10 top artificial intelligence (AI) solutions in 2022

Among the many drivers of the tech ecosystem’s rapid growth, artificial intelligence (AI) and its subdomains are at the fore. Described by Gartner as the application of “advanced analysis and logic-based techniques” to simulate human intelligence, AI is an all-inclusive system with numerous use cases for individuals and enterprises across industries.

There are many ways of leveraging AI to support, automate and augment human tasks, as seen by the range of solutions available today. These offerings promise to simplify complex tasks with speed and accuracy, and to spur new applications that were impractical or possible previously.  Some question whether the technology will be used for good or perhaps become more effective than humans for certain business use cases, but its prevalence and popularity cannot be doubted. 

What is artificial intelligence (AI) software?

AI software can be defined in several ways. First, a lean description would consider it to be software that is capable of simulating intelligent human behavior. However, a broader perspective sees it as a computer application that learns data patterns and insights to meet specific customer pain points intelligently.

The AI software market includes not just technologies with built-in AI processes, but also the platforms that allow developers to build AI systems from scratch. This could range from chatbots to deep and machine learning software and other platforms with cognitive computing capabilities. 

To get a sense of the scope, AI encompasses the following:

• Machine learning (ML): Allows a computer to collect data and learn from it to generate insights.
• Deep learning (DL): A step further in ML used to detect patterns and trends in large volumes of data and learn from them.
• Neural networks: Interconnecting units that are designed to learn and recognize patterns, much like the human brain. 
• Natural language processing (NLP): NLP supports AI’s ability to read, understand and process human language.
• Computer vision: Teaching computers to collect and interpret meaningful data from images and videos. 

These capabilities are leveraged to build AI software for different use cases, the top of which are knowledge management, virtual assistance and autonomous vehicles. With the large volumes of data that enterprises must comb through to meet customer demands, there’s an increased need for faster and more accurate software solutions.

As expected, the rise in enterprise-level adoption of AI has led to accelerated market growth of the global AI software market. Gartner places the growth at an estimated $62.5 billion in 2022 — a 21.3% increase on its value in 2021. By 2025, IDC projects this market to reach $549.9 billion. 

4 critical functions of an AI solution

Whether it powers surgical bots in healthcare, detects fraud in financial transactions, strengthens driver assistance technology in the automotive industry or personalizes learning content for students, the overarching purpose of AI solutions can be grouped into four broad functional categories, including:

1. Automate processes

The automation function of AI applications meets AI’s primary objective to minimize human intervention in executing tasks, whether mundane and repetitive or complex and challenging. By collecting and interpreting volumes of data fed into it, an AI solution can be leveraged to determine the next steps in a process and execute it seamlessly. It does this by leveraging the capabilities of ML algorithms to create a knowledge base of structured and unstructured data.

Process automation remains a top enterprise concern, with one survey exhibiting that 80% of companies expect to adopt intelligent automation in 2027.

2. Data analysis and interpretation

A core function of AI solutions, especially for enterprises, is to create knowledge bases of structured and unstructured data and then analyze and interpret such data before making predictions and recommendations from its findings. This is called AI analytics and it uses machine learning to study data and draw patterns. 

Whether the analytic tools are predictive, prescriptive, augmented, or even descriptive, AI is at the center of determining how the data is prepared, discovering new insights and patterns and predicting business outcomes. Enterprises are also turning to AI for improved data quality. 

3. User personalization and engagement 

Building a relationship has become the holy grail of customer acquisition and retention. A study from McKinsey shows that one sure way to do this is through personalization and engagement. AI technologies allow enterprises to make personalized offers to customers and predict and solve their concerns in real-time. This function manifests in programs like conversational chatbots and product recommendations generated from learned customer behavior.

Many organizations are still getting up to speed with the technology. Gartner reports that 63% of digital marketers struggle to maximize personalization technology. Their survey of 350 marketing executives revealed that only 17% are actively using AI and ML solutions across the board, although 83% believe in its potency.

4. Business efficacy

Along with greater automation of traditional processes, AI enables new services and capabilities that were not previously feasible. From driverless vehicles and natural language services for consumers to medical breakthroughs that could only have been imagined previously, AI is becoming the base of new products and markets that will continue to unfold.

Also read: Creating responsible AI products using human oversight

10 top artificial intelligence (AI) software solutions in 2022

AI software solutions include general platforms for supporting a range of applications and products for more narrow, industry-specific use cases. We include a sampling of both in the following representative list. With 56% of organizations adopting AI for at least one business function, there are many options on the market today.

Below are ten examples of AI software solutions available in 2022.

Google Cloud AI

Google’s dominant cloud offering includes assorted tools to support developer, data science and infrastructure use cases. Several speech and language translation tools, vision, audio and video tools and deep and machine earning capabilities bring AI functionality to skilled technology practitioners and mass consumer markets. Google was named a leader in Gartner’s Magic Quadrant for Cloud AI Developer Services in 2022. 

IBM Watson Studio

Like Google, IBM offers a platform for building and training AI software. The IBM Watson Studio provides a multicloud architecture for developers, data scientists and analysts to “build, run and manage”’ AI models collaboratively. With capabilities ranging from AutoAI to explainable AI, DL, model drift, modelops and model risk management, the studio gives subject-matter experts the tools they need to either gather and prepare data or create and train AI models.

It also allows these professionals the flexibility to deploy AI models on either public or private cloud (IBM Cloud Pak, Microsoft Azure, Google Cloud, or Amazon Web Services) and on-premises. IT teams can open source these models as they build them with embedded Waston tools like the Natural Language Classifier. Its hybrid environment may also provide developers with more data access and agility.

Salesforce Einstein

Named a leader in Gartner’s Magic Quadrant for CRM Customer Engagement Center thirteen times in a row and the #1 CRM solution for eight consecutive years by the International Data Corporation (IDC), Salesforce provides an advanced kit of sales, marketing and customer experience tools. Salesforce Einstein is an AI product that helps companies identify patterns in customer data. 

This platform has a set of built-in AI technologies supporting the Einstein bots, prediction builder, forecasting, commerce cloud Einstein, service cloud Einstein, marketing cloud Einstein and other functions. Users and developers of new and existing cloud applications can also deploy the platform’s predictive and suggestive capabilities into their models. For example, at Salesforce Einstein’s launch in 2016, John Ball, general manager at Einstein, revealed that by creating Einstein, the company “enables sales professionals to find better prospects and close more deals through predictive lead scoring and automatic data capture to convert leads into opportunities and opportunities into deals.”

Oculeus

Oculeus provides an industry-specific solution. For service providers, network operators and enterprises in the telecom industry that need to protect and defend their communication infrastructure against cyber threats, Oculeus offers a portfolio of software-based solutions that can help them better manage network operations. According to founder and CEO Arnd Baranowski, Oculeus uses AI and automation “to learn about an enterprise’s regular communications traffic and continually monitor it for exceptions to a baseline of expected communications activities. With its AI-driven technologies, suspicious traffic can be identified, investigated and blocked within milliseconds. This is done before any significant financial damage is caused to the enterprise and protects the brand reputation of the telecoms service provider.”

The Communications Fraud Control Association (CFCA)’s 2021 survey of international telecommunication fraud loss discovered losses amounting to over $39.89 billion, a 28% increase in losses over the previous year. Similarly, network security and operators are experiencing more fraud threats and attacks. 

Among other things, these insights amplify the need for enterprises to switch to a proactive defense approach that outwits adversaries, and this what Oculeus claims to provide with its AI-powered telecoms fraud protection solutions. In Baranowski’s words, Oculeus’ AI-driven approach to telecoms fraud protection does not only “…stop fraudulent telecommunications traffic before any significant financial damage is caused” but also includes extensive automation tools that weed out threats thoroughly.

Edsoma

Edsoma represents another narrow use case. Its AI-based reading application software features real-time, exclusive voice identification and recognition technology designed to uncover the strengths and weaknesses in children’s reading. This follow-along technology identifies users’ spoken words and speaking speed to determine if they are saying the words correctly. A correction program helps put them back on track if they mispronounce something.

As Edsoma founder and CEO Kyle Wallgren explained, once “…the electronic book is read, the child’s voice is transcribed in real-time by the automated speech recognition (ASR) system and immediate results are provided, including pronunciation assessment, phonetics, timing and other facets. These metrics are compiled to help teachers and parents make informed decision.”

This technology aims to improve children’s oral reading fluency skills and provide them the necessary support to inculcate a healthy reading culture. Edsoma seeks to establish a share of the $127 billion global edtech market. By leveraging real-time data to provide real-time literacy, Edsoma looks to provide future-focused learning powered by AI.

Appen

Appen has been one of the early leaders as a source for data required throughout the development lifecycle of AI products. This platform provides and improves image and video data, language processing, text and even alphanumeric data. 

It follows four steps in preparing data for AI processing: the first step is data sourcing which offers automatic access to over 250 pre-labeled datasets — then data preparation, which provides data annotation, data labeling and knowledge graphs and ontology mapping.

The third stage supports model building and development needs with the help of partners like Amazon Web Services, Microsoft, Nvidia and Google Cloud AI. The final step combines a human evaluation and AI system benchmarking, giving developers an understanding of how their modes work. 

Appen boasts a lingual database of more than 180 languages and a global skill force of over 1 million talents. Of its many features, its AI-assisted data annotation platform is the most popular.

Cognigy

Cognigy is a low-code conversational AI and automation platform recently named a leader in Gartner’s 2022 Magic Quadrant for Enterprise Conversational AI platforms. As the need for more excellent customer experience (CX) intensifies, more enterprises rely on conversational analytics solutions that dive deep into its customer’s text and voice data and discover insights that inform smarter decisions and automate processes. 

This is why Cognigy automates natural communication among employees and customers on multimodal channels and in over 100 languages. In addition, its technology allows enterprises to set up AI-powered voice and chatbots that can address customer concerns with human-like accuracy.

Cognigy also has an analytics feature — Cognigy Insights — that provides enterprises with data-driven insights on the best ways to optimize their virtual agents and contact centers. In addition, the platform allows users to either deploy the technology on the cloud or on-premises. Particularly praised by Gartner for its customer references, flexibility and sustainability, this platform helps businesses create new service experiences for customers.

Synthesis

Synthesis AI‘s solution generates synthetic data that allows developers to create more capable and ethical AI models. Engineers can source several well-labeled, photorealistic images and videos in deploying its models on this platform. These images and videos come perfectly labeled with labels ranging from depth maps, surface normals, segmentation maps, and even 2D/3D landmarks.

Virtual product prototyping and the chance to build more ethical AI with expanded datasets that account for equal identity, appearance and representations are also some of its product offerings. Organizations can deploy this technology across API documentation, teleconferencing, digital humans, identity verification and driver monitoring use cases. With 89% of tech executives believing that synthetic data would transform its industry, Synthesis.ai’s technology may be a great fit.

Tealium

Tealium’s data orchestration platform is positioned as a universal data hub for businesses seeking a robust customer data platform (CDP) for marketing engagement. This CDP provider offers a tray of solutions in its customer data integration system that allows businesses to connect better with their customers. Tealium’s offerings include a tag management system for enterprises to track and unify its digital marketing deployments (Tealium iQ), an API hub to facilitate enterprise interconnectedness, an ML-powered data platform (Tealium AudienceStream) and data management solutions.

The company recently sponsored a comprehensive economic impact study from Forrester, calculating ROI on reference customers.

Coro

Coro provides holistic cybersecurity solutions for mid-market and small to medium-sized. The platform leverages AI to identify and remediate malware, ransomware, phishing and bot security threats across all endpoints while reducing the need for a dedicated IT team. In addition, it’s built on the principle of non-disruptive security, allowing it to provide security solutions for organizations with limited security budgets and expertise.

This cybersecurity-as-a-service (CaaS) vendor shows how AI can support higher-level services brought to lower-level business market tiers.

The wave of AI innovation

As AI-powered technologies continue to advance and more organizations adopt them, IT leaders must be sure to ask themselves how the solutions they choose fit into their goals as a business. With so many vendors riding the wave of AI innovation, buyers must select their solutions carefully.

IDC predicts that AI platforms and AI application development and deployment will continue to be the fastest-growing sectors of the AI market. This list provides a starting point for organizations to evaluate the approaches and solutions that best fit their needs.

Read next:New AI software cuts development time dramatically