Dubai, UAE – May 2024: Imran Moinuddin, Senior Vice President of Data and AI at VentureDive, a leading technology solutions company, was at the forefront as a panelist at the Data Innovation Summit MEA 2024. The summit, held on May 8-9 at the Hilton Dubai Palm Jumeirah, brought together over 300 delegates and industry leaders to explore the future of technology and innovation.
The Data Innovation Summit MEA 2024 provided a platform for organizations to accelerate their advanced analytical capabilities and stay competitive in the Digital and AI Economy. Imran joined a distinguished panel moderated by, Armin Kech, from a diverse set of industries including, but not limited to healthcare, technology, and AI. The panel included David Dadoun, President of Data, Analytics, and IT Innovations at BRP, Rizwan Tufail, Chief Data Officer at PureHealth, and Giuseppe Mura, Director of Solution Engineering, EMEA at Software AG, to discuss the theme “From Experimentation to Industrialisation – How to truly create impact with Advanced Analytics and AI.”
Imran offered valuable insights on scaling AI initiatives within organizations during the panel discussion. He emphasized that achieving small, consistent successes is key to building confidence and momentum around AI projects. By starting with focused, manageable use cases and gradually expanding their scope, organizations can pave the way for significant achievements and cultivate a culture of innovation.
Imran also highlighted, “Data adoption is an ongoing process that necessitates a commitment to continuous learning and a clear set of priorities”. His insights underscored the importance of investing in technical expertise and cultural change to unlock the potential of data-driven transformation within organizations.
It became clear that three key trends are shaping the future of AI. Imran reflected, “Firstly, we’re seeing a significant push towards power efficiency, aligning AI models with climate priorities and reducing their environmental impact. Secondly, computing efficiency is advancing rapidly, enabling more decision-making at the edge and closer to the data source. Finally, there’s an acceleration in the commodification of advanced AI capabilities, freeing practitioners to focus on adoption, integration, and value creation rather than complex model development.”
These trends emphasize a significant shift for AI in business, making it more accessible, sustainable, and integrated. VentureDive is leading this transformation, with their deep expertise and commitment playing a pivotal role in shaping data-driven, AI-powered solutions, earning them the trust of leading Fortune 50.
For more details, visit VentureDive to learn more about our Data & AI capabilities.
Banks are increasingly using Large Language Models (LLMs) to enhance their digital services, applying them in areas like fraud detection and customer service. These models process and analyze large data volumes efficiently, providing quick insights crucial in the banking sector. With their capacity to understand and interact using natural language, large language models in banking are helping offer more personalized and responsive services to their customers.
The effectiveness of LLMs stems from their training on diverse and extensive datasets, which equips them to handle the complex language often used in finance. With the market for natural language processing expected to grow significantly, from $11 billion in 2020 to an estimated $35 billion by 2026, the integration of LLMs in banking operations is becoming vital. This is an important strategy for maintaining competitiveness and improving customer satisfaction. This guide will outline practical steps for integrating LLMs into your banking operations, focusing on both the benefits and the straightforward implementation of this technology.
Understanding LLMs in Banking and Financial Services
Large Language Models (LLMs) are a subset of Generative AI specializing in understanding, generating, and interacting with human-like text. They are built on deep learning techniques and trained on extensive datasets containing billions of words sourced from various mediums like websites, books, and articles. This training allows LLMs to comprehend and generate responses with remarkable proficiency, including the nuances of grammar and context.
Advancements in AI for Enhanced Banking Services
Integrating LLMs into Digital Banking Operations
The integration of LLMs into digital banking operations is crucial for several reasons. First, it allows banks to enhance efficiency, particularly in compliance and reporting tasks. LLMs automate the collection of vetted data from diverse sources, improving speed and accuracy in decision-making with AI, essential for meeting regulatory obligations.
Moreover, LLMs facilitate a more personalized banking experience. AI-driven tools can engage customers with human-like interactions, making services like customer support more efficient and convenient. This level of personalization not only improves customer satisfaction but also drives operational efficiencies.
Additionally, the implementation of LLMs in banking can significantly bolster security measures. These models are adept at identifying patterns and anomalies that could indicate fraudulent activities, thus protecting the financial assets of both the institution and its customers.
Key Use Cases of LLMs in Banking
Fraud Detection and Prevention
By analyzing extensive financial data in real-time, LLMs can significantly enhance fraud detection in banking. This integration enables banks to proactively identify and respond to threats more quickly than traditional methods, thereby improving both immediate security measures and long-term defenses against evolving fraud tactics.
Credit Risk Assessment and Management
Due to LLMs, there has been a significant change in the way banks assess and manage credit risk by efficiently analyzing extensive data sources like customer data, credit history, and market conditions. This allows financial institutions to make quicker, more informed decisions. Additionally, LLMs enhance credit evaluations by simulating various economic scenarios and predicting their impacts on credit risk, thus providing a dynamic and sophisticated risk assessment tool.
Enhancing Customer Service
In customer service, LLMs augment interactions via AI-driven chatbots and virtual assistants that simulate human conversations. These systems can address a wide array of customer queries, from simple to complex, autonomously improving service availability 24/7, and reducing costs related to hiring and retaining human staff. Additionally, these LLM-powered tools continuously learn from each customer interaction, progressively refining their responses and overall interaction quality.
Streamlining Onboarding Processes
LLMs in banking optimize customer onboarding by guiding new clients through the setup process, swiftly and accurately verifying personal information, and providing timely support. This fast-tracks the onboarding process while enhancing the overall customer experience, potentially boosting customer retention and satisfaction during these crucial initial interactions.
Investment and Trading Assistance
LLMs are increasingly utilized in the investment and trading sectors of banking to analyze market data and trends, thereby aiding investors and traders in making well-informed decisions. They generate trading signals from real-time market analysis, forecast market movements, and provide personalized investment advice. By integrating into trading platforms, LLMs enhance investment strategy effectiveness and risk management by efficiently processing and analyzing large datasets much faster than human analysts.
Challenges and Considerations
While the usage of large language models in financial services presents a number of opportunities, banks need to be vary of possible challenges that stand in the way of their adoption too.
Navigating Regulatory Compliance
Banks must navigate a complex web of financial regulations that vary by region, making LLM compliance challenging. They must ensure that LLM-driven operations adhere to laws regarding transparency, accountability, and consumer protection. Developing compliance frameworks adaptable to changing regulations in financial technologies is essential, requiring regular audits and possibly even specialized legal expertise to maintain compliance.
Addressing Data Privacy and Security Concerns
The integration of LLMs raises significant data privacy and security issues, as these systems process vast amounts of sensitive information. To mitigate risks, banks must implement robust encryption methods, secure data storage solutions, and access controls. Regular security assessments and adherence to international data protection standards (such as GDPR) are crucial in preventing data breaches and unauthorized access.
Managing Technology Updates and Maintenance
LLM systems require ongoing maintenance to perform optimally and securely. This includes routine software updates to incorporate the latest AI advancements and security patches to protect against new vulnerabilities. Establishing a schedule for regular system reviews and updates can help ensure that the LLMs remain smooth and secure over time.
Training and Upskilling Employees for LLM Usage
As LLMs become integral to banking operations, employees must be trained on how to use these technologies as well as their potential implications. Training programs should be focused on the technical aspects of LLMs and the ethical Generative AI considerations regarding their usage. Upskilling employees ensures they are competent in managing AI tools and can leverage them to enhance productivity and decision-making.
Balancing AI and Human Intervention in Customer Services
While LLMs can significantly enhance efficiency in customer service, maintaining a balance with human oversight is crucial. Banks should design their AI systems to escalate complex or sensitive issues to human operators. This hybrid approach ensures that while routine queries are handled efficiently by AI, the nuances and empathy of human interaction are available when needed. This further maintains trust and satisfaction among customers.
Best Practices for Integrating LLMs in Banking
Potential Future Developments
The banking and financial services sector is poised to see significant advancements in LLM technology, including more sophisticated AI models that better understand and predict customer behaviors. Upcoming innovations may involve deeper integration of LLMs with blockchain for enhanced security and transparency. Moreover, the use of LLMs in creating more dynamic, real-time financial products tailored to individual customer needs as well.
LLMs are expected to transform the banking landscape by improving efficiency in customer service, risk management, and compliance. Over time, these technologies will likely lead to more personalized banking experiences. Its faster and more accurate risk assessments, and more stringent controls against financial crimes, potentially reducing costs and increasing trust and loyalty among customers.
Embracing LLMs in Banking by Partnering with a FinTech Provider
The integration of LLMs offers vast potential to enhance various aspects of banking, from customer interactions and fraud detection to compliance and operational efficiency. For banks looking to harness the power of this technology, partnering with experts like VentureDive can provide the necessary expertise and innovative solutions to ensure successful integration. By embracing LLMs in banking services, the institutions can achieve significant competitive advantages, and future-proof their operations.
The traditional manual approaches to compliance are becoming outdated. As regulations increase in number and complexity, businesses face a growing challenge in staying compliant with an ever-expanding array of regulations. This issue spans across sectors, impacting companies of all sizes, and leading to a significant drive towards automated regulatory compliance.
Automated compliance solutions offer a streamlined way to ensure adherence to regulations, reducing the risk of human error and the anxiety over potential non-compliance penalties.
Greg, a compliance officer in a fintech company, typically occupies himself with two major concerns:
Safeguarding personal data
Securely handling sensitive user information
The stakes are high, as failure in these areas could lead to severe financial penalties and erosion of customer trust. Greg’s search for robust security measures is driven by the need to adapt to both emerging threats and changing regulations.
For Greg and many others in similar positions, automated compliance tools promise simplified compliance processes. This makes understanding the complexities of data protection and handling of sensitive information easy. It is a strategic move towards more efficient, reliable, and secure business operations.
Outcomes of Automated Regulatory Compliance
Automated regulatory compliance refers to using technology solutions to manage and meet regulatory requirements efficiently. In essence, it’s about employing software that streamlines the complex and often cumbersome process of keeping up with regulations.
Automated regulatory compliance is increasingly becoming a staple in modern business operations. It is due to its potential to significantly reduce the time and resources spent on compliance tasks, minimizing human error and ensuring a higher level of precision.
The significance of this shift towards automation is underscored by the growing costs and risks associated with non-compliance. Failing to meet regulatory standards can lead to hefty penalties, which have reached an alarming average cost in recent years, emphasizing the financial risks of non-compliance.
Non-compliance with financial rules and regulations can have severe repercussions beyond financial penalties, severely damaging a business’ reputation. Such violations may lead to customer churn, and negative media coverage, and deter potential clients and investors. The tarnished reputation can decrease market value and increase regulatory scrutiny, raising compliance costs. In severe cases, it can result in legal actions and potentially lead to business closure. Hence, maintaining compliance is essential not just legally but for sustaining a company’s credibility and financial stability.
Sector-Specific Compliance Challenges
The implementation of AI-based regulatory compliance presents unique challenges across different sectors that businesses need to navigate carefully. Some use cases are as follows:
CPG Retail
In the CPG (Consumer Packaged Goods)/retail sector, the regulatory compliance workflow focuses heavily on consumer safety and product standards. The requirements are often related to product labelling, safety testing, and environmental impact, ensuring that products are safe for consumption and accurately represented to consumers. Key compliance protocols in this sector include:
Fintech
The Fintech sector deals with more stringent compliance requirements, driven by the need to protect consumers’ financial information, ensure transaction security, and prevent financial crimes. Important FinTech regulations include:
Healthcare
Healthcare compliance is critically focused on patient data protection and clinical standards to ensure patient safety and confidentiality, especially when it comes to healthcare technologies. The sector must comply with the following regulations such as:
Implementing Automation for Compliance Management
Businesses are increasingly adopting automation compliance solutions to manage complex regulations. This involves using advanced tools that streamline processes by applying data analytics to improve compliance practices.
Benefits of Automated Compliance Management
Automated compliance solutions empower how businesses approach regulatory obligations, offering several advantages that significantly enhance overall operations.
Improved Efficiency and Accuracy
Automation in compliance management streamlines the collection, processing, and reporting of compliance-related data, reducing the need for manual intervention. This not only accelerates workflows but also minimizes the risk of human errors, ensuring that compliance data is both accurate and reliable. For example, using automated systems for tracking and managing compliance requirements can cut down the time teams spend on manual data entry and verification, allowing them to focus on more strategic tasks.
One of the world’s largest banking and financial services organizations, HSBC, utilized automation and AI in its compliance processes to enhance efficiency and accuracy. The bank implemented AI-driven tools to improve its anti-money laundering (AML) and fraud detection processes. This led to a significant reduction in false positives and more effective identification of potential fraudulent activities.
Enhanced ROI
By automating compliance tasks, businesses can allocate their resources more effectively, resulting in a better ROI. Automated systems reduce the need for extensive manual labor, lower the risk of non-compliance penalties, and help avoid the costs associated with compliance failures, such as data breaches or regulatory fines. Moreover, these systems can adapt to new regulations quickly, preventing costly overhauls of compliance programs whenever there are regulatory changes.
A leading internet infrastructure provider tackled compliance with the Sarbanes-Oxley Act (SOX) and the Health Insurance Portability and Accountability Act (HIPAA) by leveraging MetricStream’s solutions. This approach centralized their internal controls, streamlined workflows, and enhanced reporting capabilities, resulting in improved operational testing and risk assessment abilities. The comprehensive dashboard provided by MetricStream offered a panoramic view of internal controls and compliance processes, highlighting high-priority cases and facilitating effective communication with stakeholders.
Impact on Audit Processes
The implementation of automated compliance management significantly improves the auditing process. It provides auditors with easy access to a centralized repository of compliance information, streamlining the audit workflow. This means audits can be completed faster and with greater confidence in the integrity of the compliance data. Automated logs and tracking of compliance actions also offer auditors detailed insights. These insights convert into the compliance posture of a business at any given time, enhancing the overall auditing experience.
To comply with various global regulations, Airbnb has employed automated systems to manage and monitor its vast amount of user data and transactions. This has enabled the company to efficiently handle regulatory requirements across different countries, promising user safety and data protection while maintaining a strong compliance posture.
Conclusion
Automated regulatory compliance reduces risks by optimizing and enhancing the accuracy of compliance processes. With technological advancements and AI integration, the future of compliance management is set to become more efficient and less prone to human error. Businesses can focus more on their core activities by leveraging technology to ensure compliance. It will allow them to innovate and grow while staying compliant with their regulatory obligations.
VentureDive is your trusted partner in this journey, leveraging AI to offer tailored regulatory compliance solutions that safeguard your operations against compliance pitfalls. Discover how VentureDive can transform your compliance management with innovative, AI-powered solutions.
The Quick Service Restaurant (QSR) or the fast food industry has undergone a dramatic shift, moving from assembly line-style cooking to advanced, digital-first operations. This reflects the industry pivoting towards automation and intelligent solutions to serve the modern customer’s needs. The fast-food market is expected to boom from $647.7 billion in 2021 to $998 billion by 2028. This growth emphasizes the essential role of AI and machine learning in refining food preparation and kitchen processes, meanwhile also elevating customer service experiences. This is through the integration of AI in QSRs for delivering quick, personalized, and digital-first dining experiences for demanding customers.
The push towards adopting artificial intelligence tackles the industry’s challenges head-on, from managing high volumes of inquiries to personalizing the customer experience. This shift meets the expectations of today’s consumers and positions QSRs to tap into the significant market growth opportunities ahead. Let’s explore how QSRs or fast food restaurants using AI are pioneering customer experiences today.
AI in Action in Quick Service Restaurants
McDonald’s leads the Quick Service Restaurant industry by integrating geofencing technology and AI in its restaurants to enhance service efficiency. The “Ready-on-Arrival” technology allows McDonald’s to optimize order preparation time based on the customer’s proximity. This ensures fresh meals with reduced latency. AI further aids McDonald’s in predicting customer behavior and trends, managing inventory, and offering personalized menu recommendations. With the infusion of AI, McDonald’s AI restaurant has set a benchmark for innovation in the QSR sector, demonstrating a commitment to leveraging digital advancements to meet evolving consumer expectations.
Following a similar example, Chick-fil-A implemented geofencing and AI technologies. By predicting customer arrivals and personalizing services, they ensure timely food preparation and efficient queue management. This technology optimized operations and experience by reducing wait times and offering tailored services.
Wingstop, Taco Bell, and Chipotle stayed caught up in this competitive, tech-driven industry, pioneering digital-only restaurant concepts, and leveraging AI and automation in the QSR sector. These brands are adopting digital innovations like automated ordering systems and AI-driven kitchen operations, aiming to meet the demands of a digital-first consumer base.
AI-Driven Kitchen Operations
Key Applications of AI in Fast Food Restaurants or QSRs
Challenges of Implementing AI in Fast Food Restaurants & QSRs
Implementing AI in fast food restaurants and QSRs presents several challenges, which are as follows:
While such challenges present hurdles in the adoption of AI in fast-food restaurants, the potential benefits far outweigh these obstacles. QSRs that strategically embrace AI and ML technologies stand to overcome these barriers. Moreover, it will significantly enhance their competitive position in the progressing digital marketplace.
Future Directions of AI in the Fast Food Industry
Looking ahead, the potential and examples for examples of AI in restaurants and food services extend into exciting territories:
Final Thoughts
AI’s role in the QSR industry is pivotal, enhancing both efficiency and engagement while tackling significant challenges. It offers opportunities for fast-food restaurants to stand out by focusing on aspects like staff scheduling, automation, and personalized service; enhancing the customer experience in QSRs.
VentureDive’s Data and AI solutions are positioned to support QSRs in leveraging these advancements for improved operations, underscoring AI’s growing influence on the sector’s future. The technology solutions provider is actively pursuing and working in the progressive QSR industry to create Data and AI solutions that make a difference.
Connect with us to discuss more on our work in industries like CPG, Retail, BFSI, Healthcare, and more.
Imagine a store where each experience is uniquely tailored, so it feels as though the whole store has been specially crafted for you.
Well, you’re not alone, as the majority of shoppers are increasingly undergoing such a shopping experience, causing a significant hike in sales. It’s not some sorcery, its the use of AI to predict customer behavior. In 2023, the global retail e-commerce market soared to an impressive 5.8 trillion U.S. dollars, with projections forecasting a 39% surge, aiming to eclipse eight trillion dollars by 2027. This growth is largely attributed to a shift in customer engagement, transforming shopping into a journey of deep personalization, driving the retail sector to new heights.
Source: Statista
You must have received product recommendations or advertisements on your social media app that are so spot-on. So much that it feels like Google, Facebook, or Instagram are eavesdropping in on your conversations or browsing history, even in Incognito mode! It turns out, they kind of are—using AI. This technology has been making waves across the retail sector, with its market size leaping from USD 6 billion in 2022 to a projected USD 100 billion by 2032. That’s a Compound Annual Growth Rate (CAGR) of 30%! And while only a fraction of retail harnesses AI effectively, a staggering 80% of retail executives predict their operations will be powered by AI-driven automation by 2027.
AI has driven $150 billion in value in overall AI technologies for the GCC countries alone, contributing to more than 9% of their combined GDP. Talk about economic growth; in this $150 billion, the retail industry is a contributing factor. AI is creating shopping experiences that are more engaging, efficient, and tailored to individual preferences. Moreover, the GCC region is focused on adopting this sort of shopping experience for their customers.
Retailers use AI to predict consumer behavior with greater accuracy, what the user is likely to want, and offer seamless and uniquely tailored shopping experiences (details and examples mentioned below). But how is AI enabling it all at the back end? Let’s discuss this and more below.
AI’s Role in Predicting Customer Behavior in Retail and CPG
Artificial Intelligence and Machine Learning are foundational technologies reshaping retail and CPG sectors. AI refers to computers performing tasks that typically require human intelligence, while ML, a subset of AI, involves algorithms learning from data to make predictions or decisions without being explicitly programmed.
Retailers and CPG companies leverage these technologies to understand and anticipate consumer behavior, preferences, and trends. By analyzing historical and current data, AI and ML enable customer behavior prediction for businesses. Through this, they are filling the gap between traditional guesswork and data-driven decision-making. This strategic use provides a competitive edge, allowing for more personalized customer experiences and efficient inventory management.
Data Collection Methods
Data collection in retail encompasses a wide range of methods, making the entire operation successful. It’s crucial because it allows retailers to tailor their offerings and communications precisely to individual preferences and needs. This personalized approach meets customer expectations and drives loyalty and sales, ultimately creating a difference in how retailers connect with their audience.
Together, these methods provide a comprehensive view of consumer preferences and behaviors, enabling retailers to deliver highly customized shopping experiences. This approach not only increases customer engagement and satisfaction but also optimizes inventory management and marketing strategies, leading to improved sales performance and customer loyalty.
How AI Processes Data to Predict Consumer Needs and Preferences
After data collection, the data is processed by AI, which involves the following steps:
The sophistication of AI in processing and analyzing data allows retailers to anticipate the needs and preferences of their consumers. All this to craft highly personalized and engaging shopping experiences. This technology-driven approach is making retailers more responsive, efficient, and customer-centric than ever.
Real-Time Personalization and Predictive Analytics
It is no secret that brands are investing in AI predictive analytics for consumer behavior to create shopping experiences that resonate with individual consumer preferences. Amazon launched Amazon Go back in 2016, and has since evolved with multiple other tools within the domain. One notable example is Amazon Rufus, which uses AI to power its recommendation engine. It suggests products based on browsing and purchase history, leading to significant increases in customer engagement and sales.
Stitch Fix
Another example is Stitch Fix, an online styling service that uses AI to tailor clothing selections based on customer preferences, sizes, and feedback, demonstrating the potential of AI to customize services at scale.
Impact on Inventory Management
For inventory management:
AI forecasts demand: Predicting product needs to optimize inventory.
Optimizes stock levels: Balancing inventory to avoid overstock and stockouts.
Improves supply chain efficiency: Streamlining operations for better performance.
Reduces costs: Lowering expenses associated with excess inventory and missed sales.
Increases customer satisfaction: Ensuring products are available when desired by customers.
Impact on Product Recommendation
Product recommendations are among the most visible impacts of using AI to predict customer. By analyzing customer data, AI can suggest products that a customer is likely to be interested in. It thereby increases the chance of purchase.
The Consumer Experience Today VS. Before
Today, shoppers engage with retail through various means of technology in ways that were unimaginable just a decade ago, leading to a more personalized, efficient, and engaging retail therapy experience. Traditionally, shopping involved visiting physical stores, browsing through aisles, and often dealing with the uncertainty of item availability or fitting. Today, the use of AI to predict customer behavior has transformed this experience into one that is more tailored to individual preferences and needs.
Enhancing Customer Experience with Augmented Reality in Retail
Nike’s AR-based application
Virtual try-ons and augmented reality (AR) solutions represent some of the most innovative uses of AI in retail. Nike’s use of AR for shoe sizing allows customers to find the perfect fit without physically trying on the shoe, combining convenience with personalization.
Too Faced virtual makeup application
Similarly, beauty brands like Too Faced employ AR to enable customers to ‘try’ makeup or hair colors virtually, ensuring the product suits their preferences before making a purchase.
Tommy Hilfiger’s in-store virtual try-on
Clothing apparel brand Tommy Hilfiger isn’t too far behind either. It has ventured into the Metaverse by partnering with augmented reality technology company Zero10 to introduce an AR try-on feature in three of its European stores. This innovative approach allows customers to virtually try on clothes using an AR kiosk, leveraging 3D body tracking and cloth simulation to create realistic representations of the clothing on the user. This significant step by Zero10 and Tommy Hilfiger marks a move towards blending the physical and digital shopping experiences. It offers customers with a new, interactive way to engage with the brand’s fashion offerings.
Furthermore, AI-driven customer service, such as chatbots and virtual assistants, is boosting customer support by providing instant, 24/7 responses to inquiries. This not only improves the efficiency of customer service but also significantly enhances customer satisfaction by offering timely assistance.
Time to reap the benefits of using AI to predict customer behavior
AI’s evolution is transforming shopping into deeply personalized experiences, using AI to predict customer behavior is exceeding customer expectations globally. By focusing on consumer-brand interactions and decision-making, AI helps retailers boost sales and loyalty. VentureDive’s Data and AI services are at the forefront of this change, offering solutions that empower businesses to navigate this transformation successfully. Connect and explore how we can enable your business with the best AI technology that resonates with your customers.
Picture this: Sarah, a working mother, is trying to resolve a billing issue with her internet provider. She dials the customer service number, only to be greeted by a never-ending loop of hold music. As minutes turn into an hour, Sarah’s frustration mounts—not only is her issue unresolved, but she also feels ignored and undervalued as a customer.
Sarah’s struggle with her internet provider’s customer service highlights the inefficiencies of traditional contact centers, underscoring the critical need for modernization. AI technology, including agents and chatbots, offers a solution by ensuring instant, efficient, and personalized customer support.
Capable of handling thousands of interactions simultaneously, AI significantly cuts down response times and adapts through learning from each engagement. This not only elevates customer service quality but is also poised to save businesses nearly $11 billion, marking a substantial shift towards AI-driven customer service excellence.
Furthermore, AI-powered virtual assistants can quickly analyze and anticipate customer needs through data offering solutions in a breeze. However, the transition to AI-driven customer service has its challenges. Despite these hurdles, the potential benefits of AI and chatbots in transforming customer service are undeniable. They not only promise to modernize contact centers but also AI and chatbots are transforming the customer experience and defining the standards of customer engagement in the digital age, making scenarios like Sarah’s a thing of the past.
Challenges before AI chatbots in contact centers
Historically, contact centers have been the epicenters of customer service challenges. In an era where customer experience directly influences brand loyalty, such inefficiencies can be detrimental. Some of the common challenges before contact center modernization included:
High volume, low-efficiency
Traditional contact centers often struggle with high volumes of inquiries, leading to customer dissatisfaction. The inability to quickly process and resolve these queries efficiently results in a significant roadblock on resources and impacts overall service quality.
Inconsistent customer experience
Without AI, personalizing customer interactions at scale is a challenge. Human agents may not have immediate access to a customer’s history or preferences, leading to a disjointed experience that fails to meet the individual’s needs or expectations.
Agent burnout and attrition
Handling repetitive queries and managing tasks can lead to burnout, negatively affecting their performance and motivation. This not only impacts the quality of customer service but also leads to higher turnover rates, further destabilizing service continuity.
The evolution of contact centers – How chatbots improve customer experience?
The integration of AI and chatbots in contact centers represents a pivotal shift in the landscape of customer service, marking a significant leap toward contact center modernization.
Contact center modernization
Traditional contact centers, often criticized for their sluggish response times and reliance on manual processes, have been transformed into dynamic, data-driven hubs. AI and chatbots automate repetitive tasks, allowing for the reallocation of human resources to areas where they are most needed. This shift not only optimizes resource utilization but also ensures that contact centers can adapt swiftly to changing customer demands and market conditions.
By employing AI-powered chatbots, contact centers can offer 24/7 customer support, ensuring that help is always available when customers need it. This immediacy and the personalized nature of interactions greatly enhance customer satisfaction by nearly 70%.
Moreover, AI systems can analyze customer data to provide tailored recommendations and solutions, making each customer feel understood and valued. In an era where customer experience is a key differentiator for businesses, AI-enabled decision-making stands as a crucial enabler of meaningful and memorable customer interactions.
Operational efficiency
AI enhances contact center efficiency by automating routine queries and enabling agents to tackle complex issues, thereby improving service quality and reducing wait times. It offers insights into customer preferences, streamlining operations, and saving costs, leading to better and more cost-effective customer service. This includes various industries such as AI in retail, fintech, mobility, healthcare, and more.
Solutions to implementing AI chatbots in contact centers
Implementing AI in contact centers is a step towards digital transformation and revolutionizing customer service. Solutions like these ensure customers like Sarah are immediately engaged, their problems understood, and solutions provided swiftly, often without the need for human intervention.
Key technologies behind AI in contact centers
The backbone of AI in contact centers includes Natural Language Processing (NLP), machine learning, and sentiment analysis. Implementing these AI-driven solutions and technologies in contact centers not only addresses existing challenges but also paves the way for a more dynamic, responsive, and efficient customer service ecosystem.
Natural language processing (NLP)
NLP enables chatbots and virtual assistants to understand and interpret human language, allowing for more natural and engaging conversations with customers. This technology is crucial for analyzing inquiries and providing accurate, context-aware responses.
Machine learning (ML)
ML algorithms learn from data over time, improving the accuracy of responses and the efficiency of AI applications. By analyzing patterns in customer interactions, ML helps in continuously refining the AI’s understanding and problem-solving capabilities. For more information on this, check out our executive’s guide to machine learning.
Sentiment analysis
This technology assesses the emotional tone behind customer inquiries, enabling AI chatbots in contact centers to respond with empathy and adjust their approach based on the customer’s mood. Sentiment analysis plays a key role in personalizing interactions and customer satisfaction.
Virtual assistants in contact centers around the world
Globally, virtual assistants are making waves in contact centers across various industries. As the technology continues to evolve, their role becomes even more pivotal. Here are more detailed examples showcasing the impact of AI chatbots in contact centers:
North America: Macy’s On Call, powered by IBM Watson
Use Case: Macy’s, a prestigious retail giant in the United States, revolutionized customer service by launching Macy’s On Call, a cognitive mobile web tool developed in collaboration with IBM Watson. This AI-powered virtual assistant transforms the shopping experience by providing instant, accurate information on product locations, availability, and in-store services through natural language processing.
Impact: IBM Watson’s AI-enhanced Macy’s customer service, boosting engagement, and sales. Macy’s On Call provided personalized, efficient shopping assistance, bridging online and in-store experiences for a unified retail journey. With Watson’s machine learning, the service improved with every interaction, demonstrating Macy’s dedication to using advanced technology for superior customer care.
Asia: SoftBank’s Erica in Japan
Use Case: SoftBank, a leading telecom provider in Japan, leverages an AI-driven virtual assistant named Erica. Erica handles millions of customer inquiries each month, from billing questions to plan changes, with remarkable efficiency.
Impact: The deployment of Erica has dramatically reduced wait times for SoftBank customers, from several minutes to virtually instant responses. This not only ensures customer happiness but also optimizes the workload on human agents, allowing them to focus on more complex customer needs.
Europe: Swedbank’s Nina in Sweden
Use Case: Swedbank, one of the largest banks in Sweden, introduced Nina, a virtual assistant designed to process customer transactions and inquiries around the clock. Nina assists customers with various services, from account balance inquiries to transaction processing.
Impact: Nina has been instrumental in offering a seamless banking experience, handling over 30,000 conversations monthly and successfully resolving the majority of inquiries without human intervention. This 24/7 availability has significantly improved customer engagement and efficiency.
These examples illustrate the global reach and versatility of AI-powered virtual assistants in enhancing customer service across industries. By providing instant, accurate, and personalized assistance, these virtual agents also streamline operations and contribute to significant cost savings for businesses.
The future of AI in contact centers
Companies like VentureDive are leading this transformation with advanced AI and ML solutions, including chatbots and virtual assistants, aimed at elevating customer service to new heights. These technologies are revolutionizing contact centers by tackling issues of efficiency, personalization, and scalability, making them essential for businesses aiming to excel in the digital era and provide unmatched customer service.
Generative AI is a powerful technology that can create new content or data from existing data, such as images, text, audio, or code. It has many applications in various industries, mainly mobility, e-commerce, fintech, and healthcare. But one of the most promising and impactful areas where generative AI can make a difference is software testing and quality assurance (QA).
Software testing and QA are essential processes in software development, as they ensure that the software meets the requirements, specifications, and expectations of the stakeholders and users.
However, software testing and QA are also challenging, time-consuming, and costly, as they involve many manual tasks, such as writing test cases, executing tests, finding bugs, and reporting results. Moreover, as software becomes more complex, dynamic, and diverse, the demand for software testing and QA increases, putting more pressure on human testers and the software development lifecycle. This is where generative AI in software testing can help.
Artificial intelligence is the future of testing because it can automate and optimize many aspects of software and QA testing, such as test case generation, test execution, bug detection, and feedback learning. As per a recently published report by Data Bridge Market Research, the automation testing market is experiencing substantial growth with a compound annual growth rate (CAGR) of 17.06%, projected to achieve a market value of USD 93.6 billion by the year 2032, according to Future Market Insights.
Benefits of Generative AI in Software Testing
By using generative AI, software testers and QA testing engineers can achieve the following benefits:
Comprehensive test coverage
Deployment of quality assurance software testing tools enhances test coverage across various platforms and environments, ensuring that every component interacts as intended, under a wide range of conditions, before being released. The tools also facilitate regression testing, ensuring that new code does not disrupt existing functionalities. As a result, this leads to a more robust and reliable software product, as comprehensive test coverage is a cornerstone of high-quality software development.
Generative AI represents a transformative leap in quality assurance and software testing by automating the generation of test cases aligned with specified requirements and scenarios. This advanced capability ensures comprehensive coverage of potential inputs, outputs, and program paths, thereby mitigating the risk of overlooking critical bugs or errors in the software.
Faster and more accurate test execution
With the help of Generative AI in software testing, one can execute tests faster and more accurately than humans, detecting bugs and anomalies in the code and reporting them in real-time. It enables faster feedback loops and shorter delivery times, as well as higher software quality and reliability.
Continuous improvement and learning
Learning from past data and feedback is another quality of Generative AI utilized to improve the quality and efficiency of test generation and execution over time. This allows it to adapt to the changing requirements, scenarios, and environments of the software, as well as to discover new patterns and insights that can enhance the software testing and QA testing processes.
Enhanced creativity and productivity
Generative AI in software testing can free up human testers from repetitive and mundane tasks, allowing them to focus on more creative and strategic aspects of software and QA testing. This can improve the satisfaction and motivation of the human testers, as well as their collaboration and communication with the developers and other stakeholders.
Artificial intelligence has the potential to transform the quality assurance and software testing processes, resulting in faster delivery times, higher software quality, and lower costs.
However, generative AI also poses some challenges, such as the need for human supervision, ethical issues, and the evolving testing role in the era of AI in software test automation. Therefore, software testers and QA engineers need to be aware of the opportunities and limitations of generative AI and how to leverage it effectively and responsibly in their work.
It All Comes Down To…
Generative AI is not a replacement for human testers but a powerful tool that can augment and enhance their capabilities and performance. By combining the strengths of AI and human testers, quality assurance, and software testing can reach new levels of excellence and innovation.
In today’s digital age, the world of finance has undergone a significant transformation. With the advent of digital wallets, financial transactions have become more convenient, allowing users to make payments and manage their money with just a few taps on their smartphones. However, with convenience comes the need for security. In this blog, we will explore the concept of digital wallets, their types, and most importantly, the steps you can take to build a safe and secure digital wallet that protects your financial information.
What’s a digital wallet?
A digital wallet, also known as an e-wallet or mobile wallet, is a virtual wallet that allows users to store their payment card information securely on their mobile devices. It eliminates the need to carry physical payment cards and offers a convenient and streamlined payment experience. Popular digital wallet apps such as Apple Pay®, Samsung Pay®, and Google Pay® have gained widespread adoption, transforming the way we make transactions.
Top Regions Using Digital Wallets:
Digital wallets have gained immense popularity across the globe. Countries like China, the UK, and the United States have seen significant adoption rates, with millions of users relying on digital wallets for their everyday transactions. The convenience and versatility of digital wallets have made them an integral part of the financial landscape.
Transaction Growth Rate:
The growth rate of digital wallet transactions has been remarkable in recent years. With increasing acceptance from businesses and consumers, the volume of transactions conducted through digital wallets has experienced a substantial surge. This trend is expected to continue as more individuals embrace the convenience and speed of digital payments.
Types of digital wallets
Digital wallets can be broadly classified into three types: closed wallets, semi-closed wallets, and open wallets. Each type has its own unique characteristics and level of accessibility.
Closed Wallet:
Closed wallets are issued by specific merchants and can only be used to make payments within their respective ecosystems. These wallets offer convenience for customers loyal to a particular brand, but their usage is limited to transactions with the issuing merchant.
Semi-Closed Wallet:
Semi-closed wallets allow users to make payments at multiple merchants within a specific network. They are not limited to a single merchant, making them more versatile and widely accepted. Popular examples include PayPal and Paytm.
Open Wallet:
Open wallets are interoperable and can be used for transactions across multiple merchants and service providers. These wallets provide the highest level of flexibility and are often integrated with various banking services, enabling users to manage their finances seamlessly.
Why do you need to create a secure digital wallet?
While digital wallets offer convenience and efficiency, it is crucial to prioritize security to protect your financial information from unauthorized access and potential threats. Building a secure digital wallet ensures that your sensitive data remains protected and provides peace of mind while making transactions online.
Top tips on how to build a safe and secure digital wallet
Use a Unique and Difficult Password:
Choose a strong password that combines a mix of uppercase and lowercase letters, numbers, and special characters. Avoid using easily guessable information, such as your name or birthdate. Regularly update your password and avoid reusing it across multiple platforms.
Enable Two-Factor Authentication:
Two-factor authentication adds an extra layer of security to your digital wallet. Enable this feature, which typically involves entering a verification code sent to your registered mobile number or email address, ensuring that even if your password is compromised, unauthorized access is prevented.
Regular Software Updates:
Keep your digital wallet app and your mobile device’s operating system up to date. Software updates often contain crucial security patches that address vulnerabilities and enhance the overall security of your device and apps. VentureDive maintains a great reputation for curating secure and well-functioning apps with a transparent mobile app development timeline for various industries including, Fintech, Healthcare, Enterprise Integration, and more.
Download Apps from Authentic Websites and Sources Only:
To minimize the risk of downloading malicious software, only download digital wallet apps from trusted sources such as official app stores or the official websites of reputable providers. Avoid third-party app stores or unofficial download links that may expose your device to security risks.
Stay Updated on the Latest Technologies and Upgrades:
Keep yourself informed about the latest security technologies and upgrades in the digital wallet industry. Stay abreast of new features such as biometric authentication (e.g., fingerprint or facial recognition) and take advantage of them to enhance the security of your digital wallet.
Beware of the Harms and How to Overcome Them:
Educate yourself about potential risks associated with digital wallets, such as phishing attacks, malware, and unauthorized access. Be cautious when sharing your personal information and financial details, and always verify the authenticity of requests or messages before taking any action. Regularly review your transaction history and report any suspicious activity immediately.
Parting thoughts…
Building a safe and secure digital wallet is essential for enjoying the convenience and ease that digital payments bring. By following the tips outlined in this blog, you can enhance the security of your digital wallet and protect your financial information from potential threats. Embrace the digital revolution with confidence, knowing that your transactions are secure and your financial well-being is protected.
Living with a mental health disorder can be an uphill battle, and it only gets harder when people around you fail to understand the gravity of the situation and simply portray the disease as laziness or procrastination. In both developed and underdeveloped nations, mental health issues continue to be the leading cause of disability and death by suicide. Alarming statistics suggest that about 20% of adolescents live with a mental health disorder, highlighting the pressing need for effective treatment and support systems.
In this age of technological advancement, healthcare app development and mental health apps have emerged as a boom for individuals grappling with psychological issues. These apps offer a safe and anonymous platform for seeking help, without the need for in-person therapy. Moreover, with the convenience and accessibility of smartphones, mental health apps can be a valuable resource for individuals looking to improve their mental health and well-being.
However, while developing a mental health app, ensuring the privacy and confidentiality of users should be a top priority. Regulatory guidelines, such as GDPR and HIPAA compliance, should be strictly adhered to in software development. The availability of the app on different platforms, along with the features it offers, are also crucial aspects that developers need to consider.
The possibilities for the features that a mental health app can offer are endless, ranging from guided meditation to therapy sessions with licensed professionals. In order to make a positive impact on the mental health of users, developers must ensure that their apps are inclusive, user-friendly, and comprehensive in addressing a range of psychological illnesses.
In this article, we will delve deeper into the development processes of mental health apps and explore the essential elements that go into creating a successful and effective mental health app.
How to develop a mental health app?
The increasing use of mobile phones amounts to more than half of the audience on the web, hence the reason why businesses are steering towards creating mobile-friendly systems and apps for various mental health services. You’ll find some of the best mental health apps trending due to their accessible nature that allows patients and doctors to stay connected remotely, always keeping a check on their cases. And it further creates a sense of relief for users that a solution is available, just a click away.
But before we get into how these apps are created, and what makes them some of the best mental health apps, let us take a look at the most common types of mental health apps.
Types of Mental Health Apps
Mental disorder apps: These apps offer features like mood monitoring and psychotherapy to help individuals cope with mental health disorders like depression, anxiety, bipolar disorder, and schizophrenia.
PTSD apps: These apps provide tools to manage anger and anxiety, including breathing exercises, music selection, and self-talk methods. Some apps allow integration with healthcare apps and emergency contacts for easy access to assistance.
Eating disorder apps: These apps include mental health tracking features in addition to diet tracking to help individuals with eating disorders manage their symptoms. They also provide goal-setting and meal recommendations.
Meditation and self-improvement apps: These apps focus on stress management, relaxation, mindfulness, effective breathing, and anxiety management to help users practice relaxation techniques and manage stress.
CBT-based apps: These apps use cognitive-behavioral therapy techniques to help users reframe negative thoughts, develop coping skills, and change harmful behaviors, especially among women. They often provide interactive tools and activities to guide users through the therapy process.
Understanding vital KPIs & target audience for mental health apps
Did you know, every 1 in 4 people may suffer from mental health issues at some point in their lives, and nearly 970 million are currently struggling with major mental issues and drug abuse? While the disease is very common, the symptoms and cases heavily vary from person to person, so mental health apps must be feasible in catering to all demographics. An integral part of a mental health app will focus closely on the following aspects:
Audience
Similar/common issues
Current mental state
In order to create a well-rounded and effective application, one must comply with the following KPIs, and abide by these holy grail elements while creating mental health apps
User needs and goals
One of the most crucial aspects to consider when developing a mental health application is the needs and goals of the target audience. Mental health issues vary from person to person, and the app should be tailored to meet the specific needs of the users. Conducting user research, user interviews, and surveys can help in understanding the user’s mental health needs and designing an application that is useful and engaging.
Mental health experts
It is important to consult with mental health experts when developing a mental health app. Mental health professionals, including psychologists and psychiatrists, can provide valuable insights into the latest trends in mental health, common mental health issues, and the most effective treatment approaches. This can help ensure the application’s content and features are accurate, effective, and relevant to the target audience.
Features and functionality
The features and functionality of the application should be developed keeping in mind the specific mental health issues it aims to address. For example, if the app focuses on anxiety, it may include guided meditations, breathing exercises, and cognitive-behavioral therapy techniques. It is essential to develop an app that is easy to navigate, engaging, and meets the user’s needs. Additionally, the app should be developed with the ability to personalize and adapt to the user’s progress and feedback.
Design and user experience
The design and user experience of the application should be intuitive and engaging. A user-friendly interface with easy-to-navigate features can encourage users to engage with the app regularly. The app’s design should be visually appealing, with clear and concise messaging that aligns with the app’s intended purpose. Design elements, such as typography, color, and imagery, should also be consistent with the app’s branding and messaging.
Accessible
The app should be accessible to all users, regardless of their ability, age, or ethnicity. This includes providing language options and ensuring the app is compatible with assistive technology such as screen readers. Moreover, the app should be optimized for different devices and screen sizes, ensuring a seamless user experience across multiple platforms.
Evidence-based
The application should be developed using evidence-based practices, including research and studies conducted by mental health professionals. Evidence-based practices ensure that the app’s interventions are effective and supported by scientific research. The app should also provide clear information on the source of its content and interventions and cite any relevant studies or research.
Regular updates
Regular updates can ensure that the app is up-to-date with the latest research and developments in mental health. This includes adding new features, updating content, and fixing bugs. Additionally, regular updates can help maintain the user’s engagement and interest in the app.
Ethical considerations
To ensure ethical standards in the mental health app development process, it is important to take into account factors such as the use of non-stigmatizing language and obtaining informed consent from users before collecting their personal information. These considerations should be integrated into the development process to ensure that the end product is ethical and respects the rights and privacy of users.
Privacy and security
Mental health information is highly sensitive and should be handled with the utmost care. The application should have robust privacy and security features to protect users’ personal and health-related information. It is important to comply with local data protection laws and regulations, such as the General Data Protection Regulation (GDPR) in the European Union and HIPPA in the United States of America. Moreover, the app should provide clear information on how user data is collected, stored, and used.
Partnerships and collaborations
Developing partnerships with mental health organizations and professionals can help ensure that the application is relevant, effective, and based on the latest research. It can also help to increase the app’s credibility and reach.
In the end
Mental health and wellness app development is a niche that requires special attention to detail in order to succeed. And their success depends on how well their customer retention rate is, or how well it is in helping customers reach their goals. Developing a mental health and wellness app requires careful consideration of various aspects, including the type of application, target audience, features, privacy, and availability.
These apps can provide a safe and anonymous space for individuals to access treatment and support for their mental health disorders. The use of technology and mobile applications has significantly reduced the stigma surrounding mental health and increased access to care for individuals who may not have otherwise sought help.
Did you know that the global e-learning market was valued at nearly $197 Billion in 2020, and the numbers are projected to soar to up to $840.11 billion by 2030. Out of this, the healthcare learning market itself reached USD 110.54 Billion in 2022, and the forecast for upcoming years is only projecting a brighter future. Of all these figures, Learning management systems (LMS) are still dominating the e-learning market as 84% of organizations use this system.
In a fast-paced and rapid industry like healthcare, engaging and organizing educational content and upkeeping skills traditionally is near impossible. In a high-functioning healthcare workplace, gathering all the employees and arranging crucial teaching sessions is not only stressful, but it will create a bottleneck of patients and workload within the organization and the hospital, resulting in disrupted patient care and an endless backlog.
What’s the best possible way to not only help your employees gain the knowledge and skill they require to perform their current job in the healthcare sector with ease but also keep a track of all of this information? A learning management system for healthcare. Before diving deeper into the technicalities, let’s take a look at what a healthcare learning management system is and how has benefited the industry itself throughout its inception.
Essence of LMS: What is a learning management system for healthcare?
A learning management system is a software solution developed for the healthcare sector, mainly hospitals, clinics, and other major medical facilities. It aims to provide an update on medical professionals with courses, education, and training to maintain their skill sets based on current trends, research, and discoveries. With rapid changes in healthcare and medicine, it is a necessity for e-learning for healthcare.
Keeping oneself updated with the right operating procedures, research papers, and discoveries bring about numerous benefits to not just the healthcare professional, but their patients and the people associated with them as well. LMS is simple and highly accessible, like a pocket educational system that teaches, helps you revise, and tests your knowledge based on what you have learned.
LMS is affiliated with all the top educational systems, making it legit, and is always working in compliance with all the top medical regulations like HIPPA, OSHA, ACTM, etc. Complying with the official rules set by them will
7 Vital Benefits of Healthcare LMS
1. Efficient Talent Management
Healthcare is a competitive field that requires employees and workers to always be equipped with the best and most recent knowledge, along with the right expertise to cater to their patients. Therefore, healthcare learning management systems not only help you add to your pool of talent but also keep in check their expertise, helping them stay ahead of the curve through various training programs.
A well-trained healthcare professional will not only boost the quality of patient cares within your system but also help promote a healthy culture amongst its peers, maintaining a good reputation throughout.
2. Updated Training Material
LMS companies always have the best and most recent learning materials available for their workers at all times. In a tech-driven era where everything changes at the bat of an eye, keeping up was never this easy with an agile learning management system. By frequently updating and deploying new courses on a healthcare LMS it lets geographically dispersed students get first-hand access to top-quality resources in a well-documented e-learning environment.
3. Reduced Training Cost
Remote learning and agile learning methods globally have resulted in reduced costs that yield way better results than traditional physical learning and education system. From saving costs of hiring trainers, to their accommodations and classroom rents, Healthcare LMS has evolved the way teaching and learning take place altogether.
4. Flexible Schedule Management
With learning materials readily available 24/7 on a remote device, who needs to take time out for classes separately from an already busy schedule? LMS allows workers to create their own study schedule, and take lessons and training bit-by-bit, chunk-by-chunk, helping them not only process and retain information in a relaxed environment.
5. Security and Confidentiality
To ensure doctor-patient confidentiality, an LMS is encrypted well enough to maintain the security and confidentiality it requires. It always abides by the state’s healthcare security and ethical laws like HIPPA, PIPEDA, IEC 62304, and GDPR. A good learning management system will always be secure with two-factor authentication and permission-based access to ensure the data stays in place.
6. Effective Monitoring
Organizations running on healthcare LMS rely on the progress and performance of each employee presented within the system. It helps them track their current trainings that are in progress, and the ones that they have scheduled for later. Administration can also include their own assessments to test out how well the training went, and how well they retained information.
7. Blended Learning Support
A well-made healthcare LMS supports different mediums and modules of work for workers to benefit from. Whether it be e-learning, offline, or web-based training, a blended learning system helps gain insights and experiences that simple e-learning cannot on its own.
Conclusion
Healthcare is a complex and extremely challenging field that requires its workers to stay up to date constantly with new technologies, reports, research, and patient-care techniques. And with their busy schedules, a reliable and well-constructed learning environment is the only way to achieve these business goals. A custom-made healthcare learning management system is the key that bridge the gap between a well-maintained healthcare system.
FAQs for Learning Management System for Healthcare
An LMS is an overall tool used by medical professionals that allows them to not only track but maintain their workload, medical certifications, and necessary data to help them grow their expertise and professional knowledge.
A learning management system that keeps doctors and medical professionals up to date with patient care, personal goals, and training, while tracking the growth of their fellow trainee staff.
It is essential for healthcare professionals to upskill their prior educational learning as it is not only a necessity, but is beneficial. The four common benefits are:
Refine skills for patient care
Stay up to date with recent research
Updated on practice
Address real-world challenges
Tracking skills and certifications is the key feature within an LMS that helps organizations stay up-to-date on their employees’ professional progress and their overall expertise.
We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept”, you consent to the use of ALL the cookies.
This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
