Generative AI and AI (collectively referred to as AI in this article) has the potential for transforming industries by automating creative and analytical tasks that so far could only be done by humans. The potential in the Business Process Management (BPM) industry is vast and largely untapped, and can revolutionize how companies operate, deliver significant efficiencies, and augment customer experience (Cx).
The BPM industry is uniquely positioned to drive Adoption of AI due to several inherent factors:
Process Orientation and Domain: Process optimization is foundational to the BPM industry which is built on deep process SME knowledge. This makes BPM a natural fit for AI which can analyse and automate processes at scale and speed beyond human capabilities with the application of emerging technologies and models.
Data: BPM operations operate with and generate vast amounts of data providing a fertile training environment for AI models and their evolution. With access to extensive data sets, BPM firms can deploy AI to gain insights, predict trends, and automate complex processes.
Technology: BPM firms are reliant on and early adopters of digital processes. The mature players often have advanced technological infrastructures in place making it easier to integrate AI solutions into existing systems.
Innovation Focus: The BPM industry is inherently competitive, making the ability and agility to innovate, adopt (to) and adapt new technologies a differentiator for the players. AI offers new avenues for innovation, from automating existing services to creating entirely new offerings.
Cost Efficiency and Cx Enhancement: Value Creation is a key differentiator for players within BPM and cost sensitivity is high. AI-driven insights and automation directly contribute to improved customer experiences, a critical focus area for BPM firms. Gen AI can personalize interactions and enhance customer service without increasing the workload on human employees.
Regulatory Compliance: BPM often involves navigating complex regulatory landscapes. AI can help manage and monitor compliance across different regions and standards, reducing the risk of human error and enhancing the industry's ability to adapt to legislative changes swiftly.
These factors collectively position the BPM industry not just to utilize AI but lead its adoption and to transform traditional processes into more efficient, innovative, and customer-focused solutions.
Application and Potential
Application of AI in general, and specifically in the BPM industry can be versatile and there is significant potential for AI in transforming the BPM industry, delivering both operational efficiency and enabling strategic decision-making. The continuous evolution of these technologies will create even more innovative applications in BPM. Some of the use cases are in the space of (a) Intelligent Automation, (b) Augmented Customer Experience (Cx), (c) Process Optimization, (d) Compliance, (e) Insight & Analysis and (f) Content Generation.
Intelligent Automation: Robotic Process Automation (RPA) + AI results in Intelligent Process Automation by enhancing capabilities of basic RPA by adding decision-making and learning from past actions to improve outcomes. Integration of Gen AI models can further augment the ability to handle ambiguous or changing situations by generating novel solutions based on previous outcomes.
Augmented Customer Experience: Gen AI takes existing AI based automation of customer services further by generating contextually relevant and personalized responses, improving customer experience. These AI systems can learn from past interactions to enhance their responses and provide more accurate information over time.
Process Optimization: In addition to streamlining operations and improving workflow efficiency, Gen AI based models can be used for simulating process scenarios prior to implementation, predicting the outcomes of changes to workflows. This supports strategic planning and resource allocation, driving efficiency.
Compliance: BPM often requires strict compliance with regulatory standards. Gen AI can be trained to understand and interpret complex regulatory documents to automatically update systems and processes whenever new regulations are introduced.
Insights & Analysis: In addition to tasks like processing invoices, extracting information from unstructured data sources, and more, Gen AI can generate insights and reports by analyzing complex datasets, identifying patterns, and making predictions that enable decision-making. For instance, predicting customer behavior or market trends based on historical data.
Content Generation: In scenarios requiring creation of digital content or documentation, Gen AI can automatically generate written content, reports, or even code, based on guidelines and data inputs, significantly reducing the human effort.
Considerations and challenges:
As with any new technology, while AI has immense potential it also has challenges that need to be addressed and poses risks that need to be managed. Challenges associated with Gen AI are varied and include ethical considerations, technical limitations, security, and practical deployment issues. Some key challenges and considerations include:
- Ethical: The adoption of Gen AI comes with ethical risks that need to be managed carefully. These include Bias in training data and lack of control over generated content. Organizations must ensure Gen AI is used responsibly, prioritizing accuracy, safety, honesty, empowerment, and sustainability. (Reference & credit: https://hbr.org/2023/06/managing-the-risks-of-generative-ai).
- Data Privacy and Security: AI systems require vast amounts of data to train and operate effectively raising risk in terms of data privacy and security, especially where sensitive or personal information is involved. Compliance to regulations like GDPR is critical but can be challenging.
- Traceability and Reproducibility: There are deep concerns about limited traceability and inability to reproduce Gen AI outcomes which raise the possibility of incorrect or inappropriate decisions, especially in complex scenarios that require human empathy and understanding. These errors can lead to poor customer experiences and potential legal issues. (Reference & credit: https://www.bcg.com/publications/2023/c-suite-genai-concerns-challenges ).
- Resilience: Over-reliance on AI can make organizations vulnerable to technological failures. Downtime or malfunctions in AI systems can disrupt business processes, leading to operational delays and loss of customer trust.
- Strategic and Governance Issues: While the potential is significant and there is tremendous interest as well as pressure for organizations to show adoption of AI, there is a need for being strategic and intentional in the approach to AI implementation. As with any big change and opportunity, AI needs a strategic roadmap, investments, clear governance and to be driven at the highest levels within the organization. (Reference & credit: https://www.bcg.com/publications/2023/c-suite-genai-concerns-challenges ).
- Scarcity of Talent: The field requires specialized knowledge, and the necessary expertise to develop and manage AI systems is scarce. Developing the right set of skills is key, and in addition to technical and domain skills also requires expertise in fields such as legal, data privacy, and compliance needed for implementing the use cases. (Reference & credit: https://www.bcg.com/publications/2023/c-suite-genai-concerns-challenges ).
- Job Displacement: There is great concern that automation can lead to significant job displacement, as machines take over tasks traditionally performed by humans. This requires significant investment in workforce reskilling programs.
Addressing these issues proactively will be crucial for organizations looking to harness the benefits of AI in BPM. Businesses need to adopt a strategic approach to AI implementation, ensuring robust data governance, continuous monitoring for biases, investment in employee training, and maintaining a balance between automation and human oversight.
Disclaimer
The views, thoughts, and opinions expressed in this blog are solely those of the author and any content provided on this blog is for informational purposes only.
