Paul Holmes-Higgin is co-founder and former Chief Product Officer of Flowable. With a PhD in Artificial Intelligence, Dr. Holmes-Higgin offers a unique perspective on the intersection of AI and business processes. In this conversation, we delve into the intriguing question of whether Artificial Intelligence (AI) will replace Business Process Automation (BPA) and discuss the complexities of this dynamic landscape.
Question 1: A while back, Neil Ward Dutton, posted a Tweet asking if Generative AI (GAI) is the new RPA. What are your thoughts on this?
Paul Holmes-Higgin: The comparison between AI and RPA is right in some ways. Current Generative AI is at the level of individual tasks, just like RPA, and we have seen it taking over some activities previously done by RPA. The hype and investment frenzy is totally focused on AI, like it was on RPA a few years ago. So, in that sense, you could say that Generative AI is indeed the new RPA.
However, that’s where the similarity ends. RPA has singularly failed to deliver the promised transformative impact. Whereas, the capabilities of Generative AI, as well as other AI approaches, are groundbreaking, with world-changing potential. As a consequence, it's crucial to acknowledge any risks and manage expectations. The opportunity is immense, but it's equally important to understand the complexities and nuances involved in using AI effectively.
Question 2: And what about AI and BPA? How do you perceive the relationship between these two and should businesses worry about it?
Paul Holmes-Higgin: AI is a transformative force, but it won't replace BPA/BPM. They are complementary technologies. You should use AI for its one-off, creative capabilities, while BPA continues to provide the predictable and repeatable framework in which tasks are done. BPA is the best platform to orchestrate and audit the appropriate AI operations. You might also use AI to generate processes or cases to be executed by BPM, as a way of doing Process Mining, or even directly using BPM for one-off execution of a complex sequence of tasks.
Question 3: Talking about risks. Could you elaborate on how organizations can effectively constrain and manage these risks in their automation processes?
Paul Holmes-Higgin: In my opinion, dynamic orchestration is crucial in managing AI risks. For example, Case Management can play a significant role in determining when AI can autonomously handle tasks and when human intervention is necessary. Case Management facilitates intelligent suggestions and contextual support for human activities. For instance, an AI could use data from various stages of a case to prepare additional information or present relevant documents, enhancing overall efficiency and decision-making by a person.
In this early phase of this new wave of AI, Flowable can assist by enabling controlled experimentation with it. AI services are leaping up everywhere and the leading offerings are changing rapidly. By using BPM, businesses can assess the effectiveness of different AI services on specific processes. Even orchestrating services from different vendors in parallel to get the best possible output.
By finding the right blend of AI, Process and Case Management, organizations can create robust, highly-intelligent solutions.
Question 4: When talking about AI, we also need to take costs into consideration. How can organizations manage these costs and what are the factors to consider in leveraging it effectively?
Paul Holmes-Higgin: External AI services provide opportunities for accessing cutting-edge capabilities, such as data analysis, content generation and decision-making, that you might not be able to produce inhouse. A serious business challenge lies in ensuring cost optimization of these services.
Organizations that want to leverage AI should, in my opinion, manage its use to specific areas, as a means to constrain costs and monitor the value derived from its implementation. Focusing on efficient platforms with the capability to connect to a range of AI services can be more cost-effective, rather than investing in “the one” that has some AI built-in from the get-go, optimizing the balance between opportunity and expense. This allows organizations to invest in their core competencies while choosing the best AI tools available for their specific area of business.
Question 5: Given the evolving landscape of AI and its potential impact on various industries, how can organizations strike a balance between leveraging advanced AI technologies and ensuring ethical considerations and responsible AI practices?
Paul Holmes-Higgin: Obviously, prioritizing transparency in AI decision-making processes, regular monitoring, and adherence to ethical guidelines are essential for good business practice. Continuous adaptation to evolving ethical standards will ensure responsible AI practices, contributing positively to both innovation and societal impact. It’s critical not to treat AI as a magic black box. You need people within your organization that understand the implications of the technology: the opportunities and pitfalls. It’s also important that people remain in the loop, as AI isn’t yet ready to do everything we might hope it can.
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