A guide has been created to assist in moving your application from Cloud Foundry to the Liberty Buildpack by Paketo.

IBM has announced the end-of-life for the liberty-for-java buildpack in Cloud Foundry, and users are in need of a migration option. The recommended solution is to use the Paketo Buildpack for Liberty, a cloud-native alternative. The key benefit of using Paketo Buildpack is the capability to convert application source code into consistent container images, which can be used across various platforms, providing greater flexibility and ease of updates.

Additional benefits of using the Paketo Buildpack for Liberty include the capability to construct your application image without the need for a Dockerfile, efficient rebuilds due to built-in caching, and simple modification and updating options.

What’s in the migration guide?

To simplify the migration process, we have created a guide that is divided into two primary parts: creating your Liberty application using the Paketo Buildpack for Liberty, and advanced capabilities for Liberty applications. The guide contains a feature-by-feature comparison of Cloud Foundry and Paketo Buildpack commands in each section. These sections are intended to assist you in moving your application from Cloud Foundry to the Paketo Buildpack for Liberty.

The section of the guide on constructing your Liberty application with the Paketo Buildpack includes the following steps:

• Building a container image from application source code
• Building an application with a simple war file
• Building an application from a Liberty server
• Building an application from a Liberty-packaged server
• Building an application by using UBI images

The section of the guide on advanced capabilities for Liberty applications that utilize the Paketo Buildpack for Liberty includes the following areas:

• Providing server configuration at build time
• Using Liberty profiles to build applications
• Installing custom features
• Installing interim fixes

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Imagine this scenario: A traveler goes on a camping trip and decides to extend their RV rental during their trip. However, when they try to call customer service for help, they have a difficult time getting through and are redirected multiple times. The process becomes frustrating and causes them to question whether the additional rental is worth the hassle. From the perspective of the customer service agent, they are also dealing with a frustrated customer and trying to gather information quickly, which can be stressful. These types of situations are unfortunately common and can be costly for the company, as well as frustrating for both the customer and the agent.

Artificial intelligence has made significant progress in improving customer service through conversational solutions. These solutions allow organizations to better meet customer expectations, streamline operations, and reduce expenses while also increasing customer satisfaction. By implementing AI into customer service processes, companies can achieve more cost-effective operations and satisfied customers.

In today’s fast-paced environment, how can conversational AI assist in meeting customer expectations?

By implementing conversational AI in your call center, you can achieve the following benefits:

1. Increased customer and agent satisfaction. Prolonged wait times and unanswered questions can lead to dissatisfaction for both customers and agents, as well as hinder the efficiency of the business. However, by utilizing advanced natural language understanding (NLU) and automation, resolution can be achieved more quickly, leading to a win-win situation for all parties involved.
2. Improved call resolution rates. AI and machine learning can provide more self-service options and route customers to the appropriate support channels, using data from past customer interactions to improve responses. This also helps agents handle high call volumes more effectively and improve resolution rates, leading to better customer experiences and a stronger brand reputation.
3. Reduced operational costs. By using AI-powered virtual agents, it is possible to handle up to 70% of calls automatically, potentially saving your business an estimated $5.50 per contained call and saving time for customers.

Not all AI platforms are built the same

At the most basic level, you have AI bots that follow a set of predetermined rules and can only provide limited responses. For example, if you call customer service for your telecom provider and ask about an unlimited data plan, you might be asked a series of questions based on strict if-then scenarios “…say yes if you want to review service plans; say yes if you want unlimited data.”

One step higher on the AI ladder is level two AI with machine learning and intent detection. For example, if you accidentally type “speal to an agenr,” this type of virtual assistant would be able to understand your intention and provide a proper response: “I’m sorry, did you mean to say ‘speak to an agent’?”

IBM Watson® Assistant is a virtual agent that constantly learns and utilizes extensive resources. It is classified as level three AI, which is the most advanced and powerful form of AI with access to vast amounts of data and research capabilities.

IBM Watson Assistant, deployed at Vodafone, a leading telecommunications company in Germany, exhibits level three capabilities. In addition to answering questions across various platforms like WhatsApp, Facebook, and RCS, it can also retrieve and respond to requests from databases and communicate in multiple languages. It is able to analyze data, customize interactions, and continually learn and improve. “*Insert Name*, transferring you to one of our agents who can answer your question about coverage abroad.” 

By using Watson AI, you can improve the performance of your call center with round-the-clock support, fast response times, and high resolution rates. This AI can be seamlessly integrated with your current systems and processes across all customer channels and touchpoints, without the need to switch your technology stack. Watson AI offers the following benefits:

• Best-in-class NLU
• Intent detection
• Large language models
• Unsupervised learning
• Advanced analytics
• AI-powered agent assist
• Easy integration with existing systems
• Consulting services

These features can help transform customer service and support to meet the needs and pace of your business.

Why add complexity when you can simplify with AI? 

A Gartner® report predicts that by 2031, chatbots and virtual assistants powered by conversational AI will handle 30% of interactions that would have previously been handled by human agents, an increase from the 2% projected for 2022. In order to stay competitive, modern contact centers need to keep up with AI advancements. Leading companies, like Watson, continuously learn and analyze data in order to continually improve and evolve.

Watson Assistant can be easily integrated into your company’s infrastructure and provides reliable, user-friendly support and self-service options. For example, Camping World, the top retailer of recreational vehicles, used IBM Watson AI-powered virtual assistant Arvee to handle an increase in customer demand during the COVID-19 pandemic. By implementing Arvee in their call center, Camping World was able to improve agent efficiency by 33% and increase customer engagement by 40%.

Watson Assistant can improve efficiency and streamline processes, allowing human agents to provide higher quality, personalized service when necessary. For instance, a frustrated customer who was previously on hold can now enjoy their camping trip thanks to the capabilities of Watson Assistant, eliminating the need for hold music and replacing it with the sounds of nature.

Here at CourseMonster, we know how hard it may be to find the right time and funds for training. We provide effective training programs that enable you to select the training option that best meets the demands of your company.

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Virtual assistants are powerful business tools that provide a strong return on investment while enhancing the customer experience. By 2030, chatbots and virtual assistants powered by artificial intelligence will handle 30% of customer interactions that would have otherwise been handled by a human agent, a significant increase from the current 2% in 2022. Despite these benefits, there are still many companies that have yet to adopt this solution.

Implementing a conversational AI platform can be a complex and time-consuming process, but it can ultimately bring significant benefits. However, one obstacle to implementing this technology is the need for specialized technical experts, such as data analysts, AI professionals, and graph technology specialists. These experts can be in short supply and often command high salaries, often exceeding $175,000 per year. While this can be a challenge for many companies, there are more cost-effective options available for creating a chatbot to provide an optimal support experience.

A multitude of solutions to create and maintain virtual assistants to improve customer service.  

It is important to note that not all pre-developed tools are the same. In the past, conversational AI solutions have generally fallen into one of two categories:

Simple-to-use solutions

Some pre-built tools may be easy to use but ultimately provide poor customer experiences. These chatbots often rely on hardcoded responses or minimal artificial intelligence and machine learning. They are designed to provide fixed responses to specific questions, rather than adapt to the needs of the customer. As a result, they may provide the same response every time, regardless of whether it is accurate or appropriate.

Robust solutions that can create powerful experiences

At the other end of the spectrum are tools that enable you to create more sophisticated and personalized customer experiences, but they may be difficult and costly to use. For example, these tools may use advanced natural language processing to power their AI, but creating a customer-facing solution with them may require specialized technical skills such as computer science and front-end development. Additionally, integrating the chatbot into your website can be a complex process.

Watson Assistant changes the game with actions and the low code/no code interface

If your organization lacks the technical expertise or resources to build and manage a complex, expensive AI solution, you may want to consider a conversational AI platform that offers “low code” or “no code” interfaces. These interfaces allow non-technical business users to quickly develop and deploy conversational AI applications that can handle dialog, process natural language, and integrate with existing systems.

Breaking the code: Get started fast with a Conversational AI platform 

Watson Assistant has introduced a no-code visual chatbot builder that allows users to build customer conversations using actions. The tool is designed to be easy to use, even for those without technical expertise, and includes a build guide that is tailored to customer-facing professionals.

Pre-built conversation flows

IBM’s approach to building customer conversations with actions focuses on logical steps and powerful artificial intelligence that can understand the user’s intent, identify specific pieces of information from the message (called “entities”), and keep the conversation on track without being repetitive. For example, if a customer says “I want to buy one large cheese pizza,” the assistant should not ask for the size, toppings, or quantity, but rather move on to the payment process. Watson Assistant’s AI is designed to understand that if the conversation progresses to a certain point, certain steps can be skipped.

Disambiguation

Creating a virtual assistant that can ask clarifying questions is straightforward. If the assistant does not understand something that is said in the chat, it can automatically ask for more information to help get the conversation back on track. This means that the chatbot does not have to be perfect from the start, and can automatically recover if the conversation veers off course. You can also program the chatbot to have predetermined responses, such as asking for an account number and clarifying that it is looking for a numerical value only.

Maintenance

Using Watson Assistant, you can quickly identify and fix any issues with your chatbot, rather than going through a lengthy traditional development cycle. This can save you time and resources, as you can avoid having to go through IT and wait for a developer to make changes that may take weeks or even months to implement.

Low-code and no-code interfaces such as Watson Assistant’s visual chatbot builder allow companies to empower a new class of citizen developers, who may not have professional development or technical skills. With these tools, businesses can easily build chatbots for customer service without needing to know how to code. This gives them more control and flexibility to quickly create chatbots that meet their needs and improve the customer experience.

Here at CourseMonster, we know how hard it may be to find the right time and funds for training. We provide effective training programs that enable you to select the training option that best meets the demands of your company.

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Today, data leaders are expected to not only provide business intelligence to management, but also to make organizations more efficient, improve business value, and promote innovation. They must ensure that data strategy aligns with business strategy and create a data-driven culture in which the entire organization can use automation and AI technologies to improve ROI, leading to cost savings, revenue growth and the creation of new business opportunities.

Building the Foundation: Data Architecture

Effective data management involves collecting, organizing, managing, and storing data, which can be a complex task. A data architecture that is tailored to the needs of the business is essential for data-driven organizations. This architecture defines how data is collected, processed, and used within the organization, and can be based in the cloud to provide high availability, scalability, and portability. Modern data architectures also often include features such as intelligent workflows, real-time analytics, and integration with legacy systems. Choosing the right data architecture can significantly impact an organization’s revenue and efficiency, while the cost of making a poor choice can be substantial.

Here at CourseMonster, we know how hard it may be to find the right time and funds for training. We provide effective training programs that enable you to select the training option that best meets the demands of your company.

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Recently, ChatGPT, a product of OpenAI, has received a great deal of attention from people all over the globe. This is a major achievement for the field of generative artificial intelligence (AI) and the underlying technology that supports many applications. This marks the end of a successful year for the industry.

IBM is currently at a pivotal moment for artificial intelligence (AI). More and more businesses are implementing AI, and advances in foundational research and development are making it possible for AI applications like generative AI to become even more advanced and effective. The possibilities for AI are vast, as it can help you analyze large amounts of data to find new solutions to various business and societal issues, such as sustainability, life sciences, customer service, employee experience, and more.

While these advancements are exciting, they also raise important questions within the industry. For example, how can we ensure that these models are being used responsibly and that their algorithms and outputs can be trusted? Generative AI models, in particular, can produce responses that are highly convincing and well-structured, making it difficult to identify incorrect responses without specialized knowledge of the relevant subject matter.

IBM is constantly engaging with clients and partners on these issues. The rapid pace of AI technological advancements means that businesses have a chance to put internal safeguards in place to govern the development and deployment of AI. At the same time, governments around the world are evaluating and implementing new AI regulations and guidelines.

To increase the adoption of responsible AI, it is necessary to have a system in place to establish policies and accountability throughout the entire lifecycle of the AI. This system, known as AI governance, helps ensure that the AI follows principles such as fairness, transparency, robustness, and privacy. A successful AI governance strategy involves the coordination of people, processes, and technology.

Here at CourseMonster, we know how hard it may be to find the right time and funds for training. We provide effective training programs that enable you to select the training option that best meets the demands of your company.

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Recently, IBM announced that they have added three new containerized libraries to their portfolio of embeddable AI software. These new libraries allow partners to incorporate popular IBM Watson capabilities, such as natural language processing, speech-to-text, and text-to-speech, into their own applications and solutions. But what exactly is embeddable AI and what are its uses?

Embeddable AI is a suite of IBM’s core AI technologies that can be easily integrated into enterprise applications for a variety of purposes. It can be thought of as an engine that can be used in different ways, just like how an engine in a plane or a car helps those vehicles to function.

Similar to how using a pre-made engine is easier than building one from scratch, using embeddable AI allows developers to take advantage of a set of pre-made, flexible AI models that can be used to provide enhanced user experiences, such as automatically transcribing voice messages and video conferences into text.

However, many organizations still struggle to find talented individuals with the necessary skills to build and deploy AI solutions within their businesses. Therefore, it is important for companies that want to incorporate specific AI capabilities into their applications or workflows to do so without having to expand their technology stack, hire additional data science talent, or invest in expensive supercomputing resources.

To solve this problem, businesses have begun using embeddable AI technology and specific models to harness AI’s potential in the way that best fits their needs, whether it is through domain-optimized applications or containerized software libraries.

Differentiated solutions drive business success

IBM Research has added three new software libraries to IBM’s portfolio of embeddable AI solutions. These libraries are not tied to any particular platform and can be run across a range of environments, including public clouds, on-premises systems, and at the edge.

As a result, businesses may now use this technology to improve the apps they already have or to create new ones.

The new libraries include:

• IBM Watson Natural Language Processing Library for Embed
• IBM Watson Speech-to-Text Library for Embed
• IBM Watson Text-to-Speech Library for Embed

The embeddable AI portfolio also includes IBM Watson APIs and programs including IBM Watson Assistant, IBM Watson Discovery, IBM Instana Observability, and IBM Maximo Visual Inspection in addition to the libraries.

These libraries are lightweight and offer stable APIs that can be used across different models, making it easier for organizations to develop and introduce new solutions to the market.

Partner solutions using embeddable AI

Embeddable AI is being used by IBM partners in a lot of situations and industries.

Natural language processing is used by LegalMation, an IBM partner that helps the legal industry in using AI and reducing technologies, to automate contract privacy. Information that should be carefully considered by businesses is usually included in contracts and agreements. Redacting data from a contract is typically a manual process that involves marking passages for redaction line by line. Instead, LegalMation develops an automated approach using embeddable AI. The legal firm now employs a natural language processing technology to automatically identify and mark sensitive information.

Here at CourseMonster, we know how hard it may be to find the right time and funds for training. We provide effective training programs that enable you to select the training option that best meets the demands of your company.

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Around the world, there are growing regulatory requirements in place to address environmental, social, and governance (ESG) issues and create a more sustainable future. While environmental regulations have been in existence for over 50 years, governments are increasingly taking action to address forced labor, unfair working conditions, and modern slavery. These efforts reflect concerns about human rights in modern supply chains, as highlighted by the International Labor Organization (ILO) in its 2021 report. The report stated that 50 million people globally are in conditions of modern slavery, including forced labor, an increase of 10 million people from the ILO’s report five years earlier.

Environmental concerns, modern slavery, and forced labor require a global response. This can take the form of initiatives such as the Sustainable Development Goals (SGDs), such as Goal 8 on Decent Work and Economic Growth and Goal 13 on Climate Action, as well as country-specific regulations like the UK Modern Slavery Act and the California Transparency in Supply Chain Act. In June 2021, Germany passed its own legislation addressing these issues, the Supply Chain Due Diligence Act, also known as the Lieferkettensorgfaltspflichtengesetz (LkSG).

LkSG requirements and considerations

Beginning on January 1, 2023, companies based in Germany or German-registered branches of foreign companies with more than 3,000 employees will be required to create or update their business processes to identify, assess, remediate, prevent, and report on human rights and environmental risks and related actions in their own operations and those of their direct and indirect suppliers. Failure to comply with the LkSG can result in fines of up to 2% of annual turnover and/or exclusion from being awarded public contracts.

In response to the requirements of the LkSG, they believe that companies should consider the following three points:

• Risk is a variable and should not be treated as a static, one-time check. Instead, companies should prioritize finding a holistic way to proactively address dynamic environmental, social, and governance risks.
• Companies throughout the supply chain often want to be efficient in meeting regulatory compliance requirements. Suppliers may receive multiple questionnaires from their clients, which can be time-consuming and resource-intensive to respond to. It is important to find solutions that minimize the burden on suppliers.
• Regulatory requirements are increasing globally, including those related to ESG practices. Companies can improve their operational efficiency by implementing compliance solutions that are responsive to evolving regulatory requirements and that can scale to meet their business needs.

To address these challenges, IBM and FRDM have partnered to develop a solution for human rights and environmental risk sensing and management. The solution uses big data and AI to generate real-time risk signals up to the third tier of the supply chain and provides teams with the ability to respond to these signals and connect with suppliers to mitigate risks. This enables companies to detect and address issues in a timely and dynamic manner. To minimize the cost of compliance for suppliers, the platform is free to sign up for, and it can be expanded to meet changing regulatory requirements.

Addressing risk proactively and dynamically

The LkSG requires companies to establish a risk management system, perform a regular risk analysis, implement preventive measures for their own operations and multiple tiers of suppliers, and take remedial action. Its scope is broad and goes beyond companies’ tier 1 suppliers. The main challenge with risk management is that risks are not static. Self-assessment and survey-based tools, which are commonly used to assess risks, can only provide a snapshot of a company’s business and supply chain risks. These tools are also often unverifiable, and companies must trust the accuracy of responses. To proactively and dynamically address risks, it would be beneficial to implement a solution that can constantly update and keep track of changes in the supply chain and risk levels while ensuring that the information is current.

The IBM FRDM solution uses big data to generate insights on supply chain environmental, social, and governance (ESG) risks from tier 1 suppliers to tier 3 suppliers. This platform leverages a company’s spending data and third-party data (including news sources, trade databases, and sanctions databases) to map supply chains and commercial relationships and generate a live risk assessment of a company’s supply chain. The platform’s proprietary product genome database can create a predictive bill of materials (BOMs) that breaks down a company’s purchases to identify the material and service inputs, allowing the platform to map risks up through the third tier. The platform generates dashboards for companies and their suppliers with live risk ratings and issue alerts powered by machine learning. It also provides a forum for supplier engagement on remediation and enables report generation on progress updates and impact tracking.

The IBM FRDM solution also offers risk management and response management services that assist companies with taking remedial action, documenting, and reporting on their due diligence obligations. These services include coverage of the first-level response to risk alerts, supplier questionnaires, and risk assessment changes. IBM can provide third-party review of supply chain whistleblower reports and help ensure timely escalation to the appropriate parties and prompt remedial action.

Reducing the burden on suppliers

Suppliers often have to go through audits, surveys, and training as required by their customers. This can be exhausting for their teams and also expensive. It would be beneficial for businesses to create effective and efficient processes for meeting LkSG requirements, and to work with their suppliers to reduce the overall burden of compliance.

Many risk assessment platforms currently rely on supplier-filled questionnaires and do not verify this information through audits. These platforms often ask suppliers about the measures they have taken to address risks, but do not verify the accuracy of this information. Additionally, some of these platforms charge suppliers a fee to respond to the questionnaire and make their data available to their customers. In contrast, the IBM FRDM joint solution uses big data to generate insights and risk ratings without requiring suppliers to pay a fee and does not require them to sign up for any special platform. Companies can also use this solution to conduct a more in-depth analysis of their suppliers using a free digital supplier assessment.

The IBM FRDM joint solution does not charge suppliers a fee to participate, which means that companies can collect information from all of their suppliers, regardless of their size or order volume. This is important because LkSG requires companies to consider all of their suppliers, including small ones that may not be able to afford to pay platform or survey fees. Additionally, companies do not have to pay out-of-pocket to cover these costs for smaller suppliers, which makes it easier for them to comply with LkSG requirements.

The IBM FRDM solution saves suppliers time and effort because they do not have to pay for the platform or module, and they do not have to be trained on a new system for entering data. This allows suppliers to focus on more valuable activities and reduces their survey fatigue. As a result, companies can build better relationships with their suppliers and create more efficient supply chain operations.

The evolving regulatory environment

LkSG is a German law that aims to hold companies accountable for creating and maintaining fair and sustainable supply chains. It is similar to other regulations in Europe, such as the French Duty of Vigilance law and the UK Modern Slavery Act, as well as regulations in other parts of the world, such as the Australia Modern Slavery Act and the California Supply Chain Transparency Act. A European Union-wide law with similar provisions is expected to take effect in January 2024.

Companies need to be able to adapt quickly to changing regulatory demands. However, with so many different requirements, it can be difficult to build teams with expertise in all aspects of environmental, social, and governance (ESG) issues. IBM’s managed services can help companies understand these requirements, manage the data, and prioritize follow-up and remediation efforts. IBM also has a global presence, which allows it to set up local teams that understand the specific requirements and can work directly with clients. These teams can provide a first-level response to risk alerts, supplier questionnaires, and other changes in risk assessments, and can escalate any necessary issues to the responsible parties within the company.

The IBM FRDM solution uses AI and machine learning to adapt to changing or expanding regulations. It also provides local teams with deep expertise and support.

Conclusion

LkSG is just one of many recent government regulations regarding supply chain responsibility. As regulatory bodies, consumers, and employees continue to demand greater accountability for protecting the people and environment involved in the supply chain, companies must create and implement solutions that can address risk as a dynamic and changing variable. Risk is not static, and companies need more than a one-time snapshot of their supply chain risks in order to adequately address any issues. The IBM FRDM solution is designed to be forward-looking and adaptable to new risk factors and indicators and can be expanded to meet new legislation and requirements. IBM and FRDM are ready to support companies as they work to improve their practices and safeguard the planet and its people.

Here at CourseMonster, we know how hard it may be to find the right time and funds for training. We provide effective training programs that enable you to select the training option that best meets the demands of your company.

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IBM has announced the availability of three containerized Watson libraries, expanding their offering of embeddable AI software. With this update, IBM partners may now include well-known IBM Watson features like natural language processing, speech-to-text, and text-to-speech into their products and services. But what exactly is embeddable AI and how may it be used?

Embeddable AI is a new set of IBM core AI technologies that can be quickly linked to business applications to address a wide range of use cases. Imagine embedded AI to be an engine. Engines are used to move both planes and cars, but they generally serve a different way.

The similarity doesn’t end there either, as it is far easier to use a pre-made item than to create it yourself, just like with a car or airplane engine. Developers can improve end-user experiences by using embeddable AI to deliver a set of adaptable, fit-for-purpose AI models, such as those that automatically convert voicemails and video conferences to text.

However, a lot of businesses still have difficulty finding employees who have the necessary skills to create and implement AI solutions in their business. Therefore, it’s important for businesses that want to add specialized AI capabilities to their applications or workflows to be able to do so without having to upgrade their IT infrastructure, hire more data science, or make expensive supercomputing resource investments.

Businesses have discovered value in integrating strong technology with specific models to take advantage of AI’s potential in the manner that best suits their requirements in order to address this, whether it be through domain-optimized applications or containerized software libraries.

Differentiated solutions drive business success

Three new software libraries from IBM Research have been added to IBM’s portfolio of embeddable AI solutions. These libraries are not systems and may be used in public clouds, on-premises, and at the edge.

As a result, businesses may now use this technology to improve the apps they already have or to create new ones.

The new libraries include:

• IBM Watson Natural Language Processing Library for Embed
• IBM Watson Speech-to-Text Library for Embed
• IBM Watson Text-to-Speech Library for Embed

In addition to the libraries, the embeddable AI portfolio includes IBM Watson APIs and applications like IBM Watson Assistant, IBM Watson Discovery, IBM Instana Observability, and IBM Maximo Visual Inspection.

Embeddable AI libraries are small and offer consistent APIs for use across models, making it simpler for businesses to market creative solutions.

Here at CourseMonster, we know how hard it may be to find the right time and funds for training. We provide effective training programs that enable you to select the training option that best meets the demands of your company.

For more information, please get in touch with one of our course advisers today or contact us at training@coursemonster.com

Learn how automation of the procure-to-pay process and other snipping technology is giving businesses a competitive advantage.

The success of a business is significantly influenced by procurement. Nearly every area of a business is addressed by managing the flow of materials and services, planning expenses, and negotiating the best prices, from manufacturing to customer satisfaction. Organizations now have a way to track this complex activity from the moment of need to the last payment thanks to the procure-to-pay procedure. Its goal is to seamlessly integrate the functions of purchasing, supply chain management, and accounts payable.

The procure-to-pay cycle is susceptible to backlogs and inefficiencies because it is so complicated and affects so many areas of the business. Businesses want procure-to-pay software that can locate bottlenecks, improve workflows, and meet all procurement requirements. In an era with unstable supply chains, this includes improving the procure-to-pay process with real-time information and flexible decision-making.

In this article, we’ll look more closely at the procure-to-pay process and how cloud-based procure-to-pay solutions improve productivity and the bottom line.

What is the procure-to-pay process flow?

The cycle of acquiring and accounting for the goods or services required to run a business in a timely way and at an affordable price is known as procure-to-pay, also known as purchase-to-pay or P2P.

For businesses to improve their purchasing decisions and realize cost benefits, such as early payment discounts, a P2P solution aims to document the procurement process and provide a framework for accountability.

Stages of the P2P process

Depending on a wide range of factors, including cost, availability, sustainability, and many more, various companies adopt various ways of the procurement process. Based on its business plan, each organization will create its procurement strategy. Here is an example of a typical enterprise purchasing procedure:

Source-to-pay process

The source-to-pay process, which is usually confused with procure-to-pay, focuses on the first steps of identifying a need and choosing the appropriate items or services to meet that need. These steps include the following:

• Identifying a need: Stakeholders look at business needs and decide what products or services are needed, whether an outside vendor must provide them, and which departments will benefit.
• Selecting goods and services: The manager or department creates a formal purchase requisition that specifies the goods, services, and suppliers they want to work with. This begins the vetting process used by the finance team to place the correct order, choose the best vendor, and look into potential cost savings.

Procurement process

• Purchase order: The finance team issues a purchase order approval and places an order with the vendor for the products and services after analyzing the purchase request’s details and finalizing a contract with the vendor.
• Receiving: Receiving goods and services includes checking to make sure the correct order was given, as well as putting them into the workflow of the department making the request. Examples include onboarding software or delivering components to the appropriate manufacturing department. The accounts payable team receives a goods receipt, which attests that the order was received exactly as it had been.
• Invoice processing and payment: Payment is given to the vendor when the invoice is compared to the order that was placed. Using invoice matching, such as two-way matching, which compares the vendor’s invoice to the specifics of the purchase order, or three-way matching, which compares the specifics of the purchase order, the invoice, and the delivery receipt to ensure they are all in a contract before the vendor payment is made, can be used to accomplish this.

Benefits of an efficient P2P process

There are lots of benefits to a procurement solution that connects the accounting system, the organization workflows, and the procurement department:

• Better supplier relationship: Good relationships are the foundation on which business is built. Being a good customer increases the likelihood that a supplier will go above and beyond in times of need and helps to ensure that products are high quality and delivered on schedule. Keeping a supplier happy requires a system that not only ensures that invoices are paid on time but also provides visibility into the status of the invoice.
• Visibility: Organizations have a complete understanding of their cash flow and financial commitments due to internal control and visibility across the whole P2P cycle. They are simpler to track when all transactions are being captured. Additionally, the data may provide areas for optimization.
• Fraud prevention: While having strong business connections is helpful, it can also provide opportunities for fraud and favoritism. Strict invoice matching and many points of review in a P2P system guard against fraud, such as granting a contract to an unqualified vendor who is personally connected to a customer or making purchases at a lower price than was originally agreed.
• Efficiency: When a system is extremely complex or segregated between a few departments, human error happens. The supply chain accounts payable, and procurement processes can all be centralized to assist businesses to find methods to simplify their operations and pay their bills more quickly.
• Cost saving: The P2P approach helps businesses in finding preferred suppliers, develop good relationships with them, and ultimately secure the best costs. P2P automation and software solutions improve spending management while saving time. Better forecasting is made possible as a result, helping to avoid making an urgent purchase to meet a need as output demand rises.
• Speed: Automation and e-procurement tools help companies to react more quickly to supply chain issues. The procurement process can be improved to save time, free up resources, and accelerate the approval process for a new supplier.
• Predictive modeling: Additional chances for process optimization can be found using data analytics, process mining, and other digital tools. Emerging platforms simulate changes in advance to make sure they have no unexpected consequences.

Addressing the challenges of the P2P process

The procurement process has five key challenges, according to IBM:

• Maverick buying: Spot buying, poor contract management, and unauthorized purchases increase the cost of products and services. Prices are higher during periods of great demand or when purchases are made in lower amounts.
• Deviations: Deviations, such as unexpected changes in the capital markets, technical advances, and spikes or drops in customer demand, are typical parts of corporate processes. Examining data on these deviations can show where the P2P cycle may need to modify, even though deviations are a problem that can increase costs.
• Rework: In the P2P process, manual and repetitive processes are prone to mistakes and eventually slow down a workflow. Reworks take a lot of time, taking the procurement team’s attention away from more important tasks.
• Enabling automation: With outdated or semi-manual P2P systems that do not support automation, organizations may be missing out on a chance for efficiency and cost savings.
• Cash discount losses: Early payment plans provide cash discounts that have been negotiated with suppliers who also help the organization’s supply chain. Organizations miss out on significant cash discounts and run the risk of losing the supplier’s trust if payments are not managed regularly and deadlines are not met.

The role P2P software plays in solving challenges

There are still some businesses today that manage to purchase accounts payable through manual or semi-manual processes, despite the trend toward cloud solutions. Others make use of purchasing software that is integrated with an accounts payable or enterprise resource planning (ERP) system. This may work, however, an ERP system may lack deeper key performance indicator insights (KPIs).

In addition to enhancing compliance and control, a cloud-based P2P software solution with analytics and process mining capabilities can offer deeper insights into global spending and process inefficiencies. A few of the ways it can enhance the process are as follows:

• Automation: Send purchase orders, verify payments, and start costs automatically to get an invoice paid more quickly.
• Track KPIs: KPIs, like lead time, average invoice approval time, and the average cost to process an invoice, offer data about how to improve operations, and hold teams responsible.
• Deeper insights: A P2P system can assist businesses in making informed decisions at a low cost, such as working out which supplier discounts offer genuine value or identifying common approval process bottlenecks.
• Root cause analysis: Within P2P networks, process mining can go further into the root factors of issues to identify where inefficiencies are present.
• Process optimization: It takes time and comes with some uncertainty to modify processes. Changes may be opposed by organizations because they could result in unanticipated issues elsewhere. To find the optimum course of action and protect against unexpected consequences of process changes, an advanced software system can simulate processes.

Here at CourseMonster, we know how hard it may be to find the right time and funds for training. We provide effective training programs that enable you to select the training option that best meets the demands of your company.

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To utilize enterprise data, forward-thinking businesses understand the value of removing data silos.

Within the modern data management stack, metadata management serves an important role. By helping data and analytics teams to better understand the context and quality of data, it helps in the combining of data silos. In turn, this increases confidence in the data and the ensuing decision-making. Manual approaches to metadata management are less than ideal and can lead to missed opportunities as data quantities continue to increase. Consider a situation in which a new data asset becomes available but is kept from your data consumers due to incorrect or insufficient tagging. How can you provide real-time data governance for consistent data while keeping up with increasing data volumes and consumer demand?

To keep up with the growth of enterprise data, it is essential to adapt metadata management strategies. This clarifies the role of active metadata management. Active metadata management, according to Gartner, entails a group of abilities that allow constant access to and processing of metadata.

What is Active Metadata management?

Active metadata management automates metadata processing using machine learning, and then uses the outcomes of that analysis to inform decisions through suggestions, warnings, and other means. In other words, an active metadata system improves the actionability of data in the present. It has some features that enable automated data discovery, boost data confidence, and enable scaleable data security and governance.

Common use cases for active metadata management

Improve data discovery

According to research, most organizations don’t analyze up to 68% of their data. Improving data consumption requires an understanding of the data assets that are available across the organization. AI-driven recommendation engines that examine active metadata and suggest new assets to data users based on their usage habits can enable advanced data discovery.

Provide early indicators of data quality

A challenge to becoming data-driven that organizations must overcome is poor data quality. The majority of data quality management strategies are reactive, only being activated when users express issues with data teams about the accuracy of datasets. Proactive data quality management can improve from active metadata management. Data observability capabilities support solid data by improving it and identifying anomalies in data pipelines, enabling IT teams to quickly identify and fix issues before they have an impact on the company.

Regulatory and compliance

Legal consequences, reputation loss, and loss of customer trust are too great risks to take on. More than 80% of businesses globally will have to deal with at least one privacy-focused data protection policy by 2023, says the Gartner Hype Cycle for Data Privacy 2021. Instead of addressing each issue individually, organizations may increase customer trust by taking a proactive approach to data privacy, protection, and risk management. Organizations may better comply with new data standards by enforcing data policies automatically and putting data protection principles into action at scale with active metadata management.

3 Benefits of an Active Metadata Management Solution

A data fabric solution connects the appropriate data to the appropriate people at the appropriate time and from any location as needed. The IBM Watson Knowledge Catalog for Cloud Pak for Data’s active metadata capabilities are one of the key features of the IBM data fabric system. This data catalog gives data creators and consumers the tools they need to confidently use data throughout its lifecycle while analyzing, trusting, and protecting it.

Know your data

For advanced data discovery and increased trust in data, it is important to make sure that the data is enriched with all the relevant context. Watson Knowledge Catalog provides a solid metadata foundation made up of business keywords, data classifications, and reference data, supported by AI/ML-driven automation, to assist data consumers in finding and understanding data. Users are given the power to identify important assets from throughout the company at scale with the help of intelligent recommendations from IBM Watson and peers. Additionally, Watson Knowledge Catalog’s automated metadata enrichment uses machine learning to automatically assign business keywords to data assets at scale. This makes it easier for customers to find material quickly, decide whether it is acceptable and trustworthy, and decide how to use it.

Trust your data

Data teams must spend a lot of time managing data spread across distributed data environments and providing trusted data due to complex data landscapes and the consequent data silos. Watson Knowledge Catalog does data quality analysis to award quality scores to data assets based on factors such as data class and type violations, duplicate values, missing values, and suspect values to increase the trust in data. Then, specific custom data quality rules can be created to enhance curation processes. The partnership between IBM and MANTA also adds automatic data lineage capabilities, allowing you to track and examine how data is used and consumed across all of your applications and data sources. By actively using past trends and statistics to detect data anomalies in data pipelines, this complements IBM’s acquisition of Databand.ai and its data observability solutions to enable trustworthy data so that IT teams can quickly identify problems before they have an impact on the business.

Protect your data

IBM provides amazing data privacy management tools for the global, dynamic application of your data protection policies. Make data protection rules to restrict access to data assets regardless of where they are located, mask data at the column level, and filter data rows according to row attributes. Through the de-identification of private and confidential information, IBM can aid in the protection of sensitive and important data.

Here at CourseMonster, we know how hard it may be to find the right time and funds for training. We provide effective training programs that enable you to select the training option that best meets the demands of your company.

For more information, please get in touch with one of our course advisers today or contact us at training@coursemonster.com