AI + automation: Why AI is the next step for automation

Traditional automation systems rely on predefined rules and structured data, which means they hit a wall when facing complex and unstructured workflows. AI, however, is a perfect match for unstructured processes and environments where data is often messy, inconsistent, or incomplete.

AI excels at processing and interpreting unstructured information, such as text, images, and audio, using advanced techniques like natural language processing (NLP) and computer vision. This allows AI models to understand and extract meaningful insights from data that would otherwise be inaccessible to conventional automation systems. By processing unstructured information, AI empowers enterprises to accelerate workflows that were previously considered too complex or variable to automate.

In the insurance industry, for example, AI can analyze unstructured data from claims forms, emails, and customer communications to automate the claims processing workflow. By interpreting the context and content of these documents, AI systems can make informed recommendations about claim approvals, reducing processing time and improving accuracy.

Similarly, in customer service, AI-driven chatbots can handle inquiries that involve unstructured conversations, understanding customer intent and providing relevant responses.

In healthcare, AI can process unstructured data from medical records, lab reports, and imaging studies to support clinical decision-making and automation of administrative tasks. By extracting critical information and identifying patterns, AI systems can assist healthcare professionals in diagnosing conditions, recommending treatments, and better managing patient care.

By maintaining a balance between automation and human input and oversight, enterprises can harness the potential of AI while ensuring trust in the integrity of AI-driven decisions. The "human-in-the-loop" model, where human input refines and enhances automated systems, is particularly important in scenarios where AI-driven decisions need validation or adjustment based on human judgment.

Processes that leverage human-AI collaboration can increase the time available for human interaction where there is high interaction value, such as in customer experience.

In all cases, optimizing human-AI collaboration is a core factor in the success of workflows supported by AI and automation. Best practices to drive effective collaboration include embedding access to AI-driven automations directly within work applications and providing training in both the use and purpose of the technology, including understanding the AI system.

Providing training on both the effective use of the AI system and its underlying mechanics, including data sources, can greatly enhance successful human-AI collaboration. Understanding what the system does—its purpose and intended uses—enables employees to evaluate the AI performance critically and identify possible issues or biases. This awareness makes it possible for everyday users to determine if AI operates as intended and aligns with organizational goals and standards.

Optimal use of AI is further supported by robust oversight mechanisms. Effective oversight of AI systems requires a multifaceted approach that includes continuous monitoring, real-time analytics, regular audits, and impact reviews. Continuous monitoring involves keeping a constant watch over the AI system's operations to detect anomalies, performance issues, or deviations from expected behavior. Regular audits examining the system's algorithms, data usage, and decision-making processes help to maintain transparency and accountability.

At the organizational level, impact reviews that assess the broader effects of AI systems are essential for ensuring their long-term success and safe application within enterprise operations. Assessing whether the application is achieving its intended objectives and identifying any unintended consequences on employees, customers, and broader enterprise operations helps inform the overall AI and automation strategy. This process also drives adjustments or improvements to the AI system, ensuring it continues to deliver value while minimizing potential risks.

Augmenting and automating complex and creative work hinges on the ability of AI systems to adapt over time and improve on their own through continuous learning processes.

At the heart of adaptive AI are advanced learning mechanisms such as machine learning and reinforcement learning, which empower AI systems to refine their performance and decision-making capabilities without constant human intervention.

Machine learning enables AI to identify patterns and extract insights from enormous datasets, allowing it to make predictions and decisions based on historical data. And as AI systems process more data, they continue to learn and improve accuracy and efficiency.

Reinforcement learning allows AI systems to learn from interacting with their environment. That means when they receive feedback on their actions, these systems can adjust strategies to maximize desired outcomes. This age-old trial-and-error approach enables AI to adapt to new challenges and optimize its performance over time, which is particularly valuable in the context of creative and complex tasks where solutions are not always straightforward.