How can AI supercharge process automation? A dedicated guide on how AI enhances process automation, driving efficiency & innovation across industries.
Some innovations shape the world slowly, while others explode onto the scene, instantly transforming industries, cultures, and societies at large.
AI is undoubtedly one of these innovations. While artificial intelligence has existed in some form for many decades now, it's only recently advanced to a point where it's impacting nearly every sector. From healthcare, where it's being used to predict patient outcomes and assist in surgeries, to the creative industries, where AI-generated art and music are challenging our perceptions of creativity.
And then there are core business processes - the way companies on the ground leverage AI to supercharge their operations, enhance decision-making, and personalize customer experiences. The business setting is arguably where AI is having the widest-reaching impact on our daily lives.
Why? For employees, it means a shift towards more strategic and creative roles as AI handles routine tasks, enhancing job satisfaction and opening new career opportunities. Financially, it boosts the bottom line by optimizing resource allocation and opening new revenue streams. For consumers, interactions with businesses become more tailored and responsive, significantly improving service quality and satisfaction.
Let's rewind a little here. Before first-rate AI solutions, companies the world over were already using automation to achieve some of these goals. For example, more than one in three businesses have five or more automated divisions. Moreover, more than 80% of corporate executives are committed to speeding up work process automation. But how? How can companies get more from process automation? You guessed it - by implementing AI.
Today, 22% of firms are already aggressively pursuing AI across various business workflows and technology products. However, a larger percentage, 78% to be precise, are either engaging in limited AI implementation or are still in the exploration phase. So, what can these companies do to reap the benefits of AI in process automation? How should they go about it? And how can they ensure they don't get left behind while their competitors race ahead? Let's get into it.
At its core, business process automation is about using technology to complete business processes with minimal human input.
Most businesses today use some form of process automation. For example, they will use an automated employee onboarding process, complete with automated document management and training schedules. Similarly, most companies manage support tickets with automation. These systems prioritize issues, route them to the appropriate department, and provide automated responses for common queries, ensuring swift and efficient resolution.
However, traditional process automation typically involves minimal AI. The process automation workflows of old were clunky, rules-based, and could not adapt or learn from new data, often requiring manual intervention to handle exceptions or unforeseen scenarios. Depending on the system, these exceptions could crop up regularly. This slows down the entire process, leading to bottlenecks, increased errors, and frustrated customers and employees alike. Ultimately, it impacts the efficiency and scalability of operations - the very thing it's supposed to combat. This is where AI comes in.
Integrating AI into business process automation not only solves many of the problems with traditional automation but also offers more functionality, speed, and accuracy. A few key AI technologies are leading the charge here.
First, we have Machine Learning (ML), and Deep Learning (DL), which act as the brains behind the operation. By sifting through and analyzing extensive datasets, these technologies uncover patterns and learn from them. This means these systems can make predictions about customer behavior, and market trends, and suggest operational optimizations.
The fashion retailer Zara is a prime example of ML-based operational optimizations in action. Unlike many of its competitors, Zara's use of AI extends throughout its entire supply chain and inventory management systems, not just consumer behavior analytics. By analyzing sales data, customer preferences, and fashion trends, these algorithms predict which products will be in demand in different regions. This predictive insight allows Zara to adjust its production and distribution strategies dynamically, ensuring that popular items are readily available while minimizing overstock.
Complementing this duo is natural language processing (NLP), the AI linguist that bridges human communication and machine understanding. NLP allows systems to parse and interpret both spoken and written language, allowing for highly adaptive virtual assistants and chatbots.
These systems can engage with users naturally, offering support, answering questions, and making recommendations around the clock. Apple's virtual assistant, Siri, is an excellent example of NLP. Siri understands spoken language, interprets user requests, and performs actions such as setting reminders, answering questions, and navigating apps.
Lastly, there are expert systems. These are the workhorses of AI, tirelessly automating repetitive tasks and freeing your team to focus on critical thinking, problem-solving, and activities that only humans can handle. These systems mimic expert human decision-making by using data, algorithms, and a detailed knowledge base to solve complex problems and make predictions.
For instance, expert systems can analyze data to predict customer behavior or sales trends, recommend products or services, and even advise CEOs on strategic decisions. You can think of an expert system like a highly skilled consultant trapped inside your computer, offering advice and solving problems in specific fields without needing a coffee break.
The architecture of an AI-powered expert system includes three primary components: the knowledge base, the inference engine, and the user interface. The knowledge base stores data, the inference engine applies algorithms to pull insights based on user queries, and the user interface is how users interact with the system. These systems have shown to be incredibly versatile, being used for forward chaining to predict future events and backward chaining to understand why something occurred.
AI-powered process automation offers real-world tangible benefits.
So we've covered what AI-driven process automation means, how it works, and what benefits you can expect, but what specific technologies fall under this umbrella? What tools can you implement on the ground? Let's take a look.
Robotic process automation (RPA) automates repetitive, rules-based tasks typically performed by humans. You'll sometimes see RPA bots called smart bots, especially when they use artificial intelligence. Unlike traditional RPA systems, these smart bots can adapt to changing inputs.
For instance, it can process invoices by extracting relevant data, regardless of format variations, improving efficiency in financial operations. AI-powered RPA solutions are capable of handling complex tasks, such as data extraction, data validation, decision-making, and even fraud detection, with minimal human intervention.
One of the primary challenges in process automation is handling large volumes of unstructured data in documents. For example, this is a major challenge in the insurance industry when it comes to processing claims.
Here, information comes in various formats like emails, PDFs, and handwritten notes, making it difficult for traditional automation tools to extract and process data efficiently. In the past, a process automation tool was typically capable of handling data only if it followed a particular format, and anything that fell outside of this format would need human input.
AI-powered IDP solutions combat this by leveraging advanced natural language processing and machine learning algorithms to extract relevant information from various documents, including invoices, contracts, and forms. Automating this previously manual and time-consuming task significantly improves accuracy, reduces processing time, and enhances overall productivity.
Intelligent process automation combines RPA with advanced cognitive capabilities. IPA can analyze unstructured data, make complex decisions, adapt to new situations, and even use generative AI to provide human-centered solutions. It enables businesses to automate processes that involve natural language understanding, pattern recognition, and prediction, creating a more versatile automation system.
A hospital might use IPA to analyze patient feedback, medical records, and clinical notes—data that is mainly unstructured and varied. By integrating natural language processing, the system can identify patterns in patient symptoms, feedback trends, and treatment outcomes, leading to more personalized patient care plans. It can also automate appointment scheduling based on the urgency inferred from patient communications and historical data, adapting to changes such as cancellations or rescheduling requests in real-time.
Cognitive virtual agents, like chatbots or virtual assistants, leverage AI to interact with users and provide intelligent responses. These AI-powered virtual agents can handle customer inquiries, perform basic troubleshooting, and guide users through various processes. By automating customer interactions, you can enhance customer service, improve response times, and reduce the workload on human customer support teams.
Tools are only one part of the equation when it comes to technology-powered transformation. Without the right brains and the attitude, tools are left underutilized, much like a high-performance engine running idle. With this in mind, let's look at some of the ways you can prepare your organization to reap the most benefits from AI-driven process automation.
Identify Opportunities for Disruption: Begin by pinpointing areas within your organization where AI can create the most value. This involves assessing current processes, products, and services to identify pain points or inefficiencies that AI technologies can address. Look for opportunities where AI can enhance decision-making, automate routine tasks, or create new customer experiences.
Capture Data and Identify Data Sources: Data is the lifeblood of AI systems. An essential step in preparing for AI is to identify and capture high-quality data from diverse sources within your organization. This includes customer interactions, operational processes, and external data feeds. Establishing a robust data infrastructure that can aggregate, store, and process data is critical for feeding AI algorithms the information they need to generate insights.
Ensure Data and Process Quality: The accuracy of AI outputs is directly related to the quality of the input data. Ensuring data integrity involves establishing protocols for data collection, validation, and cleaning. It's equally important to review and refine your organization's processes to ensure they're streamlined and documented. Clear, efficient processes not only improve data quality but also enhance the effectiveness of AI applications.
Get Key Employees Onboard: AI transformation is not solely a technology initiative; it's a business strategy that requires buy-in across the organization. This means engaging key stakeholders early in the process to align AI initiatives with business objectives. Training and development programs are essential to equip your workforce with the skills needed to work alongside AI technologies.
Furthermore, fostering a culture that encourages innovation and adaptability is vital in making the most of AI's potential. This last point is crucial because many people have negative preconceptions of AI but quickly come around once they realize AI is a tool to support them in their roles and free them up for more exciting tasks.
Many companies face challenges in integrating AI process automation due to a significant skills gap. The rapid pace of AI technology development outstrips the rate at which the workforce can acquire the necessary skills, leading to a shortage of workers proficient in AI and machine learning.
Additionally, companies often struggle with aligning AI initiatives with business strategies, as there is sometimes a disconnect between the technological capabilities of AI and the practical needs of the business.
AI can enhance your business operations, boost productivity, and drive growth. But that doesn't mean you can jump in head first without getting your company ready. Embracing AI applications in process automation is not just adopting new technology; it's about preparing your organization for a paradigm shift.
BP3, a leader in process automation, can guide you on this journey. We blend digital process automation, RPA, AI, IDP, application development, and workload automation to create intelligent solutions tailored to your business. Our focus on process automation and our AI expertise make us the perfect partner for your business needs. And crucially, we're well-equipped to handle all of the challenges along the way.
Take the first step toward an AI-driven future with BP3. Unlock the full potential of AI applications in process automation and redefine what's possible in your business.
Book a consultation, and let us show you how you can streamline your processes and tackle complex challenges using AI and automation. We guarantee that you will confidently know how to innovate your business and step into the future.
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