Appier named as consumer marketing AI company to know
Enterprise companies comprise a
$3.4 trillion market worldwide of which an increasingly larger share is being allocated to artificial intelligence technologies. By our definition, “enterprise” technology companies create tools for workplace roles and functions that a large number of businesses use. For example, Salesforce is the primary enterprise software used by sales professionals in a company. Also known as a type of customer relationship management software, or CRM, it is the system of record for sales professionals to enter in their contacts, progress of leads, and for sales metrics to be tracked. Any company directly selling their products and services would benefit from a CRM. Plenty of enterprise companies use combinations of automated data science, machine learning, and modern deep learning approaches for tasks like data preparation, predictive analytics, and process automation. Many are well-established players with deep domain expertise and product functionality. Others are hot new startups applying artificial intelligence to new problems. We cover a mix of both. To help you identify the best tools for your business, we’ve broken up our
landscape of enterprise AI solutions into functional categories to match organizational workflows and use cases. Most of these enterprise companies can be classified in multiple categories, but we focused on the primary value add and differentiation for each company. You’re welcome to re-use the infographic below as long as the content remains unmodified and in full.
Business intelligence (BI)
This function derives intelligence from company data, encompasses the business applications, tools, and workflows that bring together information from all parts of the company to enable smart analysis. From streamlining data preparation like
Trifacta, to connecting data more effectively from different silos like
Alation, and even automating reports and generating narratives like
Narrative Science and
Yseop, enterprise companies are improving BI workflows with artificial intelligence.
Productivity at work is often stunted by a myriad of tiny tasks that consume your attention, i.e. “death by a thousand cuts.” Many productivity tools have emerged to eliminate such tasks, such as the endless back and forth required to schedule meetings. Luckily, many of these productivity tools are virtual scheduling assistants like
Taking care of your customers is no easy task. Enterprise companies have recognized this critical area as ripe for disruption with artificial intelligence.
DigitalGenius utilizes AI to sift through your customer service data and automate customer service operations.
Inbenta’s AI-powered natural language search enables delivery of self-service support in forums and virtual agents.
Luminoso creates visual representations of customer feedback, allowing companies to better understand what consumers want.
With the average tenure of a hire getting shorter, hiring and talent management is arguably one of the most difficult areas for every company to tackle. Where can you find the right candidates and how do you keep hires engaged? Companies like
Scout work from the top of the funnel to get you the most qualified candidates while others like
hiQ Labs utilize public data to warn you of staff attrition risks and enable you to create retention strategies.
No one likes to waste time tediously doing data entry or spend hours sometimes googling and sifting through LinkedIn trying to get that marginal bit of information on a lead. Perhaps that’s why professionals in these functions are willing to embrace and experiment with new tools. Some automate data entry and improve forecasting accuracy like
Fusemachines and the AI-powered sales assistant
Tact, while others like
Lattice Engines and
Mintigo utilize thousands of data sources to surface the most qualified prospects and opportunities. You also have
Salesforce’s Einstein who has the intention of bringing AI and automation throughout the entire sales ecosystem.
So much data and intelligence can be gathered about your consumers through social channels, distribution channels, media channels, etc. Smart tools can not only crawl through this data, but analyze and understand what’s being said or done.
Lexalytics is a text analytics platform that translates billions of unstructured data pieces and online signals into actionable insights for the company.
Affinio uses deep learning to surface social fingerprints for the brand by creating interest-based clusters. Brands now have a better understanding of their customer segments, behaviors, and sentiments.
Finance & operations includes the back office, forecasting, accounting, and operational roles required to run a company. Since nobody likes paperwork, this area is ripe for automation.
HyperScience recently came out of stealth with their
$18 million Series A in December of 2016 to completely automate back office operations like form processing and data extraction through AI. Another company called
AppZen is an automated audit platform that can instantly detect fraud and compliance issues, freeing up T&E teams from tedious manual audits and checks. The tools in this space reap immediate returns for companies due to the volume and repetitive nature of some of the tasks.
More consumers are transacting online, making conversions a critical area of focus for retail and e-commerce companies.
Sentient Technologies analyzes user actions against the product catalog and is able to recommend products more effectively while also creating a personalized shopper experience while the shopper explores. Utilizing NLP and machine learning techniques,
BloomReachis able to adapt site content to capture traffic and provide personalized search and categories to make it more relevant to the user.
Companies need the right format, volume, and understanding of data before they can effectively deploy artificial intelligence solutions, making data science and management critical to any ambitious enterprise. With the rise of data science professionals came new tools and platforms.
Domina Data Lab are all data science platforms that assists data scientists in building and deploying models quickly and more efficiently.
Even software engineering can be accelerated and automated by AI.
Diffbot uses a combination of AI techniques like computer vision, NLP, and machine learning to enable developers to extract and understand objects from any web page.
Bonsai removes layers of complexity to make programming AI models more accessible.
Rainforest utilizes intelligent crowdtesting to QA in order to keep up with fast-moving development teams.
As more users transact online, security & risk becomes a bigger challenge for enterprises. Security and risk companies usually fall into two categories: 1) companies that focus on detection and risk mitigation of potential fraud and cybercrime and 2) companies that automate and scale security operations. In the first categories are companies like
Sift Science and
Darktrace which are AI-driven platforms that monitor and track thousands of anomalies to detect fraud and cybercrime.
Demisto falls into the second category, working as an incident response platform, reducing time spent responding and following the proper SLA guidelines while also automating inquiries into the incident.
Industrials are related to the manufacturing, supply chain, and distribution of goods. This sector is generally not vertically integrated and therefore usually suffers from decentralized data.
DeepVu leverages deep reinforcement learning to assess supply chain risk and accurately forecast future demand.
Arimoanalyzes historical data in order to provide equipment downtime monitoring, manage energy being used efficiently, detect anomalies in production, and more.
We encourage you to use this list of 133 enterprise AI companies as a starting point for your own research into technology solutions for your organizations. As we mentioned in the introduction, there are many ways to define and categorize companies in a complex enterprise taxonomy. Many of the companies we listed above add value across multiple functional categories and verticals. Most of them also use a wide range of AI and machine learning technologies, ranging from computer vision to NLP / NLU. You’ll need to conduct further investigations to find the right fit for you. Source:
113 enterprise AI companies you should know