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Re-imagining Business Processes

Re-imagining Business Processes

GenAI copilots are enabling AI-assisted redesigning of business processes, leading to more outcome-focused operations. One example is the realm of knowledge management. In the manufacturing sector, you often have vast repositories of operating manuals, standard operating procedures, logbooks and past incidents. However, retrieving the correct information quickly and effectively from these repositories can be a daunting task. GenAI can assist in this process by summarising the relevant information, thereby enabling operators to focus more on their tasks without unnecessary delays. This also fosters collaboration among different teams by providing them with the right information at the right time.

Fuelling Innovation

Product development in the manufacturing industry is being impacted by GenAI, enabling a level of innovation and efficiency that was previously unattainable. A compelling example of this is in the automotive industry where companies like Toyota2 are leveraging GenAI to create more efficient and aerodynamic vehicle designs. Technology allows them to optimise the shapes and structures of vehicle components, leading to significant improvements in fuel efficiency and performance. This level of optimisation was not possible with traditional design methods, which were often time-consuming and lacked the ability to fully explore the vast design space. GenAI and agentic workflows, on the other hand, can quickly generate and evaluate a multitude of design options, identifying solutions that human designers might overlook. Moreover, GenAI can adapt to changing requirements and constraints, making it a powerful tool for agile product development.

GenAI and Banking and Financial Services

Banking and financial services industries have always been among the first adopters of any new technology. So, it’s not surprising that when it comes to GenAI, the financial services sector is among the biggest adopters of the technology, using it to improve operations and customer experience.

Enhancing Efficiency

Back-office processes are one area where GenAI is being used to enhance efficiency. Traditionally, tasks such as classifying documents, processing applications, verifying accounts and opening accounts were manual and time-consuming. However, with the advent of GenAI, these repetitive tasks are now automated. GenAI models are trained to understand and process unstructured data in documents with varied formats. This not only speeds up the process but also reduces the chances of human error, thereby enhancing efficiency and accuracy. Moreover, the automation of these back-office processes allows banking professionals to focus on more complex tasks, thereby increasing overall productivity.

Re-imagining Business Processes

A prime example of how GenAI copilots are re-imagining business processes is in insurance underwriting. Underwriting, a critical function in the insurance industry, involves evaluating potential risks and determining appropriate coverage levels. Traditionally, this process was manual and time-consuming and required extensive expertise. However, with the advent of GenAI, underwriters are now assisted by AI copilots that can analyse vast amounts of data, including historical claims, customer information and external factors. These AI models generate risk profiles and recommend appropriate coverage levels, enabling underwriters to make more informed decisions quickly.

Fuelling Innovation

GenAI is helping the financial services industry innovate to promote financial inclusion. For example, the Asian Development Bank (ADB) leverages GenAI to lower the barriers to entry for the unbanked and underbanked in Asia and the Pacific, enabling them to become part of the formal financial system.3 The AI system extracts data from potential customers to create a lending profile. This innovative use of AI allows ADB to extend financial services to individuals who may lack traditional identification or credit history. This not only expands ADB’s customer base but also promotes financial literacy and inclusion among traditionally underserved communities.

GenAI Copilots in the HR Business Function

GenAI is revolutionising the HR function, making it more human-centric by automating repetitive tasks and enabling personalised employee experiences. A compelling example of this is the use of a GenAI-based avatar in the recruitment process by a large automotive player. This avatar, powered by advanced algorithms, interacts with each applicant, providing personalised feedback on their application process. It can answer queries, provide updates and even offer tips for improvement, thereby enhancing the candidate experience. The avatar also streamlines the recruitment process by automating tasks such as screening résumés and scheduling interviews. This not only increases efficiency but also potentially reduces the risk of human bias, ensuring a fair and objective selection process. It is important, however, to ensure that the AI system is itself fair and free from bias. By taking over these routine tasks, the avatar allows HR professionals to focus on candidate engagement and people development.

练习题

In manufacturing knowledge management, what is a key role of GenAI copilots described in the source material?

A. Replacing all operators on the factory floor
B. Summarising relevant information from manuals, SOPs, logbooks, and incident records
C. Restricting information sharing between teams
D. Eliminating the need for documented procedures

Why are GenAI and agentic workflows especially valuable in automotive product development?

A. They avoid evaluating more than one design at a time
B. They remove all design constraints from engineering work
C. They can quickly generate and evaluate many design options across a vast design space
D. They only reproduce designs already selected by human engineers

Which banking use case best illustrates GenAI improving back-office efficiency?

A. Manually reviewing each account application one by one
B. Automating document classification, account verification, and account opening
C. Reducing all banking work to customer-facing sales only
D. Ignoring unstructured documents because their formats vary

Which statements about GenAI in banking and insurance are supported by the source material? Select all that apply.

A. GenAI can process unstructured banking documents in varied formats
B. AI copilots in insurance underwriting can analyse historical claims, customer information, and external factors
C. Traditional underwriting was already fully automated and required little expertise
D. Automation can allow banking professionals to focus on more complex tasks
E. GenAI in banking necessarily increases human error

In manufacturing, GenAI-supported knowledge management can improve collaboration by delivering the right information to teams at the right time.

Traditional automotive design methods described in the source were faster and better at exploring the full design space than GenAI-based approaches.

The financial services sector is presented as one of the biggest adopters of GenAI for improving operations and customer experience.

In the ADB example, the AI system extracts data from potential customers to create a ___ profile.

How does GenAI support financial inclusion according to the ADB example?

Compare one way GenAI re-imagines business processes in manufacturing with one way it does so in insurance underwriting. Include how prior ideas about cross-functional collaboration help explain the benefit.

Which scenario best shows how GenAI uses unstructured information to improve efficiency in both manufacturing and banking?

A. In manufacturing, GenAI summarises manuals, logbooks and past incidents for operators; in banking, GenAI processes varied document formats to automate classification and account-related tasks.
B. In manufacturing, GenAI only replaces all human operators; in banking, GenAI is used only for marketing campaigns.
C. In manufacturing, GenAI is limited to physical assembly-line robots; in banking, GenAI is limited to branch interior design.
D. In manufacturing, GenAI mainly increases fuel prices; in banking, GenAI mainly slows account opening for additional review.

Which statements correctly compare how GenAI copilots re-imagine business processes across manufacturing, insurance and prior cross-functional collaboration use cases? Select all that apply.

A. In manufacturing knowledge management, GenAI can deliver the right information at the right time to support collaboration.
B. In insurance underwriting, GenAI copilots can analyse historical claims, customer information and external factors to recommend coverage levels.
C. Unified AI platforms can support cross-functional collaboration when trends, outcomes and actions are accessible across the organisation.
D. Insurance underwriting with GenAI removes the need for any human decision-making.
E. In manufacturing knowledge management, GenAI is used only for aerodynamic design exploration.

Because GenAI and agentic workflows can explore a vast design space in manufacturing, they are similar in principle to prior examples where GenAI generated novel molecular designs that human experts might not have identified on their own.

In both banking back-office automation and prior healthcare note-generation, GenAI reduces manual, time-consuming work so professionals can focus on more ___ tasks.

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