Generative AI can be used to enable the creation of efficient, consistent, and personalized content across a range of modalities.

Issue / Opportunity

Companies face a significant challenge in managing and optimizing marketing content. With hundreds of websites for brand portfolios each in dozens of languages, companies struggle to allocate enough time and resources to create customer group-specific product descriptions, images, video, and even audio. Enterprises also wrestle with consistency across descriptions, imagery, ads, and other media, and the materials may not always be optimized for the necessary purposes (e.g., product descriptions for search versus e-mail). Companies need a method to provide a seamless and personalized brand experience across different ecosystems and touchpoints.

How Gen AI can help

Next-gen content generation

With Generative AI, the enterprise can create product descriptions, imagery, video, and more much faster and more consistently than with exisiting tools and processes. Personalization at scale. Generative AI models can draw from multimodal data (e.g., text, image, geospatial data) to create personalized and contextually relevant content. The model can be used to catalog content and adapt content and user flow based on language, region, and customer behavior trends.

Assisting compliance 

Due to the consistency Generative AI enables across modes, languages, and contextual factors, the enterprise can enhance regulatory compliance for materials across different geographies, cultures, and topics.

Managing risk and promoting trust

Reliability

While tasked with producing superior marketing materials, Generative AI systems may invent inaccuracies, which will lead to poorer customer engagement and outcomes.

Fair and impartial

Biases in the data (e.g., due to incomplete datasets) could lead to unequal quality of content in the face of different geographical or cultural factors.

Generative AI can enable banks to increase digitization at a faster pace through code assistants.

Issue / Opportunity

Many FSI enterprises are pursuing cloud and data transformations, which are essential steps in preparing the organization for using AI tools (of many kinds). In some cases, legacy hardware is retired as data is curated and shifted to the cloud, freeing up humans for more valuable work while bringing down the costs associated with on-premise infrastructure. Yet, these kinds of transformations are significant undertakings that can bring long lead times and high costs. There is also a risk of failure and error.

How Gen AI can help

Supercharge your human capital

Generative AI can be used as a component of cloud and data transformations to empower developers working across the enterprise on applications, data engineering, machine learning, and frontend development.

A helping hand in code development

As organizations explore new digital and cloud capabilities, development teams can accelerate and simplify their work by using Generative AI as a force multiplier when writing, debugging, and documenting code, as well as translating ideas to code.

A shorter path to software

Part of success in transformation hinges on how quickly new enabling software can be deployed. There are opportunities to use Generative AI in software development to shorten the lifecycle and more quickly reach a stable and deployable version, such as by helping rapidly write APIs, ETL, data pipelines, or even frontend code.

Managing risk and promoting trust

Reliability

Partial automation of programming-related tasks requires the system to be reliably availability and accurate. If availability cannot be guaranteed to an acceptable extent, weigh the benefits of automation against the risk of erroneous or buggy code.

Responsibility

The training data for foundation models may create legal risks related to intellectual property or copyright infringement. If the training data contains copyrighted material, the organization deploying the model needs to evaluate whether the presence of intellectual property in the training set could lead to legal challenges against the enterprise.

Security and privacy

By using a Generative AI system, proprietary code bases may be exposed to third parties, raising questions around the security of the data and controlled access to it. An inadvertent breach of confidential intellectual property could have significant enterprise impacts.

Accountability

While the use of Generative AI can accelerate the work of developers, without a human in the loop (e.g., validating and debugging code), critical failures may occur. Shoring up accountability may include documenting and communicating standards and expectations for employees using Generative AI.

Generative AI can be used to draft denial appeal letters, drawing from patient records and medical policies and guidelines in a faster, more cost-effective way than human staff.

Issue / Opportunity

When a medical insurance claim is denied, hospital billing staff face a costly and lengthy process of reviewing patient records and medical policies to create an appeal letter. For U.S. hospitals, appeals-related administrative costs are measured in billions of dollars. Part of the challenge is the amount of time required for staff to compile an appeal. While more than 60% of denied claims are recoverable, vague reasons for denial and limited hospital billing resources result in only 0.2% of in-network claims being appealed, with millions of dollars written off as uncollectable loss each year.¹

How Gen AI can help

Retrieving policies and guidelines

A Generative AI retrieval model can reach across large volumes of medical policies and member plans to identify the necessary information for a claims appeal. Extracting patient data. Using extractive algorithms, the organization can rapidly consult unstructured medical notes, medications, lab results, and other electronic health records. Writing the appeal. With the necessary information gathered with AI, an LLM can be used to generate an appeal letter.

Managing risk and promoting trust

Accountable

When consulting highly detailed guidelines, policies, and records to appeal a claim denied for vague reasons, the Generative AI models working together to create the appeal may misinterpret the denial or the records, leading to an unsuccessful appeal. Ultimately, a human needs to be accountable for validating appeal letters.

Privacy

By drawing from electronic health records, the model is consuming health information whose protection is subject to laws and regulations. Ensure that the data ingestion and information outputted aligns with data protection and patient privacy expectations.

Source: 1 https://www.beckershospitalreview.com/

Generative AI in asset maintenance planning can improve equipment uptime, reduce maintenance costs and enhance operational efficiency.

Issue / Opportunity

In mining and oil and gas operations, maintenance planning helps prevent premature equipment failure, costly repairs and replacements, and extends the life of an asset. Facing near- and long-term constraints and factors, maintenance plans and the subsequent downstream processes can be changed to align with production, in response to resource availability, or because of unexpected events. Making maintenance plan alterations, however, can be costly and labor intensive.

How Gen AI can help

Continuous improvement

Generative AI can be used to reconcile lessons learned from prior shutdowns, identify opportunities for maintenance alignment, provide planners with the information needed to challenge assumptions on maintenance alignment, and develop strategies to minimize the impact across the system.

Optimal maintenance scheduling

Generative AI helps optimize maintenance schedules by weighing operational factors (e.g., equipment use, production requirements, and maintenance costs), recommending the most efficient and cost-effective schedules, and analyzing equipment use and performance data to minimize downtime and maximize equipment availability.

Simulation and optimization

Generative AI can simulate maintenance scenarios and evaluate the impact of maintenance strategies on equipment performance, productivity, and operational efficiency. This helps reveal the most effective maintenance approaches and optimizes resource allocation for maintenance activities

Managing risk and promoting trust

Robust and reliable

Generative AI applications for asset maintenance planning depend on the quality of the data. Data that is incorrect, incomplete, or is not representative of the current operational environment or maintenance practices can lead to a suboptimal and potentially inappropriate maintenance plans that may even be detrimental to asset health management and future maintenance planning activities.

Accountable

There is no machine substitute for a human asset maintenance planners' knowledge, experience, and expertise. Overreliance on AI-generated outputs without critical human review may lead to important contextual factors and valuable insights being overlooked.

Safe and secure

Generative AI models may struggle to account for the uncertainties inherent in asset maintenance planning, like unexpected equipment failures or changing production requirements. Suboptimal or unrealistic Generative AI recommendations due to overfitting can lead to inaccuracies or poor performance when applied to real-world maintenance scenarios. The degree of human intervention and oversight needed must be considered in the design phase of the solution. This is especially true in complex maintenance scenarios with interdependent systems or intricate operational constraints may also prevent Generative AI from providing accurate and feasible solutions.