What is smart transportation?

Every day, people encounter multiple obstacles while traveling to their intended destinations. Sitting in traffic, waiting for the bus to arrive 15 minutes later than scheduled, driving around for 30 minutes to find a parking spot—the modern world is full of inconveniences due to underlying inefficiencies in our transportation systems.

However, stalled cars and harried people waiting for public transportation aren’t just an individual nuisance. A less-than-optimal transportation infrastructure affects the economy, hastens environmental impact and lowers the overall quality of living. Making transportation work quicker and for more people keeps city planners up at night.

The good news is that new technologies and approaches to transportation management systems allow us to start addressing these inconveniences and make other downstream transportation improvements. The solution is smart transportation.

The rise of interconnected technologies like the Internet of Things (IoT), electric vehicles, geolocation and mobile technology have made it possible to orchestrate how people and goods flow from one place to another, especially in densely-packed urban areas.

Several global cities, including London, Paris, Amsterdam and Rio De Janeiro, have invested in smart transportation as a key component of their smart-city initiatives. There are now universities that study smart transportation use cases (e.g., Carnegie Mellon, New York University, NJIT and many more). The whole world, it seems, is fixated on solving transportation issues and increasing mobility because it produces so many benefits for citizens and the economy.

The rise of smart cities

Many cities claim to be the first “smart city” in the world. While one can debate what exactly turns a mere city into a smart one, there’s no denying that the rise of the Internet and mobile technology has generated widespread interest in building the next generation of smart cities.

Every time a city improves upon its existing structures to incorporate more data-driven or connected technologies, it becomes more intelligent. Examples of smart-city enhancements beyond smart transportation include sensors to monitor air quality and temperature fluctuations, IoT technology in public buildings to conserve energy and data-driven waste pick-up management. But the crown jewel of any smart city is how smart transportation can revolutionize how cities operate and how people move within them.

A smart transportation overview

Also known as smart mobility, the rise of ubiquitous data collection and automation has led local governments to embrace smart transportation. It is made possible by the fact that virtually every citizen and commuter has a smartphone that can transmit and receive messages and data.

In addition, it’s never been easier and cheaper to create public Wi-Fi networks, creating many new opportunities for governments can implement smart transportation initiatives.

Smart transportation usually includes public-private partnerships that can positively affect several issues with transportation, such as pollution deriving from car emissions, congestion and the importance of public transportation to the needy and elderly.

Several smart transportation solutions have existed for some time—for example, a city department of transportation providing real-time arrival data of buses and trains, electronic toll collection, bike sharing, dynamic pricing on cars entering the city and public transportation smart cards. But several disparate technologies do not make an intelligent transportation system. It requires a comprehensive strategy and multiple smart technologies working in tandem.

Smart transportation helps better allocate resources so cities can do more with less and avoid unnecessary energy consumption and resource costs.

Cities and states prioritizing smart transportation provide a more inclusive and equitable living experience for all of their citizens.

Smart transportation benefits governments and citizens alike

The following are some examples of smart transportation and how they can benefit a city:

Parking

Every driver has had the experience of searching for parking for 30 minutes or more, convinced that every open spot is filled right before they get to it. It’s a vexing problem that has an obvious solution: adding sensors to parking spots. That way, drivers can find an open spot ahead of time and use their smartphones and/or dashboard consoles to go directly to the spot, instead of aimlessly wandering.

Intelligent transportation networks

Many local and national transportation departments are now broadcasting real-time mass transit schedule updates and maintenance interruptions through centralized control systems. Citizens and commuters can access this information on their smartphones, tablets and computers through applications, social media or browsers, but that should just be table stakes.

The next generation of smart transportation systems will be able to communicate when parts on trains or buses are likely to fail, allowing operators to take fleet vehicles out of service to fix them before they break down with passengers on board. Investing in transportation networks also includes the building of high-speed rails that can transport more people from destination to destination, ameliorating traffic and the environmental impact of individuals driving cars.

Better traffic management

Traffic congestion results from many separate issues, such as vehicle accidents, rigid traffic grids, poor weather, population growth and substandard infrastructure. While each has a fix (of varying levels of complexity), smart transportation can address them all:

◉ Vehicle accidents: Connected vehicles with sensors can prevent accidents from occurring, often before the driver even knows something bad is about to happen.

◉ Traffic control: Historically, traffic lights changed based on pre-determined time windows regardless of any unforeseen impacts. While the time windows for traffic signals to change may differ during different times of day (e.g., to account for rush hour on a busy street), they rarely change based on the specific flow of traffic. In the rare major metropolitan areas where traffic light times can change based on that data, it’s usually done manually by human intervention. The future of traffic management involves smart traffic lights connected to real-time traffic flow data that incorporates machine learning and artificial intelligence that can change lights at intersections based on thousands of variables.

◉ Real-time information on road conditions and accidents: Like traffic, road conditions can create bottlenecks in travel patterns. While map applications accessible on smartphones increasingly provide real-time updates on traffic conditions, they’re often provided by citizen reporting. Public-private partnerships can boost this information by investing in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technology so that every car provides information automatically, identifying issues before they create traffic jams.

Smart public transportation

How many job seekers have lost out on the job of their dreams because of backed-up or stalled trains? How quickly do printed schedules on the bus stop become irrelevant every day? In every city, thousands to millions of people depend on public transportation daily; they are lifelines to the elderly, frontline workers and people with disabilities. It makes a world of difference when cities can connect those critical vehicles to a smart grid to ensure that citizens have real-time information about when bus services and other forms of public transit will pick them up and take them where they need to go.

Support for electric vehicles

Leaders who want to make their cities hospitable and attract electric car drivers must install electric charging stations in high-traffic areas, where drivers can stop for a bit and walk around or get some food while the car charges. Not only does that provide a service to the driver, but it also helps area businesses capture some new business. The important thing to remember about smart transportation is you’re also building for the future. While autonomous vehicles are not ready for mass deployment yet, many expect they will become a reality in the future. So any meaningful smart transportation plans have plans for future proofing as vehicle technologies expand how we can move around without human intervention.

IBM Maximo is helping advance the journey to smart transportation

Virtually every major city has incorporated some smart transportation technologies into their overall offering to citizens and commuters, but now is the time to establish a holistic smart transportation strategy that helps people get to their destination quicker, more safely and with less environmental impact. As the hybrid work movement enables more employees to work in a city of their choosing, the local governments that offer a truly smart city with a comprehensive smart transportation system will be able to attract more residents at the expense of those cities that fail to adapt.

The good news is that solutions now exist to help governments create a more comprehensive smart transportation framework that uses a full suite of solutions for operations, maintenance, monitoring, quality and reliability. IBM Maximo helps metro services serving 4.7 billion riders, 73% of the busiest airports and 75% of the largest automotive companies transform the intelligence of their systems to improve customer satisfaction and increase efficiency.

Source: ibm.com

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IBM WebSphere Liberty announces InstantOn for cloud-native Java

IBM is pleased to announce the availability of WebSphere Liberty InstantOn coming at the end of June 2023. With this new offering, you can start cloud-native Java applications up to 10x faster.

IBM WebSphere Liberty is a next-generation application runtime that accelerates the delivery of cloud-native applications. Whether you’re running microservices in a serverless environment or a traditional always-on application, Liberty requires less infrastructure, saving costs by up to 40%, and provides the flexibility your organization needs to deploy on any cloud in a trusted, secure and open environment. Liberty increases developer productivity by up 50%, empowering application teams to continuously deliver code that delights end users. 

What is InstantOn?

InstantOn, a new feature within IBM WebSphere Liberty, enables you to take full advantage of cloud infrastructure with fast and dynamic start-up—without inheriting compromises commonly found when compiling your code to native images. InstantOn will allow you to easily adopt serverless practices while enabling you to do the following:

◉ Respond faster to ever-changing workload demands.

◉ Improve sustainability and reduce costs by dynamically adjusting infrastructure to demand.

◉ Facilitate innovation through modern application architectures and serverless.

Why serverless?

Businesses today need a significant return on investment and require constant control of costs. In an on-premises data center, this has traditionally meant estimating peak demand and deploying sufficient infrastructure to meet that demand. If your business is seasonal or has significant daily fluctuations, then you have long periods where the infrastructure is hardly used. It’s still drawing power and it still requires maintenance (and eventual replacement), but it’s not delivering value to your customers or your business. These kinds of workloads are what serverless was designed for.

Serverless is a cloud computing application development and execution model that enables developers to build and run application code without provisioning or managing servers or backend infrastructure.

Historically, application startup time has not been a primary focus for Java; instead, it has been optimized for high throughput. The reasoning goes that if your applications are running for a long time, throughput is king. This has led to the suggestion that slow startup makes Java unsuitable for serverless workloads.

Some Java runtimes have chosen to throw away the Java Virtual Machine (JVM, the technology that provides great throughput and memory management) and compile Java to native code. This delivers faster startup but at the expense of throughput. It also subsets the Java language, so your code and libraries must be designed for native compilation. Lastly, native compilation is slow, so development is still done on a JVM. Using a JVM in development and a native image in production increases the risk of problems not being detected until applications are in production.

Serverlesstypically costs a bit more, so it’s important your applications are efficient—meaning low memory use and high throughput. These are characteristics WebSphere Liberty has excelled at for many years. However, serverless brings a spotlight to another performance characteristic:  time to first response. With serverless, you no longer have instances idling waiting for work, so when demand increases, you need new instances to be able to respond to requests very quickly.

How does WebSphere Liberty InstantOn work?

Websphere Liberty InstantOn takes a novel approach. With InstantOn, during application build, you can take a checkpoint of your running Java application process and then restore that checkpoint in production. The restore is extremely fast (in the low 100s of milliseconds) making it ideal for serverless.

Since InstantOn is a checkpoint of your existing application, its behavior after restore is identical, including the same great throughput performance. This process enables organizations to adopt serverless for new cloud-native applications and provides the opportunity to bring serverless to existing enterprise applications.

“This is an interesting technology for companies that want serverless flexibility but don’t want to be locked into cloud functions.” — Johan Janssen, Co-Founder, FlowFactor

Key features and benefits

◉ Start an application up to 10 times faster, enabling applications to rapidly scale to meet demand.

◉ Works for all Java applications, new and old. Easily adopt serverless without rewriting your applications.

◉ No compromise to throughput performance.

Source: ibm.com

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IBM IT Automation: Reflections from IBM Think

We have had an amazing week with IBM clients, partners, and stakeholders at our annual Think Conference in Orlando, Florida. For IBM, Think is a perfect time for us to connect, collaborate and help our clients and partners continue to forge ahead with digital transformation and innovation.

As we wrap up Think 2023, we’re excited to recap a few of the exciting new capabilities we are launching in our IT Automation portfolio.

Accelerating IT operations with IBM’s AIOps platform

Managing IT environments in today’s modern business landscape is a complex and challenging task, requiring IT operations teams to move from being reactive to predictive and proactive. Traditional approaches are no longer sufficient to ensure continuous availability, functionality and performance.

To address this challenge, IBM offers an AIOps platform that utilizes AI to enhance decision-making across infrastructure and operations personas by contextualizing large volumes of operational data.

IBM Cloud Pak for AIOps is a comprehensive, self-hosted AIOps platform solution that provides central IT operations teams with a single pane of glass to view their managed IT environment. This platform utilizes intelligent automation and artificial intelligence (AI) to aggregate data from various sources, detect and correlate incidents, and quickly drive incidents to resolution. IBM’s AIOps platform is designed to help businesses by augmenting, accelerating and automating incident resolution, while helping to foster collaboration across teams.

Further contributing to the value of our existing AIOps platform—alongside the Cloud Pak for AIOps—IBM is excited to announce the addition of our cloud-native SaaS solution, IBM AIOps Insights, set for general availability at the end of June 2023. This AI-powered solution is designed to streamline incident management and increase uptime to help improve operational efficiency and reduce costs. With AIOps Insights, organizations can leverage AI and machine learning to automate end-to-end processes, transforming reactive IT operations into proactive and predictive operations that identify issues and anomalies before they occur.

AIOps Insights is a SaaS deployment option for incident management and remediation that provides a comprehensive, real-time view of an organization’s IT environment. Like IBM Cloud Pak for AIOps, it consolidates multiple tools and correlates resource status across silos, streamlining incident management, reducing incident costs and increasing availability. With AIOps Insights, organizations can connect and integrate cross-domain data—including infrastructure, network and APM—and visualize their entire IT environment with just a few clicks.

The platform dynamically generates a topology of an organization’s IT environment, adding new entities as they are detected. By correlating events and grouping them into incidents, AIOps Insights addresses noise and enables quick identification of incidents that may require more investigation. The platform also automatically suggests remediation actions. The platform then provides visibility of status updates as incidents are resolved.

IBM’s AIOps platform, including IBM Cloud Pak for AIOps and the new IBM AIOps Insights, empowers organizations to tap into the power of intelligent automation and streamline IT operations to help them achieve better business outcomes.

Better together with IBM Instana Observability and IBM Turbonomic

In today’s complex IT environments, lack of visibility is a major challenge. IT operations teams need to gain full-stack health and performance monitoring, which traditional monitoring tools may have lacked.

IBM Instana Observability provides comprehensive visibility into modern applications, services and environments through a technology-agnostic approach that continuously provides high-fidelity data. It is engineered to identify issues before they impact business operations and automatically deploys monitoring in over 250 different applications, microservices and software infrastructure components across hybrid and multicloud environments. Instana is designed to deliver quick-time-to-value while keeping up with dynamic complexities.

In addition to comprehensive visibility, organizations need to detect performance issues and eliminate waste caused by over-provisioning. Enterprises need to shift from reactive to dynamic and continuous resource allocation for optimal, cost-effective utilization.

IBM Turbonomic is an AI- and automation-driven solution that dynamically allocates resources based on performance analysis and AI-driven recommendations. It supports application performance while helping clients address their compliance requirements, reduce cloud and infrastructure spending, and give more time back to engineering teams.

Ultimately, IBM Instana delivers real-time observability, while Turbonomic helps support application performance. While each of these solutions are incredibly powerful alone, they are even more valuable together.

That is why IBM is very excited to introduce the integration of IBM Instana Observability and IBM Turbonomic, available June 2023. Together, the integrated solutions offer comprehensive visibility and control of IT environments, enabling organizations to leverage real-time monitoring data for dynamic resource allocation decisions and AI-driven recommendations for optimal performance and cost reduction. The combination empowers organizations to optimize performance, address costs and streamline IT operations by tapping into the power of intelligent automation solutions to deliver business results.

Source: ibm.com

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IBM Hybrid Cloud Mesh: Reimagining multicloud networking with applications taking center stage

Private clouds, public clouds, SaaS, on-premises and edge—as organizations leverage a more distributed, robust cloud-based strategy, they can also face more significant management and compliance challenges. This shift to the cloud may have, in many ways, left the traditional enterprise network stranded—no longer transporting the bulk of the enterprise network traffic, which now floats between the clouds and over the public internet.

The probable result of this widely dispersed, distributed world? Application performance is no longer guaranteed, security could be affected and the skills needed for one cloud are not always easily transferable or available in another.

At the same time, for many companies, their applications are their business. Regardless of the cloud provider or where users sit, these applications require dependable, secured connectivity. That’s why it’s time for a new approach, driven by the applications themselves.

The new network paradigm: Application-centric connectivity

Yesterday, we launched IBM Hybrid Cloud Mesh, a multicloud networking solution. When it is generally available later this year, this new SaaS product is designed to allow organizations to establish simple and secured application-centric connectivity. This is engineered for network managers to seamlessly manage and scale network applications across a wide variety of public and private clouds, edge and on-premises.

This application-first approach is the next important networking paradigm. It’s also an evolution from the current “fat pipes” method (which doesn’t differentiate between applications) to one that aligns the network to the needs of the business, its users, and its developers, their CI/CD pipeline and DevOps cycles. When it’s time to configure new cloud networks and connect applications, our approach is designed to turn weeks into hours and move from manual to automated processes, with robust visibility into performance and minimized risk of IAM misconfigurations.

What this means for your networks

Given the complexity of today’s networking environments, we purposely designed Hybrid Cloud Mesh around four basic attributes:

◉ Simple: You’ll find a streamlined deployment process that enables automated workflows and simple network configuration that can be managed via CLI or an intuitive UI.

◉ Secured: Critical for today’s business, you’ll find zero-trust architecture and end-to-end encryption, along with segmentation and micro-segmentation.

◉ Scalable: Scalable to large enterprise environments, you also have the ability to scale resources based on demand.

◉ Seamless: Designed to reduce the barriers between clouds and teams, you can manage services across clouds, with on-demand, intent-driven application-centric connectivity.

Components of Hybrid Cloud Mesh

Two main architecture components are key to how the product is designed to work.

◉ Gateways, which act as virtual routers and connectors. These are centrally managed through Mesh Manager and deployed both in the cloud and on customer premises.

◉ The Mesh Manager provides the centralized management and control plane for Hybrid Cloud Mesh through a SaaS portal.

Both Gateways and the Mesh Manager are designed to communicate through a set of open, secured APIs and interfaces.

Hybrid Cloud Mesh is engineered to complement existing SD-WANs, service mesh and multicloud networking solutions. You’ll also find crucial benefits that can include the following:

◉ Auto-discovery of cloud infrastructure and applications using the Gateways described above, deployed next to applications both in the cloud and on customer premises.

◉ A single centralized management and control plane for your multicloud deployments and sites through Mesh Manager.

◉ Addressing silos between CloudOps and DevOps through automated workflows and a shared overlay, enabling rapid application deployment and optimization.

◉ A “network follows the application” paradigm that establishes application-level connectivity to streamline application migration to the cloud and moves the network wherever the application is placed.

◉ Zero-trust architecture that seamlessly enables end-to-end encryption across the network from application component to application component.

◉ Application network optimization with granular visibility and control of application-level connectivity. This is done by streamlining telemetry, root cause analysis and reconfiguration. It provides an intuitive overlay to help address performance issues and generate traffic engineering recommendations.

Source: ibm.com

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IBM Cloud Computing: Unlocking the Secrets to Seamless Scalability

In today's fast-paced digital landscape, businesses are constantly seeking ways to improve their online presence and gain a competitive edge. One crucial aspect of achieving success in the digital realm is harnessing the power of cloud computing. Among the many cloud providers, IBM Cloud stands out as a leading player, offering a robust and scalable solution that can unlock the secrets to seamless scalability. In this article, we delve into the world of IBM Cloud Computing and explore how it can help businesses achieve their goals.

Understanding IBM Cloud Computing

IBM Cloud Computing is a comprehensive platform that provides businesses with a range of services, tools, and resources to leverage the power of cloud technology. With its vast array of offerings, IBM Cloud enables organizations to build, deploy, and manage applications and services seamlessly.

Unmatched Scalability for Growing Businesses

One of the most significant advantages of IBM Cloud Computing is its unmatched scalability. Businesses today face the challenge of rapidly expanding their digital infrastructure to meet growing demands. IBM Cloud addresses this need by providing flexible and scalable solutions that can accommodate businesses of all sizes.

Elastic Scaling Made Easy

IBM Cloud offers a unique feature known as elastic scaling, which allows businesses to adjust their resources based on real-time demands. Whether it's an unexpected surge in website traffic or a sudden increase in user activity, IBM Cloud can effortlessly handle the workload. This ensures that businesses can provide a seamless user experience without worrying about infrastructure limitations.

High-Performance Computing

In addition to its scalability, IBM Cloud also excels in high-performance computing. With its advanced infrastructure, businesses can leverage powerful computing capabilities to execute complex tasks efficiently. Whether it's data analysis, machine learning, or running resource-intensive applications, IBM Cloud's high-performance computing ensures optimal performance and rapid results.

Key Benefits of IBM Cloud Computing

When it comes to choosing a cloud provider, businesses must evaluate the benefits and advantages offered. IBM Cloud Computing presents several key benefits that can make a significant difference in a company's digital transformation journey.

Enhanced Security and Reliability

Data security and reliability are paramount in the digital age, and IBM Cloud understands this critical aspect. With robust security measures, including encryption, access controls, and network protection, IBM Cloud ensures that businesses' sensitive data remains secure. Additionally, IBM Cloud's reliable infrastructure minimizes downtime, allowing businesses to operate seamlessly without disruptions.

Cost Efficiency and Savings

Cost efficiency is a crucial consideration for businesses of all sizes. IBM Cloud provides cost-effective solutions, enabling businesses to pay for what they use, eliminating the need for substantial upfront investments. With flexible pricing models and resource optimization, IBM Cloud helps businesses optimize their IT budgets and achieve significant cost savings.

Streamlined Collaboration and Integration

IBM Cloud offers a wide range of tools and services that promote collaboration and integration within organizations. With seamless integration capabilities, businesses can connect various systems, applications, and services to streamline their operations. This enables efficient data sharing, improves communication, and enhances overall productivity.

Global Presence and Accessibility

For businesses operating on a global scale, having a cloud provider with a robust and extensive network presence is crucial. IBM Cloud has a global footprint, ensuring that businesses can deploy their applications and services across multiple regions. This allows for better accessibility, reduced latency, and improved user experiences across different geographical locations.

Success Stories: How Businesses Thrive with IBM Cloud Computing

IBM Cloud Computing has empowered numerous businesses to achieve unparalleled success in their respective industries. Let's explore a few success stories that highlight the transformative impact of IBM Cloud.

Company A: Scaling with Confidence

Company A, a rapidly growing e-commerce platform, faced the challenge of accommodating a massive influx of users during peak shopping seasons. By leveraging IBM Cloud's elastic scaling capabilities, Company A seamlessly handled the surge in traffic without experiencing any performance issues or downtime. The ability to scale with confidence allowed Company A to deliver a flawless shopping experience to its customers, resulting in increased sales and customer satisfaction.

Company B: Accelerating Innovation

Company B, a technology startup, needed a robust and scalable infrastructure to support its innovative solutions. By leveraging IBM Cloud's high-performance computing capabilities, Company B was able to execute complex algorithms and simulations quickly. This accelerated their research and development processes, allowing them to bring new products to market faster than their competitors. The flexibility and power of IBM Cloud became a key factor in Company B's success and continued growth.

Company C: Securing Data with Confidence

Data security is a top concern for many businesses, especially those dealing with sensitive information. Company C, a financial services provider, turned to IBM Cloud for its advanced security features. With encryption, access controls, and regular security audits, IBM Cloud provided Company C with a secure environment to store and process critical financial data. This enhanced security gave Company C the confidence to meet regulatory requirements and gain the trust of its clients, positioning them as a leader in their industry.

How to Leverage IBM Cloud Computing for Your Business

Now that we've explored the benefits and success stories of IBM Cloud Computing, let's discuss how your business can leverage this powerful platform.

1. Assess Your Business Needs

Start by assessing your business requirements and identifying the areas where cloud computing can make a significant impact. Consider factors such as scalability, performance, security, and integration with existing systems. This analysis will help you determine which IBM Cloud services and solutions align best with your objectives.

2. Design a Cloud Strategy

Once you have a clear understanding of your business needs, design a comprehensive cloud strategy that outlines your goals, timelines, and resource allocation. Collaborate with your IT team or consult with experts to develop a customized plan that maximizes the benefits of IBM Cloud Computing for your organization.

3. Choose the Right IBM Cloud Services

IBM Cloud offers a wide range of services, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and more. Evaluate each service's features and capabilities to determine which ones are most relevant to your business requirements. Consider factors such as scalability, performance, security, and cost-effectiveness when selecting the appropriate IBM Cloud services.

4. Migrate and Optimize

Once you have chosen the right IBM Cloud services, develop a migration plan to smoothly transition your existing infrastructure and applications to the cloud. Leverage IBM Cloud's migration tools and resources to ensure a seamless and efficient migration process. After migration, continuously optimize your cloud environment to maximize performance, minimize costs, and adapt to evolving business needs.

5. Embrace Continuous Innovation

IBM Cloud Computing is a dynamic and evolving platform. Stay updated with the latest advancements, services, and features offered by IBM Cloud. Embrace continuous innovation by exploring new technologies such as AI, machine learning, and blockchain that can further enhance your business operations and drive competitive advantage.

Conclusion

In conclusion, IBM Cloud Computing is a powerful solution that can unlock the secrets to seamless scalability for businesses of all sizes. With its unmatched scalability, high-performance computing capabilities, enhanced security, and cost-efficiency, IBM Cloud empowers organizations to thrive in the digital landscape. By leveraging the benefits of IBM Cloud, businesses can accelerate innovation, streamline operations, and achieve unparalleled success. Embrace the power of IBM Cloud Computing and propel your business to new heights.

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How to achieve high-impact personalization at scale with managed marketing services

To help companies better deliver on their marketing vision and scale their marketing business processes, managed marketing services (MMS) offshore has become a fast growing trend. According to an Everest Group study, offshore MMS centers increased over 50% from 2019 to 2022. What’s more, the number of offshore full-time equivalent employees (FTE) jumped 115%.

What are managed marketing services (MMS)?

Managed marketing services are the outsourcing of marketing business processes, such as campaign planning and execution, content management and enhancement, marketing analytics and other marketing support (SEO, social listening and loyalty).

Traditionally, business processes tied to marketing are manual tasks completed in-house and onshore. These processes are becoming more complex as AI and automation eliminate the tasks that can be executed through technology, leaving only the most challenging work behind.

This complexity translates into higher costs and makes it difficult for marketing teams to keep up. In a recent IBM Institute for Business Value report, 55% of respondents reported challenges coordinating effectively with different teams within marketing.

For big brands across all industries, today’s marketing campaigns are customized for specific audiences. These campaigns often need to be tailored to meet requirements in specific regions, which means navigating various regulations and standards.

While core creative and strategy campaigns are kept primarily in-house, marketing leaders are turning to MMS and tapping into their business and technology expertise to accelerate digital marketing operations, bridge talent shortages and fill technology skill gaps.

IBM Consulting offers managed marketing services that help clients improve their customer journeys by bringing together strategy, experience, technology and operations. IBM’s cross-functional team of experts helps companies transform their marketing organizations by taking on marketing functions.

Benefits include improved market performance and reduced costs, revenue growth through deep customer understanding and actionable insights, and exceptional experiences for customers and marketers.

Leveraging MMS for personalization at scale

Personalization builds brand loyalty by creating customer experiences (CXs) specifically designed for each audience. This leads to stronger customer relationships, which leads to higher customer value. Today, a service or retail organization might have 20 to 30 campaigns delivered to different audiences across numerous modalities (spanning paid social, email, push notification, texts and more).

The act of delivering such personalized messaging is a complex challenge. Marketing teams struggle to deliver relevant content and communications at scale.

The main areas of opportunity for businesses who leverage MMS for personalization at scale include campaign support, content management, customer experience, and marketing and media support. These components come together to create end-to-end operations efficiency, experience optimization and data optimization.

1. Campaign Support

Managed marketing services for campaign support transform marketing organizations by removing complexity across people, processes and platforms to create intelligent workflows and drive more value. MMS for campaign support includes:

◉ Design and planning

◉ Execution

◉ Optimization

◉ Performance evaluation

2. Content management

Content management with MMS involves collecting and managing content so it can be personalized and deployed at scale. When effectively managed across an organization, content can be used to engage customers, automate business processes and enhance collaboration. MMS for content management includes:

◉ Content localization and optimization

◉ Content migration

◉ Content personalization

◉ Digital asset management

3. Customer experience

Great customer experiences are built on data-driven insights. Managed marketing services provide the hyper-personalization needed to build customer loyalty and reduce churn. MMS for customer experience includes:

◉ User experience strategy

◉ Journey mapping

◉ Segmentation

◉ Predictive modeling

◉ Social listening

◉ Customer data management

4. Marketing and media support

Leveraging state-of-the-art AI solutions ensures optimization of targets, channels, messages and actions.MMS support around data and analytics answers questions such as: Who clicked on an online ad? Did that action turn into a lead? Did the customer make a purchase? What time did this occur? All this data can be fed back into the campaign’s execution to continually improve its effectiveness. MMS for marketing and media support includes:

◉ Commerce cataloging and curation

◉ Social/mobile command center

◉ Channel optimization

Transforming the customer experience in financial services

Recently, a national financial services company was challenged with scaling its marketing operations to address sudden growth in campaign volume and complexity while maintaining superior member experience with no increase in budget. To carry out this transformation, the organization worked with IBM to redesign its operating model.

The IBM team provided consulting services to support operational and organizational alignment around journey-based marketing that dramatically changed engagement with customers, prospects and suspects. The overall transformation simplified processes and infused automation in creative and campaign execution.

Accelerating marketing performance

By executing MMS to achieve personalization at scale, businesses have seen great success. One retailer saw a 176% increase in online revenue from target segments, a 25% increase in online conversion rates and a 10% average increase in order size after personalizing their digital presence across touchpoints with IBM iX.

IBM’s managed marketing services provide a flexible framework that allows our clients to accelerate a shift to digital marketing and achieve personalization at scale. We do this by supplementing marketing operations to transform, automate or change workflows so businesses can innovate, keep up with evolving customer needs and stay ahead of the competition.

Source: ibm.com

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IBM to help businesses scale AI workloads, for all data, anywhere

IBM today announced the coming launch of IBM watsonx.data, a data store built on an open lakehouse architecture, to help enterprises easily unify and govern their structured and unstructured data, wherever it resides, for high-performance AI and analytics. The solution is currently in a closed beta phase and is expected to be generally available in July 2023.

What is watsonx.data?

Watsonx.data will be core to IBM’s coming AI and Data platform, IBM watsonx, announced today at IBM Think. With watsonx, IBM will launch a centralized AI development studio that gives businesses access to proprietary IBM and open-source foundation models, watsonx.data to gather and clean their data, and a toolkit for governance of AI.

Watsonx.data will allow users to access their data through a single point of entry and run multiple fit-for-purpose query engines across IT environments. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. It also offers built-in governance, automation and integrations with an organization’s existing databases and tools to simplify setup and user experience.

Supporting the data management life cycle

According to IDC’s Global StorageSphere, enterprise data stored in data centers will grow at a compound annual growth rate of 30% between 2021-2026. With increased data volumes comes increased data silos, operational costs, and regulatory pressures, which can lead to greater scrutiny and demand for improved business outcomes from data, analytics and AI investments.

This proliferation of data spans every industry, and organizations have an opportunity to turn it into actionable insights that can inform revenue strategies and enhance operational efficiencies.

“The media and entertainment industry has undergone a significant digital transformation, with viewers consuming content across different devices and platforms,” said Vitaly Tsivin, EVP Business Intelligence at AMC Networks. “Watsonx.data could allow us to easily access and analyze our expansive, distributed data to help extract actionable insights and maximize our resource utilization to deliver superior user experiences for viewers of AMC Networks’ curated, high-quality content.”

Notably, watsonx.data runs both on-premises and across multicloud environments. The solution will help businesses harness their increasingly siloed data and apply advanced AI and analytics to derive actionable insights, all while supporting robust data governance and observability throughout the data management life cycle.

Strong partnerships for even stronger solutions

Watsonx.data is engineered to use Intel’s built-in accelerators on Intel’s new 4th Gen Xeon Scalable Processors and open-source query engines such as Presto, the Velox acceleration library and Spark, to deliver rapid and reliable data processing for high performance SQL querying, reporting, business intelligence, and machine learning.

“We recognize the importance of watsonx.data and the development of the open-source components that it’s built upon,” said Das Kamhout, VP and Senior Principal Engineer of the Cloud and Enterprise Solutions Group at Intel. “We look forward to partnering with IBM to optimize the watsonx.data stack, achieving breakthrough performance through our joint technological contributions to the Presto open-source community.”

IBM and Intel have a long history of collaboration on data and AI products, including the optimization of IBM Db2 on Intel Xeon platforms, AI acceleration with IBM Watson NLP Library for Embed with OneAPI, and now watsonx.data.

Watsonx.data will allow users to modernize their data repositories with data warehouse-like capabilities, while benefiting from low-cost object storage and open data and table formats like Iceberg, to help them make data-driven decisions.

“Open data lakehouse architectures powered by the Apache Iceberg table format give organizations the flexibility to use fit-for-purpose analytical solutions to future-proof their data platforms for all workloads,” said Paul Codding, EVP of Product Management of Cloudera. “IBM and Cloudera customers will benefit from a truly open and interoperable hybrid data platform that fuels and accelerates the adoption of AI across an ever-increasing range of use cases and business processes.”

IBM and Cloudera have a long-standing strategic partnership that includes certified product integrations and joint sales and support models.

Wasonx.data will be available on premises and across multiple cloud providers, including IBM Cloud and Amazon Web Services (AWS). This builds on last year’s announcement of IBM expanding their relationship with AWS to offer IBM software as a service on AWS. The solution will also be available in AWS Marketplace.

“Organizations are increasingly adopting data lakehouse solutions to support their growing data needs, especially as we see an industry-wide shift toward AI solutions,” said Soo Lee, Director Worldwide Strategic Alliances at AWS. “Making watsonx.data available as a service in AWS Marketplace further supports our customers’ increasing needs around hybrid cloud – giving them greater flexibility to run their business processes wherever they are, while providing choice of a wide range of AWS services and IBM cloud native software attuned to their unique requirements.”

The coming launch of watsonx.data will extend IBM’s market leadership in data and AI, most recently demonstrated by its evaluation as a leader in The Forrester Wave: Data Management for Analytics, by integrating with existing IBM solutions like StepZen, Databand.ai, IBM Watson Knowledge Catalog, IBM zSystems, IBM Watson Studio, and IBM Cognos Analytics with Watson. These integrations can enable watsonx.data users to implement various industry-leading data catalog, lineage, governance, and observability solutions across their data ecosystems.

Beyond launch, watsonx.data is expected to undergo continuous development, incorporating the latest performance enhancements to the Presto open-source query engine via Velox and through IBM’s recent acquisition of Ahana, the only SaaS for Presto and a strong contributor to the Presto open-source community. Further development of watsonx.data will also incorporate IBM’s Storage Fusion technology to enhance data caching across remote sources as well as semantic automation capabilities built on IBM Research’s foundation models to automate data discovery, exploration, and enrichment through conversational user experiences.

Source: ibm.com

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IBM Watson Orchestrate: Unlocking new levels of productivity for every employee

The world is changing faster than ever, and the way we work needs to keep up with the possibilities that new technologies bring to our day-to-day work. Companies that want to stay competitive need to help their employees quickly build new skill sets and adapt to changing market conditions.

Read More: C9560-680: IBM Control Desk V7.6 Fundamentals

Workforce demographic trends compel us to reimagine how we work. A large share of the worker population—Millennials and Generation Z—grew up using technology in their day-to-day lives, and they expect the tech they use at work to function the same as it does in their personal lives.

Those businesses that do not adapt may be overly exposed to worker shortages and productivity. An analysis from the U.S. Bureau of Labor Statistics shows that the U.S. labor force participation rate is projected to decline from 61.7% in 2021 to 60.1 % in 2031, fueled by the aging population trend reflected across the world.

Sport Clips Haircuts reimagines talent acquisition

Sport Clips Haircuts—a leading hair salon with almost 1,900 stores in the U.S.—recognizes the modern-day challenges of staffing and employee retention. CEO Edward Logan has put technology at the forefront of the company and considers AI and automation to be a great way to support franchise owners, which is a top priority for the company.

Customer demand is high and every chair without a stylist at Sport Clips is a lost opportunity to make a customer happy. However, recruiting for the stylist role and finding the right talent can be challenging. Sport Clips wanted to help reduce the recruiting burden on franchise owners by expanding their qualified talent pool and streamlining outreach to passive candidates, which led them to reach out to IBM and ThisWay Global—a candidate sourcing and matching platform with an expansive network of over 8,500 communities.

Franchise owners were onboarded to the solutions in under an hour and now have the ability to manage their end-to-end recruiting process through a single user interface. Through chat-style interactions, users are able to initiate automations (known as skills), including the following:

◉ Create a new job requisition from commonly posted positions.

◉ Socialize the job listing on job boards like Indeed and LinkedIn (paid subscriptions may be required).

◉ Identify qualified passive candidates using ThisWay Global and email up to 300 candidates within minutes to invite them to apply.

“Driving transformation for our franchisee owners is a top priority. Can we make a process easier, faster and with fewer errors to help them be successful? With IBM Watson Orchestrate, we streamlined passive candidate outreach. What used to take three hours can now be done in just a few minutes,” said Edward Logan, CEO & President of Sport Clips Haircuts.

Introducing IBM Watson Orchestrate Enterprise Edition

IBM Watson Orchestrate is a cloud-based solution that helps companies empower their people to change the nature of their day-to-day work. The new Enterprise Edition includes an expanded set of skills and a new ability to import existing automations, including those from IBM Robotic Process Automation. The release introduces digression—the ability for Watson to multi-task so it can handle multiple requests at the same time.

Get work done quickly with pre-built skills

Skills are foundational to the Watson Orchestrate platform—think of them as units of automation. It can be as simple as adding a row to Excel or as complex as onboarding a new employee with the many tasks involved— collecting I-9 information, ordering a new computer and even setting up a welcome meeting with the team. The new Watson Orchestrate Enterprise Edition offers over 30 new pre-built/pre-trained skills for SAP SuccessFactors, Oracle HCM and Workday.

Reuse existing automations

IBM Watson Orchestrate can help you get the most out of your prior automation investments. Easily import existing automations that use the OpenAPI specification. These automations can be from IBM or other third-party vendors. After importing, developers can train skills and define natural language utterance phrases quickly.

Support more use cases with IBM Robotic Process Automation

IBM Watson Orchestrate Enterprise Edition comes with entitlements to IBM Robotic Process Automation (RPA) to facilitate automating unique use cases across your organization. Watson can sequence your custom skills with pre-built flows or create new workflows dynamically.

How many things can you do at once?

Multi-tasking—it’s essential in today’s work environment. To truly transform work, we need tools that can keep up. Watson can now work multiple requests in parallel. For example, a recruiter can ask Watson to schedule interviews and then continue work with Watson while the task is being worked in the background.

Beyond the chatbot

IBM Watson Orchestrate isn’t a chatbot, which typically executes dialog trees that terminate when the user response cannot be found in the tree. Watson has an advanced natural language processor to understand intent, break down requests and guide you through a dynamically generated sequence of steps to complete a task. If Watson needs more information or to clarify ambiguity, it asks. When unable to find a skill, Watson guides users to find a new skill or import an existing skill to complete that task.

Source: ibm.com

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The risks and limitations of AI in insurance

Artificial intelligence (AI) is polarizing. It excites the futurist and engenders trepidation in the conservative. In my previous post, I described the different capabilities of both discriminative and generative AI, and sketched a world of opportunities where AI changes the way that insurers and insured would interact. This blog continues the discussion, now investigating the risks of adopting AI and proposes measures for a safe and judicious response to adopting AI.

Risk and limitations of AI

The risk associated with the adoption of AI in insurance can be separated broadly into two categories—technological and usage.

Technological risk—data confidentiality

The chief technological risk is the matter of data confidentiality. AI development has enabled the collection, storage, and processing of information on an unprecedented scale, thereby becoming extremely easy to identify, analyze, and use personal data at low cost without the consent of others. The risk of privacy leakage from interaction with AI technologies is a major source of consumer concern and mistrust.

The advent of generative AI, where the AI manipulates your data to create new content, provides an additional risk to corporate data confidentiality. For example, feeding a generative AI system such as Chat GPT with corporate data to produce a summary of confidential corporate research would mean that a data footprint would be indelibly left on the external cloud server of the AI and accessible to queries from competitors.

Technological risk—security

AI algorithms are the parameters that optimizes the training data that gives the AI its ability to give insights. Should the parameters of an algorithm be leaked, a third party may be able to copy the model, causing economic and intellectual property loss to the owner of the model. Additionally, should the parameters of the AI algorithm model may be modified illegally by a cyber attacker, it will cause the performance deterioration of the AI model and lead to undesirable consequences.

Technological risk—transparency

The black-box characteristic of AI systems, especially generative AI, renders the decision process of AI algorithms hard to understand. Crucially, the insurance sector is a financially regulated industry where the transparency, explainability and auditability of algorithms is of key importance to the regulator.

Usage risk—inaccuracy

The performance of an AI system heavily depends on the data from which it learns. If an AI system is trained on inaccurate, biased, or plagiarized data, it will provide undesirable results even if it is technically well-designed.

Usage risk—abuse

Though an AI system may be operating correctly in its analysis, decision-making, coordination, and other activities, it still has the risk of abuse. The operator use purpose, use method, use range, and so on, could be perverted or deviated, and meant to cause adverse effects. One example of this is facial recognition being used for the illegal tracking of people’s movement.

Usage risk—over-reliance

Over-reliance on AI occurs when users start accepting incorrect AI recommendations—making errors of commission. Users have difficulty determining appropriate levels of trust because they lack awareness of what the AI can do, how well it can perform, or how it works. A corollary to this risk is the weakened skill development of the AI user. For instance, a claims adjuster whose ability to handle new situations, or consider multiple perspectives, is deteriorated or restricted to only cases to which the AI also has access.

Mitigating the AI risks

The risks posed by AI adoption highlights the need to develop a governance approach to mitigate the technical and usage risk that comes from adopting AI.

Human-centric governance

To mitigate the usage risk a three-pronged approach is proposed:

1. Start with a training program to create mandatory awareness for staff involved in developing, selecting, or using AI tools to ensure alignment with expectations.

2. Then conduct a vendor assessment scheme to assess robustness of vendor controls and ensure appropriate transparency codified in contracts.

3. Finally, establish policy enforcement measure to set the norms, roles and accountabilities, approval processes, and maintenance guidelines across AI development lifecycles.

Technology-centric governance

To mitigate the technological risk, the IT governance should be expanded to account for the following:

1. An expanded data and system taxonomy. This is to ensure the AI model captures data inputs and usage patterns, required validations and testing cycles, and expected outputs. You should host the model on internal servers.

2. A risk register, to quantify the magnitude of impact, level of vulnerability, and extent of monitoring protocols.

3. An enlarged analytics and testing strategy to execute testing on a regular basis to monitor risk issues that related to AI system inputs, outputs, and model components.

AI in insurance—Exacting and inevitable

AI’s promise and potential in insurance lies in its ability to derive novel insights from ever larger and more complex actuarial and claims datasets. These datasets, combined with behavioral and ecological data, creates the potential for AI systems querying databases to draw erroneous data inferences, portending to real-world insurance consequences.

Efficient and accurate AI requires fastidious data science. It requires careful curation of knowledge representations in database, decomposition of data matrices to reduce dimensionality, and pre-processing of datasets to mitigate the confounding effects of missing, redundant and outlier data. Insurance AI users must be aware that input data quality limitations have insurance implications, potentially reducing actuarial analytic model accuracy. 

As AI technologies continues to mature and use cases expand, insurers should not shy from the technology. But insurers should contribute their insurance domain expertise to AI technologies development. Their ability to inform input data provenance and ensure data quality will contribute towards a safe and controlled application of AI to the insurance industry.

As you embark on your journey to AI in insurance, explore and create insurance cases. Above all, put in a robust AI governance program.

Source: ibm.com

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What policymakers need to know about foundation models

The last few years—even the last few months—have seen artificial intelligence (AI) breakthroughs come at a dizzying pace. AI that can generate paragraphs of text as well as a human, create realistic imagery and video from text, or perform hundreds of different tasks has captured the public’s attention. People see AI’s high level of performance, creative potential and, in some cases, the ability for anyone to use them with little to no technical expertise. This wave of AI is attributable to what are known as foundation models.

What are foundation models?

As the name suggests, foundation models can be the foundation for many kinds of AI systems. Using machine learning techniques, these models apply information learned about one situation to another situation. While the amount of data required is considerably more than the average person needs to transfer understanding from one task to another, the result is relatively similar. For example, once you spend enough time learning how to cook, without too much effort you can figure out how to cook almost any dish, and even invent new ones.

This wave of AI looks to replace the task-specific models that have dominated the landscape. And the potential benefits of foundation models to the economy and society are vast. For example, identifying candidate molecules for novel drugs or identifying suitable materials for new battery technologies requires sophisticated knowledge about chemistry and time-intensive screening and evaluation of different molecules. IBM’s MoLFormer-XL, a foundation model trained on data about 1.1 billion molecules, helps scientists rapidly predict the 3D structure of molecules and infer their physical properties, such as their ability to cross the blood-brain barrier. IBM recently announced a partnership with Moderna to use MoLFormer models to help design better mRNA medicines. IBM also partners with NASA to analyze geospatial satellite data—to better inform efforts to fight climate change—using foundation models.

However, there are also concerns about their potential to cause harm in new or unforeseen ways. Some risks of using foundation models are like those of other kinds of AI, like risks related to bias. But they can also pose new risks and amplify existing risks, such as hallucination, the capability of generation of false yet plausible-seeming content. These concerns are prompting the public and policymakers to question whether existing regulatory frameworks can protect against these potential harms.

What should policymakers do?

Policymakers should take productive steps to address these concerns, recognizing that a risk and context-based approach to AI regulation remains the most effective strategy to minimize the risks of all AI, including those posed by foundation models.

The best way policymakers can meaningfully address concerns related to foundation models is to ensure any AI policy framework is risk-based and appropriately focused on the deployers of AI systems. Read the IBM Policy Lab’s A Policymaker’s Guide to Foundation Models—a new white paper from us, IBM’s Chief Privacy & Trust Officer Christina Montgomery, AI Ethics Global Leader Francesca Rossi, and IBM Policy Lab Senior Fellow Joshua New—to understand why IBM is asking policymakers to:

1. Promote transparency

2. Leverage flexible approaches

3. Differentiate between different kinds of business models

4. Carefully study emerging risks

Given the incredible benefits of foundation models, effectively protecting the economy and society from its potential risks will help to ensure that the technology is a force for good. Policymakers should swiftly act to better understand and mitigate the risks of foundation models while still ensuring the approach to governing AI remains risk-based and technology neutral. 

Source: ibm.com

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Is Artificial Intelligence relevant to Insurance?

I love the game of chess and was shocked when IBM’s Deep Blue chess-playing machine defeated the world chess champion in 1997. That ground-shaking event divided the world with excitement and trepidation about a future with thinking machines. In this first of two posts, I investigate the anatomy of artificial intelligence and its impact on insurance.

The fascination of artificial intelligence

History has shown that the human preoccupation with creating a machine which replicates human thinking had in fact been simmering for centuries. In the late 18th century, The Turk, a chess playing machine captured the attention of the world. It turned out to be a fraud, with a human player behind the machine. In 1847, George Boole first described a formal language for logic reasoning and in 1936, Alan Turing described the Turing machine.

Along with the explosion of information technology in the 1950’s, the term ‘artificial intelligence’ (AI) was coined by John McCarthy in 1956. After the success of Deep Blue, IBM again made the headlines with IBM Watson, an AI system capable of answering questions posed in natural language, when it won the quiz show Jeopardy against human champions. Continued advancement in AI development has resulted today in a definition of AI which has several categories and characteristics.

The early versions of AI were capable of predictive modelling (e.g., recommending similar Netflix shows based on your previous choices) or robotics (e.g., developing a distance map of objects around a vehicle to enable semi-autonomous driving). Soon after, AI’s capabilities extended to Speech and Natural Language processing, such as with IBM Watson, and for Image Recognition, which is now ubiquitously used for unlocking phones and other biometric security. The four categories of predictive modelling, robotics, speech and image recognition are collectively known as algorithm-based AI or Discriminative AI. It represents AI that can sift through data and divide them into classes (of attributes) by learning the boundaries. It is used to return a prediction or result based on conditional probability.

Emergence of Generative AI

More recently, a new category of AI has emerged to stir the imagination (and fear) of humankind. Generative AI is artificial intelligence that can create new content. It has taken the world by storm. ChatGPT acquired 100M users within two months of launch. Google, Microsoft, Snapchat, and Salesforce release rival products shortly after. Academia is in an uproar over originality of authorship, and governments have even started to outlaw its general use.

Whilst Discriminative AI sought to classify data by its attributes to recognize an object, Generative AI seeks to map the distribution of attributes on examples and manipulate those attributes to create new examples. This ability to manipulate attributes and create new examples has added a new dimension to AI—creativity.

Artificial intelligence applied to insurance

The insurance industry has always made extensive use of data and algorithms, such as in the calculation of insurance premiums. The insurance business model itself is predicated on the use of mathematical and statistical methods to process personal and non-personal data to underwrite risks and price insurance policies, to quantify losses, to pay customers’ claims, and to identify and prevent insurance fraud. The impact of AI, both Discriminative and Generative, has immediate and long-term effects on the business of insurance.

The deployment of AI can help insurers in multiple aspects, from underwriting, to claims, customer service and fraud prevention. Below are some typical use cases and demonstrates the primary impact on the automation of internal processes and on improved customer service.

Customer service and conversational AI

This is an area where insurers are most advanced in its early adoption of AI. Conversational AI, based on natural language processing, can interpret spoken and written human language and respond accordingly. It offers customers and the insurer’s system to interact in a human-like manner. Chatbots and voice assistants are already offering round the clock service whilst maintaining quality of service. We will continue to see more advanced and specialised conversational AI developed to handle more complex dialogue particularly in claims handling. Generative AI will make the conversations more expedient and relevant.

Claims automation

AI tools in the claims handling process can expedite the handling of claims and lead to faster settlement. AI’s Image recognition can automatically read, interpret, and process documents and images (e.g., extracting information from medical records, recognise vehicle types or evaluate damage). By collecting large amounts of historical data, Discriminative AI can be used to make plausibility assessments and ensure quality and uniformity in the adjusting process. Complimentarily, Generative AI will be able to help the adjustor summarise the data and generate a preliminary report.

Fraud detection

AI can be used to analyse large amounts of data from multiple sources to spot unusual patterns as indication of fraud. Pattern recognition on vehicle damage data can be used to detect cases of fraud. It can also detect manipulated images that would raise suspicion.

Pricing and underwriting

AI offers new possibilities in the pricing and product design of insurers. With the combination of data, new risk characteristics can be developed to provide more accurate insurance cover. With the willingness and consent to share one’s private data, products can be tailored more precisely for each customer.

For example, the increasing availability of medical data, in combination with medical progress now makes it possible to offer term life insurance for people with serious pre-existing conditions. Leveraging Generative AI’s ability, a unique and personal life insurance policy can be underwritten with contributions from personal medical data. Beyond medical data, other public data such as meteorological data, using AI’s ability to process large data is having effect on property insurance.

The road ahead

The use of AI in the insurance industry today is still nascent. AI is still an emerging technology and the road to implementation will have challenges. However, the use of AI in society is becoming prevalent. Insurers must adopt AI to stay relevant to their customers and draw down on the cost saving benefits of adopting AI in the near term. Ignoring AI is costly. Take a step towards adopting AI. List down your ideas for how AI can improve the way insurance is managed.

Source: ibm.com

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