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Artificial intelligence (AI) has become a key technology included in the strategic roadmap of most organizations. Gartner estimates that by 2025, 70% of organizations will have implemented AI architectures due to the quick maturity of AI orchestration initiatives. Forrester also predicts that one in five organizations will increase their investment in AI. These forecasts have significant implications for business leaders, which will discuss in this blog post. Additionally, it will provide examples of common business scenarios in which AI can be applied.
Several factors in the present business landscape are pushing organizations to accelerate their AI plans:
- Business applications are moving towards the cloud, which enables organizations to conveniently access the necessary data.
- Low-code/no-code platforms are utilizing pre-built machine learning (ML) models, leading to a surge in democratization.
Furthermore, complementary technologies in various industries are progressing, with AI offering substantial advantages.
- 5G is disrupting the telecom sector
- The Internet of Things (IoT) is influencing manufacturing, automobile, and oil and gas spaces
- Omnichannel experiences are driving the retail and e-commerce segment
- Blockchain is influencing financial services, procurement, logistics, etc.
Artificial Intelligence is not a universal solution that can be implemented similarly across all businesses. Companies must comprehend the fundamental functional capabilities that AI can assist in achieving.
Artificial Intelligence applications for common business scenarios
Document interpretation
In this situation, AI helps companies categorize and extract data from unstructured documents, as the name suggests. With the increasing sophistication of ML models, organizations can extract data with greater accuracy and confidence levels, even with fewer datasets. For example, using Forms AI, we can train the ML model accurately by simply dragging and dropping two to three invoices.
Artificial Intelligence Computer Vision
Computer vision enables the interpretation of on-screen elements with human-like recognition, allowing companies to develop vision-based automation that can operate on most virtual desktop interface (VDI) environments, irrespective of the framework or operating system. The AI Computer Vision technology enables robots to identify and interact with on-screen fields and components.
Natural language processing (NLP) for Artificial Intelligence
NLP (Natural Language Processing) capability facilitates language detection, unstructured data extraction, and sentiment analysis. NLP can also be applied to business communications, a process known as communication mining. It extracts intent data (such as customer inquiries and reasons for contact), tone, and sentiment to enhance automation and comprehension of business procedures.
Email automation is one of the primary applications of communication mining:
- Extracting emails from underlying systems
- Classifying based on the target scenarios
- Extracting information from the respective email
- Processing the information as per the requirement
Scientific discovery: process mining and task mining
By utilizing ML models, Process Mining empowers organizations to detect bottlenecks in their business processes by analyzing digital footprints, such as transactional logs from various applications or systems. The Business Automation Platform provides convenient access to both Process Mining and Task Mining. Task Mining interprets data collected across multiple agents over a specific timeframe, enabling organizations to identify various paths employees take to accomplish the same task.
Predictive analytics
ML models, with access to historical data, now enable businesses to make more informed decisions. Companies are utilizing this capability to improve demand forecasting, offer personalized products or services, anticipate network outages, prevent fraudulent transactions, and achieve other valuable outcomes.
Industry relevance
AI has potentially impacted every industry, with a broad range of proofs-of-concept (PoCs) and industry-specific ML models readily available in plug-and-play mode. As a result, businesses have increased their investments in this transformative technology.
Banking and financial services for Artificial Intelligence
According to Forrester’s predictions, AI is poised to become one of the top technologies that banks will adopt in 2022. Interest in AI, microservices, and analytics remains strong, and budgets for AI/machine learning are notably high.
Pharmaceuticals
According to a report by McKinsey, the implementation of AI technologies leads to better decision-making, optimization of innovation, improved efficiency in research and clinical trials, and the creation of useful new tools for physicians, consumers, insurers, and regulators.
Telecommunications
According to reports, the global AI in the telecommunications market is expected to grow at a CAGR of 42.6% from 2021 to 2027. This growth is driven by the adoption of 5G technology by service providers, which is creating new use cases for both B2B and B2C segments.
IT for Artificial Intelligence
CIOs are being acknowledged as the key drivers of AI adoption at a larger scale within organizations. Regarding IT, there are two angles to consider. Firstly, IT leaders are implementing AI for internal functions such as optimizing IT operations and executing zero-touch service desks. Secondly, IT teams are advocating for AI best practices for businesses, encouraging adoption with minimal risks. As a result, CIOs are taking charge of leading the transformation and driving business outcomes.
Human resources (HR)
A Gartner article from 2020 stated that within the HR function, 17% of organizations are currently utilizing AI-based solutions. HR leaders have indicated that they are deploying AI to achieve cost savings, make data-driven decisions more accurately, and improve the employee experience.
Below is a table highlighting some of the industries where AI can be applied:
Conclusion
AI has become an integral part of modern business operations, enabling organizations to streamline processes, improve decision-making, and gain a competitive edge. From process automation to customer service, the potential use cases for AI are endless. By understanding the core functional capabilities of AI and leveraging the right tools, businesses can effectively implement AI and reap its benefits.
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