Want to Succeed in the AI Economy? Embrace AI Workflow Automation

Ready or not, AI workflow automation is poised to transform business operations from the shop floor to the C-suite in the AI economy. As organizations embrace digital-first initiatives, IT teams will be able to do much more with less. The situation is a byproduct of the generative AI boom. And yet, so many companies have hardly scratched the surface of AI automation’s full potential in their business operations.

The more these companies get AI workflow automation to work for them, the more agility, efficiency, productivity, and economies of scale they’ll get back for themselves. Per Gartner, by 2024, organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes. This blog highlights strategies, insights, and use cases to drive better business outcomes from AI workflow automation.

AI workflow automation involves using artificial intelligence to automate and streamline repetitive tasks within a workflow to enhance efficiency and productivity in business operations. Beyond that, AI workflow automation makes it easier to efficiently scale growth by integrating a wide range of digital tools (e.g., robotic process automation, APIs, software robots) and orchestrating the automation of routine tasks within and between core business processes.

However, that kind of orchestration doesn't just stop at tech integration—it also means creating a mixed-autonomy scenario in which technology, data, and human workers complement each other to optimize business outcomes.

AI workflow automation overview.

To turbocharge efficiency, organizations are implementing hyperautomation initiatives and using artificial intelligence and machine learning to streamline, monitor, and orchestrate automated processes. That’s where AI workflow automation shines, leveraging the power of artificial intelligence to orchestrate and optimize complex workflows. It's a cutting-edge approach to workflow automation that integrates intelligent algorithms and machine learning into routine workflows seamlessly, fundamentally reshaping how work gets done.

Fueled by machine learning, AI replicates human cognitive processes, adapting and improving over time. This is where intelligent automation comes into play, infusing workflows with advanced cognitive capabilities. In contrast to traditional automation, which efficiently executes predefined tasks, intelligent automation allows machines to think, learn, and make decisions autonomously (with human oversight).

Advances in intelligent automation have ushered in a new era of mixed-autonomy innovation, where the strategic integration of AI into front- and back-office workflow automation plays a major role in maximizing employee productivity and driving game-changing improvements in decision-making processes.

AI-driven customer interactions and the implementation of AI-enhanced process automation turbocharge operational efficiency and showcase the intelligent capabilities that set AI workflow automation apart in the modern business landscape.

Traditional business process management (BPM) and AI workflow automation work together to streamline business processes. While BPM provides the structured framework for end-to-end process management, AI automates specific tasks within those processes, enhancing decision-making and optimizing workflows. 

However, integrating AI into BPM takes workflow automation to another level, enhancing its design, execution, and monitoring capabilities and propelling it forward by introducing intelligent decision-making, adaptability, and learning into workflows.

While acknowledging the foundational role of BPM, our focus remains on how AI enhances and transforms workflows and helps organizations like yours navigate the challenges of modern business operations. When all is said and done, AI workflow automation isn't an option; it's a necessity.

Case in point: In the insurance industry, complex workflows, siloed data, and disparate systems can turn the claims process into a frustrating customer experience. That’s why leading insurers prioritize claims process automation to optimize efficiency and service quality and reduce expenses related to preparing, processing, paying, and adjusting claims, which represent a huge share of an insurer's operating costs. 

That’s where AI workflow automation shines. For example, Appian AI Copilot allows insurers to leverage generative AI to quickly design a claims submission interface by uploading customer-filled PDF claim forms, verifying fields, and configuring data in a fraction of the time it used to take.

Instead of getting bogged down in manual tasks like sorting through  claims documents, AI-powered custom models automatically delegate claims to specific teams based on the damage location. Computer vision analyzes claim images, determines damage location, and routes claims to the right teams.

Insurers can also create an artificial intelligence skill to recognize document types in claims using machine learning (ML) models. Users train the model with representative data sets to identify the unique characteristics of each document type. Then, they configure the process model for different workflows based on document types for incoming claims.

Consider a large law firm managing class action lawsuits with extensive case data. Searching for common traits among thousands of plaintiffs manually can be time-consuming. Enter Appian’s records chat, an AI-powered component in Appian that allows law clerks to ask questions using natural language. Powered by AI Copilot, it swiftly finds answers within related records, significantly speeding up research. Crucially, Appian’s records chat component ensures privacy and protection of legal data. As an entirely private AI model, it keeps inputs and outputs within your Appian Cloud site's boundaries, putting you in complete control.

AI workflow automation has emerged as a transformative force in the healthcare industry, significantly improving patient care and operational efficiency. AI-powered systems can do everything from streamlining administrative processes to enhancing diagnostic capabilities to automating appointment scheduling and medical record management to assisting in medical imaging analysis. This reduces the administrative burden on healthcare professionals and ensures faster and more accurate patient care. Embracing AI workflow automation means optimizing operational processes and elevating the overall quality of patient care.

In the financial sector, AI workflow automation and document extraction tools are revolutionizing how institutions handle vast amounts of financial documents and data, mitigate risks, and deliver faster, more personalized customer experiences. For instance, document extraction and anti-fraud measures benefit from AI algorithms that analyze transaction patterns and identify anomalies in real time, providing a proactive approach to security.

AI-powered data extraction streamlines document processing workflows.

Moreover, automated chatbots that handle routine queries can streamline customer service workflows, freeing up human agents for more complex interactions. Integrating AI workflow automation in finance translates into heightened security, improved customer satisfaction, and a more agile response to heightened regulatory compliance and fast-evolving risks.

While investments in isolated task automation using robotic process automation, AI, and other digital technologies have led to efficiency gains, many IT leaders still need more tools to orchestrate broader, end-to-end process automation. Faced with numerous disconnected islands of automation, large organizations need help managing increasingly complex automation challenges across the enterprise.

Maintaining islands of automation presents several challenges, including:

• Higher IT maintenance costs.

• Need for skilled resources to manage various automation tools.

• Need for complex integrations to connect different automation technologies.

• Technology upgrades that conflict with each other.

• Overlapping tools and misaligned governance.

Altogether, these challenges create costly inefficiencies, defeating the point of adopting automation technology in the first place. To overcome these challenges, IT leaders must take a platform approach to AI workflow automation to address enterprise-wide automation needs. Investing in workflow automation is useless if the technologies you purchase don’t work well together or if the data used in your AI workflow automation is unreliable. Convergence in AI, automation, and well-governed data is a critical success factor to ensure the capabilities of your tech stack align with your business goals and customer expectations.

Just about every large organization wants to harness AI workflow automation to overcome the challenges of disconnected islands of automation, rising IT maintenance costs, and complex integrations. But there’s also a need to stay on top of hot trends that are transforming AI workflow automation, including AI + RPA synergy, transparent decision-making, advanced conversational AI, and enhanced cybersecurity:

1. AI + RPA synergy: AI's integration with robotic process automation (RPA) is a game-changer, automating cognitive and repetitive tasks while streamlining processes and boosting workflow efficiency for your team.

2. Transparent decision-making: Are you concerned about the mysterious nature of AI decisions? The rise of explainable AI and transparent algorithms brings more clarity, helping AI adopters understand how decisions are made.

3. Advanced conversational AI: As natural language processing (NLP) improves, expect more sophisticated conversational AI and virtual assistants. This isn't just about streamlining workflows; it's a strategic move that drives seamless interactions with customers and your internal team.

4. Cybersecurity boost: The AI and machine learning integration surge enhances cybersecurity. Automated threat response and centralized incident workflows make security measures more robust. The fusion of cybersecurity with DevOps, known as DevSecOps, ensures security seamlessly integrates into the software development lifecycle, promising a future where efficiency, agility, and security benefit from AI workflow automation.

As you navigate the dynamic demands of the AI economy, success lies in not merely adopting AI workflow automation as an option but recognizing it as paramount for sustained success. Choosing a platform that aligns with your strategic business and workflow automation goals is crucial to leveraging AI workflow automation to drive innovation, resilience, and competitive advantage.

Navigating the dynamic AI economy means harnessing AI workflow automation for sustained success. Align your platform with strategic goals to drive innovation and operational resilience. Now, let’s explore how to implement this effectively.

As IT leaders navigate the dynamic demands of the AI economy, they need to recognize that AI workflow automation is essential for sustained success. However, to effectively implement these advanced technologies and reap their full benefits, it's crucial to follow strategic best practices. Here are five key steps to guide you on this journey.

1. Understand your current-state workflows
   To move forward, you must first know where you stand. Take an unfiltered look at your existing processes. Grasping the intricacies of your current workflows lays the foundation for effective orchestration.
2. Integrate data throughout your workflows
   Reliable data is your lifeline. Integrating data seamlessly boosts efficiency, ramps up productivity, and sharpens decision-making. A platform with a data fabric capability provides a unified data view, essential for orchestrating end-to-end processes.
3. Use the right automation tools in the right places
   Don’t jump the gun with automation tools. Identify process bottlenecks and understand the type of work occurring there. Match the right tool to the right task to unlock the full potential of your technology.
4. Build your workflows with external stakeholders in mind
   Your processes likely extend beyond your department walls. Keep partners, contractors, and customers integrated into your workflows. Ensure they’re part of the orchestration platform from the get-go, not an afterthought.
5. Think outside the workflow box
   Change is the only constant. Regulations shift, technologies evolve, and business strategies pivot. Treat your processes as ongoing projects, continually seeking opportunities for improvement and adaptation.

To thrive in the AI economy, embracing AI workflow automation is non-negotiable. It's not just about staying competitive; it's about unlocking unprecedented agility, efficiency, and productivity. As we’ve seen, integrating AI into your workflows transforms operations from the ground up, offering a competitive edge. By adopting these practices and leveraging advanced AI technologies, you can drive innovation and operational excellence, positioning your organization for sustained success.