compete on the speed of democratization of cloud AI technologies by lowering the requirements for level of expertise, increasing the level of integrations for the purpose of using and operationalizing AI.
Improving the Operationalization of AI Solutions by Supporting Openness and Interoperability With Third-Party Solutions and Services
By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, accelerating AI deployment. 2 The increasing complexity of operationalizing AI requires cloud providers’ support for multicloud, open-source framework, integration with third- party solutions and operating in a services-based architecture.
Trust with responsible AI
Many organizations are already exploring certain responsible AI capabilities such as bias mitigation/fairness, explainability, trust/transparency and privacy/regulatory compliance.
Explainable AI is a critical component of responsible AI t
that gives visibility into how a model arrived at a particular decision. This helps in building trust, confidence and understanding in AI systems. In highly regulated sectors such as insurance or banking, regulations directly or indirectly mandate the need for model explainability to properly manage model risk.
GCP on responsible AI
Google also has a series of responsible AI tools — such as ML-fairness-gym, Model Cards, Know Your Data and Fairness Indicators — for mitigating fairness and bias issues and improving models’ transparency and explainability.
