Optimizing finished vehicle logistics with blockchain solutions

When a customer orders a product on an ecommerce site, through quick commerce or through a traditional courier service, they receive an update of each action taken to deliver that product. This includes basic status updates such as shipped, in transit, reached destination, out for delivery and delivered.

Service providers manually update some of these statuses, and some updates are enabled through technology by using devices such as GPS trackers, RFIDs and sensors.

But with all the technological advances in the transportation of goods, one area that lags behind is the automotive logistics industry.

Challenges of finished vehicle logistics

Finished vehicle logistics in the automotive industry typically involves moving vehicles from the assembly plant to the National Sales Organization (NSO), then to dealerships or to large fleet operators. The multimodal transportation is done via road, rail and sea. Several stakeholders and processes are involved as vehicles move from the original equipment manufacturers (OEMs) factory or compound to their customer’s destinations, resulting in a plethora of challenges as listed after this:

• Limited customer visibility of Estimated time of Arrival (ETA): Unlike e-commerce, automotive end customers generally lack visibility into the delivery process after placing an order with the dealer. Customers usually rely on verbal confirmation from the dealer regarding the probable delivery date.
• Lack of route optimization: Delivering vehicles within stipulated timelines while keeping logistics costs low requires route optimization. Logistics service providers (LSPs) do not always take the most optimized route, and unexpected events (such as the Suez Canal blockage and weather impacts) can complicate matters further. Route intelligence is crucial for deciding the best possible route or mode of transportation based on delivery timelines.
• Damage during transit: Many vehicles are damaged during transit each year. Identifying the stakeholder responsible for a particular damage leads to accountability and timely resolution. Indirect implications include extra logistics and handling costs to manage the damaged vehicles. 
• Insurance claims management: OEMs must file insurance claims in case of damages, which involves providing relevant documentation and capturing evidence of activities as the vehicle changes custody among stakeholders.
• Liability for damages: Identifying the party responsible for any damage during transit is important to fix liability.
• Delivery delays: Transit damages can delay delivery to customers. Proper evaluation of the vehicle’s condition is necessary to decide whether it can be delivered or if a new vehicle must be ordered.
• OEM losses: OEMs suffer both reputational and monetary losses due to damages during transit.

Building collaborative stakeholder networks for digital transformation

A blockchain-based automotive logistics platform can address the issues associated with finished vehicle logistics. Stakeholders on the blockchain platform can collaborate to track and trace where a vehicle is located at any specific point in time. They can also connect any relevant document to a vehicle, and upload or retrieve documentation and events of the vehicle throughout its journey in the supply chain.

The solution can provide near real-time actionable supply chain information for all contributors in the network (including OEMs, LSPs, warehouse operators, compounds, dealers and insurers). These network contributors collectively generate value by analyzing various factors, such as production locations and target markets and determine the most efficient and cost-effective routes.

Real-time actionable insights can include vehicle registration, creation of transport orders, warehouse orders, instructions that LSPs provide to sub-contractors among others. They can also encompass consignment notes, SalesOrder and events related to transport such as pickup or drop off. Further aspects include warehouse events such as gate-in, gate-out, ready for pickup, commercial events and damage events like submission of damage events, damage reports or cost estimates for damage.

Using insights to optimize, track and protect the transportation process

Stakeholders can tie these insights to documents associated with the shipping, such as contractual documents, bill of lading, inspection reports and various other evidence to fix liabilities in case of damage. Digitizing customs documents and tying them to a single source (such as a vehicle) expedites the custom clearance process and can reduce the delivery timelines.

The blockchain-based solution makes it possible to calculate ETA with real-time actionable insights from multiple stakeholders. This can be done by factoring in vehicle location, traffic, historical data, weather conditions, vehicle and driver performance, distance and speed calculations, and any other available. Vehicle tracking is possible through various technologies such as GPS, IoT sensors and RFID and telematics.

OEMs and LSPs can optimize the transportation process by planning the movement of the vehicles from manufacturing plants to various markets to meet delivery schedules and cost requirements.

LSPs can adjust the routes based on the trade lanes and the associated transportation legs in the case of any route deviations due to external factors. These external factors include adverse weather conditions, traffic conditions, political or social unrest, infrastructure issues or any other supply chain disruptions. Pre-defined trade lanes can help in identifying the next best route that is available from the current location in case of disruptions.

LSPs can inform truck drivers and other personnel of their responsibility to inspect and report the vehicle status before loading, during transit and upon delivery. Inspection reports document the condition of the vehicle to identify when and where damages occurred.

They can provide photographic evidence in case of any damage in real time. Users can store and publish evidence over a blockchain network for transparency across all involved stakeholders. This gives the insurer solid, accurate evidence.

Bringing the blockchain solution to life

Blockchain satisfies the key requirements of transparency and data sharing among stakeholders. Blockchain’s distributed ledger technology allows network participants to create a single source of truth for everything that happens to a vehicle throughout the supply chain, and that data is immutable.

Car buyers know exactly where their car is in the delivery cycle. OEMs can assess progress and act in case of any financial or operational challenge. Meanwhile, LSPs optimize routes, reduce customs clearance times, and increase efficiency and accuracy. Dealers plan vehicle inventories to meet customer needs, and insurers accurately assess claims and the extent of damage, leading to faster settlement and a reduction in fraudulent claims.

The blockchain solution builds an ecosystem that is accessible to all the players in the finished vehicles logistics space. Because of the integrity and security that blockchain provides, partners can work collectively to provide end-to-end visibility to the automotive logistics industry.

Source: ibm.com

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Hybrid cloud success: The role of Red Hat OpenShift Virtualization

Many organizations have aligned their technology strategy to achieve business success, but they recently encountered a disruption in their plans amidst the ever-changing portfolio after VMware’s acquisition by Broadcom. This disruption has caused an uproar in the IT industry and led to mass confusion with large product modifications, licensing changes and financial implications. IBM Consulting® recognizes that organizations have many options for transformational modifications to revise their technology strategy, and we are here to help.

Clients must prioritize productivity, scalability and efficiency to stay ahead of the competition. Red Hat® OpenShift® Virtualization is leading the industry in providing the ideal platform to meet these demands. IBM Consulting, along with Red Hat, can craft the correct solution to update a client’s technology strategy with the preeminent products and services to meet or exceed business goals. With deep expertise in hybrid cloud transformations, IBM Consulting offers guidance that can elevate the technology strategy across any major cloud provider by using the power of Red Hat OpenShift.

In this blog, we explore the benefits of Red Hat OpenShift Virtualization and how it is revolutionizing the way our clients operate.

What is Red Hat OpenShift Virtualization?

Red Hat OpenShift Virtualization is an innovative technology that provides a modern application platform to host new and existing virtual machines alongside containers. It also comes prebuilt with key capabilities for easier migration and management of traditional virtual machines on a comprehensive hybrid cloud platform. Red Hat OpenShift Virtualization is included in a Red Hat OpenShift subscription and is quickly deployed as an operator.

Technological advantages of Red Hat OpenShift Virtualization

1. Improved productivity:

Red Hat OpenShift Virtualization streamlines the delivery of virtual machines through an innovative approach that uses DevOps pipelines. These pipelines can be used to deliver containers as well, giving developers a common set of tools and runtimes for building and deploying enterprise-grade software. Having containers and virtual machines on the same platform provides a consistent environment across the hybrid cloud, reducing the skills and time needed by operations teams for management and maintenance.

Red Hat OpenShift Virtualization also allows Windows and Linux® virtual machines to run side by side and includes unlimited Red Hat Enterprise Linux (RHEL) subscriptions. Having virtual machines running on Red Hat OpenShift Virtualization allows for gradual migration to cloud-native applications that uses the supremacy of containerization and orchestration on Red Hat OpenShift. This migration culminates in improved efficiency, lower operational costs and increased overall productivity.

2. Enhanced scalability:

With Red Hat OpenShift Virtualization, organizations can scale their infrastructure environments as needed on standard x86 hardware, often without requiring an expensive hardware refresh. There is also the option to deploy the infrastructure on public clouds and even take advantage of a managed environment, such as Red Hat OpenShift on AWS (ROSA). This flexibility allows businesses to quickly adjust to new opportunities and changing market demands.

3. Superior efficiency:

Red Hat OpenShift Virtualization provides a single, unified platform for developers, operators and administrators to collaborate on container and virtual machine development and deployment, streamlining the process and improving communication. It delivers a faster time to market by including prebuilt components such as monitoring, log aggregation, service mesh and pipelines, which improve developer and operations productivity. Security features are also included to ensure that applications and virtual machines are protected from data breaches and unauthorized access. Together, these capabilities create a platform that reduces the need for manual configuration and minimizes downtime, which in turn decreases the total cost of ownership.

Evolve your digital transformation journey

Red Hat OpenShift Virtualization is a powerful platform that helps organizations evolve their digital transformation journey and provide a consistent environment for their hybrid cloud strategy. IBM Consulting supports these efforts with its vast experience in assisting clients through hybrid-by-design journeys. This support is possible due to strong Red Hat OpenShift Virtualization capabilities and strong ecosystem partners such as Amazon Web Services (AWS), Azure, IBM Cloud®, Google Cloud Platform (GCP) and Oracle Cloud Infrastructure (OCI).

Together, Red Hat OpenShift Virtualization and IBM Consulting can drive business success by improving productivity, enhancing scalability and providing superior efficiency aligned to application and infrastructure modernization goals.

Source: ibm.com

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The power of embracing distributed hybrid infrastructure

Data is the greatest asset to help organizations improve decision-making, fuel growth and boost competitiveness in the marketplace. But today’s organizations face the challenge of managing vast amounts of data across multiple environments.

This is why understanding the uniqueness of your IT processes, workloads and applications demands a workload placement strategy based on key factors such as the type of data, necessary compute capacity and performance needed and meeting your regulatory security and compliance requirements.

While hybrid cloud has become the dominant IT architecture, we believe that adopting an intentional hybrid-by-design approach is pivotal for enterprises to use their data irrespective of where it resides to further drive business value and outcomes with the combined power of hybrid cloud and AI. A distributed hybrid infrastructure provides the flexibility and agility to deploy and operate workloads and applications wherever needed. This allows for reliable and secured cloud-connected experiences that pave way for speedy innovation with IT environments designed to be both open and continuous.

Harnessing IBM Power as-a-service in distributed infrastructure

Clients that are furthest along in their hybrid cloud journey have well-thought-out, hybrid-by-design strategies. Not only are they making intentional workload placement decisions, are also designing an infrastructure with interoperability and security at the forefront. We are helping our clients modernize workloads and infrastructure with a hybrid cloud experience.

IBM® Power® Virtual Server, for example, can help clients expand their on-premises servers to modern-day hybrid-cloud infrastructures. Within a distributed hybrid environment, IBM Power Virtual Server is designed to help clients quickly adopt and expand their on-premises infrastructures both efficiently and economically at any moment to remain competitive in the marketplace. Its validation under the IBM Cloud Framework for Financial Services® also ensures compliance with stringent industry standards, making it particularly valuable for regulated sectors. A great example of this would be our client Safeguards CS Sdn Bhd (SCS), a cash solution services provider in Malaysia. By optimizing costs and maintaining robust security, this approach is designed to support billions of daily banking transactions across Southeast Asia, highlighting the platform’s critical role in expanding financial services to underserved populations.

Further, to provide clients with an additional choice of where to use IBM Power, IBM recently released IBM Power Virtual Server Private Cloud, which combines configurable compute, storage and network infrastructure within your data center, owned and managed by IBM on IBM Cloud®. This setup provides enterprises the consumption and management capabilities of the cloud while the data remains on premises to help clients address their regional compliance and governance requirements of the business.

A path forward: Power through an XaaS lens

The future of cloud computing lies in adopting distributed hybrid infrastructure, bolstered by the XaaS model, which promotes agility, reliability and security. This approach is designed so that businesses can modernize applications, enhance data management and optimize IT operations, paving the way for a more resilient and cost-effective IT landscape. IBM Power Virtual Server stands at the forefront of this transformation, offering innovative XaaS solutions to meet the diverse needs of modern enterprises.

Source: ibm.com

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Seamless cloud migration and modernization: overcoming common challenges with generative AI assets and innovative commercial models

As organizations continue to adopt cloud-based services, it’s more pressing to migrate and modernize infrastructure, applications and data to the cloud to stay competitive. Traditional migration and modernization approach often involve manual processes, leading to increased costs, delayed time-to-value and increased risk.

Cloud migration and modernization can be complex and time-consuming processes that come with unique challenges; meanwhile there are many benefits to gen AI assets and assistants and innovative commercial models. Cloud Migration and Modernization Factory from IBM Consulting® can also help organizations overcome common migration and modernization challenges and achieve a faster, more efficient and more cost-effective migration and modernization experience.

Leveraging the same technologies that are driving market change, IBM Consulting can deliver value at the speed that tomorrow’s enterprises need today. This transformation starts with a new relationship between consultants and code—one that can help deliver solutions and value more quickly, repeatably and cost efficiently. 

The power of gen AI assets and assistants

Gen AI assets and assistants are revolutionizing the cloud migration and modernization landscape, which offer a more efficient, automated and cost-effective way to overcome common migration challenges. These tools leverage machine learning and artificial intelligence to automate manual processes, reducing the need for human intervention and minimizing the risk of errors and rework.

IBM Consulting Assistants are a library of role-based AI assistants that are trained on IBM proprietary data to support key consulting project roles and tasks. Accessed through a conversation-based interface, we’ve democratized the way consultants use assistants, creating an experience where our people can find, create and continually refine assistants to meet the needs of our clients faster.

IBM Consulting Assistants allow our consultants to select from the models that best solve your business challenge.  Those models are packaged with pre-engineered prompts and output formats so our people can get tailored outputs to their queries, such as creating a detailed user persona or a code for a specific language and function. The result is that you get more valuable work, faster.  

Innovative commercial models for migration and modernization

Our innovative commercial models, such as our cloud migration services, offer a flexible and cost-effective way to migrate and modernize applications and data to the cloud. Our pricing models are designed to help organizations reduce costs and increase ROI, while also promoting a smooth and successful migration experience.

Cloud Migration and Modernization Factory from IBM Consulting

As a leading provider of hybrid cloud transformation services, IBM has extensive expertise in helping organizations overcome common migration and modernization challenges. Our experts have developed gen AI tools and innovative commercial models to ensure successful cloud migration and modernization.

The Cloud Migration and Modernization Factory from IBM Consulting enables clients to realize business value faster by leveraging pre-built migration patterns and automated migration approaches. This means that organizations can achieve faster deployment and ramp-up, getting to market faster and realizing business benefits sooner.

With Cloud Migration and Modernization from IBM Consulting, clients can achieve:

• Faster business value realization: The Cloud Migration and Modernization Factory from IBM Consulting accelerates business value realization by leveraging pre-built migration patterns and automated approaches. This enables organizations to deploy and ramp-up faster, getting to market sooner and realizing benefits earlier.   
• Scaled automation: The Cloud Migration and Modernization Factory from IBM Consulting leverages cloud-based metrics and KPIs to enable scaled automation, ensuring consistent quality and outcomes across multiple migrations. Automated approaches reduce the risk of human error, manual testing and validation, which result in improved efficiency, quality and ROI.
• Improved efficiency and quality of outcomes: By leveraging our gen AI assets, clients can automate the migration and modernization process, reducing manual effort and minimizing errors. The IBM Consulting Cloud Migration and Modernization Factory offers a library of pre-built migration patterns, allowing clients to choose the right approach for their specific needs and use cases.
• Cost savings: The Cloud Migration and Modernization Factory from IBM Consulting reduces the total cost of ownership and increases ROI by leveraging pre-built migration patterns and automated approaches, minimizing manual effort and errors.

Overcome common migration challenges

Cloud migration and modernization can be a complex process, but with the power of gen AI assets and assistants and innovative commercial models, organizations can overcome common migration challenges and achieve a faster, more efficient and more cost-effective migration experience. By automating manual processes, reducing the need for human intervention and minimizing the risk of errors and rework, gen AI tools can help organizations achieve significant cost savings and increased ROI.

Source: ibm.com

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Reduce downtime and increase agility: Mainframe observability with OpenTelemetry

Imagine your enterprise’s critical online services are suddenly down, and the IT operations team is working to identify the cause. Minutes turn into hours, and every second of downtime costs the company revenue and customer trust. In a rush to recover the systems, it is critical that your technical experts can isolate and resolve the real problem—or better yet, the ability to get ahead of growing issues and avoid the outage altogether. 

This is where an effective cross-platform end-to-end observability strategy becomes essential, allowing organizations to gain rapid insights into the health of their applications and systems.

Meeting the challenge of complex online services

With services running across the hybrid-cloud including on-prem and multiple hyperscaler platforms, locations and regions, detecting latency and resource issues before they become critical is paramount. As the number of services underpinning application flow increases, the manageability of this environment becomes more challenging.

For born-on-the-cloud applications, an observability approach is essential to provide a unified view of these dynamic dispersed environments. The role of Site Reliability Engineers (SREs) is also critical in ensuring the availability of the full end-to-end application or service. Rather than relying on a less comprehensive view of each technology, the SRE’s application-centric focus identifies which services are performing suboptimally. This guides development teams as they make detailed investigations and fixes.

OpenTelemetry as a cloud-native observability solution

Observability depends on timely and effective telemetry signals from the underlying systems. The OpenTelemetry project is a direct community-led response to this need and aims to address the head-on challenge of navigating increased complexity. 

OpenTelemetry is a vendor-agnostic, open-source framework hosted by the Cloud Native Computing Foundation (CNCF). It aims to enable effective observability across distributed applications and systems by providing an open standard and open tools that support high-quality telemetry data from any source to any target. By building on OpenTelemetry, the telemetry capabilities across different tools and domains can be simplified, making it easier to implement end-to-end observability solutions.

OpenTelemetry’s inherent concept of signal correlation enables the linking and association of different types of signals (such as traces, metrics and logs) to gain a comprehensive insight into an application’s behavior and resources. The OpenTelemetry Semantic Conventions support the correlation of signals by defining a common set of attributes, ensuring that standardized metadata facilitates their association. This is crucial for faster detection and resolution of incidents.

Bringing OpenTelemetry to the mainframe

With a growing number of enterprises unlocking the value of their mainframe investments as an integral part of these hybrid cloud environments, end-to-end observability must also span the applications and data that reside on IBM Z®.

This brings both teams to the table: SREs, for whom the transition of an application flow into the mainframe domain can obscure the full observability view, and mainframe teams, with their deep knowledge and tools.

As a widely consumed open standard, OpenTelemetry provides a richer set of tools to expedite the identification of the root cause of issues. Mainframe subject matter experts, with deep mainframe-centric diagnostic tools, can apply these skills in a more targeted and effective fashion. With observability teams and SREs able to identify what is and, critically, what is not a mainframe issue, teams can focus their time more efficiently. This reduces the risk of outages, as well as resolution time.

OpenTelemetry support on IBM Z and IBM LinuxONE

With IBM and its partners already starting to support OpenTelemetry in our observability and monitoring tools, wider adoption is increasing. We are working with the OpenTelemetry community, with our vendor partners and within our products across IBM Z and IBM LinuxONE to help enable a consistent end-to-end observability experience. Our approach complements our existing operational management tools and instrumentation and focuses on providing high-quality and timely telemetry at appropriate system overhead.

The value of observability extends beyond operational efficiency. It’s about strategic foresight and competitive advantage. Business leaders are keenly interested in how observability through frameworks like OpenTelemetry can provide clarity amidst complexity and unlock the agility of their IT systems. The rewards can be significant, as they are designed to reduce downtime, increase business agility and improve IT resource utilization.

Source: ibm.com

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Harnessing XaaS to reduce costs, risks and complexity

To drive fast-paced innovation, enterprises are demanding models that focus on business outcomes, as opposed to only measuring IT results. At the same time, these enterprises are under increasing pressure to redesign their IT estates in order to lower cost and risk and reduce complexity.

To meet these challenges, Everything as a Service (XaaS) is emerging as the solution that can help address these challenges by simplifying operations, reducing risk and accelerating digital transformation. According to an IDC white paper sponsored by IBM®, by 2028, 80% of IT buyers will prioritize XaaS consumption for key workloads that require flexibility to help optimize IT spending, augment IT Ops skills and attain key sustainability metrics.

Moving forward, we see three pivotal insights that will continue to shape the future direction of businesses in the coming years.

Simplify IT to accelerate business outcomes and focus on ROI

The need to overhaul legacy IT infrastructures is a significant pressure point for enterprises. The applications that we are writing today will be the applications that we need to modernize tomorrow.

With XaaS offerings, enterprises are able to integrate business-critical applications into a modernized hybrid environment, particularly in AI applications and workloads.

CrushBank, for example, worked with IBM to transform its IT support, streamlining help desk operations and arming staff with improved information. This created a 45% reduction in resolution time and notably enhanced customer experiences. CrushBank has reported that with the power of watsonx™ on IBM Cloud®, customers have shared feedback of higher satisfaction and efficiency allowing the organization to spend time with the people that matter the most: their clients.

Reimagine business models to foster rapid innovation

AI is fundamentally altering how business is done. Traditional business models, often constrained by their complexity and cost-intensive nature, are proving inadequate for the agility required in an AI-driven marketplace. According to recent IDC research, sponsored by IBM, 78% of IT organizations view XaaS as a critical component of their future strategies.

To meet this demand for rapid innovation and address the accompanying risks and costs, businesses see the benefits of turning to XaaS. Rather than merely providing tools, this model focuses on delivering outcomes for greater operational efficiency and effectiveness. The model allows XaaS vendors to focus on secure, resilient and scalable services, enabling IT organizations to invest their precious resources in their client requirements.

Anticipate for tomorrow by preparing for today

The shift toward an XaaS model is not just about optimizing IT spending; it is also about augmenting IT operations skills and achieving business goals faster and in a more agile manner.

At Think, CrushBank’s CTO David Tan highlighted how they enabled clients to innovate and effectively leverage data seamlessly where it resides, allowing them to craft a holistic strategy to meet the unique business needs for each of their customers. Enabling a simpler, faster and more economical path to leverage AI, while also reducing the risk and burden of managing complex IT architectures, remains paramount for companies operating in today’s data-driven environment.

The momentum toward XaaS stands out as a strategic solution that offers a multitude of benefits. From helping to reduce operational risks and costs to enabling rapid adoption of emerging technologies like AI, XaaS should be the cornerstone of every IT strategy.

IBM’s current as-a-service initiative can help enterprises achieve those benefits today. The combined capabilities across IBM software and infrastructure help clients drive outcomes, while helping to ensure that mission-critical workloads stay secured and compliant.

For example, IBM Power Virtual Server is designed to assist leading enterprises across the globe to successfully expand their on-premises servers to hybrid cloud infrastructures, granting leaders more insight into their businesses. Also, the IBM team is working collaboratively with our customers to modernize with AI, with offerings like watsonx Code Assistant™ for Java code or enterprise applications.

Enterprises are under increasing pressure to redesign their legacy IT estates—to lower cost and risk and reduce complexity. XaaS is emerging as the solution that can address these challenges head on by simplifying operations, enhancing resilience and accelerating digital transformation. IBM aims to meet our clients where they are on their transformation journey.

Source: ibm.com

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Step-by-step guide: Generative AI for your business

Generative artificial intelligence (gen AI) is transforming the business world by creating new opportunities for innovation, productivity and efficiency. This guide offers a clear roadmap for businesses to begin their gen AI journey. It provides practical insights accessible to all levels of technical expertise, while also outlining the roles of key stakeholders throughout the AI adoption process.

1. Establish generative AI goals for your business

Establishing clear objectives is crucial for the success of your gen AI initiative.

Identify specific business challenges that gen AI could address

When establishing Generative AI goals, start by examining your organization’s overarching strategic objectives. Whether it’s improving customer experience, increasing operational efficiency, or driving innovation, your AI initiatives should directly support these broader business aims.

Identify transformative opportunities

Look beyond incremental improvements and focus on how Generative AI can fundamentally transform your business processes or offerings. This might involve reimagining product development cycles, creating new revenue streams, or revolutionizing decision-making processes. For example, a media company might set a goal to use Generative AI to create personalized content at scale, potentially opening up new markets or audience segments.

Involve business leaders to outline expected outcomes and success metrics

Establish clear, quantifiable metrics to gauge the success of your Generative AI initiatives. These could include financial indicators like revenue growth or cost savings, operational metrics such as productivity improvements or time saved, or customer-centric measures like satisfaction scores or engagement rates.

2. Define your gen AI use case

With a clear picture of the business problem and desired outcomes, it’s necessary to delve into the details to boil down the business problem into a use case.

Technical feasibility assessment

Conduct a technical feasibility assessment to evaluate the complexity of integrating generative AI into existing systems. This includes determining whether custom model development is necessary or if pre-trained models can be utilized, and considering the computational requirements for different use cases.

Prioritize the right use case

Develop a scoring matrix to weigh factors such as potential revenue impact, cost reduction opportunities, improvement in key business metrics, technical complexity, resource requirements, and time to implementation.

Design a proof of concept (PoC)

Once a use case is chosen, outline a technical proof of concept that includes data preprocessing requirements, model selection criteria, integration points with existing systems, and performance metrics and evaluation criteria.

3. Involve stakeholders early

Early engagement of key stakeholders is vital for aligning your gen AI initiative with organizational needs and ensuring broad support. Most teams should include at least four types of team members.

• Business Manager: Involve experts from the business units that will be impacted by the selected use cases. They will help align the pilot with their strategic goals and identify any change management and process reengineering required to successfully run the pilot.
• AI Developer / Software engineers: Provide user-interface, front-end application and scalability support.  Organizations in which AI developers or software engineers are involved in the stage of developing AI use cases are much more likely to reach mature levels of AI implementation.
• Data Scientists and AI experts:  Historically we have seen Data Scientists build and choose traditional ML models for their use cases. We now see their role evolving into developing foundation models for gen AI.  Data Scientists will typically help with training, validating, and maintaining foundation models that are optimized for data tasks.
• Data Engineer:  A data engineer sets the foundation of building any generating AI app by preparing, cleaning and validating data required to train and deploy AI models. They design data pipelines that integrate different datasets to ensure the quality, reliability, and scalability needed for AI applications.

4. Assess your data landscape

A thorough evaluation of your data assets is essential for successful gen AI implementation.

Take inventory and evaluate existing data sources relevant to your gen AI goals

Data is indeed the foundation of generative AI, and a comprehensive inventory is crucial. Start by identifying all potential data sources across your organization, including structured databases. Assess each source for its relevance to your specific gen AI goals. For example, if you’re developing a customer service chatbot, you’ll want to focus on customer interaction logs, product information databases, and FAQs

Use IBM® watsonx.data™ to centralize and prepare your data for gen AI workloads

Tools such as IBM watsonx.data can be invaluable in centralizing and preparing your data for gen AI workloads. For instance, watsonx.data offers a single point of entry to access all your data across cloud and on-premises environments. This unified access simplifies data management and integration tasks. By using this centralized approach, watsonx.data streamlines the process of preparing and validating data for AI models. As a result of this, your gen AI initiatives are built on a solid foundation of trusted, governed data.

Bring in data engineers to assess data quality and set up data preparation processes

This is when your data engineers use their expertise to evaluate data quality and establish robust data preparation processes. Remember, the quality of your data directly impacts the performance of your gen AI models.

5. Select foundation model for your use case

Choosing the right AI model is a critical decision that shapes your project’s success.

Choose the appropriate model type for your use case

Data scientists play a crucial role in selecting the right foundation model for your specific use case. They evaluate factors like model performance, size, and specialization to find the best fit. IBM watsonx.ai offers a foundation model library that simplifies this process, providing a range of pre-trained models optimized for different tasks. This library allows data scientists to quickly experiment with various models, accelerating the selection process and ensuring the chosen model aligns with the project’s requirements.

Evaluate pretrained models in watsonx.ai, such as IBM Granite

These models are trained on trusted enterprise data from sources such as the internet, academia, code, legal and finance, making them ideal for a wide range of business applications. Consider the tradeoffs between pretrained models, such as IBM Granite available in platforms such as watsonx.ai and custom-built options.

Involve developers to plan model integration into existing systems and workflows

Engage your AI developers early to plan how the chosen model integrates with your existing systems and workflows, helping to ensure a smooth adoption process.

6. Train and validate the model

Training and validation are crucial steps in refining your gen AI model’s performance.

Monitor training progress, adjust parameters and evaluate model performance

Use platforms such as watsonx.ai for efficient training of your model. Throughout the process, closely monitor progress and adjust parameters to optimize performance.

Conduct thorough testing to assess model behavior and compliance

Rigorous testing is crucial. Governance toolkits such as watsonx.governance can help assess your model’s behavior and help ensure compliance with relevant regulations and ethical guidelines.

Use watsonx.ai to train the model on your prepared data set

This step is iterative, often requiring multiple rounds of refinement to achieve the wanted results.

7. Deploy the model

Deploying your gen AI model marks the transition from development to real-world application.

Integrate the trained model into your production environment with IT and developers

Developers take the lead in integrating models into existing business applications. They focus on creating APIs or interfaces that allow seamless communication between the foundation model and the application. Developers also handle aspects like data preprocessing, output formatting, and scalability; ensuring the model’s responses align with business logic and user experience requirements.

Establish feedback loops with users and your technical team for continuous improvement

It is essential to establish clear feedback loops with users and your technical team. This ongoing communication is vital for identifying issues, gathering insights and driving continuous improvement of your gen AI solution.

8. Scale and evolve

As your gen AI project matures, it’s time to expand its impact and capabilities.

Expand successful AI workloads to other areas of your business

As your initial gen AI project proves its value, look for opportunities to apply it across your organization.

Explore advanced features in watsonx.ai for more complex use cases

This might involve adapting the model for similar use cases or exploring more advanced features in platforms such as watsonx.ai to tackle complex challenges.

Maintain strong governance practices as you scale gen AI capabilities

As you scale, it’s crucial to maintain strong governance practices. Tools such as watsonx.governance can help ensure that your expanding gen AI capabilities remain ethical, compliant and aligned with your business objectives.

Embark on your gen AI transformation

Adopting generative AI is more than just implementing new technology, it’s a transformative journey that can reshape your business landscape. This guide has laid the foundation for using gen AI to drive innovation and secure competitive advantages. As you take your next steps, remember to:

• Prioritize ethical practices in AI development and deployment
• Foster a culture of continuous innovation and learning
• Stay adaptable as gen AI technologies and best practices evolve

By embracing these principles, you’ll be well positioned to unlock the full potential of generative AI in your business.

Unleash the power of gen AI in your business today

Discover how the IBM watsonx platform can accelerate your gen AI goals. From data preparation with watsonx.data to model development with watsonx.ai and responsible AI practices with watsonx.governance, we have the tools to support your journey every step of the way.

Source: ibm.com

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Revolutionizing community access to social services: IBM and Microsoft’s collaborative approach

In an era when technological advancements and economic growth are often hailed as measures of success, it is essential to pause and reflect on the underlying societal challenges that these advancements often overlook. And to consider how they can be used to genuinely improve the human condition.

IBM Consulting and Microsoft together with government leaders, are answering that call, partnering to develop a platform to bridge the division and enhance the delivery of social services support to communities in need.

Our shared purpose

Communities face a myriad of pressing societal challenges daily, from homelessness and juvenile justice to violence, mental health and food insecurity. In response, many government organizations are adopting transformative strategies with the goal of creating a society where all individuals have access to the necessary support and resources to flourish.

Take, for instance, government leaders who are adopting a “Care First” strategy. This approach is about redirecting thousands from the criminal justice system into supportive programs tailored to their “re-entry into society” needs. These programs help communities with housing, transportation, access to substance use treatment, and other essential services.

Other innovative leaders are dedicated to preventing violence, enhancing maternal health and equipping transitional age youth for success. A broader segment of leaders are embracing a whole-person care approach, focusing on the community at large rather than specific groups, thereby integrating social services across health, education and other vital sectors.

Introducing IBM Connect360

While many government organizations across the world have a wealth of different services and programs available to them. However, they are not able to bring the power of these systems to the people and communities that really need them. In many cases, the delivery models, technology (systems), and underlying data have been developed in silos and users need to work with each system and services separately.

IBM Connect360 facilitates integrated social services delivery and transforms data into actionable information and promotes cross-agency collaboration. This solution is focused on achieving five key goals:

• Enable collaboration by creating an electronic information exchange system
• Improve citizen access to services and resources through shared information
• Empower the citizen by permitting active participation in service decisions and delivery
• Strengthen decision‐making ability through data integration and business analytics
• Increase the region’s connection to community data through interoperability

IBM Connect360 is a platform that seamlessly integrates data from various siloed social services agency systems. This capability is designed to transform and align disparate data sources with the HL7 FHIR (Fast Healthcare Interoperability Resources) and HSDS (Human Services Data Specification) standards. This is so that information is standardized, protected, and easily accessible.

By adhering to the HL7 FHIR specification, IBM Connect360 also facilitates interoperability of health-related data with a wide range of healthcare systems to drive continuity and coordination of care for individuals in need.

This transformation not only makes the data readily available within IBM Connect360 but also enables seamless interoperability with other applications. As a result, service providers can exchange and update information to provide coordinated and effective assistance for those who rely on these crucial services.

IBM Connect360, hosted on Microsoft Azure, provides the level of isolation, security, performance, scale and reliability, required to support sensitive workloads across a broad spectrum of unique requirements. As we look to the future, IBM® and Microsoft’s investment in AI and their commitment to advance responsible, secured and trustworthy AI sets the baseline for future enhancements of IBM Connect360.

Transforming lives with IBM Connect360 and Microsoft Azure

Social services encompass a broad spectrum of programs from multiple departments, such as health, behavioral health, social services, housing, justice and more, all aimed at supporting individuals with complex needs.

Meeting these needs depends on interdepartmental collaboration, which is essential for improving client outcomes. Another key factor in achieving better results is the participation of Contracted Service Providers (CSPs) and Community-Based Organizations (CBOs) through the departments’ network. Beyond directly offering services, departments also have the responsibility for providing a central resource repository that their partners can use to deliver services.

The operational model that is being used is supported by IBM Connect360. In this model, the government agency provides the foundational systems and APIs, which provide access to the core systems in near real-time. This allows various business applications to interact with each other across different departments by using these APIs.

Each entity involved in this model focuses on its core competencies: The agency manages data, establishes business rules, ensures compliance and evaluates performance, while CSPs and CBOs can create service-oriented applications that are closer to the client. This model facilitates the swift introduction of new public assistance and healthcare programs by making efficient use of the existing agency resources. The ecosystem works together during the launch process to ensure that services are delivered to clients promptly, without any delays.

“At IBM, we understand the importance of effective communication and collaboration across government agencies,” said Cristina Caballé Fuguet, Vice President, Global Public Sector at IBM Consulting. “With IBM Connect360, government agencies can connect with citizens, share information, and gather feedback, all in a protected and scalable environment. We’re proud to see how IBM Connect360 with Microsoft Azure are helping governments around the world better serve their citizens and build stronger, more resilient communities.”

Case Study: How is IBM Connect 360 helping transform one citizen’s life?

Let’s consider the fictional story of Michael, a veteran who was finding the transition to civilian life challenging. Dealing with challenges including PTSD, mental health issues and substance abuse, he found himself trapped in a vicious cycle of homelessness. How did IBM Connect 360 help him?

With a care coordinator’s help, Michael set up his account on IBM Connect360. He provided information about his circumstances, IBM Connect360 assessed his needs and provided tailored recommendations. These recommendations were for services such as interim housing, substance use treatment, mental health support, transportation, skills training and ultimately helped him find a job.

With each step, Michael grew more confident and independent. He used the recommended services diligently, found solace in his supportive community of care providers, and slowly rebuilt his life, piece by piece. The technology solution was not just a guide but a constant companion in his journey to stability.

This is not just about one man’s path to stability; it shows that the right tools, combined with a supportive network, can bring about real, positive change in a person’s life. Michael’s example is a testament to the power of compassionate intervention and the potential applications of technology in social support systems. With the right tools and support, transformation is always within reach, and a brighter future is not just a dream but a possible reality. When IBM Consulting, Microsoft, Governments and Communities join forces these outcomes can happen at scale.

Experience the transformative power firsthand

IBM Connect360 along with IBM Community Health user interface and Microsoft Azure, is a powerful solution that has the potential to bring about real, positive change in people’s lives. This comprehensive and open platform is designed to support all stages of service delivery. From understanding individual needs, to locating and connecting with the service providers that can support them and effectively measure outcomes and quality of care.

“IBM Connect360 ensures that every aspect of community service delivery is enhanced, fostering a more connected, efficient and impactful system,” said Angela Heise, Corporate Vice President, Worldwide Public Sector at Microsoft. “We are looking forward to continuing our strategic partnership with IBM Consulting and take the solution to the next level.”

A distinctive feature of this platform is its versatility in catering to a wide range of stakeholders, community members, service navigators, care coordinators, service providers and government leaders alike, will find immense value in its features.

We recommend you experience the transformative power of this solution firsthand. Reach out to your IBM Consulting and Microsoft representatives to schedule a personalized demo and witness how this solution can be tailored to meet your unique needs and requirements.

Source: ibm.com

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Optimizing data flexibility and performance with hybrid cloud

As the global data storage market is set to more than triple by 2032, businesses face increasing challenges in managing their growing data. This shift to hybrid cloud solutions is transforming data management, enhancing flexibility and boosting performance across organizations.

By focusing on five key aspects of cloud adoption for optimizing data management—from evolving data strategies to ensuring compliance—businesses can create adaptable, high performing data ecosystems that are primed for AI innovation and future growth.

1. The evolution of data management strategies

Data management is undergoing a significant transformation, especially with the arrival of generative AI. Organizations are increasingly adopting hybrid cloud solutions that blend the strengths of private and public clouds, particularly beneficial in data-intensive sectors and companies embarking on AI strategy to fuel growth. 

A McKinsey & Company study reveals that companies aim to have 60% of their systems in the cloud by 2025, underscoring the importance of flexible cloud strategies. Hybrid cloud solutions address this trend by offering open architectures, combining high performance with scalability. For technical professionals, this shift means to work with systems that can adapt to changing needs without compromising on performance or security. 

2. Seamless deployment and workload portability

One of the key advantages of hybrid cloud solutions is the ability to deploy across any cloud or on-premises environment in minutes. This flexibility is further enhanced by workload portability through advanced technologies like Red Hat® OpenShift®.  

This capability allows organizations to align their infrastructure with both multicloud and hybrid cloud data strategies, ensuring that workloads can be moved or scaled as needed without being locked into a single environment. This adaptability is crucial for enterprises dealing with varying compliance requirements and evolving business needs. 

3. Enhancing AI and analytics with unified data access

 Hybrid cloud architectures are proving instrumental in advancing AI and analytics capabilities. A 2023 Gartner survey reveals that “two out of three enterprises use hybrid cloud to power their AI initiatives”, underscoring its critical role in modern data strategies. By using open formats, these solutions provide unified data access, allowing seamless sharing of data across an organization without the need for extensive migration or restructuring. 

Furthermore, advanced solutions like IBM watsonx.data™ integrate vector database like Milvus, an open-source solution that enables efficient storage and retrieval of high-dimensional vectors. This integration is crucial for AI and machine learning tasks, particularly in fields like natural learning processing and computer vision.  By providing access to a wider pool of trusted data, it enhances the relevance and precision of AI models, accelerating innovation in these areas. 

For data scientists and engineers, these features translate to more efficient data preparation for AI models and applications, leading to improved accuracy and relevance in AI-driven insights and predictions. 

4. Optimizing performance with fit-for-purpose query engines

In the realm of data management, the diverse nature of data workloads demands a flexible approach to query processing. With watsonx.data, multiple fit-for-purpose open query engines are offered such as Presto, Presto C++ and Spark, along with integration capabilities for data warehouse engines like Db2® and Netezza®. This flexibility allows data teams to choose the optimal tool for each task, enhancing both performance and cost-effectiveness. 

For instance, Presto C++ can be used for high-performance, low-latency queries on large datasets, while Spark excels at complex, distributed data processing tasks. The integration with established data warehouse engines ensures compatibility with existing systems and workflows. 

This flexibility is especially valuable when dealing with diverse data types and volumes in modern businesses. By allowing organizations to optimize their data workloads, watsonx.data addresses the challenges of rapidly propagating data across various environments. 

5. Compliance and data governance in a hybrid world

With increasingly strict data regulations, hybrid cloud architectures offer significant advantages in maintaining compliance and robust data governance. A report by FINRA (Financial Industry Regulatory Authority) demonstrates that hybrid cloud solutions can help firms manage cybersecurity, data governance and business continuity more effectively than by using multiple separate cloud services. 

 Unlike pure multicloud setups, which can complicate compliance efforts across different providers, hybrid cloud allows organizations to keep sensitive data on premises or in private clouds while using public cloud resources for less sensitive workloads. IBM watsonx.data enhances this approach with built-in data governance features, such as having a single point of entry and robust access control. This approach supports varied deployment needs and restrictions, making it easier to implement consistent governance policies and meet industry-specific regulatory requirements compromise on security. 

Embracing hybrid cloud for future-ready data management

The adoption of hybrid cloud solutions marks a significant shift in enterprise data management. By offering a balance of flexibility, performance and control, solutions like IBM watsonx.data are enabling businesses to build more resilient, efficient and innovative data ecosystems. 

As data management continues to evolve, using hybrid cloud strategies will be crucial in shaping the future of enterprise data and analytics. With watsonx.data, organizations can confidently navigate this change, using advanced features to unlock the full potential of their data across hybrid environments and be future ready to embrace AI. 

Source: ibm.com

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10 tasks I wish AI could perform for financial planning and analysis professionals

It’s no secret that artificial intelligence (AI) transforms the way we work in financial planning and analysis (FP&A). It is already happening to a degree, but we could easily dream of many more things that AI could do for us.

Most FP&A professionals are consumed with manual work that detracts from their ability to add value to their work. This often leaves chief financial officers and business leaders frustrated with the return on investment from their FP&A team. However, AI can help FP&A professionals elevate the work they do.

Developments in AI have accelerated tremendously in the last few years, and FP&A professionals might not even know what is possible. It’s time to expand our thinking and consider how we could maximize the potential uses of AI.

As I dream up more ways that AI could help us, I have focused on practical tasks that FP&A professionals perform today. I also considered AI-driven workflows that are realistic to implement within the next year.

10 FP&A tasks for AI to perform

1. Advanced financial forecasting: Enables continuous updates of forecasts in real time based on the latest data. Automatically generates multiple financial scenarios and simulates their impacts under different conditions. Uses advanced algorithms to predict revenue, expenses and cash flows with high accuracy.
2. Automated reporting and visualization: Automatically generates and updates reports and dashboards by pulling data from multiple sources in real time. Provides contextual explanations and insights within reports to highlight key drivers and anomalies. Enables user-defined metrics and visualizations tailored to specific business needs.
3. Natural language interaction: Enables users to interact with financial systems that use natural language queries and commands, allowing seamless data retrieval and analysis. Provides voice-based interfaces for hands-free operation and instant insights. Facilitates natural language generation to convert complex financial data into easily understandable narratives and summaries.
4. Intelligent budgeting and planning: Adjusts budgets dynamically based on real-time performance and external factors. Automatically identifies and analyzes variances between actuals and budgets, providing explanations for deviations. Offers strategic recommendations based on financial data trends and projections.
5. Advanced risk management: Uses AI-driven risk models to identify potential market, credit and operational risks. Develops early warning systems that alert to potential financial issues or deviations from planned performance. Helps ensure compliance with financial regulations through automated monitoring and reporting.
6. Anomaly detection in forecasts: Improves forecasting accuracy by using advanced machine learning models that incorporate both historical data and real-time inputs. Automatically detects anomalies in financial data, providing alerts for unusual patterns or deviations from expected behavior. Offers detailed explanations and potential causes for detected anomalies to guide corrective actions.
7. Collaborative financial planning: Facilitates collaboration among FP&A teams and other departments through shared platforms and real-time data access. Enables natural language interactions with financial models and data. Implements AI-driven assistants to answer queries, perform tasks and support decision-making processes.
8. Continuous learning and improvement: Develops machine learning models that continuously learn from new data and improve over time. Incorporates feedback mechanisms to refine forecasts and analyses based on actual outcomes. Captures historical data and insights for future decision-making.
9. Strategic scenario planning: Analyzes market trends and competitive positioning to support strategic planning. Evaluates potential investments and their financial impacts by using AI-driven analysis. Optimizes asset and project portfolios based on AI-driven recommendations.
10. Financial model explanations: Automatically generates clear, detailed explanations of financial models, including assumptions, calculations and potential impacts. Provides visualizations and scenario analyses to demonstrate how changes in inputs affect outcomes. Helps ensure transparency by enabling users to drill down into model components and understand the rationale behind projections and recommendations.

This is not a short wish list, but it should make us all excited about the future of FP&A. Today, FP&A professionals spend too much time on manual work in spreadsheets or dashboard updates. Implement these capabilities, and you’ll easily free up several days each month for value-adding work.

Drive the right strategic choices

Finally, use your newfound free time to realize the mission of FP&A to drive the right strategic choices in the company. How many companies have FP&A teams that facilitate the strategy process? I have yet to meet one.

However, with added AI capabilities, this could soon be a reality. Let’s elaborate on how some of the capabilities on the wish list can elevate our work to a strategic level.

• Strategic scenario planning: How do you know what choices are available to make? It can easily become an endless desktop exercise that fails to produce useful insights. By using AI in analysis, you can get more done faster and challenge your thinking. This helps FP&A bring relevant choices and insights to the strategy table instead of just being a passive facilitator.
• Advanced forecasting: How do you know whether you’re making the right strategic choice? The answer is simple: you don’t. However, you can improve the qualification of the choice. That’s where advanced forecasting comes in. By considering all available internal and external information, you can forecast the most likely outcomes of a choice. If the forecasts align with your strategic aspirations, it’s probably the right choice.
• Collaborative planning: Many strategies fail to deliver the expected financial outcomes due to misalignment and silo-based thinking. Executing the right choices is challenging if the strategy wasn’t a collaborative effort or if its cascade was done in silos. Using collaborative planning, FP&A can facilitate cross-functional awareness about strategic progress and highlight areas needing attention.

If you’re unsure where to start, identify a concrete task today that aligns with any item on the wish list. Then, explore what tools are already available within your company to automate or augment the output using AI.

If no tools are available, you need to build the business case by aligning with your colleagues about the most pressing needs and presenting them to management.

Alternatively, you can try IBM Planning Analytics on your work for free. When these tools work for you, they can work for others too.

Don’t overthink the issue. Start implementing AI tools in your daily work today. It’s critical to use these as enablers to elevate the work we do in FP&A. Where will you start?

Source: ibm.com

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