Empowering Human Decision-Making: The Incremental Approach to AI in Business

Summary: Artificial intelligence (AI) is redefining the role of technology in business processes. Business process automation and case management provide the perfect foundation for AI modelling designed to support rather than supplant human efforts.

Takeaways:

• AI enhances, not replaces, human capabilities, optimizing decision-making and efficiency in business processes.

• Effective AI integration relies on robust systems like BPA and case management to provide the necessary data and operational context.

• Incremental, strategic AI deployment ensures alignment with business goals and ethical standards, promoting continuous improvement.

As our digital landscape evolves faster than ever, the way we perceive artificial intelligence (AI) is changing. Far from the initial perspective that AI would replace the human workforce, we now see this novel technology as an assistive enhancer of employee capabilities. However, for AI to succeed, it needs clean and relevant information from the tools and systems already at work in the business, such as business process automation (BPA) and case management.

These established systems are critical for the successful integration of AI technologies, providing a robust framework that AI can enhance to deliver more accurate and impactful results. By capitalizing on the structured workflows and rich data repositories already in place through BPA and detailed oversight via case management, businesses can ensure that AI implementations are both effective and seamlessly integrated. This approach not only maximizes the utility of AI in streamlining operations and enhancing decision-making but also ensures that these technologies support and extend the capabilities of human workers, fostering a synergistic relationship between human intelligence and machine efficiency.

The roll-out of AI is not a single, all-out project — but a series of small, strategic implementations that cumulatively refine and build on existing business systems and processes. By defining working strategies for implementation in manageable steps, organizations can ensure AI becomes a tool that supports human workers and delivers a balanced approach to leverage the best of both human insight and machine efficiency.

The foundational role of BPA

Business process automation (BPA) is traditionally seen as a tool for streamlining operations and reducing costs. But what happens with the introduction of AI into the mix?

According to Paul Holmes-Higgin of Flowable, "AI is a transformative force, but it won't replace BPA." Far from replacing process automation, there is a significant benefit when considering AI and BPA as complementary technologies.

A BPA platform provides the perfect framework to execute, measure, and refine AI models. The ability to consistently experiment and deploy AI into existing workflows offers a low-risk mechanism to begin embedding models into the workplace.

However, a less recognized benefit of the integration is that BPA not only supports the operational background but also ensures that when introducing AI, it has a clean, well-organized data environment from which to work.

AI can significantly elevate the capability to process and analyze large volumes of data swiftly and with greater precision — but only with good-quality data. By utilizing the data that BPA platforms are already collecting, AI can drive better decision-making and identify opportunities for further process optimization.

Combining human and artificial intelligence with case management

Many organizations use case management to organize, store, and process information relating to customers, projects, or cases — especially within healthcare, legal, and government sectors. These systems combine data and processes that allow employees to manage the dynamic nature of business interactions in these areas. Classic automation tools such as RPA struggle in this field — they need clearly defined processes, whereas case management activity tends to be dynamic.

By integrating AI and case management, businesses can combine human and artificial intelligence for a more personalized approach to case handling, faster turnaround times, and improved client satisfaction. AI supports by providing predictive analytics and decision support that can suggest the next best actions. AI can analyze past cases to identify patterns and predict outcomes, thereby advising on potential strategies and solutions. This enhancement not only speeds up the process but also improves the accuracy of the outcomes.

For organizations, this means improved operational efficiencies and an enhanced reputation for customer care and responsiveness, both of which are incredibly valuable.

Implementing AI: Strategies for success

Successfully integrating artificial intelligence (AI) into an existing IT infrastructure requires a strategic and thoughtful approach. Rather than seeking to overhaul entire systems, the most effective implementations focus on using existing systems and data to train AI models, leveraging the results to enhance and augment the capabilities already in place — something Accenture calls "an AI-enabled, secure digital core."

With this core in place, organizations are in a perfect position to continually measure results and refine them accordingly —cyclically repeating the process to ensure ongoing improvement.

Of equal importance when embedding AI into business workflows is to ensure that any deployment acts as a complement to human expertise and not a replacement. By focusing on alignment with existing ways of working and continual improvement, businesses can realize the full potential of AI while maintaining the crucial balance between automation and human oversight.

Best practices for AI integration

Successfully integrating AI into business operations goes beyond mere technology deployment; it involves a nuanced understanding of how AI complements and enhances existing tools and workflows. The best practices below are crucial to ensure AI implementations are effective and synchronized with established business processes.

1. Connect high-quality data:

The integrity and cleanliness of data form the foundation for all AI usage. By leveraging BPA , you ensure only accurate and relevant data enters the AI modeling and deployment process.

2. Adopt a cyclic approach to process automation:

Implement a continuous loop of "create, deploy, measure, review, refine, redeploy." This iterative process mirrors AI's learning capabilities, allowing for constant improvements and adjustments based on real-time feedback.

3. Align AI with business objectives:

Clearly define how AI technologies align with your business goals. Ensure each AI initiative has a clear purpose and directly links to strategic outcomes.

4. Foster collaboration between AI and human workers:

Design workflows that enable effective collaboration between AI systems and human workers. Ensure that the primary focus of AI is as a support tool to enhance human decision-making and efficiency.

5. Address ethical and security concerns:

Prioritize the development of ethical AI practices and ensure robust security measures are in place to protect data and privacy. Ensure transparency about AI use and maintain control over AI decision-making processes.

By adopting these best practices, organizations can ensure that AI becomes an integral, connected component in their existing processes and actively enhances them at every step.

This approach leads to an agile and resilient business model where AI supports workers and improves business outcomes through smarter, data-driven decisions. The cyclical nature of AI's integration and refinement allows organizations to remain ultra-competitive, adapting and evolving faster than the rest of the market as new data and technologies emerge.

Final thoughts

The journey towards AI-enabled business is paved with potential but requires careful preparation and strategic implementation. Recognition that AI is part of a wider, connected ecosystem of technologies rather than a single solution is critical.

Existing applications such as business process automation and case management are fundamental in this environment as both a reliable data source for AI model creation and as active deployment vehicles for AI outputs.

By building on these robust structures, organizations can ensure their AI initiatives are successful and fully integrated with the business, employees, and existing ways of working. Far from being a technology to replace human capabilities, smart deployment of AI enhances them — to create a dynamic, responsive, and highly efficient business environment.