Artificial intelligence (AI) having a transformative impact on business processes is old news. Its proven capabilities for task automation, insightful data analysis, and enhanced decision-making are well known. Nevertheless, the exponential rate at which developers are refining AI business technologies constantly produces new, substantial process improvements.

Perhaps none are more impressive than what’s happening in a core component of business process management (BPM), namely robotic process automation (RPA), which is business process automation (BPA) taken to the “nth” degree.

Hyperautomation: Combining RPA with AI to Expand Business Capabilities

RPA’s conjoining with AI, known as hyperautomation (i.e., the integration of multiple automation proficiencies), is taking automation to a level that easily surpasses RPA’s pre-AI.

For instance, you couldn’t previously have a bot analyze invoices and route them to the appropriate people in your company until you standardized every invoice or programmed the bot to process a lot of document variations. That makes the task both difficult and costly.

By itself, RPA can only perform straightforward, organized and objective tasks. But the layer of intelligence that AI adds to RPA provides tools with nuanced, subjective or unstructured data. Now, you can automate more of your processes:

• You can train an AI model to accurately read and sort documents, so there’s no need to manually program bots to handle different document formats. An experience that we had is a case in point. We recently worked with UiPath, the world’s biggest RPA vendor, to help a client deploy hyperautomation to manage its large number of multiformat, multilanguage purchase orders more effectively. It trained an AI tool on more than 100 purchase orders from one of the client's vendors. Linking up this tool with RPA automated a process that previously involved a lot of back-end work.
• AI can interpret free-form text, chats, emails and other unstructured data. By using an AI-powered bot to read and properly direct emails to the team members needing to see them, you can free up staff to focus on more valuable tasks.
• An AI-linked RPA can analyze images and detect anomalies or errors. An example is ensuring product labels are accurate by inspecting and analyzing design drafts, flagging inconsistencies, and notifying the appropriate design agencies. It’s a much quicker and more precise method than what was available before AI.
• Hyperautomation produces and analyzes complex reports rapidly, saving employees many hours of manually extracting data and accelerating the decision-making process that depends on that data.
• A real-world demonstration of how AI plus RPA can classify complex information saw a bank implement a custom machine learning model. This model automates about 90 percent of the work needed to generate living expense reports – information crucial to making loan determinations. The result was a much quicker and more accurate loan application process.

Hyperautomation results like these are only some of the latest proofs of how AI has automated routine tasks, providing insightful data analysis, and enhancing decision-making business processes. AI has been doing this in more streamlined, effective ways over time through advancements in its core purpose: the creation of computer systems that perform tasks more intelligently. Like people, trying things, learning from mistakes, and changing behaviors to meet future challenges.

In its most mature development to date, as generative AI, it provides large language models that power ChatGPT, Google Bard, and other such applications. Generative AI uses sophisticated algorithms to answer queries --- in the user’s human language --- by detecting patterns in words, numbers and data.

The Software Tools Elevating Business Process Automation

There are many intelligent automation software tools that expand the power and application of business process automation, particularly as it applies to RPA functionality.

Enterprise Automation Platforms

Distinguished by their offering of a modular solution to customer automation needs, enterprise automation platforms include UiPath, whose 10,00 worldwide customers range from independent RPA developers to large businesses like Verizon and Coca-Cola. And IBM’s suite solution to BPA offers process mining functionality developed on process discovery and mining technology acquired from MyInvenio.

Robotic Process Automation

RPA involves using software robots to mimic human interactions with digital systems and software. Some examples of this technology include:

• Robocorp, an open-source RPA intelligence software that allows developers to automate repetitive tasks.
• Worksoft, which provides RPA-based software test automation and automated website testing capability.
• And Turbotic, a platform with multivendor integrations that helps coordinate RPA projects across the entire process lifecycle and enables RPA project management across different toolkits.

Intelligent Document Processing (IDP) Technology

IDP tech allows AI to automate information extraction and validation from key documents such as invoices, receipts, forms, and contracts. It is available through the likes of:

• Kofax, an IDP and process automation solution provider that does digital and print scanning of financial documents.
• ABBYY, which extracts data from print and digital documents through optical character recognition (OCR).
• And Super.AI is a tool with automatic data capturing ability that can automate audio, video, images, and text document processing.

Conversational AI

This AI processes and understands human speech and text through natural language processing (NLP), natural language understanding (NLU), and automated speech recognition (ASR). The products that feature it include:

• Boost.ai, a platform leveraging NLP and NLU for scalable enterprise use cases (e.g., in customer service).
• Cognigy, a platform that lets businesses create and post intelligent automated conversations, with custom dialogue flows, on any platform.
• And Avaamo, whose intelligent assistants work in areas such as supply chain management, HR, sales support and claims processing.

AI Decision-Making: Making Smarter Business Decisions

Generative AI is the most sophisticated, “the-future-is-now” exploration of AI technology. But it is predictive AI that has given businesses the power to make more enlightened and effective decisions. That’s because predictive AI uses machine learning (ML) algorithms extracted from historical data to constantly recognize patterns, identify trends, and make predictions. These insights, which may elude people, inform strategic decisions by helping businesses anticipate what customers need, develop optimal pricing strategies, and see where the market is going.

This is a separate dimension from what conventional AI does. Predictive AI goes beyond basic historical data analysis by turning data into a predictive resource. More than simply looking at data to understand what happens, it reshapes the data into actionable intelligence for looking forward instead of backward.

This advance has made customer engagement much more personalized and closely targeted to providing relevant product and service offerings. It has also penetrated AI into virtually every aspect of business infrastructure management: from predicting and pre-empting manufacturing equipment failures to smoothing out supply chain logistics to human resource retention strategies that anticipate potential employee attrition by assessing factors such as job satisfaction, performance statistics, and work-life balance.

Note that predictive AI isn’t the same thing as predictive analytics, with which it’s sometimes confused. AI is autonomous and learns continuously on its own, while predictive analytics depends upon humans to arrive at decisions by manually assessing past and present datasets.

Generative AI, on the other hand, uses the data from advanced algorithms and deep learning methods to fashion new content with attributes derived from that data. Plus, generative AI learns from what it did in the past. Nevertheless, using generative AI for business can have a secondary yet valuable role in supporting the decision-making process. It does things like developing decision “copilots” that can dynamically analyze information, present options, and complete tasks.

Remember Data Protection and Ethics When Introducing AI for Your Business

Before committing to adopting AI for your business, your company should take a holistic, strategic approach that considers all possible risks and rewards. Having done that, you’ll find it necessary to make significant investments in time, money and resources to implement AI. Your most significant investment will be to establish and ensure data privacy and security, given the critical and sensitive nature of the data AI systems handle.

Organizations must be acutely aware of privacy risks to keep personal and proprietary data safe from unwarranted disclosure.

The complexity of advanced AI models that train using personal data can make it difficult to give regulators and shareholders a clear understanding of what the models produce and how they make decisions. That can make it hard to discover, diagnose and fix undetected biases and ethical challenges and to determine accountability for the decisions that flow from those models. Minimizing these risks requires the adoption and encouragement of ethical AI development principles, rigorous testing, and transparency. It also requires making users more aware of and diligent about data protection imperatives.

It behooves your organization to adopt best practices for AI security. These include:

• Formalize data processing agreements to prevent AI providers from using your data for training.
• Enhance user input validation to weed out potentially hostile prompt inputs.
• Audit and monitor AI outputs on a continual basis to ensure they’re appropriate.
• Monitor your network traffic for data exfiltration attempts.
• Integrate security into data governance to manage data privacy concerns.
• Enhance robust cybersecurity tools to monitor for AI enhanced attacks.
• Implement new cybersecurity tools for deepfake detection.
• Improve or augment personnel training for cybersecurity threats impacted by AI (e.g. phishing, social engineering).
• Enact robust AI governance covering usage, development, monitoring, risk management, regulatory compliance and incident response as it all relates to AI.
• Embrace continuous learning to improve AI literacy of the workforce.

Establishing an ethical culture for AI adoption is no less important than making the data safe. The emphasis here is living by the principles of transparency, fairness and non-discrimination (i.e., the absence of both deliberate and inadvertent bias). Then you build a responsible AI environment with well-defined accountability frameworks and ethical data sourcing.

People must be able to understand why an AI model made a particular decision. They should have confidence that data is securely stored, with encryption methods and firewalls in place to thwart data breaches. Access should be limited to approved personnel. You should also adopt and encourage effective data deletion practices to permanently remove obsolete data and data an individual asks you to delete.

The Future of AI in Business: A World of Opportunities

AI is not just a buzzword. It's a powerful tool that can transform business operations and decision-making. Businesses that embrace AI will be better positioned to navigate the complexities of the digital age and thrive in the competitive business landscape.

So, is AI right for your business? To answer this question, it's essential to understand your business needs, assess your capabilities, and weigh the potential benefits against the challenges. Then, if you decide that AI is for you, it’s worth seeking a professional consultation to ensure a successful implementation.

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Our AI Strategy experts can help you fully explore AI’s potential for your business and show you how to use AI to streamline business processes and make smarter decisions. Contact us