Generative AI’s (Gen AI) power to transform every aspect of our lives is now common knowledge

But business leaders are still unsure exactly how to make this revolutionary technology an integral part of their activities. The truth is that the integration of generative AI for operations requires a very different approach than the integration of traditional AI. This is evident in the sphere of R&D and operations.

In recent years, AI has demonstrated concrete impact across the entire operations value chain. However, the most common and widespread use cases consistently focus on optimizing core processes. One notable development is the way industry leaders use AI to optimize the “physical” manufacturing and delivery of goods. Significant changes include time series analysis to improve process yield or scrap rate, operational research optimizing goods inventory norms, flows of goods or transportation, and computer vision to detect non-conformities.

With its unmatched ability to navigate, digest, and interact with unstructured information and documentation, Gen AI reoriented the focus from the “physical” to the “information” world. This means a shift from sensors and connected assets data to documents, which in turn leads to a retreat from manufacturing and supply chain core processes to R&D and industry enabling functions (e.g., sourcing, maintenance, quality and regulatory, etc.).

With this new paradigm in mind, leaders in operations see a new world of opportunities opening up before them, all powered by Gen AI .

Key capabilities for R&D and operations

Generative AI for research and operations is a gamechanger for all industries worldwide, optimizing organizations by automating data analysis and customer service tasks. Right now, many organizations are rightly experimenting, developing best practices, and identifying scalable solutions. We at ��ѻ��ý Invent were some of the first movers in this space, using our expertise to envision applications.

Even though we are only at the dawn of this transformation, several concrete use cases are already shining bright with more emerging every week. All that remains is to explore the untold opportunities.

Smart products

The ability of Gen AI to emulate a human conversation makes it a prime candidate for enriching user experience with “companion apps”. These apps leverage data collected by connected devices and expose it through a virtual assistant, enabling interactions in natural language and access to insights generated in real time. In the near future, we foresee the emergence of autonomous, edged Large Language Models (LLMs) embedded directly within products, enabling a new range of usage.

Engineering and R&D

As it is probably the most document-intensive area of the industry, Gen AI offers a multitude of opportunities for engineering and R&D. The ability of Gen AI to digest and synthetize complex information, combined with Retrieval-Augmented Generation (RAG) , enables engineers to easily search and query knowledge base or technical documentation using natural language. LLMs have multiple applications all along the technical documentation lifecycle, accelerating creation of a draft, proofreading or consistency checks against existing standards.

From the innovative use of AI algorithms for Automated Molecule Design in Drug Discovery to the Fragrance Formulation Generator streamlining perfume creation, the applications are far-reaching and go way beyond LLMs. It’s all down to Gen AI’s ability to craft detailed 3D simulation scenarios.

Manufacturing quality and maintenance

Leveraging its abilities to extract, synthesize, and classify information, Gen AI can optimize and turbocharge several manufacturing, quality, and maintenance processes. For instance, it can accelerate classification, summarization, search and analysis of quality incidents. It can also automate generation of quality documentation in domains with heavy compliance requirements (e.g., life sciences) and automate the documentation of non-conformities.

Gen AI-powered software enables human operators in maintenance or engineering to rapidly navigate through documentation of assets in a targeted way. Furthermore, generative AI in manufacturing and operations will be instrumental in the consolidation of information from other external sources, such as weather forecasts, market insights, and assessment of geopolitical risks. This capacity to integrate data from multiple sources is why Gen AI maximizes maintenance planning, accelerates operations, and improves efficiency.

This is all the truer in the case of distributed operations, where data coming from different systems is often heterogeneous and sometimes inconsistent. Gen AI’s ability to automatically harmonize and retreat data from distributed environments to ensure it meets quality standards will be key to enable seamless utilization of these data flows and to de-risk associated field operations.

Supply chain and purchasing

Gen AI can also significantly increase productivity of interactions across a network of partners, assets, and inventory. More specifically, Gen AI can boost the resiliency and efficiency of supply chains in the following ways:

• Enriched demand planning: Complementary to “standards” ML-based forecasting, by analyzing various exogeneous data sources (customer reviews, social-media trends, articles, etc.) to better understand demand drivers.
• Efficient sourcing: For instance, through improved upstream suppliers’ intelligence, by crossing external information regarding the upstream supply chain with the analysis of internal suppliers’ documentation and deriving insights from these data. Gen AI can also improve procurement process efficiency by automating transactional tasks involving interactions with suppliers or processing of external documentation (contract analysis, CSR compliance check, automated review of tenders, etc.).
• Improved Customer interactions: Automating and augmenting customer service and back-office operations, by accelerating search, summarization, classification and processing of trade, logistics and customer claims.

Three detailed use cases

The following three case studies provide an idea of how AI will change R&D and operations in the near future.

1. Search, synthesis, and reconciliation of engineering documentation

In the engineering industry, accelerating development processes while securing the quality on most complex products or major industrial projects are key challenges.

The main difficulties usually encountered are:

i) Limited access to engineering knowledge, and/or inefficiencies inherent to collaboration across various business entities involved in one given project.

ii) Time-consuming – or even occasionally unattainable – compliance requirements (e.g., traceability demonstration standards) generating significant complexity to retrieve and match key information from contracts, engineering specs, test procedures and results.

Generative AI in research is proving to be profoundly transformative. It can be used to research and analyze extensive amounts of documentation, both internal and external, such as engineering standards, design practices checklists, papers, and industry benchmarks, encompassing various formats, languages, and structures. It can identify and extract the relevant information and find the best way to display the requested information. It can also document user feedback, measuring engineers’ satisfaction and contributing to enhanced model performance.

Gen AI can also support the reconciliation of information across the V-cycle, by extracting key elements, such as specifications and technical requirements, from technical documents (even when these are not properly referenced). Once retrieved, Gen AI correlates the data to improve requirements traceability (e.g., high-level requirements with low-level specifications).

Finally, Gen AI can accelerate the generation of test procedures and reports.

2. Smart search for technical documents

Engineering teams face the rising complexity of new or changing regulations, extended commercial and industrial ecosystems, technological constraints, customer expectations, and even their own organizational structure and business process management systems. As such, relevant information is often managed by different stakeholders, widely dispersed, and non-homogeneous in various regards (e.g., format, granularity, languages, etc.).

Engineering teams struggle to manage this complexity. Despite being a critical task, information management is highly time-consuming and potentially a source of new risks or missed opportunities.

Gen AI can help human operators face these challenges, by leveraging its ability to research and analyze the extensive amounts of documentation required for maintenance and engineering, both internal and external, such as maintenance report, operating protocols.

Additionally, it can facilitate interactions between human operators and databases by writing and executing queries in natural language, which can prove decisive in accessing certain information.

3. Customer service efficiency

Within the consumer products industry, customer service faces several recurring challenges related to searching, summarizing, and classifying clients claims, as well as process logistics:

i) Customer service processes involve interacting with multiple IT systems and requires communication with various stakeholders to respond to customers’ requests, making it complex and tedious to gather relevant information.

ii) These processes are still largely manual, which is time-consuming and increases the risk of errors. As a result, end-to-end claims resolution can take months to complete, impacting both customer satisfaction and cash flow optimization.

To address these challenges, Gen AI can be used at several levels:

• Data retrieval and synthesis: To search for and retrieve relevant information related to the claim from various data sources, such as invoices and delivery receipts and contracts, all of which exist in different formats.
• Proposition of insights and validation recommendations: Comparing collected information and received claims, Gen AI can swiftly detect inconsistencies, highlight discrepancies, provide insights, assess whether the customer’s claim is well-founded, and make recommendations on potential outcomes to suggest to customers.
• Document processed claims: Capitalization is of the essence. Documenting processed claims, connecting the claim, its outcome, and the evidence used paves the way for easier information retrieval and decision making, should similar cases arise.

Factories are transforming and becoming smarter through the introduction of powerful multi-agent AI systems.

In this blog, we’ll take a close look at how these revolutionary AI-powered systems can help drive the factories of tomorrow. 

A lesson from history 

The industrial revolutions of the past can be described in two ways: firstly, as the emergence of new types of power. The transition from using humans and animals to using steam power in the 18th century was a significant revolution that enabled huge productivity gains as well as transportation innovations and urbanization. Secondly, the industrial revolutions marked the emergence of specialization: splitting up work into smaller tasks, with dedicated humans or machines for each part of the process. This enabled standardization and mass production. 

Coinciding with this, education and knowledge became specialized as well – people were only trained on their individual part of a process. Eventually, the innovation of machinery introduced automated reactivity to factory processes. Machines could now use condition-based “if this, then that” actions to complete a task. 

In today’s factories, we are seeing the emergence of innovative multi-agent AI systems, which reflect the above themes in many ways, while also exhibiting some differences. In this blog, we’ll take a closer look at some of these new developments. 

What are multi-agent AI systems? 

Multi-agent AI systems consist of autonomous agents or bots equipped with AI capabilities that work together to achieve a desired outcome. An agent in this context can be defined as  “an entity which acts on another entity’s behalf.” In these multi-agent systems, AI agents cooperate to achieve the goals of people who own certain processes and tasks. 

Multi-agent systems can be thought of as having five dimensions in terms of complexity when compared to a single agent system: 

1. Single to multi – adding more agents. 

2. Homogenous to differentiated – having fundamentally different roles between agents. 

3. Centralized to decentralized – removing the need for a single/central point of orchestration. 

4. Generic to specialized – adding in different backgrounds and knowledge to create different expert agents. 

5. Reactive to proactive – agents that can act independently in response to changes in the environment, without needing to be prompted. 

Are there parallels with the previous industrial revolutions that suggest agents might accelerate the next one? 

Let’s take the principles of multi-agent AI systems and apply them to a smart factory.  

• Each machine can have its own AI agent, while multiple machines or types of work can be managed by supervisor agents.  

• Most industrial tasks require multiple machines to work together, either in a streamlined, one-piece flow or in batches. Even machines working in “islands” need to be coordinated for the work in progress to be controlled, with no idle time. This requires that many different roles need to be assigned to different agents.   

• Adding a decentralized AI management layer can be very beneficial for a factory. There are many advantages to having sub-teams of agents with the ability to act independently of each other and run different areas of a factory to meet objectives.  

• Each machine works in a different way, and each area of a factory requires specialized knowledge. Therefore, each agent needs its own pertinent information to be able to act effectively. Higher levels of agent specialization would be very valuable to a smart factory. 

• Agents would benefit from autonomously determining when and how they need to act, rather than waiting for permission or being told when to do so. If agents were connected to the market, they could independently decide what to do. For example, an agent might exhibit this reasoning: “although the plan says that we have to produce this mix, I will change it because I think that there will be an increase in that particular product due to X and Y.”  

Multi-agent AI systems deliver clear improvements to factory processes and outcomes, including reduced downtime and increased optimization and efficiency. We also have the ability to add AI agents to data processing tasks, such as image and video analysis. This unlocks the potential of understanding input data in ways that were not possible before.  

Unlocking new ways of understanding data in smart factories 

In-line process control (IPC) is an approach that provides immediate feedback and adjustments based on real-time monitoring to maintain desired performance, quality, or output. If this is done well, it improves efficiency and reduces waste. However, the approach is difficult to implement, especially in systems based around humans. There are many data sources that need to be reviewed and understood in real time, and very experienced individuals tend to be the ones relied upon for this task. This experience is hard to acquire, potentially expensive, and still may not be sufficient to get the best results. This is, therefore, a great area of opportunity for multi-agent AI systems, which are very good at taking in lots of information, understanding what it means, and making real-time adjustments.  

Let’s look at two examples of how this works. First, let’s say you are making potato crisps, and need to understand how the cooking time of the chrisps differs depending upon the size and growing conditions of the potatoes. This can be a complex problem involving lots of disparate data sources that a multi-agent AI system could cope with well. The system could also help to determine the root cause of any problems that arise. 

A second example: if you are processing rubber in an extrusion line, the composition of the raw materials, their current mechanical and thermal characteristics, and the line parameters all influence the quality and speed of extrusion. This is a very complex problem, and in-line process control performed by an AI multi-agent system could add a lot of value. 

Another advantage of this application is that it can be integrated into factories of varying levels of infrastructure quality. Sensors may not be perfect, and information from outside the factory may have data quality issues, but removing even some of the problems will give great productivity and quality benefits. This can be especially true if costly manual inspections could be streamlined, alongside the more obvious benefits of reduced waste.

Multi-agent AI systems are revolutionary for factories 

We see parallels between the industrial revolutions of the past and what we are seeing today in multi-agent AI systems being adopted into factories. The difference now is that we are not transitioning power sources from people or animals to steam, or substituting humans in physical parts of processes. Instead, we’re allowing AI to perform tasks where it is beneficial to do so, and where it can perform the task better than the human. It is also worth bearing in mind that the real world is messy, and multi-agent AI systems can help us have more resilience and be more flexible.  

New innovations like real-time AI processing on edge can accelerate the next AI-powered industrial revolution, and give similar productivity benefits as seen in the first one. The edge component is critical, as it is more responsive than cloud, permitting real-time control. It also offers higher levels of data security, enables off-line operations (which are critical to factories), and significantly reduces the cost of the operation. 

However, AI will likely not be operating alone. I believe we will have human-AI hybrid systems for quite some time, and this is in no way a bad thing. It will be essential that humans and AI work effectively together – because for AI systems to bring value, they need to empower people, rather than replace them.  

This blog article was written in collaboration with Ramon Antelo (��ѻ��ý Engineering)

About the Generative AI Lab 

We are the Generative AI Lab, expert partners that help you confidently visualize and pursue a better, sustainable, and trusted AI-enabled future. We do this by understanding, pre-empting, and harnessing emerging trends and technologies. Ultimately, making possible trustworthy and reliable AI that triggers your imagination, enhances your productivity, and increases your efficiency. We will support you with the business challenges you know about and the emerging ones you will need to know to succeed in the future.  

We have three key focus areas: multi-agent systems, small language models (SLM) and hybrid AI. We create blogs, like this one, points of view (POVs) and demos around these focus areas to start a conversation about how AI will impact us in the future. For more information on the AI Lab and more of the work we have done, visit this page: AI Lab. 

From data deluge to insights: How Gen AI is transforming document search and knowledge management in financial services

Organizations, particularly in the financial services sector, have long mastered the management of structured data within relational databases. These firms have honed their expertise in data storage, ensuring data quality, and leveraging this data for applications, reporting, and analytics. However, the advent of Gen AI has transformed the handling of unstructured data, unlocking new possibilities in knowledge management and search capabilities across enterprise processes and workflows.

While structured data benefits from centralized storage and easy retrieval through tables and keys, managing unstructured data presents unique challenges. Ensuring that documents are not duplicated across various storage platforms like SharePoint, Teams, and Content Management Systems is less straightforward. Although some progress has been made in solving storage issues, the rigor seen in relational databases is often lacking.

The time-consuming process of gathering and auditing information from large collections of documents can significantly hamper productivity. The complexity increases when integrating structured and unstructured data to provide a seamless and efficient user experience for business, technical, and operational purposes. The value of advanced, Gen AI-powered search and knowledge management systems becomes evident, offering speed, accuracy, and scale, thus enhancing overall organizational efficiency.

Approaching the problem from multiple fronts

Now that we’ve examined the challenges and business value of this organizational capability, let’s discuss how to address it from multiple angles. The following chart offers an overview of the key dimensions involved in building this capability. In the subsequent sections, we will delve into the specifics of how AI and advanced techniques can be effectively implemented across the organization.

1) Information Stewards for feedback loop

The role of Information Stewards in ensuring ongoing data readiness is crucial. Information Stewards are responsible for monitoring and managing the quality, security, and compliance of the data environment. Their oversight ensures that the data remains accurate and secure. Additionally, integrating feedback from Information Stewards is essential for continuously improving data quality and AI model performance. This ongoing process helps maintain a high standard of data readiness and enhances the effectiveness of AI implementations.

The organizational structure of the financial services firm will determine the specific responsibilities of each Information Steward. For example, every line of business (LOB) and operational horizontal, such as contact centers, back-office operations, and strategy teams, will have designated stewards. If the firm uses disparate content management systems, additional effort will be required to standardize unstructured data governance processes, ensuring the integrity of the unstructured data landscape.

2) AI-augmented data enhancement

To ensure the quality, accuracy, and completeness of data, several capabilities are essential. Deploying classification algorithms to automatically identify document types and topics is crucial for effective data classification. Tag generation and metadata management play a significant role by automatically generating metadata tags for roles, topics, and divisions. Additionally, adhering to data standards is necessary to ensure that documents are reviewed and approved before publishing.

Document standards, such as mandatory sections for an intended audience, role-based security permissions, and change audit trails, must be strictly enforced. Approaches need to be developed to automate data augmentation from system logs, incorporating this information into service desk tickets to record which systems were accessed for resolving issues. The goal is to enhance human entries with automated data from logs and other sources, thereby reducing user friction and improving the accuracy and completeness of information.

3) Database for unstructured data augmented with structured data

Combining structured and unstructured data involves several key strategies. Implementing vector databases for dynamic indexing of unstructured data significantly improves the speed and accuracy of search queries. Enhancing unstructured data with structured data, such as document metadata and access permissions, adds valuable context.

Adding user role-based context makes large language models (LLMs) more effective in addressing queries. By including roles and their key performance indicators (KPIs) as additional context, Gen AI applications can better understand the motivations behind specific questions. This enables them to respond to general queries, such as “What are the top three things I should worry about today?” with greater expertise and relevance.

Additionally, exploring advanced techniques like combining Retrieval-Augmented Generation (RAG) architecture with knowledge graphs can further augment the enterprise context, providing a more comprehensive and efficient data management solution. GraphRAG approaches add an extra advisory layer that helps identify related document chunks specific to the document repository being queried.

4) Hybrid search vector + semantic search

To enable quick and effective data search and presentation to end users, a hybrid search and agentic architecture is essential. This approach combines the precision of vector search with semantic search to enhance search accuracy. Result enhancement is achieved through ranking fusion techniques, which merge results from both search types.

Additionally, the ability to call APIs across multiple domains, such as CRM, document repositories, service desks, and requirements, further enriches the search capabilities. An agentic architecture, with libraries for specific functionalities, ensures an improved customer experience (CX). This architecture allows AI libraries to augment Gen AI applications’ capabilities, such as performing mathematical calculations, rendering reports, and creating SQL queries against specific databases.

This evolution is crucial as it enables applications to explore areas like intelligent decision-making, rules execution, and product recommendations. The goal is twofold: first, to enhance enterprise context retrieval, and second, to augment Gen AI with AI and other APIs to deliver a superior customer experience.

5) Establish process for alerts for missing information with workflow

To automate continuous monitoring of processes and workflows, it’s essential to integrate systems for alerts and monitoring. Establishing a monitoring and alerts system allows for the oversight of data quality and completeness, promptly notifying teams of any anomalies or gaps.

Once alerts are triggered, workflow automation is used to respond efficiently, with predefined workflows in place to address and rectify identified data issues. This ensures timely and effective resolution of data quality problems.

Given that this is an ongoing effort, there is a pressing organizational need to keep the data fresh, up-to-date, and complete with the highest level of quality. This dedication to data integrity ensures that users receive the best possible information when they need it.

Bringing it all together

While financial services firms have long excelled in managing structured data within relational databases, the advent of Gen AI has opened up transformative possibilities for handling unstructured data. This evolution is crucial for enhancing knowledge management and search capabilities across enterprise processes and workflows. Managing unstructured data poses unique challenges, including preventing document duplication across various storage platforms and ensuring data accuracy and completeness.

To overcome these challenges, the problem needs to be approached from multiple fronts:

• Information Stewards ensure data quality, security, compliance, and continuous improvement of AI performance.
• Classification algorithms and metadata management ensure data quality and adherence to standards.
• Combining structured and unstructured data with vector databases and RAG architecture improves search accuracy.
• Incorporating hybrid vector and semantic search, ranking fusion, and API integration further refines search precision.
• Monitoring and alert systems with automated workflows maintain data quality and completeness.

By addressing these challenges from multiple fronts and leveraging advanced AI techniques, financial services firms can unlock the full potential of their data, driving superior decision-making and operational efficiency.

Want to learn more?

Check out the latest reports from the ��ѻ��ý Research Institute, packed with cutting-edge insights on Generative AI. Explore topics such as Turbocharging software with Gen AI, Harnessing the value of Gen AI, and Why consumers love Gen AI.

Click here to download a PDF copy of this expert perspective.

Generative AI (Gen AI) is transforming nearly every industry, and financial services is no exception. As banks and insurance companies navigate an increasingly competitive and complex business landscape, harnessing the power of Gen AI can unlock significant business value. While nearly every financial institution has begun experimenting with and piloting this transformative technology, only a few have successfully scaled their Gen AI models for widespread production.

AI agents promise smoother automation and enhanced productivity

Source: ��ѻ��ý Research Institute, Generative AI executive survey, May–June 2024, N = 1,031 organizations who are at least exploring Generative AI capabilities

In this article, we explore how financial services organizations can effectively implement and scale Gen AI to transform their operations and drive innovation.

Scaling and operationalizing Gen AI

In recent months, many financial services firms have shifted from experimenting with Gen AI to scaling up and operationalizing use cases. This transition is fueled by the recognition of Gen AI’s transformative potential, particularly in enhancing business processes and customer experiences. To maximize the value of Gen AI investments, financial institutions should focus on several key activities:

1) Identify high-impact use cases

A crucial first step in activating Gen AI is pinpointing use cases that have a high impact on business goals and can be developed within a reasonable timeframe. It’s important to avoid burdening these initiatives with unrealistic expectations that cannot be implemented promptly.

In financial services, many organizations are focusing their attention on the following key areas:

• Personalized customer experiences: Gen AI can tailor product recommendations, generate targeted content, and automate communication and marketing campaigns, boosting customer satisfaction and loyalty.
• Enhanced risk management: Gen AI can significantly improve processes like fraud detection, credit risk assessment, and anti-money laundering (AML) compliance, increasing operational efficiency and accuracy.
• Streamlined operations: Automating repetitive tasks such as report generation, data analysis, and customer service interactions allows human capital to focus on higher-value activities.
• Expert-in-the-loop: Gen AI-powered contact center co-pilots can assist customer service representatives by providing real-time guidance on products, services, and support questions.
• Tailored technology integration: Instead of relying solely on Gen AI, organizations can leverage a combination of technologies, including other AI models and Robotic Process Automation (RPA).
• Governance and trust: Creating robust governance frameworks helps organizations maximize AI’s potential while mitigating risks. Transparent, consistent, and accountable AI implementation promotes stakeholder collaboration, ensuring that strategies prioritize risk management, ethical practices, and alignment with organizational values.
• Establish a value framework: Create a system for business users to experiment with, measure, and benchmark Gen AI applications. This enables the quantification and justification of high-value use cases.

High Impact use case selection process

2) High Impact use case selection process

Financial Services firms have been heavily investing in cloud infrastructure to address legacy technical debt and democratize access to data. Augmenting these platforms with tools like ��ѻ��ý’s new RAISE (Reliable AI Solution Engineering) accelerator enables faster deployment and management of Gen AI solutions.

3) Focus on data quality and management

Gen AI models rely heavily on high-quality data. Financial institutions are investing in several critical areas to ensure optimal AI performance. Data cleansing and standardization are vital to maintain accuracy, completeness, and consistency across various sources. Proper data labeling and annotation guide AI models to learn desired patterns effectively. Additionally, establishing clear data governance frameworks is crucial. These frameworks define ownership, access control, and security protocols to protect sensitive information, thereby enhancing the reliability and integrity of Gen AI models.

Real-world implementations

Financial services organizations are leveraging Gen AI in diverse ways, often employing a blend of automated and human-augmented processes.

Adoption of generative AI has grown across functions

Source: ��ѻ��ý Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive survey, May–June 2024, N = 1,031 organizations who are at least exploring Generative AI capabilities

Here are several examples we’re working on now with clients:

Legacy modernization

Gen AI excels in tasks like code documentation, code generation, and test case creation. However, maximizing code reuse while refactoring legacy systems is complex and typically involves the following steps:

1. Documentation: Explaining the purpose and functionality of the code to help developers understand the rationale behind specific changes.
2. Discovering code relationships: Identifying and mapping dependencies between different code elements to streamline the refactoring process.
3. Analysis and design for target architecture: Deciding how the code should be structured, which patterns to apply, and how components will interact in the new architecture.
4. Code generation: Analyzing the code to identify areas for improvement, such as eliminating code smells (e.g., duplicated code, long methods, complex logic, or inconsistent naming conventions).
5. Generate test cases and test data: Automatically generating scenarios and input data for testing purposes, ensuring that refactoring does not inadvertently introduce bugs or regressions.

Business operations

In banking and insurance operations, back-office processes are being examined for automation opportunities using Gen AI. Examples include dynamic Q&A systems and automated document creation. The principle here is to automate tasks where feasible and augment human effort where automation is not possible, thereby enhancing and transforming operations.

Business processes

For business-critical functions like legal, finance, and underwriting, financial services firms are using Gen AI to augment human capabilities in key areas:

• Surfacing answers from data: Searching large sets of documentation to support more informed decision-making.
• Learning from historical data: Utilizing historical data from service desks and other sources to improve manual adjudication processes.
• Summarizing information: Providing starting points or drafts for documentation, streamlining the creation process.
• Reimagining business processes: Combining Gen AI with existing tools and AI/ML models to enhance and optimize business operations.

Precautions for deploying Gen AI

When implementing Gen AI solutions, financial services companies must consider several critical areas before developing or deploying any models:

• Data privacy and security: Financial data is highly sensitive, making data privacy and security paramount when using AI models for generation or analysis. Robust encryption, strict compliance with regulations (such as GDPR or CCPA), and strong defenses against cyber threats are essential to protect against privacy breaches and security vulnerabilities.
• Biases (intentional and unintentional): Gen AI models can inherit biases from the data they are trained on, potentially leading to unfair outcomes in financial decision-making. This can result in discriminatory practices or skewed recommendations, exposing institutions to regulatory scrutiny and reputational damage. Financial firms must rigorously evaluate model outcomes during the training phase to identify and address any biases. Additionally, augmenting in-house data with third-party data can help overcome inherent limitations and provide a more balanced perspective.
• Regulatory compliance: Financial Institutions operate within stringent regulatory frameworks (e.g., Basel III, Dodd-Frank, etc.). The introduction of AI models requires compliance with these regulations, which often do not explicitly address AI technologies, leading to uncertainties. Careful interpretation of existing regulations is needed to ensure that AI applications align with compliance requirements.
• Interpretability and transparency: AI models, particularly complex generative models, often function as black boxes, making it difficult to understand how they arrive at decisions or generate outputs. This lack of transparency poses challenges for financial institutions that must justify their decisions and ensure accountability. To address this, banks and insurers should carefully document input data and its relevance to the model’s decision-making process, creating a consistent audit trail that supports transparency and facilitates compliance reviews.

Best practices for winning with Gen AI

Successful Gen AI adoption requires a well-defined strategy, persistence, and a willingness to learn from previous experiences. Here are some best practices to guide financial services firms in their AI initiatives:

• Educate and align: Educating stakeholders at every level—from board members to developers—is critical for Gen AI success. For instance, our own Gen AI Center of Excellence (COE) hosts a bi-weekly Gen AI Hour meeting, where key stakeholders are updated on successful case studies and cross-sector use cases. This forum helps keep everyone informed, aligned, and engaged with the AI strategy.
• Build a culture of AI readiness: Invest in employee training programs to build awareness, understanding, and comfort with AI technologies. Cultivating an organization-wide mindset that embraces AI is essential for successful Gen AI adoption.
• Establish trust and governance: Address ethical considerations related to bias, fairness, and data privacy by setting clear ethical guidelines and committing to responsible AI development practices, such as those outlined in ��ѻ��ý’s Code of Ethics for AI. Strong governance frameworks will help build trust and promote ethical AI use across the organization.
• Prioritize continuous improvement: Gen AI is an evolving field. Establish a culture of continuous learning and improvement by regularly monitoring model performance, gathering feedback, and iterating on models.
• Partner and co-invest: Collaborate with partners to develop joint solutions, educate partner sales organizations about your Gen AI go-to-market strategies, and sell solutions together.

��ѻ��ý’s Comprehensive AI framework

Ready, set, go!

Gen AI is reshaping the financial services industry, providing banks and insurers with powerful tools to drive innovation, improve customer experiences, and streamline operations. While many institutions have started experimenting with Gen AI, the real challenge lies in effectively scaling these models for widespread use.

Success in this space requires a strategic approach, focusing on high-impact use cases, leveraging cloud infrastructure, and prioritizing data quality and governance. Real-world implementations show that Gen AI can modernize legacy systems, optimize business operations, and augment key business processes. However, it’s equally important to address concerns around data privacy, bias, regulatory compliance, and model transparency to ensure ethical and responsible AI adoption.

By educating stakeholders, fostering a culture of AI readiness, building robust governance frameworks, and continuously improving AI models, financial services firms can unlock the full potential of Gen AI. Partnering with the right allies will further amplify success, enabling organizations to navigate the complexities of this transformative technology and achieve a competitive edge in the industry.

As a recognized “Leader” in The Forrester Wave: AI Services, Q2 2024 report, ��ѻ��ý is uniquely positioned to help your organization activate and scale Gen AI. Contact us today to start your AI journey.

Special thanks to: Clement de Balby de Vernon, Ryan Toa, Tom Nicholson

As we start into 2025, the ever-dynamic telecom industry finds itself at yet another crossroads. It is set to explore new ways of working, unlocking new revenue streams, optimizing operations, and enhancing capabilities.   

Praveen Shankar, Global Head of Telecommunication at ��ѻ��ý dives deeper on each of these trends and its impact on the telcos. Watch the full video here.

 The quest for revenue growth will intensify

 Satellite companies will move into the mainstream

 Cybersecurity will ascend to a top priority

 Focus will increase on home markets and on simplification

 Data and AI will return to the center stage

 Summary

The high volume world

In its, Airports Council International (ACI) projected a 10% growth for passenger traffic in 2024, to reach 9.5 billion. According to , the top 20 ports in the world generated 387.5 million twenty-foot equivalent units (TEUs) of traffic in 2023, a 1.24% increase from the previous year. 

Against this backdrop of increased volume, the modernization of airport and seaport operations is crucial for the efficient and secure transportation of people and goods. Integrating 5G and edge computing based applications can revolutionize these operations, by enabling real-time monitoring, automation, and data-driven decision making.

With 5G’s high-speed and low-latency connectivity, airports and seaports can leverage applications like smart surveillance, predictive maintenance, and autonomous vehicles, ensuring enhanced safety and reduced congestion.

Edge computing enables data processing and analysis closer to the source, reducing latency and improving response times. This modernization will streamline operations, increase cargo throughput, and enhance the overall passenger experience, making transportation more efficient, sustainable, and resilient. By embracing these technologies, airports and seaports can stay ahead of the curve and meet the growing demands of global trade and travel.

5G and edge computing are mainly technology platforms – they allow enterprises to ‘connect’ and ‘host’ services. But they also offer unparalleled levels of flexibility because they enable customers to tune both network and server resources to meet the specific requirements of each airport or seaport service. For example, a highly reliable, bandwidth-prioritized network for airport security, but a ‘best effort’ network for airport entertainment services.

This flexibility to provide different quality of service (QoS) for both data and applications sets 5G apart. Similarly, edge computing uses virtualization and cloud-native architecture to provide scalable compute for different applications. For example, in a seaport, the usage of container management and fleet control applications can increase when a ship berths in or leaves port.  

According to a report prepared by Avinor AS and Heathrow Airport Ltd., another factor driving the trend towards 5G and edge computing in airports has been the move towards. This calls for airport systems to communicate with each other over secure, but known interfaces and APIs. It helps the airport to absorb the most competitive applications from multiple vendors. These open architectures also need a platform for compute, communications and edge – which 5G does well.

The key benefits of 5G and edge based ‘smart services’ are discussed below.

Enhanced operational efficiency

One of the primary benefits of 5G and edge computing in airports and seaports is the significant improvement in operational efficiency. For instance, in airports, 5G networks facilitate real-time data exchange between ground handlers, aircraft teams, and operations centers. According to a blog by Ericsson, this seamless communication . Similarly, another Ericsson report argues that .

Improved safety measures

Safety is paramount in both airports and seaports, and 5G combined with edge computing offers robust solutions. According to an article by Airport Technology, in airports, are now possible. These technologies ensure that potential threats are identified and mitigated promptly, enhancing passenger safety. In seaports, Ericsson reports that smart sensors and IoT devices can . This proactive approach significantly reduces accident risk and ensures a safer environment for port staff.

Increased profitability

The integration of 5G and edge computing also drives profitability, by enabling new revenue streams and reducing operational costs. Airports, for example, can leverage these technologies to enhance retail operations within terminals. Additionally, predictive maintenance powered by real-time data analytics minimizes downtime and extends the lifespan of critical assets, reducing maintenance costs. In seaports, automated logistics and real-time tracking improve cargo handling efficiency, reducing turnaround times and increasing throughput.

Future proofing infrastructure

Investing in 5G and edge computing not only addresses current challenges, but also future-proofs airport and seaport infrastructure. These technologies provide a scalable, flexible foundation that can adapt to evolving demands and integrate with emerging technologies like augmented reality (AR) and AI. This adaptability ensures that airports and seaports remain competitive and capable of meeting future operational and security requirements. Digital twins for operations is another area of interest for both airports and seaports. These digital twins can provide valuable real time insights to drive better decision making; for example, on traffic flow, energy use, hardware status or local climate conditions.

The challenges of implementing 5G and edge

Seaports and airports are large areas of real estate with massive inventories of devices. Additionally, much of this inventory is mobile. This inventory requires efficient tracking, surveillance and operational management. As described above, digital twins of products and processes can bring operational efficiency, but to build such digital twins, data must be collected from the vast amount of mobile devices over a large area. 5G/edge is perfectly suited to be the digital fabric that enables the realization of these digital twins.

Smart services: a path to monetization for CSPs

CSPs and telcos worldwide see smart services as a way to offer complete packages to enterprise customers. This helps drive adoption for their 5G private network and SD-WAN communication platforms, as enterprises don’t just get a data pipe in their premises – they also get all the necessary application suites and devices. It also positions the CSP as a ‘tech-co’ instead of a ‘tel-co’, with offers in specific markets like manufacturing, mining, warehousing, etc. To that end, CSPs often partner with system integrators (like ��ѻ��ý) to tailor these smart service offerings to enterprise – right from early strategy to eventual roll-out.

Our smart service solutions are powered by ��ѻ��ý’s Intelligent Edge Application Platform (IEAP), which is designed to accelerate the development and deployment of edge applications, particularly for industries leveraging the Internet of Things (IoT) and real-time data processing. The platform enables efficient integration of edge computing with cloud infrastructure, facilitating faster data analysis and decision-making at the network edge. It supports scalable, low-latency solutions for industries like telecommunications, manufacturing, and automotive, enhancing operational efficiency, automation, and intelligent decision-making. IEAP simplifies the management of distributed systems and enhances the overall agility of edge-based digital services.

A smart services example: ��ѻ��ý’s collaboration with AWS

��ѻ��ý is collaborating with AWS to build a solution, based on IEAP, that provides the advantages of a 5G network connected to edge computing running on AWS Hybrid cloud. This platform serves as a delivery vehicle for smart services for airports, seaports and other enterprises that want to run various applications locally, but with access to the AWS ecosystem of enterprise applications. It allows for localized and AI-powered services, providing security, traffic control, track-and-trace and end services to airports and seaports.

Conclusion: the transformation is happening now

The transformation of airports and seaports through 5G and edge computing, which is happening now, is a testament to the power of smart services. By enhancing operational efficiency, improving safety measures, increasing profitability, and future-proofing infrastructure, these technologies are setting new standards for the transportation industry. As 5G and edge computing continue to evolve, we can expect even more innovative solutions that will further revolutionize how airports and seaports operate, ensuring they remain vital hubs in the global economy.

Rail operators want to replace old mechanical rail systems with modern digital alternatives, enabling the rapid deployment of innovative digital services.

These will include intelligent traffic management, automated shunting, infrastructure monitoring, and connected workers. Such systems however will require advanced connectivity delivered over high-bandwidth communications, with service-oriented architectures, and safety-critical cloud infrastructure. But many currently use legacy communications systems such as the Global System for Mobile Communications (GSM-R), based on decades-old 2G technology.

A future intelligent rail will need to upgrade. Since such upgrades happen irregularly, a number of advances have been made since the last one. Many railways are therefore looking to jump straight to 5G, under the Future Railway Mobile Communication System (FRMCS) standard.

Designed by the International Union of Railways, FRMCS aims to become the worldwide standard. It is a network architecture is designed to provide a software platform with rail in mind, onto which new digital services to be easily built and launched, and services easily upgraded over-the-air.

FRMCS is targeted to replace GSM-R in the next 7-10 years. But this will be no easy task. Railways are complicated and new services take time because of extensive testing, verification, and

stringent safety requirements. The technological challenges are immense; including dual operation during the co-existence period, network type and technology deployment decisions, and new security threats.

But these are challenges we must overcome to deliver future rail networks – and all the safety improvements and cost savings that will come with them.

In our new whitepaper, Future Railway Mobile Communication Systems, we discuss the benefits and challenges of deploying 5G under FRMCS, and propose a model for a migration strategy.

Five years of 5G trials across industries have confirmed its truly transformational potential. The development of 5G presents a unique opportunity to further strengthen digitalization, efficiency, and security.

But for various reasons, industry adoption has been slower than expected. Now, as the technology and ecosystem mature, we see rising interest in private 5G networks, and growing benefits in the fields of logistics, offshore, manufacturing and aviation. In 2021, my French colleagues published a report on a project of theirs, “5G for airport operations”, which is as relevant now as it was then. Recently, many airports and aviation companies have embarked on the 5G journey, with ongoing implementations in European hubs. Following this, the Norwegian airport operator Avinor has started the journey towards 5G with a clear target to digitalize and make aviation safer and more efficient across their 44 airports in Norway.

Supporting Avinor 5G pilot project

Avinor selected ��ѻ��ý together with one of the leading telecom operators in Norway to help them build up strong internal 5G knowledge and test the technology through a real and impactful use case: Autonomous Guided Vehicles (AGV) for tarmac inspections.

Driving the project based on a specific use case has proven to be very beneficial. Instead of running a theoretical exercise, the real-life testing of the AGV performance with different radio technologies (sometimes in harsh weather conditions) has been a driving force through the project and enabled fast conclusions and proof of 5G private network capabilities.

At the same time, the technical complexity and dependencies across the organization have given strong insights in how to successfully run early phase 5G projects – insights that apply in any industry.

What this project revealed about 5G transformations

With the newest developments in network spectrum allocations in Norway, industries can now implement private 5G networks and achieve unmatched coverage, low latency, and capacity close to fixed broadband installations. However, what a technology promises is not always what it delivers. Out-of-the box solutions often need to be tweaked for a specific use case. Dependencies must be tested and verified, and everything from the radio access network to hardware and back-end set up must be adapted.

A clear learning from the tests of the AGV use case is that the end-to-end 5G set up must be carefully thought through; Back-end integrations are vital to secure the low latency required, and once in place, private 5G outperform other connectivity options. In addition, hardware such as routers and antennas on the receiving end is critical. Without the hands-on experience, it’s easy to assume that other radio alternatives such as public 4G/5G perform as well as private 5G. However, experience shows that these options often fall short of requirements.

Guidance for your 5G project

In general, the numerous use cases and varying requirements in 5G challenge project management. From our recent experience with 5G installations across different industries, we’ve gathered some interesting insights. Arguably, not revolutionary, however often overlooked.

Gate closing: Be clear on what to achieve and what is required to pass each gate before moving forward to next step of the project. It’s easy to end up in a spiral of different use cases and technical requirements, paralyzing the organization and addling little value. The key is to identify and agree on the potential, quickly narrow down what to test and how, and secure that value is proven early. Once this is achieved for one use case, the next becomes a priority exercise instead of another proof of concept.

Leverage experience: What you are set out to do has most likely been done before. In this project, ��ѻ��ý facilitated a visit to our Paris 5G lab, enabling hands-on experience of network set up, use cases and meetings with industry experts, sharing experience, challenges, and solutions. This proved to be a great experience and facilitated a steep learning curve on 5G radio networks and use cases. In addition, it can be very helpful to contact other companies in the same (or similar) industry to share experiences.

Think basics: There are multiple use cases enabled through a private 5G network, everything from improving onsite coverage, to drones, predictive maintenance, digital twin and AI applications. However, it might be the basics, that drives tangible benefits with little effort. One great example is replacing old, wired connections with 5G. Various sensors, cameras, and technical equipment across the airport are vital to operations and often dependent on wired connectivity, installed many years ago. Substituting old wire across and around the tarmac, and in the airport itself, with 5G radio is expected to save substantial time and money, compared with the cost of installing new cables.

Set the roadmap: Once the pre-study is completed, the work starts for real. Knowing what use cases to prioritize, how they relate to network requirements, and what the benefits are, will help set the direction and focus needed to succeed over time. Implementing 5G and associated use cases will be a long project, and it is vital to divide it in smaller, prioritized deliveries. This is not only a technology requirement; breaking down a big project into manageable chunks is easier for the workforce and organization and facilitates the transition to new ways of working. For the aviation case, the roadmap must cover different stakeholders across, and outside, the organization, such as ground operations, maintenance, different carriers, baggage handling and security – of which some are potential customers, keen to leverage the installed 5G network.

Final note: Relevance of 5G for your industry

A question that many companies ask is: “Is 5G really relevant for my business?” The short and honest answer is yes. Looking beyond the technology and focusing on the possibilities a private 5G network enables, you can see significant operational and financial benefits. No matter if it is the future of aviation operations, offshore worker safety or implementation of AI in the factory production line, 5G is often the missing link in your journey towards digitalization and the connected future.

Exploring 5G for your organization? Connect with our 5G Private Network team and let us know what you need.

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