Will Gen AI fulfill the promise of process optimization?

Generative AI (Gen AI) revolutionizes process optimization. We explore mass adoption, rapid implementation, and the role of Centers of Excellence (CoEs)

Gen AI solutions are profoundly transformative, captivating industries with its potential to revolutionize how businesses operate. This is primarily due to its ability to streamline processes and enhance efficiency. However, despite the excitement, AI process optimization has faced significant practical application challenges. Historical attempts at process improvement, from re-engineering to robotic process automation (RPA) have often fallen short of expectations. As organizations stand at the cusp of a new technological era, the critical question remains: Can Gen AI finally fulfill the long-awaited promise of process optimization? As part of our value proposition, Gen AI strategy and solutions for business growth, we answer that question and more in the below blog post.

Exploring the hype

Gen AI solutions have rapidly emerged as leading drivers of the adoption of technological innovations, with 96% of executives in our recent Capgemini Research Institute survey citing it as ‘a hot topic of discussion in their respective boardrooms’ (Oost et al 2023: 9). In addition, Capgemini Research Institute concluded that, of executives surveyed, over half (59%) say ‘their leadership became strong advocates for Gen AI after only six months of the technology having hit the mainstream’ (Ibid: 10). This high level of interest highlights Gen AI as probably the fastest new technology to attract such strong senior-executive interest. It gives non-technical users the ability to interact with very advanced and powerful AI models, generating widespread excitement and driving fast adoption of this transformative tool across various functions and industries. But how widespread is the adoption of sound Gen AI strategies.

Despite the significant excitement and investment around Gen AI, the practical application of AI for process optimization and other enterprise-level activities has been limited. While promoted as a revolutionary optimization tool, Gen AI solutions yet to fully realize their potential in this domain. Many large corporations are motivated by its potential to streamline processes and enhance efficiency as ‘60%’ of surveyed executives believe that ‘generative AI will completely revolutionize the way we work’ (Ibid: 16). But so far, the results have fallen short of the promise.

What surveyed executives say about Gen AI (Capgemini Research Institute)

The key reason is that the journey from experimentation to adoption and realization has been filled with challenges, ranging from adoption/change management challenges to ethical concerns and technical hurdles. For example, worries regarding data privacy and algorithmic bias have cast a shadow over the widespread adoption of Gen AI in process engineering. Additionally, the complexities of integrating Gen AI into existing workflows have proven to be real barriers for organizations seeking to harness its capabilities effectively.

With a combined 50 years of assisting clients by streamlining operations and process optimization, we, your authors, have time and again seen that many of these efforts have not lived up to their lofty expectations. This goes all the way back to the use of process re-engineering, adoption of workflow tools, RPA, process mining, and now AI. Our strong belief is that with this latest wave of AI and Gen AI, we have now reached an inflection point where a lot of the prior barriers to scaled adoption will be overcome and the promise of process optimization will finally become much more a reality in the short term. AI for process improvement is becoming a game-changer. Let’s take a look at some key aspects of this new way to focus on achieving process optimization outcomes.

The inflection point

With the potential to unlock unprecedented efficiency and innovation, the strategic use of Gen AI is not only an opportunity but a necessity for organizations aiming to remain competitive. This train has left the station and is only picking up speed, and each day that companies wait, they will fall further behind their more advanced competitors, emphasizing the widespread adoption and integration of this technology into business operations. Process optimization using machine learning is yielding impressive results.

Currently, the focus in process optimization revolves around leveraging process mining and RPA tools with some Gen AI embedded, enhancing existing systems with automation and predictive analytics. However, the trajectory is ready to shift as Gen AI quickly matures and deployment for break-through improvement takes center stage. This transition signals a recalibration of workforce dynamics, with mundane tasks increasingly automated and Gen AI acting as companion to human resources, enabling them to focus on higher-value activities.

The evolving role of CoEs exemplifies this shift, increasingly prioritizing value realization and sharing/scaling of solutions over previous technology deployment focus to maximize the benefits of process optimization powered by Gen AI. This new focus includes:

• Generate and Prioritize Demand: CoEs have to identify the most valuable use cases to work on to deliver material improvements and generate organizational momentum.
• Lead Development: CoEs will lead the development of new methodologies and frameworks for integrating Gen AI into existing processes, ensuring optimal utilization and performance.
• Facilitate Collaboration: CoEs will facilitate sharing of successes and cross-functional collaboration, bridging the gap between data scientists, engineers, and domain experts to drive innovation and problem-solving.
• Drive Training and Upskilling: CoEs will lead training and upskilling initiatives to equip employees with the necessary skills and knowledge to leverage Gen AI tools and technologies.

This shift requires a reevaluation of skillsets within CoEs, with more emphasis on building expertise in data analysis, algorithm development, and process optimization. Additionally, CoEs will play a pivotal role in fostering a culture of innovation and continuous improvement, driving the next evolution of automation and process engineering practices in alignment with the capabilities of Gen AI. By embracing a methodology-centric approach, rather than a technology-centric one, CoEs will be key in unlocking the full potential of Gen AI and driving organizational success in the era of process engineering driven transformation. Companies and industries at the vanguard of this transformation are embracing a methodology-centric approach to drive adoption and utilization of Gen AI.

How to get on the accelerated path to success

As organizations embark on their journey towards leveraging Gen AI for process optimization and excellence, scaling up poses an ever-increasing challenge. Achieving success at scale requires a strategic approach that encompasses both technological capabilities and organizational readiness. The path to success begins with strategic planning, effective execution, and a clear understanding of the organization’s overall objectives and how Gen AI can support these. Some key steps include the following:

Success stories

It is evident that the path to successful Gen AI integration has to be both strategic and ambitious to drive real impact across the organization. However, successful implementation requires more than just following a set of instructions. It demands a collaborative effort, drawing upon the expertise and insights of both internal stakeholders and external partners. This collaborative principle has been exemplified in recent work, where Capgemini seamlessly merged diverse perspectives and competencies to achieve transformative outcomes, as shown below:

Global Media: Capgemini worked with a global media and entertainment company on a digital people project, leveraging a Gen AI-enabled chat-engine. This resulted in an increase in cart size, revenue, net-promoter score (NPS), brand awareness, and a decrease in operating costs. A notable business outcome, according to a Capgemini Research Institute study on Gen AI, was the increase in engagement, with digital users reporting the system as easier to use (38%), more engaging (85%), and more effective than chatbot equivalents (92%).

Automotive: Capgemini successfully addressed this major automotive manufacturer’s challenge of manually comparing supplier documents by implementing an AI-driven automation solution. By leveraging its expertise in AI implementation projects and cloud technology, we delivered a PoC funded through a strategic partnership. As a result, our client experienced a significant reduction in time spent on document comparisons, enabling employees to focus on higher-value tasks while mitigating the risk of delays in this important verification process.

Global bank: Capgemini collaborated with an international bank to address complex information usage challenges by developing a customized Gen AI-driven Q&A system. Leveraging strategic partnerships, Capgemini optimized and deployed an advanced AI solution tailored to the client’s needs, ensuring confidentiality. The implemented solution significantly reduced search time for client-specific content, resulting in substantial cost savings and paving the way for future Gen AI applications across business lines.

Transportation: Capgemini played a pivotal role in a high-profile project aimed at transforming how passengers access information in the context of the 2024 Olympic and Paralympic Games in Paris, France. Our expertise in complex program steering, change management, and innovative technologies enabled the team to follow a radical approach to automation, resulting in a Gen AI-based multilingual passenger information tool developed in less than two months. This successful track record, responsiveness, and delivery team enthusiasm were key factors in Capgemini’s selection as the partner of choice for delivering this transformative initiative.

Final thoughts

Our research indicates the growing value of integrating generative AI solutions for process optimization. But as is the case with all new technologies, there is no one blueprint for implementation. Each use case demands a tailored strategy, and process optimization is no exception. At Capgemini Invent, we are dedicated to crafting solutions that meet the unique needs of our clients. Contact us to explore how Gen AI can unlock the potential of process optimization for your organization.

References: Oost, M., et al. (2023) Harnessing the Value of Generative AI. Capgemini Research Institute. [online][Accessed on the 30^th of August 2024]