One of the hottest topics today is the AI for business boom and its enormous potential for helping enterprises do exponentially more with less. According to PwC's 2023 Global Artificial Intelligence Study, the AI gold rush could contribute a massive $15.7 trillion to the global economy by 2030.
By 2030, the AI gold rush could contribute over $15 trillion to the global economy.
However, maximizing your AI investment goes beyond integration—it requires orchestrating business processes to operationalize AI throughout your organization’s value chain. That includes enabling collaboration between AI and humans. What does that look like? Think scenarios in which AI proposes solutions that humans subsequently make decisions about and refine.
Practically speaking, AI for business is about leveraging AI to transform core business processes and supporting three critical business needs: streamline business processes, gain insight through data analysis, and take friction out of the customer experience. This article explores trends and insights to help you jumpstart your AI strategy, get better outcomes from your AI investment, or simply find inspiration for AI use cases.
2023 was, without a doubt, a breakout year for generative AI. In 2024, the trend will continue. More importantly, generative AI will have a profound impact on process automation, but only if large organizations can successfully incorporate the technology into digital workflows. The best platforms incorporate generative AI and low-code development in end-to-end process automation, offering an array of productivity-enhancing AI capabilities, including:
Instant generation of digitized forms from PDFs through AI document understanding and creation of new applications from existing PDF documents.
Cutting-edge self-service analytics through AI-powered queries of a data fabric architecture with natural language.
AI-generated workflows assembled on the fly based on natural language prompts, breaking down barriers in human-machine collaboration.
Low-code development is a much more efficient and effective way to work with generative AIs. The best platforms seamlessly integrate AI into low-code design tools, allowing users to build interfaces directly from a PDF in a few clicks and generate instructions for the form based on the PDF’s content. Here's how it works:
AI-powered low-code automation creates user-friendly interfaces from PDFs in just a few clicks.
Generative AI is also changing the game for rapid workflow generation. Imagine, for example, instructing an AI to construct a workflow that performs tasks X, Y, and Z, leveraging the existing business process automation technologies within the platform, such as robotic process automation, business rules, and data fabric. Or, envision empowering users throughout your organization with instant access to analytics by simply inputting their queries into an AI assistant using natural language.
[Download the Process Automation Guide and discover how to optimize your critical business processes from end to end .]
Depending on your business requirements, you can use AI to review files, extract critical data points, monitor customer interactions, or collect and digitize handwritten information from contracts. Whatever your situation, it's best to consider how AI can improve or solve pain points in your existing business processes (rather than coming up with brand news uses that don’t align with current problems). Let's look at some enterprise AI use cases and best practices that are transforming business operations today:
A large law firm handles class action lawsuits and manages case data in an AI-powered process automation platform. A single case can include thousands of plaintiffs, so it could take a long time to look for traits that the plaintiffs have in common. For example, a law clerk may have to manually research how many plaintiffs reported a respiratory illness due to materials used to build apartments where they live.
Using an AI-powered tool like the records chat component of Appian speeds up this research significantly. The law clerk could query related records using natural language without engineering a prompt. And this particular component of Appian uses generative AI—powered by the Appian AI Copilot design assistant—to answer questions held within record data. So if the law clerk asks a question in the context of the case record, AI Copilot looks within the related records to find the answers in a flash.
Invoice and purchase order (PO) processing is essential for businesses across many industries. As organizations scale, manually processing invoices and PO documents can quickly become a bottleneck for other workflows. Let's say your application supports a rapidly growing finance department. You have a limited number of employees, and they increasingly spend an overwhelming amount of time processing invoices and POs daily.
Building a document extraction AI skill into your existing workflows can take the burden off your employees, freeing them up for more meaningful tasks. Invoice and purchase order document types are excellent for automatic document extraction. They contain semi-structured data with clearly labeled values that can be easily extracted into key-value pairs.
In the financial services industry, security is of the utmost importance when working with sensitive financial documents. Yet, many institutions manually process numerous forms on paper without a digital workflow, which makes the process error-prone and inefficient. A better approach is to choose an AI process platform with document extraction functionality and other automation capabilities. To optimize your document extraction capabilities, prioritize solutions that offer:
Robust optical character recognition capabilities
Flexibility to customize your extraction models
Seamless integration through an AI-driven platform that incorporates a robust data fabric architecture
Pre-built intelligent document processing functionality that accelerates document extraction workflows.
Automatic document extraction and performance monitoring.
Customized security settings for different document types to improve security, such as configuring a workflow to send withdrawal forms to bank tellers and loan applications to loan officers.
[Learn about other top AI trends in banking.]
Now, let's say you work for a company that specializes in supply management. Your document classification application receives several invoice forms that must be audited and filed daily for processing. Since each form is structured in the same format and contains easily identified fields, you could delegate this task to artificial intelligence–powered robots to extract the data from each field and store the results.
You can also design your process to classify documents and extract data from them automatically. If you want to confirm that the data extracted was correct before saving it to a database, you can have human workers validate the AI’s work, adding in an extra layer of quality control and human oversight.
And that's just one document extraction use case. You could also apply this process to any application that requires a human to audit, review, or organize data from a document. Since document extraction focuses on fields, consider using these capabilities when processing forms like invoices, records, or applications.
Next, let's look at an insurance industry use case. Say your claims management application receives hundreds of insurance claims a day. If your team is overwhelmed by the amount of data involved, it could slow down the process and increase the risk of inaccurate decision-making. That's a scenario where AI can improve insurance processes.
For example, you can use AI to quickly design a claims submission interface that allows users to process claims data in a fraction of the time it would otherwise take. Instead of having all of your claims go to one team to identify and sort through reams of supporting documentation manually, you could use AI to delegate claims to different teams depending on where the damage occurred. Custom ML models can identify the types of documentation that accompany each claim so that the relevant teams are notified or involved at the correct times. And when an image accompanies each insurance claim, you could leverage computer vision to analyze it, determine where the damage occurred, and then route the claim to the proper team.
Customer support-related applications are prime candidates for numerous types of AI, since they are responsible for constant and reliable communication with customers. Using AI in these use cases, you can easily detect customer sentiment with natural language processing, properly understand customer engagement through fast AI translation, or leverage document extraction capabilities to review and audit customer contracts.
Let's say your application is built to manage a global customer support center. You receive support tickets from around the world in many different languages, and your support engineers need to understand customer issues quickly to resolve them promptly. To avoid slow resolution time and misinterpreting the customer's issue, AI integration allows you to:
Utilize Google Cloud Translation for rapid text translation.
Create dynamic translation with rule inputs in the integration.
Enable translated customer issue descriptions in the support engineer's native language.
Here's the bottom line: AI will be a powerful enabler of competitive advantage for enterprises that crack the code of AI adoption across the enterprise value chain.
AI will be a powerful enabler of competitive advantage for enterprises that crack the code of AI adoption across the enterprise value chain.
As generative AI gains traction, natural language processing (NLP) has emerged as a driving force reshaping AI capabilities. NLP's influence is pervasive and transformative. NLP enables machines to better understand and interact with human language, and it's a vital component of everything from document automation to chatbots and conversational AI. In 2024, NLP's popularity will continue to soar as it empowers AI across industries. Here's how NLP will impact AI in the coming year:
Enhanced conversational AI: NLP's evolution will usher in more sophisticated chatbots and virtual assistants, significantly improving their ability to understand and respond to human speech, which promises better customer support and user experiences across sectors.
Data insights unleashed: Market leaders will harness NLP's advanced capabilities to extract valuable insights from text data sources. AI analytics will rely on NLP to decipher complex text data, enabling informed decision-making.
Hyper-personalization: Businesses will leverage NLP-driven AI to deliver hyper-personalized customer service. AI systems will provide more accurate and relevant recommendations and content by understanding user preferences and sentiments.
Healthcare revolution: NLP's impact will extend to healthcare, transforming diagnosis, data management, and patient care. It will facilitate a better understanding of medical records and improve healthcare outcomes.
Cross-industry integration: NLP's versatility will foster cross-industry integration, giving rise to domain-specific solutions and innovations. AI-NLP integration will drive innovation across industries from finance to retail and beyond.
NLP's growing popularity and capabilities reflect its crucial role in advancing AI technology. As businesses increasingly recognize its potential, NLP will continue to shape the AI landscape, making interactions between humans and machines more seamless and insightful.
[Explore more on AI and process automation. Get the Gartner® Emerging Tech Impact Radar: Hyperautomation.]
For any AI process automation venture to excel, it must be firmly grounded in robust data management. Why? Because the effectiveness of any AI implementation is intricately linked to the quality and accessibility of the data that fuels it. The most successful AI implementations prioritize not only making data instantly accessible but also readily usable for AI-driven process automation.
As organizations seek to maximize the potential of their data, they are pivoting from traditional data warehouses and lakes to more agile data management strategies, such as data fabric. When integrated with process automation technologies, a data fabric offers the advantage of no-code connectors to facilitate the seamless integration of data from diverse systems into a single application, ensuring that data becomes a valuable asset that drives efficient and effective process automation across the entire value chain.
If you prioritize protecting sensitive and confidential information, such as intellectual property and customer data, seek out fully private AI solutions, where your data remains within the confines of your cloud site and under your control.
It's essential to recognize the risks of using AI models trained on public data sets (as opposed to those trained only on proprietary data). For instance, massive language models like ChatGPT incorporate publicly available data. In contrast, private AI means the model is exclusively trained on your organization's data, ensuring that the resulting intelligence stays within your organization, preventing competitors from capitalizing on your data-driven insights.
Hyperautomation combines AI, machine learning, and robotic process automation to make enterprises more agile, efficient, and adaptable. According to Gartner, by 2024, 65% of large organizations worldwide will have deployed some form of hyperautomation.
The strategic integration of AI into front- and back-office operations is essential for maximizing employee productivity and driving meaningful improvements in decision-making processes. However, achieving this level of integration using traditional developer tools is resource-intensive and demands a profound understanding of complex business processes.
That’s why the application of AI in process modeling and product innovation holds immense potential for creating game-changing and revolutionary offerings. For example, AI enables businesses to quickly develop tailored process models and products that resonate with evolving consumer demands.
AI-powered process modelers speed application development.
The consequences of failing to effectively implement AI extend beyond hindering immediate operational effectiveness, impacting long-term strategic viability as well. Companies that are slow to embrace enterprise AI risk becoming irrelevant.
Whether you're kickstarting your enterprise AI strategy, experimenting with AI-powered business process automation, or seeking inspiration for better AI applications, this guide serves as your blueprint for success.
[For expert insights on maximizing AI's potential and navigating its future impact, download the 2024 AI Outlook: Expert Advice on Navigating the AI Economy.]