Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
Artificial intelligence is revolutionizing every industry with various use cases. Demand for AI products grows as more companies shift their legacy systems with digital products to survive in the competitive business landscape. However, the AI vendor landscape is crowded, and most executives or decision-makers have limited knowledge of the AI landscape.
Check out our comprehensive categorization of AI companies based on their sizes, technology, industry, business function, geography, business model & services they offer. Note that this list does not contain all AI vendors. If you want to see our comprehensive and up-to-date AI vendor lists, feel free to check out AIMultiple.com, where we list 8,000+ AI vendors based on their technology offerings.
The global AI race is getting fierce, and companies such as Google, Facebook, Amazon, Microsoft, and Apple are developing new AI products& services and making new AI acquisitions. Apple is leading in the number of AI acquisitions, and Microsoft has the most AI-related patents (more than 18,000) in its portfolio.1
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As of October 2020, 5 startups raised more than or equal to $1 Billion in funding:
he table below summarizes the AI startups listed by size above:
Enables companies to build and deploy ML models. These models could be in any AI domain such as NLP, machine vision, etc.
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Natural language processing is the core technology behind chatbots. NLP is a subcategory of AI that helps break down, understand, process, and determine the required action based on queries. NLP is the engine that performs tasks such as dialog control and task prediction.
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Machine vision is at the core of technology behind industrial automation. The decrease in cost of cameras and advancement in image recognition technology resulted in more accurate and cheaper machine vision systems. Therefore, industrial companies aim to achieve increased automation and efficiency through machine vision systems.
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Autonomous things include robotics, vehicles, drones, autonomous smart home devices, and autonomous software. Self-driving cars are getting the most attention among these technologies. However, there is still time before we see them on most roads due to technical and regulatory challenges.
Checkout for more on autonomous things (AUT).
AI helps analytics get automated, more accessible, and more accurate. Thanks to AI and ML algorithms, organizations’ analytics methods are better in prediction, pattern recognition, and classification.
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Task mining technologies enable businesses to collect and monitor user interaction data to understand how they perform the tasks. Most task mining solutions are integrated with process mining technologies. These enable organizations to understand processes and find ways to enhance the whole process rather than just improve how employees perform specific tasks.
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AI-powered systems can automate various business processes with the help of RPA technology. Some automation examples are
AI-powered automation vendors
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The table below lists tools by their technology category:
Roughly 70% of healthcare tasks could be optimized through automation or AI support. In nursing, 20% of routine, low-complexity duties could be automated, potentially saving $50 billion annually.4 Therefore, 45% of operations leaders in customer care said introducing advanced technologies, including AI, was a key priority.5
The most prominent applications of AI companies in the healthcare industry are early diagnosis, drug discovery, and better treatment along with data-driven administration by analyzing and interpreting the available patient and company data more precisely.
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The insurance industry heavily relies on documents and repetitive processes. AI and Insurtech companies deliver automation in back-office tasks while improving customer service (via chatbots) and enabling fraud detection (via predictive analytics).
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AI products & services can provide retailers various capabilities such as
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Most popular AI use cases in manufacturing focus on improving maintenance and quality. Manufacturing includes the orchestration of processes and full of analytical data that suits AI/ ML algorithms; therefore, manufacturers can generate value through AI adoption.
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Capabilities AI technology offers to logistics companies are:
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In the telecommunication industry, AI projects focus on the following technologies:
AI helps banks and other financial institutions reduce costs and errors with improved banking processes while ensuring data security and compliance. McKinsey estimated that AI can generate more than $250 billion in value for financial institutions.6
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Check out enterprise AI companies listed by the industry they belong to:
Most important challenge of sales reps is spending a significant time on unqualified leads due to a lack of lead prioritization and manual processes in lead generation. AI technologies can target these obstacles with its analytics and automation capabilities.
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There are numerous AI products you can purchase to enhance different marketing strategies such as SEO, content marketing, and account based marketing (ABM). Products like recommendation engines or website personalization solutions help businesses improve conversations while AI-powered analytics is enabling better customer targeting.
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AI can help customer service team enable communication with customers through chatbots while performing analytics on customer responses to enhance call experience.
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AI can facilitate recruiting and saves time for recruiters by automating processes such as candidate identification & outreach, resume screening & interview analysis.
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Artificial intelligence’s influence on security systems depends on where you look.
Regardless of perspective, businesses should rely on AI to secure themselves from cyberattacks. IBM’s 2025 report shows that global breach costs fell 9% to USD 4.44 million, the lowest in five years, as AI defenses cut containment time to 241 days, a nine-year best.7
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Here are some of the enterprise AI companies categorized by the business functions:
The top 5 countries by number of AI startups for 2013-2023 period:
San Francisco is the leading region that has the highest number of AI startups with 842 startups. Yet, the interesting fact is around one-third of startups have Chinese founders/co-founders.
Standford AI index also focus on the number of publications and AI models to measure growing interest and maturity in the regional markets.9 The top 5 countries by the number of notable AI models delivered in 2024 include:
The top Enterprise AI companies that delivered highest notable AI models are listed as:
Like tech companies, AI companies can also be classified by the size of the businesses they target:
Though most AI startups, specifically in industries such as insurance, retail, healthcare, and banking, focus on enhancing customer experience through the guidance of data and analytics, they promote their products for businesses rather than consumers.
In other words, most AI companies are B2B-focused. According to Asgard’s research, which is a venture fund for AI companies, 64% of AI companies are B2B. However, their calculation methodology doesn’t look 100% accurate since there are numerous B2B companies such as OJO Labs (in real estate) and Personetics Technologies (in Fintech) where the research below included them in the B2C environment. Therefore, we assume the ratio of B2B AI startups is higher than 64% of the AI ecosystem.
AI chips are specially designed accelerators for artificial neural network(ANN) based applications. ANN is considered a subfield of artificial intelligence and most commercial ANN applications are deep learning applications.
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Most AI products you encounter in the business world are SaaS products where vendors share APIs or deliver a product via an app or web portal.
Some vendors offer specific services based on your business needs. AI services businesses may purchase include
If your business needs are niche, you need to build custom AI solutions. For this reason, you may want to check our custom AI development whitepaper where we explained every aspect of vendors that you may encounter within the AI landscape.
Download our Whitepaper on Custom AI Solutions
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You can also check out our list of AI tools and services:
If you still have questions on AI vendors, don’t hesitate to contact us:
Sources:
*Data related to businesses’ funding is taken from Crunchbase
**Data related to businesses’ number of employees is taken from Linkedin
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