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jcmarchi · 8 days

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AI Agents Can Change the Business Dynamics in B2B ecommerce

New Post has been published on https://thedigitalinsider.com/ai-agents-can-change-the-business-dynamics-in-b2b-ecommerce/

AI Agents Can Change the Business Dynamics in B2B ecommerce

There’s a reason why today AI is all you hear about. We’ve experienced more AI innovation in the last 18 months than ever before. AI has exited the lab overnight and turned into a viable business driver.

One industry that stands to win big is B2B eCommerce. In fact, B2B eCommerce could use the technological boost to take the industry to the next level. There are a few key reasons for this:

B2B transactions have many moving parts. They often involve multiple stakeholders, complex product configurations, and customized pricing agreements. It can be downright confusing.

There’s just way too much data. B2B eCommerce generates an insurmountable amount of data from various sources such as transaction history, customer interactions, and supply chain operations.

Customers want what they want. B2B buyers increasingly expect personalized experiences similar to those in B2C. Not surprising, and they will only get more demanding.

Competition gets fiercer by the day. The competitive landscape is becoming increasingly crowded, with companies vying for market share and differentiation. Yes, your customers are likely to be using AI to get ahead already.

Supply headaches are real. Supply chains are complex, involving multiple suppliers, distributors, and logistics partners. There are so many elements that are outside of your control.

Neither of the above is surprising. But the fact of the matter is that AI is now at our fingertips. Any organization that fails to jump on the bandwagon is essentially leaving money on the table, and poised to eventually lose customers.

Let’s run through where AI could have the most impact on your organization.

Navigating the intricacies of transactions

As I previously mentioned, B2B eCommerce transactions can involve many parties and other elements. AI can tap into all of these signals to analyze data on stakeholders, product configurations, pricing agreements, and more.

This could help organizations gain a better understanding of each buyer’s and each supplier’s unique needs, which in turn facilitates smoother negotiations, optimized pricing terms, and expedited deal closures. The ultimate result? Cost savings, improved supplier relationships, and faster time-to-market for products and services.

Expense management is another area where AI can have an impact. By analyzing historical spending patterns and supplier performance data, AI agents help businesses make informed decisions, reduce procurement cycle times, and achieve greater transparency and compliance in their procurement processes.

Mo’ data, mo’ problems.

Every company wants more data but also complains about the inability to harness it at scale. AI excels at processing and analyzing large volumes of data, turning it into actionable insights. Large language models in particular are excellent at analyzing transaction history, customer interactions, and supply chain operations to identify patterns, trends, and correlations that may not be immediately apparent to human analysts. For instance, it can identify which product combinations are frequently purchased together, which customers are most likely to churn, or which suppliers have the highest on-time delivery rates.

AI can also serve as a ‘connector’, integrating data from multiple sources such as CRM systems, ERP systems, and external data sources, to provide comprehensive insights into customer behavior, market trends, and competitive dynamics. For example, it can analyze sales performance across different regions, identify emerging market trends, and predict future demand for products or services.

AI agents can make your customers happier.

One of the biggest goldmines for companies is customer conversations. Customer service agents interface with customers on all levels, as they field reviews, complaints, and issues. Customer conversations can even yield insights that could help with product development.

Yet, most companies barely scratch the surface.

The beauty of customer interactions is that they are based on language. AI agents are powered by large language models that not only have the ability to process information at great speeds and volume, but also to respond—i.e., handle orders, resolve queries, provide personalized recommendations, and more.

AI Agents are available around the clock, ensuring customer needs are met promptly and efficiently. This can boost customer satisfaction and free up human resources to focus on more complex, value-adding tasks.

The supply chain conundrum.

It’s no secret that supply chains are intricate (and delicate). AI-driven supply chain optimization tools can improve various aspects, such as inventory management, logistics, and procurement. For example, Oracle Supply Chain Management Cloud utilizes AI algorithms to optimize inventory levels and reduce stockouts while minimizing carrying costs and stockouts by analyzing historical sales data, demand forecasts, and market trends.

Additionally, UPS’s AI-powered logistics optimization platform, ORION (On-Road Integrated Optimization and Navigation), leverages AI algorithms to optimize delivery routes and schedules. By analyzing data on package volume, delivery locations, and traffic patterns, ORION calculates the most efficient routes for UPS drivers, reducing fuel consumption, vehicle wear and tear, and delivery times.

IBM’s Watson Supply Chain is another good example, which applies AI-driven analytics to streamline procurement processes and improve supplier performance. By analyzing data on supplier quality, lead times, and pricing trends, Watson Supply Chain identifies opportunities to consolidate suppliers, negotiate better pricing terms, and mitigate supply chain risks.

Robotic process automation has risen as one of the most interesting areas for companies, with 60% of manufacturing executives polled by Sikich LLC mentioning it as their main area of interest, with machine learning for demand forecasting and predictive analytics also getting some mentions.

This rise in interest is where commerce platforms are needed to act quickly, fulfill this need, and initiate beta testing. Our AI-integrated Data Pipeline saw that manufacturers and other B2B businesses required simplified data consolidation, cutting custom infrastructure costs, which can eat away at their bottom line. B2B businesses wanted an experience similar to a food delivery app where they can easily select relevant datasets, specify retrieval frequency, and destination. This helps them align commerce data with internal sales targets efficiently.

Don’t rest on your laurels.

I just went through some of the ways in which AI agents can improve efficiency, so I’ll spare you the repetition. What I will say is: act now. If you’re not already using AI in some way, be warned that your competitors are.

It’s never been easier and more accessible to tap into model APIs and build your own system. If you don’t want to build, you can buy and experiment, as long as you reap the benefits. Just don’t wait too long.

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salesmarkglobal · 12 days

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Is B2B Cold Calling Still Relevant Today?

Despite digital trends, it's a key tool for reaching prospects. You can take a look at its benefits and challenges in today's sales landscape. Explore further insights on our blog! https://lnkd.in/dAB-WFDF

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cleverstacksblog · 1 month

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#cloud call center #cloud calling #real time analytics #voip technology #business voip #voip solutions #voice broadcasting #voice message #entrepreneur #b2b #whatsapp business api #bulk sms #sms api

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commsaquitilabs · 1 month

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https://www.acuitilabs.com/q2c-customerportal/

#Q2C Customer Portal #Subscription Management Software #SAP BRIM Integration #S4HANA Cloud Billing #Self-Service Subscription Platform #Acuiti Labs Billing Solutions #Subscription Billing Automation #Customer Self-Management Tools #B2B Subscription Management #Mobile Subscription Management App #Scalable Subscription Software #Subscription Lifecycle Management #Invoicing and Payment Automation

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alpinesoft-blog · 3 months

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Transform YourBusiness Through Whatsapp API

Alpinesoft IT Solutions is your dedicated partner in transforming your business through WhatsApp API. We have established partnerships with official API providers like Wati, Gupshup, Haptic, and more. Our experienced team is ready to assist you in leveraging the power of WhatsApp API to enhance your customer communication and engagement. We offer support to get your account Meta verified. Our goal is to help your business achieve success by harnessing the full potential of WhatsApp, including obtaining the coveted Green Tick verification. Trust Alpinesoft IT Solutions to guide you towards unlocking the numerous benefits of WhatsApp API for your business transformation.

Contact: 011 4166 1883

E-mail: [email protected]

#google #blog #facebook #clouds #google workspace #office 365 #reddit #zoho #web development #b2bmarketing #b2b services

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cymetrixsoftware · 4 months

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Salesforce Commerce Cloud for B2B: How to transform your business

If you are looking for an e-commerce solution that can handle the complex and diverse needs of B2B customers, you should consider Salesforce Commerce Cloud. Commerce Cloud is a cloud-based platform that allows you to create customized online storefronts for your B2B accounts, with features such as: - Multiple storefronts and product catalogs, Self-service portal for buyers, etc. Moreover, Commerce Cloud can also seamlessly integrate with Salesforce CRM, giving you a 360-degree view of your B2B customers and their interactions. If you want to know more, we suggest reading about how Salesforce Commerce Cloud for B2B can help you transform your business.

#salesforce #salesforce cloud #salesforce commerce cloud #b2b #salesforce commerce cloud for b2b #cymetrix software #cymetrix #cymetrix salesforce partner #salesforce crm #cymetrix salesforce #crm

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callerdesk · 6 months

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Discover the power of cloud telephony for the education industry. Enhance communication and streamline operations with a reliable, scalable, and cost-effective solution. Increase efficiency, improve student satisfaction, and transform your educational institution with cloud telephony technology. Explore our offerings today.

#cloudtelephony #callerdesk #cloudcallcenter #cloud telephony #cloud telephony in noida #cloud telephony providers in india #businesssolution #b2bmarketing #boostyourbusiness #b2b

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cowidor · 6 months

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Star Moon Rainbow Explosive Shower Steamer

Our shower steamer tablets have enough bubbles to keep fragrance for a long tim.The Rainbow Series Explosive Salts give you a different experience.

#fashion #bath #bath bombs #bathfizzle #showersteamer #bathbombs #essential oils #essential spa #shower steamers #b2b services #wholesalebathbombs #wholesalers #bathbombs supplier #supplier #manufacturers #rainbowbathbomb #star #moon #clouds

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jcmarchi · 23 days

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How platformization is transforming cyber security - CyberTalk

New Post has been published on https://thedigitalinsider.com/how-platformization-is-transforming-cyber-security-cybertalk/

How platformization is transforming cyber security - CyberTalk

With more than 15 years of experience in cyber security, Manuel Rodriguez is currently the Security Engineering Manager for the North of Latin America at Check Point Software Technologies, where he leads a team of high-level professionals whose objective is to help organizations and businesses meet their cyber security needs. Manuel joined Check Point in 2015 and initially worked as a Security Engineer, covering Central America, where he participated in the development of important projects for multiple clients in the region. He had previously served in leadership roles for various cyber security solution providers in Colombia.

In this insightful Cyber Talk interview, Check Point expert Manuel Rodriguez discusses “Platformization”, why cyber security consolidation matters, how platformization advances your security architecture and more. Don’t miss this!

The word “platformization” has been thrown around a lot recently. Can you define the term for our readers?

Initially, a similar term was used in the Fintech industry. Ron Shevlin defined it as a plug and play business model that allows multiple participants to connect to it, interact with each other and exchange value.

Now, this model aligns with the needs of organizations in terms of having a cyber security platform that can offer the most comprehensive protection, with a consolidated operation and easy enablement of collaboration between different security controls in a plug and play model.

In summary, platformization can be defined as the moving from a product-based approach to a platform-based approach in cyber security.

How does platformization differ from the traditional way in which tech companies develop and sell products and services?

In 2001, in a Defense in Depth SANS whitepaper, Todd McGuiness said, “No single security measure can adequately protect a network; there are simply too many methods available to an attacker for this to work.”

This is still true and demonstrates the need to have multiple security solutions for proper protection of different attack vectors.

The problem with this approach is that companies ended up with several technologies from different vendors, all of which work in silos. Although it might seem that these protections are aligned with the security strategy of the company, it generates a very complex environment. It’s very difficult to operate and monitor when lacking collaboration and automation between the different controls.

SIEM and similar products arrived to try to solve the problem of centralized visibility, but in most cases, added a new operative burden because they needed a lot of configurations and lacked automation and intelligence.

The solution to this is a unified platform, where users can add different capabilities, controls and even services, according to their specific needs, making it easy to implement, operate and monitor in a consolidated and collaborative way and in a way that leverages intelligence and automation.

My prediction is that organizations will start to change from a best-of-breed approach to a platform approach, where the selection factors will be more focused on the consolidation, collaboration, and automation aspects of security controls, rather than the specific characteristics of each of the individual controls.

From a B2B consumer perspective, what are the potential benefits of platformization (ex. Easier integration, access to a wider range of services…)?

For consumers, the main benefits of a cyber security platform will be a higher security posture and reduced TCO for cyber security. By reducing complexity and adding automation and collaboration, organizations will increase their abilities to prevent, detect, contain, and respond to cyber security incidents.

The platform also gives flexibility by allowing admins to easily add new security protections that are automatically integrated in the environment.

Are there any potential drawbacks for B2B consumers when companies move towards platform models?

I have heard concerns from some CISOs about putting all or most of their trust in a single security vendor. They have in-mind the recent critical vulnerabilities that affected some of the important players in the industry.

This is why platforms should also be capable of integration through open APIs, permitting organizations to be flexible in their journey to consolidation.

How might platformization change the way that B2B consumers interact with tech companies and their products (ex. Self-service options, subscription models)? What will the impact be like?

Organizations are also looking for new consumption models that are simple and predictable and that will deliver cost-savings. They are looking to be able to pay for what they use and for flexibility if they need to include or change products/services according to specific needs.

What are some of main features of a cyber security platform?

Some of the main features are consolidation, being able to integrate security monitoring and management into a single central solution; automation based on APIs, playbooks and scripts according to best practices; threat prevention, being able to identify and block or automatically contain attacks before they pose a significant risk for an organization…

A key component of consolidation is the use of AI and machine learning, which can process the data, identify the threats and generate the appropriate responses.

In terms of collaboration, the platform should facilitate collaboration between different elements; for example sharing threat intelligence or triggering automatic responses in the different regions of the platform.

In looking at platformization from a cyber security perspective, how can Check Point’s Infinity Platform benefit B2B consumers through platformization principles (ex. Easier integration with existing tools, all tools under one umbrella…etc)?

The Check Point Infinity platform is a comprehensive, consolidated, and collaborative cyber security platform that provides enterprise-grade security across several vectors as data centers, networks, clouds, branch offices, and remote users with unified management.

It is AI-powered, offering a 99.8% catch rate for zero day attacks. It offers consolidated security operations; this means lowering the TCO and increasing security operational efficiency. It offers collaborative security that automatically responds to threats using AI-powered engines, real-time threat intelligence, anomaly detection, automated response and orchestration, and API-based third-party integration. Further, it permits organizations to scale cyber security according to their needs anywhere across hybrid networks, workforces, and clouds.

Consolidation will also improve the security posture through a consistent policy that’s aligned with zero trust principles. Finally, there is also a flexible and predictable ELA model that can simplify the procurement process.

How does the Check Point Infinity Platform integrate with existing security tools and platforms that CISOs might already be using?

Check Point offers a variety of APIs that make it easy to integrate in any orchestration and automation ecosystem. There are also several native integrations with different security products. For example, the XDR/XPR component can integrate with different products, such as firewalls or endpoint solutions from other vendors.

To what extent can CISOs customize and configure the Check Point Infinity Platform to meet their organization’s specific security posture and compliance requirements?

Given the modular plug and play model, CISOs can define what products and services make sense for their specific requirements. If these requirements change over time, then different products can easily be included. The ELA consumption model gives even more flexibility to CISOs, as they can add or remove products and services as needed.

How can platformization (whether through Infinity or other platforms) help businesses achieve long-term goals? Does it provide a competitive advantage in terms of agility, innovation and cost-efficiency?

A proper cyber security platform will improve the security posture of the business, increasing the ability to prevent, detect, contain and respond to cyber security incidents in an effective manner. This means lower TCO with increased protection. It will also allow businesses to quickly adapt to new needs, giving them agility to develop and release new products and services.

Is there anything else that you would like to share with Check Point’s thought leadership audience?

Collaboration between security products and proper intelligence sharing and analysis are fundamental in responding to cyber threats. We’ve seen several security integration projects through platforms, such as SIEMs or SOARs, fail because of the added complexity of generating and configuring the different use cases.

A security platform should solve this complexity problem. It is also important to note that a security platform does not mean buying all products from a single vendor. If it is not solving the consolidation, collaboration problem, it will generate the same siloed effect as previously described.

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onlinelms · 7 months

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Implementing a Learning Management System (LMS) in the field of Engineering and Construction can bring about various benefits, enhancing training, collaboration, and overall efficiency within the industry.

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cleverstacksblog · 1 month

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cloudlinkus · 7 months

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Performance Prediction: Utilizing Machine Learning for HR Decision-Making

Machine learning has garnered significant attention and is revolutionizing various facets of business operations. In the HR domain, the integration of machine learning and artificial intelligence (AI) has opened new possibilities for enhancing decision-making processes. By leveraging advanced analytics and predictive algorithms, machine learning is empowering HR professionals to make data-driven decisions and streamline various functions within the HR department.

How is Machine Learning Revolutionizing HR Processes?

Machine learning is offering numerous benefits to HR functions, transforming the way human resources are managed within organizations. The use of machine learning in HR enables the automation of routine tasks, freeing up valuable time for HR professionals to focus on strategic initiatives. Additionally, machine learning algorithms can help HR teams identify key patterns and trends in HR data, facilitating better decision-making processes and enhancing overall efficiency.

Benefits of using Machine Learning in HR

The integration of machine learning in HR brings various benefits such as automation of repetitive tasks, data analysis, and predictive analytics. This aids HR professionals in making informed decisions and streamlining processes within their departments.

Potential Use Cases of Machine Learning in HR

Machine learning can be applied across various HR functions, including recruitment and talent acquisition, employee engagement and retention, and performance prediction and people analytics. The utilization of machine learning algorithms in these areas can significantly enhance the efficiency and effectiveness of HR processes.

Challenges of Implementing Machine Learning in HR

Despite the potential benefits, implementing machine learning in HR comes with its own set of challenges. These may include the integration of new technologies, data privacy concerns, and the need for upskilling HR professionals to effectively utilize machine learning tools.

What are the Key Applications of Machine Learning in HR?

The applications of machine learning in HR are diverse, spanning across critical areas such as recruitment and talent acquisition, employee engagement and retention, and performance prediction and people analytics. Each of these applications is crucial in optimizing HR functions and processes, ultimately leading to improved organizational outcomes.

Recruitment and Talent Acquisition

Machine learning can revolutionize the recruitment process by enabling HR professionals to identify and attract top talent more efficiently. By analyzing vast amounts of data, machine learning algorithms can help predict the suitability of candidates for specific roles, streamlining the hiring process and improving overall recruitment outcomes.

Employee Engagement and Retention

Machine learning plays a vital role in gauging employee engagement and predicting potential turnover. By analyzing employee data and identifying relevant patterns, HR teams can proactively address retention issues and implement strategies to enhance employee satisfaction and loyalty.

Performance Prediction and People Analytics

With machine learning algorithms, HR professionals can leverage predictive analytics to anticipate performance trends and identify factors influencing employee productivity. This can lead to better-informed decisions regarding talent development, workforce planning, and overall performance management within the organization.

Why is Data-Driven Decision-Making Crucial for HR Professionals?

Data-driven decision-making is essential for HR professionals as it enables them to harness the power of predictive analytics and advanced algorithms to enhance various HR functions and processes. By utilizing supervised and unsupervised learning techniques, HR professionals can gain deeper insights into employee behaviors, performance patterns, and predictive trends, thereby empowering them to make informed decisions related to talent management and organizational success.

Role of Predictive Analytics in HR Functions

Predictive analytics powered by machine learning can revolutionize HR functions by enabling HR professionals to forecast future trends and make accurate predictions related to employee turnover, performance, and talent availability. This proactive approach helps HR departments in strategic workforce planning and talent management.

Utilizing Supervised and Unsupervised Learning in HR

The use of supervised and unsupervised learning algorithms empowers HR teams to categorize and analyze vast amounts of HR data, uncovering hidden patterns, and gaining valuable insights into employee behavior and performance. These insights play a pivotal role in informing HR strategies and decision-making processes.

Enhancing HR Processes with Reinforcement Learning

Reinforcement learning can be utilized to optimize HR processes by continuously learning from previous experiences and feedback. This adaptive approach can improve the efficiency of HR operations and lead to more effective decision-making within the HR domain.

How Can Machine Learning Improve HR Functionality?

Machine learning has the potential to significantly improve HR functionality by automating routine tasks, identifying key patterns and trends in HR data, and implementing predictive analytics to anticipate turnover rates. These advancements enable HR professionals to focus on strategic initiatives and make data-driven decisions that positively impact organizational performance and employee satisfaction.

Automating Routine HR Tasks and Processes

Machine learning can streamline and automate repetitive HR tasks such as resume screening, scheduling interviews, and onboarding processes. By automating these routine functions, HR professionals can allocate their time and resources more effectively to strategic HR initiatives, and enhance the overall candidate and employee experience.

Identifying Key Patterns and Trends in HR Data

Machine learning algorithms are adept at identifying complex patterns and trends within HR data, providing HR professionals with valuable insights into employee behaviors, performance indicators, and potential areas for improvement. This empowers HR teams to make informed decisions and implement targeted strategies for talent management and organizational development.

Implementing Predictive Analytics to Anticipate Turnover Rates

By leveraging predictive analytics, HR professionals can anticipate turnover rates and identify potential attrition risks within their organization. This proactive approach allows HR departments to develop retention strategies and implement interventions to mitigate turnover, thereby fostering a more stable and engaged workforce.

What Is the Future of Machine Learning and AI in the HR Domain?

The future of HR is closely intertwined with the adoption and integration of AI and machine learning technologies. These advanced analytics capabilities are poised to transform HR practices, empowering HR professionals with advanced insights and tools for enhancing decision-making processes and optimizing HR operations.

Adoption and Integration of AI in HR Decision-Making

The adoption of AI technologies in HR decision-making is expected to grow, enabling HR professionals to leverage advanced algorithms for talent management, performance evaluation, and workforce planning. This integration of AI in HR will lead to more efficient and strategic decision-making processes within HR departments.

Empowering HR Professionals with Advanced Analytics

AI and machine learning technologies empower HR professionals with advanced analytics capabilities, enabling them to gain deeper insights into employee behaviors, performance metrics, and predictive trends. These insights play a crucial role in shaping HR strategies and driving organizational success.

Transforming HR Practices through Machine Learning and AI

The integration of machine learning and AI technologies is set to transform traditional HR practices by enabling HR professionals to make more informed decisions, implement targeted strategies, and foster a data-driven culture within HR departments. This transformation will drive enhanced efficiency and effectiveness across various HR functions and processes.