The healthcare landscape faces complex challenges, including staff shortages, burnout among clinicians, decreasing profits, and deteriorating health outcomes, especially in underserved areas. Leveraging new technologies while maintaining healthcare quality has become essential. Generative AI in healthcare has the potential to address healthcare providers’ pain points by democratizing knowledge, increasing interoperability, accelerating discovery, and enabling true personalization.

A recent report stated that Generative AI in healthcare can potentially eliminate significant portions of the $1 trillion in wasted healthcare spending. With such cost containment, Generative AI in healthcare is a golden opportunity to add a personalized touch to patient care and improve healthcare outcomes.

Shifting Healthcare Market Landscape

In today’s complex world, the healthcare system is facing operational, talent, financial, and value crises that are all interconnected, creating a critical situation. Despite attempts to address these issues gradually, healthcare providers have yet to make significant progress toward providing fair, high-quality healthcare services. Some of the challenges that need quick address in healthcare are-

1. Labour Shortage

   The healthcare sector is experiencing severe worker shortages at all levels. As per a recent survey, workforce challenges are the top concern for healthcare providers. Even top-tier health systems need help to cope with the increasing demand for healthcare services. A shortage of 1.1 million nurses leads many organizations to rely on contract labor.

2. Clinician Burnout

   Clinicians face escalating workloads and administrative burdens, resulting in 81% reporting high or moderate levels of burnout. Administrative tasks like electronic health record management, patient scheduling, and follow-up with patients require a lot of manual effort. Clinicians are calling for technology and automation to allow them to focus on patient care. However, only 45% of frontline clinicians trust their leadership to prioritize patient care.

3. Lack of Patient Care

   Paperwork, documentation, and data entry are typical administrative duties for healthcare professionals. They take up valuable time and attention that could be spent on direct patient care. The administrative workload detracts from the provider-patient relationship and contributes to burnout among healthcare professionals.

4. Lower AI Adoption

   Healthcare has been slow to embrace AI compared to other industries due to technical complexities, difficulty interpreting AI outputs, and reliance on text-based data. Previous NLP techniques had limitations, especially in understanding context, which is critical in healthcare. These issues, combined with the high-stakes nature of health care, underscore the complexity and sensitivity of implementing AI.

Emergence of Generative AI in Healthcare

Healthcare is now the world’s foremost data repository, comprising 30% of yearly production and 80% of healthcare data in unstructured formats. That means it’s not neatly organized in databases or spreadsheets. This depth of healthcare data and continuous advancements hint at a promising future for Generative AI. Generative AI in healthcare can bring immediate efficiency, efficacy, and personalization benefits.

Source

Generative AI Use Cases in Healthcare

Generative AI aligns well with the functional needs in healthcare that traditional AI and ML models may overlook. In specific areas, Generative AI in healthcare has the potential to replace tasks and roles related to data entry, classification, and generation while also augmenting functions that demand empathy, innovation, and complex decision-making. Some of the potential applications of Generative AI in healthcare are-

1. EHR Management

   Traditional AI models in electronic health record management primarily focused on data entry and classification. While AI models have helped streamline certain aspects of record management, healthcare providers still face challenges in accurately capturing and organizing complex information. With its ability to understand and generate human-like text, Generative AI can summarize patient notes, extract relevant data and create a comprehensive healthcare record management system.

2. Medical Scribe

   Traditional transcription methods heavily rely on manual efforts, leading to potential errors and inefficiencies. Documenting patient encounters, assisting physicians with administrative tasks, and maintaining accurate medical records are other challenges for healthcare providers. Integrating Generative AI in healthcare system can create accurate medical notes in real time.
   With advanced speech recognition and natural language generation capabilities, Generative AI can seamlessly capture and document patient-provider interactions, enabling medical scribes to focus on patient care and communication.

3. Patient Scheduling

   Patient scheduling involves coordinating appointments, managing cancellations, and optimizing clinic schedules to minimize wait times. While traditional AI models can assist with appointment reminders and scheduling algorithms, Generative AI adds a layer of personalization and adaptability. Generative AI can recommend optimal appointment times, predict no-shows, and adjust real-time schedules by analyzing patient preferences, historical data, and clinic resources.

4. Personalized Patient Experience

   Patients frequently endure lengthy interactions with Interactive Voice Response (IVR) systems and other automated systems to address their concerns. The high volume of calls necessitates many agents to manage them effectively. Generative AI offers a solution by tailoring responses to customers’ needs and preferences.
   It supports live agents by summarizing inquiries and, in some cases, providing real-time personalized feedback to them. By decreasing resolution time, generative AI boosts customer satisfaction, improves agent productivity, and lowers operational costs.

5. Claims Denial Management

   Annually, healthcare providers incur additional costs due to denied claims. Approximately 60% of denied claims can be reclaimed, but only 0.2% have been appealed. Generative AI in healthcare presents a solution by efficiently scanning vast policy databases to retrieve relevant context for claims appeals. Additionally, it can extract pertinent patient data from Electronic Health Records (EHR) and craft appeal letters tailored to each case. Through these capabilities, Generative AI offers substantial benefits to healthcare providers by facilitating the recovery of billions of dollars in unappealed claims and streamlining the burdensome process of appealing claims.

Benefits of Generative AI in Healthcare

Generative AI in healthcare can affect everything from patient care to healthcare’s multiple domain functions. Some of its benefits are-

1. Generative AI can streamline back-office tasks by utilizing unstructured data for healthcare insurance claims and revenue cycle management. Additionally, integrating Generative AI with chatbots can address common IT and HR queries, enhance employee experience, and reduce administrative costs.
2. In healthcare operations, generative AI can automate tasks like generating discharge summaries, synthesizing care coordination notes, and creating checklists in real time.
3. Generative AI’s natural language understanding capabilities have the potential to enhance Electronic Health Records (EHRs), from prepopulating visit summaries to suggesting documentation changes and providing decision support research.

Way Forward

Generative AI can potentially revolutionize the healthcare industry in unprecedented ways, surpassing the capabilities of previously available technologies. As Generative AI matures, it could synergize with emerging technologies like virtual and augmented reality, as well as other forms of AI, to reshape healthcare delivery.

While these concepts may seem futuristic, they hold tangible potential as Generative AI progresses. However, healthcare providers must prioritize the responsible and ethical use of this technology.

AutomationEdge has seamlessly incorporated Generative AI into its solutions to scale up processes like patient scheduling, voice notes, healthcare insurance claims management and data analytics. Moreover, by integrating Generative AI with its chatbot capabilities, the AutomationEdge solution enables healthcare providers to comprehend patient inquiries in natural language and respond instantly using the enterprise knowledge base. This integration aims to simplify patient interactions with clinic or hospital support systems.

Robotic Process Automation in Banking

Did you know that human mistake in the banking industry results in over $878,000 in wasted time and labor each year? It is evident that the desire for change on the part of banking and financial services is not surprising.

Implementing robotic process automation makes sense since the human error can have a high financial cost in the BFSI industry. A Mckinsey report claims that RPA can automate more than 30% of tasks in about 60% of occupations.

The banking sector is anticipated to grow as business procedures across organizations change. The urge to automate superfluous procedures and provide end users control is expected to drive the sector over the next few years.

Automation adopters will concentrate on four areas: Quick Automation, Auto Decision, Data Security, and Instant Scalability.

BFSI is a tremendously competitive industry; banks and other financial institutions must continuously innovate, stay competitive, and deliver excellent user experiences. This is especially true given the significant counter-competition from FinTech and other virtual banking alternatives.

Banks and other financial institutions are under a lot of pressure to reduce costs and boost output. The lack of qualified workers, the need to increase process effectiveness, and the sharp rise in labor costs all contribute to the adoption of RPA in banking business.

Top 10 Benefits of RPA in Banking and Finance

You must consider which tasks in your banking and financial organization to automate.

1. Enhanced Productivity and Efficiency

   Another advantage of RPA systems is how easily and quickly they complete tasks since they listen to and carry out instructions without any space for ambiguity. Robotic accounting procedures don’t have any drawbacks, in contrast to manual ones. Gartner estimates that banking automation can prevent up to 25,000 hours of unnecessary work caused by human mistakes.

2. Scalability

   By addressing the need for bots to respond to events at record speed, robots’ high scalability enables you to manage big volumes during peak business hours. In addition, by relieving bank personnel of tedious activities, RPA deployment enables the bank to concentrate more on creative business growth ideas.

3. Accurate Information Extraction

   Because there is a vast amount of consumer data saved in systems, RPA, a combination of AI and machine learning, can readily store the necessary data for any desired query. Additionally, automated processing leverages third-party databases to gather information when there are errors or blank fields on bills in order to streamline Accounts Receivable and Accounts Payable procedures. However, similar operations can be completed quickly with RPA and particularly Intelligent Automation.

4. Cut Down Expenses

   The requirement for manual intervention will diminish as a result of the elimination of redundancy, which will allow banking and financial companies to drastically cut their additional expenses for resources, systems, and staff.
   Repeated tasks like manually processing data and adding fresh data could be avoided by employees. The financial sector can employ this technology to boost efficiency, consume less energy, and cut back on time, which can lower expenses by around 25–50%.

5. Increased Accuracy and Dependability

   It is natural for people to make mistakes. However, occasionally even the smallest errors could result in serious mistakes that would cost the company a significant amount of money. Unfortunate circumstances may even result in the customers’ excellent reputation being lost.
   But the introduction of RPA systems can quickly allay these concerns. The systems will conduct the procedure precisely and effectively with RPA. With the most recent technical marvels like AI and ML, massive amounts of data and processes may be managed effectively. Additionally, RPA solutions are accessible 24/7 and are not hindered by data failures. The data is effectively, automatically, and frequently backed up. Therefore, even if an unexpected event or downtime occurs, it will only stay a short while, and the process will resume as usual very quickly.
   According to Grand View Research, the banking and financial services sector, which accounted for over 29% of global revenue, was at the forefront of RPA adoption in 2019.

6. Better Compliance and Risk Management

   In order to comply with regulatory requirements and ensure meticulous record-keeping for potential audits, banking organizations must align their operational practices with applicable regulations. The banking industry, in particular, faces rigorous regulatory standards that necessitate strict adherence.
   Managing a lot of data coming from multiple resources and monitoring them on time takes a lot of effort from banking employees. Here RPA bot can be programmed to automatically perform real-time monitoring, and analyze the data to make sure business processes adhere to regulations.

7. Fraud Detection

   Having a lot of transactions happening across the banking operations, banking employees must keep all the data in check to prevent any fraudulent activities. Instead of banking employees handling the transaction and analyzing it in real-time, RPA can take over this repetitive process and analyze the transaction in real-time. Also, these RPA bots can also generate alerts whenever any fraudulent activities occur. That’s how RPA enables faster defect detection and maintains transparency.

8. Faster Loan Processing

   According to a poll conducted by Moody’s Analytics, manual data entry is the biggest challenge in loan origination. As banks receive these loan applications in high amounts, banking employees need to extract, analyze, and verify the data before processing loans. Here RPA can reduce the time involved in loan application processing using intelligent document processing to extract, analyze, and process the data, and the RPA bot can facilitate seamless communication with customers for information about approval and other information

9. 24/7 Assitance

   With a lot of processes encircling the banking operations, banking employees receive queries 24/7. Checking these queries extracting the information and routing the ticket to the right banking agents for instant resolution can be done by using an RPA bot. Also, this RPA bot can be available 24/7 and with pre-defined rules, it can offer self-service to customers as well.

10. Improved Data Analytics

    RPA can automate data extraction and transformation tasks, making it easier for banks to collect and analyze customer data. This facilitates data insights in one glance and enables banking organizations for data-driven decision-making, allowing banks to offer targeted products and services.

[Also Read – Top RPA Use Cases in Banking Industry in 2024]

Final Reflections

As opportunistic, system-based solutions that are quicker and simpler to adopt than extensive transformations, several banks, and financial institutions have started the journey of implementing RPA in their operations.

Robotic process automation, or RPA, services, which automate manual, repetitive, and time-consuming operations, can aid in the banking industry’s digital transformation if properly applied. Increased output, a considerable drop in error rates, and rapid turnaround times would result from automating such repetitive activities.
Throughout the RPA deployment process, having a partner with a track record of proficiency in RPA tools, technologies, and staffing is essential.

This will benefit banks and other financial organizations, but it will also show them when and how to transition from RPA to AI and other advanced technologies. And, who better than the RPA innovators – AutomationEdge!

Widespread healthcare and home care staff shortages have created a huge void that exerts heavy pressure on the currently employed staff. And guess what? These shortages are expected to continue for a while now.

The home care staff have to perform various manual tasks every day, such as scheduling medication refills, taking vitals, conducting wellness checks, etc. Stress on the nursing staff has been piling up, which eventually makes them feel fatigued, burned out, and heading for the exits. A survey conducted on 1,327 workers from hospitals, nursing homes, outpatient clinics, and home healthcare providers revealed that 62% had felt stress related to COVID-19 (Sudo, 2021). Personal Support Workers (PSWs) have dealt with burnout throughout this pandemic. Many are overwhelmed and exhausted.

What is causing home care staff burnout?

If we consider the nature of the industry, the volume of patients or clients, and the shortage of resources, these burnouts are caused by unfair treatment, pressurizing workloads, unclear job duties or roles, little or no communication from the manager, and unreasonable time frames. These caregivers have to multitask and make decisions that take a heavy toll on their physical health.

Not to forget, overtime or extra shifts are also there to blame on. Moreover, while in the patient’s home, workers have to conform to their new or controlled environment, which is one of the reasons that lead to burnout. It is said that half of all the caregivers working in the home care domain tend to experience burnout at some point in time. In simple words, these burnouts occur as caregivers help and take care of their patients, and they simply forget about themselves.

These burnouts are characterized by multiple factors, such as

• Exertion
• A sense of detachment
• Frustration or anxiety
• Panic attacks
• Inability to face demand
• Headache and extreme fatigue
• Body ache
• Change or loss of appetite
• Sleeplessness

It doesn’t stop here, a study by the American Psychological Association indicates that employees with burnouts “are 2.6 times as likely to be actively seeking a different job, 63% more likely to take a sick day, and 23% more likely to visit the emergency room” (Moss, 2019). Which ultimately increases the caregivers’ attrition rate in the domain.

How can AI save Caregivers from these burnouts?

1. Automates manual and administrative tasks:

   Home care has an overwhelming amount of repetitive or manual administrative tasks that consume a heavy chunk of caregivers’ energy and time. These tasks include scheduling, billing, reporting, testing, coding, ordering, etc.

   These tasks simply disconnect the caregiver from the core duties and their interaction with patients. AI can automate these tasks using, Natural Language Processing, Speech Recognition, Intelligent Document Processing, and Machine Learning. For example, AI can smartly create electronic health records by transcribing your voice notes. It can generate billing codes from your diagnoses, suggest optimal schedules and appointments, and also provide automated reminders and alerts. This dramatically reduces the stress and workload on caregivers as AI takes care of these tasks with precision and accuracy.

2. Augments decisions:

   The complexity and uncertainty of clinical decisions requires a large amount of data to be processed with guidelines. Here, AI can create magic by augmenting your decisions using data analytics, predictive modeling, and predictive analytics. For example, AI dives deep into a patient’s data and medical history, compares it with similar cases in the past with outcomes, and identifies and suggests potential diagnoses and treatments. It also ranks them based on their probability and efficacy. This adds speed and accuracy to caregivers’ service by expediting the diagnosis and shortening the treatment cycle by guiding them with potential treatment.

The rapid evolution of artificial intelligence (AI), especially in Generative AI, has garnered widespread attention, particularly in industries like insurance. The profound impact of this technology presents significant opportunities for companies aiming to drive innovation in the insurance industry. In the face of a continually changing competitive landscape, staying at the forefront of technological advancements is crucial for meeting customer expectations and addressing emerging challenges.

As insurers contemplate the optimal utilization of this potent tool, their initial focus should be on defining a clear vision for what they aim to achieve. In this blog, we will explore how Generative AI can play a part in scaling the process execution and efforts for insurers.

Understanding Generative AI in Insurance

In the context of the insurance industry, Generative AI offers a multitude of applications to enhance efficiency, operations, and customer experience. Some of Generative AI use cases in insurance are-

Generative AI in Insurance Use Cases

1. Claims Processing

   With automation in claims processing, insurers can set predefined rules for data extraction and process the information. Having automation bots in place reduces the manual efforts involved in processing claims documents and information that is crucial in decision-making. Here, Generative AI can scale up the process by analyzing large datasets and recognizing patterns. It can analyze multiple factors, including policy details, historical claims data, and real-time information. It can dynamically adjust the settlement amount based on the circumstances surrounding the claim.

   This way, Generative AI can identify anomalies indicative of fraudulent claims. Also, the integration of Generative AI and automation helps in creating personalized, empathetic communication with claimants. It can understand the context of the claim and provide updates or information in a manner that resonates with the individual.

2. Underwriting

   Automated underwriting utilizes technology to evaluate risk and make decisions without extensive manual intervention. Generative AI plays a crucial role in this process by constructing predictive models that consider a diverse range of variables from applicants’ documents. These models analyze factors such as age, health history, occupation, and more, offering a holistic perspective on the applicant’s risk profile.

   The implementation of automated underwriting, powered by Generative AI, accelerates risk calculations and decision-making, particularly beneficial for intricate insurance products with relatively straightforward risk assessments. Overall, Generative AI in insurance underwriting ensures consistent decision-making while mitigating biases and reducing the likelihood of human errors.

3. Policy Generation

   In the insurance industry, policy generation is a time-consuming process, considering the number of tasks, including customer information collection, risk assessment, underwriting analysis, policy document generation, taking customer review and approval and issuing the policies. Utilizing automation and AI solutions helps insurers take care of these tasks effortlessly with minimal human intervention. Here, integrating Generative AI with automation can assist in creating personalized insurance policies by analyzing individual risk factors and customer preferences. It can generate policy documents, ensuring clarity and compliance.

   Also, Generative AI can adjust policy terms based on evolving data and market trends to make sure regulatory compliances are met effortlessly.

Why settle for automating mundane tasks with basic bots and macros when you can supercharge your business processes with intelligent bots fueled by AI? Intelligent automation not only saves time and money but goes beyond fixing human errors, offering a transformative edge.

Intelligent automation harnesses the power of AI alongside traditional technologies to achieve unparalleled performance. This means more than just efficiency – it opens doors to innovation and frees up resources for seizing new opportunities.

Are you curious about how this works in real business processes? Read on for specific examples where AI-backed automation, combining technologies like intelligent document processing, robotic process automation, machine learning, optical character recognition, computer vision, and business process automation, can elevate your business game.

1. Invoice Processing

   Invoice processing is a critical but time-consuming task for businesses of all sizes, considering the amount of data required to collect, analyze and process the information. Here, AI-enabled document processing can play a role in extracting documents from multiple resources and processing the information faster. Using AutomationEdge’s IDP in conjunction with RPA bots and business process automation, a major product distributor, Transmed, improved its invoice process using intelligent automation solutions.
   

   Each day, the firm receives over 1000 invoices, most of which have unique formats. Due to the enormous workload, there was a substantial risk of human error. Moreover, if the problems weren’t addressed, any errors would have damaged the vendor-client relationship, which would have resulted in substantial losses for the company.

   Using AutomationEdge’s Intelligent Document Processing solution, Transmed was able to Process 1000 invoices daily using AI bots. Having AI in place, Transmed was able to improve its employee productivity by 70%.

2. Service Desk Automation

   The service desk is the lifeline of IT support in any organization. Right from routing service tickets to extracting information for faster resolution, the IT service desk has a lot on its hands to serve. With a lot of data management and processing involved, AI-enabled bots can benefit both service desk agents and customers at the same time. Take another AutomationEdge client example.

   Wilsonart, a manufacturing company, was handling a lot of service ticket requests like account creation, sharing folders and data access. Handling multiple data violations, managing data retrieval and providing operation support 24/7 was a constant challenge. Using intelligent automation in the process, Wilsonart was able to fast-track its service desk operations by easily assigning service tickets to the right agent to offer instant resolution and create a scalable design to handle high-volume service ticket requests. With Intelligent Automation in place, Wilsonart achieved 95% manual effort reduction and 100% error-free service desk operations.

3. Data Reconciliation

   In companies dealing with a high volume of daily transactions, the reconciliation process can consume a considerable amount of time. This challenge is particularly pronounced in financial institutions, where customer transactions form the core of the business model.
   Also, managing data extraction and checking discrepancies between data is another challenge that makes data reconciliation a suitable use case for intelligent automation.

   One of our customers, HDFC Bank, used to manually reconcile a large volume of transactions daily by cross-referencing multiple spreadsheets. Their team of employees had the task of matching entries from various external banks with their internal spreadsheet of daily transactions to ensure data accuracy. This process was not only time-consuming but also prone to errors. To reduce the manual efforts and fast-track the reconciliation process, AutomationEdge built RPA bots to automate the process and layer it with AI to make sure that bots can understand the data variations and achieve a 90% TAT reduction in the reconciliation process.

4. Employee Onboarding

   Employee onboarding is a critical process that sets the tone for an employee’s experience with an organization. Intelligent automation can simplify and accelerate this process by automating administrative tasks such as document verification, background checks, and account provisioning. Automated onboarding workflows ensure consistency, compliance, and a positive onboarding experience for new hires, allowing HR teams to focus on more strategic aspects of talent management.

   One of our biggest customers, Axis bank, was struggling to handle the manual and tedious task of matching new employee details and handling 50+ employee background verification requests per day. Here AutomationEdge’s Intelligent automation solution helped create an automated system that handled 50+ requests using AI bots and reduced 1 FTE efforts.

5. Claims Processing

   Claims processing with intelligent automation involves leveraging advanced technologies such as Robotic Process Automation (RPA) and machine learning to automate and expedite the end-to-end claims workflow. From data extraction in claim forms to validation and assessment, intelligent automation ensures a streamlined and efficient process, reducing manual intervention and enhancing accuracy. This approach not only accelerates claims processing times but also helps identify and prevent fraudulent claims through data analytics.

In the ever-evolving world of insurance, policy administration plays a pivotal role in managing insurance policies efficiently. It involves numerous processes, including policy issuance, premium billing, claims processing, etc. Manually processing policy requests and handling all the information in the policy administration process leads to inefficiencies, errors, and delayed resolution.

That’s one of the reasons Insurers are adopting insurance automation solutions to accelerate the policy administration process. Let’s learn more about the policy administration process, the challenges associated with it and how automation and AI can help insurers in transforming this process.

Understanding Policy Administration

Policy administration is a crucial process within the insurance industry, involving multiple steps-

1. Initial Application
2. Underwriting
3. Policy generation
4. Billing & Payment
5. Policy Updates
6. Claims Processing
7. Data Management
8. Regulatory Compliance

Insurers have to manage and maintain the above process that involves cross-referencing policyholder data in accordance with established regulations during the data processing phase. Handling this all together leads to multiple challenges like data complexity, & inaccuracy, regulatory compliance, policy updates & amendments and creating a seamless customer experience.

To err out these challenges and create a seamless policy administration process, an automation and AI solution can be a savior. Let’s further see how automation can help insurers get rid of administrative burdens in the policy administration process.

Automated Policy Administration Process with RPA and AI

Considering the number of challenges in the policy administration process, automation technologies like RPA and AI can significantly enhance the policy administration process. Let’s see how-
Certainly, let’s expand on each of the points to provide more information about how automation solutions, including RPA and AI, enhance the policy administration process:

1. Streamlined Underwriting

   Underwriting is a critical step in policy administration where insurers assess the risk associated with potential policyholders. Insurance automation solutions bring several benefits to this stage:

   • Data Analysis

     Automation with intelligent document processing can rapidly process vast datasets, including historical claims data, financial records, and other relevant information. This enables insurers to make more informed underwriting decisions, as they can consider a broader range of factors that humans might overlook.

   • Consistency

     Automated underwriting systems can apply predefined rules consistently. This reduces the risk of bias and ensures that every application is evaluated fairly based on the same criteria.

   • Efficiency

     The process becomes much faster and more scalable with automation. Instead of spending weeks manually reviewing applications, underwriters can focus on complex cases that require human judgment.

2. Faster Policy Issuance

   This is often used to be a time-consuming process involving paper documents and manual entry. Automated systems can swiftly generate policy documents based on the underwriting decisions. This includes policy terms, coverage details, and any applicable endorsements. Instead of waiting for postal services, automated systems can instantly send policy documents electronically to policyholders, reducing the time it takes to get coverage in place. That’s how automation and AI minimize the likelihood of errors in policy documents, ensuring that the terms are accurately reflected.

3. Precise Premium Billing

   Accurate premium billing is crucial for financial stability and customer satisfaction. Automation greatly improves this aspect of policy administration. Using automation insurers can use precise algorithms to calculate premiums based on policy details, ensuring that the amounts are calculated correctly and consistently. Also, these invoices are sent promptly, and automated systems can even issue reminders for upcoming payments, reducing the risk of missed deadlines. This way, automation allows for flexibility in premium billing, enabling insurers to tailor billing frequency and methods to meet the needs and preferences of policyholders.

In the rapidly advancing era of Artificial Intelligence (AI), these advancements offer your organization the means to fortify itself, create value, and adapt to evolving customer expectations and business models. Some innovations are AI-driven, while others facilitate efficient and secure operations and growth. You may have already pioneered certain technologies, while others might be novel; however, all contribute to establishing the necessary infrastructure, governance, and tools for enhanced resilience and autonomic activities. When implemented effectively, these technologies provide advantages such as:

• Ensuring trust and confidence in the safe utilization of AI
• Facilitating sustainable activities within your operational ecosystem
• Providing dedicated technologies tailored for specific tasks, industries, and functions
• Boosting speed and productivity with minimal additional investment or effort
• Delivering increased value for both internal and external stakeholders

Adopting the actual value of these technologies is realized when these technological advancements align and contribute to achieving overarching business objectives. So, let’s explore in this blog the upcoming technology trends that you must look for in 2024 in the age of AI to prioritize your investments. The top strategic technology trends in 2024 are-

1. AI, Trust, Risk and Security Management (TRiSM)

   Security is a significant concern when using AI models in the system or all across the organization. Also, the increasing availability of AI underscores the importance of managing AI Trust, Risk, and Security, often denoted as TRiSM by Gartner. Without robust safeguards, AI models have the potential to generate escalating adverse outcomes, undermining the positive impacts they could otherwise facilitate.

   AI TRiSM encompasses essential tools for Model Operations (ModelOps), proactive data protection and security measures, and comprehensive monitoring of models and data. It also incorporates risk controls, particularly pertinent when utilizing third-party models. According to Gartner’s projections, enterprises adopting AI TRiSM are anticipated to significantly enhance decision-making accuracy, potentially eliminating up to 80% of misleading information by 2026.

2. Continuous Threat Exposure Management

   According to Gartner’s projections, organizations prioritizing security investments guided by a CTEM program are expected to experience a substantial two-thirds reduction in security breaches by 2026.

   Continuous Threat Exposure Management (CTEM) represents a systematic methodology enabling organizations to consistently assess and oversee the accessibility, exposure, and exploitation risk associated with their digital and physical assets. By aligning these methodologies with individual projects or threat vectors instead of solely focusing on infrastructure, enterprises can gain a holistic view of vulnerabilities, including those that may be unmatchable.

3. Sustainable Technology

   Gartner anticipates that by 2027, around 25% of CIOs will witness a correlation between their personal compensation and the impact of sustainable technology initiatives.

   Sustainable technology involves a set of digital solutions designed to facilitate ESG (Environmental, Social, and Governance) outcomes, fostering a lasting equilibrium in both ecological and human rights aspects. With growing apprehensions about the energy consumption and environmental implications associated with technologies such as AI, cryptocurrency, and cloud computing, organizations are under mounting pressure to enhance the efficiency, circularity, and overall sustainability of their IT practices. Additionally, sustainable technologies provide insights necessary for decision-making and improve overall performance.

4. Platform Engineering

   Regarding IT organizations, optimizing the developer experience and accelerating business value is the utmost priority. The practice of platform engineering involves building and operating self-service development platforms for internal use. As per the Gartner report, 80% of software engineering organizations will establish platform teams as internal providers of reusable services, components and tools for application delivery.

   Also, platform engineering accelerates developer’s abilities to independently run, manage, and develop their applications while ensuring reliability and security.

5. AI Augmented Development

   This pertains to leveraging machine learning, Generative AI, and other AI technologies to support software engineers in application design, coding, and testing tasks. Integrating AI in software development enhances developer efficiency and enables teams to meet growing demands for essential business software. These tools also give engineers more time to focus on strategic activities, such as application design and composition, by automating routine tasks.

6. Industry Cloud Platforms

   It’s a specialized platform for industries that integrates Software-as-a-Service (SaaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS) elements to deliver a product with customizable features like an industry data fabric a set of business capabilities, and tools for composition. These platforms can be adapted by organizations to suit their particular requirements. According to Gartner, over 70% of enterprises are expected to leverage industry cloud platforms for expediting business initiatives by 2027, a significant increase from the current usage of less than 15%.

7. Intelligent Applications

   Intelligence lies at the core of intelligent applications, as defined by Gartner. It refers to the capacity to autonomously and appropriately respond through learned adaptation. This form of intelligence serves to enhance the efficiency and dependability of automated and augmented tasks, ultimately leading to the delivery of more dynamic user experiences. Technologies like Generative AI can truly make apps more intelligent and transform the experience of customers, users, developers, and owners. Additionally, AI can add predictions and allow organizations to add data-driven decision-making into business processes.

8. Democratize Generative AI

   Enabling widespread access to Generative AI within an organization holds the promise of automating diverse tasks, elevating productivity, cutting costs, and unlocking avenues for growth. This transformative potential extends to reshaping the competitive landscape and altering how enterprises approach their work. The dissemination of information and skills across various roles and business functions is poised to follow, allowing a broad spectrum of individuals to harness the power of Generative AI. Furthermore, business users can easily access and utilize extensive internal and external information sources through natural language conversational interfaces.

9. Augmented Connected Workforce

   The imperative to enhance talent scalability propels the strategy of an Augmented-Connected Workforce (ACWF), designed to maximize the effectiveness of human workers. This approach leverages intelligent applications and workforce analytics to offer guidance and context, fostering a supportive environment for workforce experience, well-being, and skill advancement. The ultimate goal of this approach is to achieve business outcomes and positive stakeholder impact. According to Gartner, by 2027, a minimum of 25% of Chief Information Officers (CIOs) are expected to implement augmented-connected workforce initiatives, aiming to cut the time to competency by 50% for crucial roles.

10. Machine Customers

    Gartner has identified a new trend called “custobots,” which are nonhuman entities capable of independently negotiating and purchasing goods and services. They predict that by 2028, there will be 15 billion connected products with custobot capabilities, potentially generating trillions of dollars in revenue by 2030. Gartner advises organizations to consider supporting existing custobots or creating new ones as part of their strategic development.

With changing times, these technology trends can write a success story for any industry and business model. These technologies can change an organization’s picture of growth by implementing them to reduce the burden of human efforts for repetitive tasks. It’s time for organizations to evaluate their business strategy and look for ways to implement these trends right and add more business value to it.

In the contemporary workforce, the traditional HR process has lost its appeal. From employee onboarding to resolving employee queries, repetitive tasks take a toll on the HR team’s productivity. Employee performance management is one such process that is a routine and uninspiring task that employees endure rather than a valuable exercise. Employees perceive these appraisals as merely a checkbox activity, and this sentiment extends to managers and HR leaders as well. The manual nature of performance management systems exacerbates the issue, leading to employee disengagement and creating more problems than they resolve.

Both employees and those responsible for appraisals, such as managers and HR leaders, are growing weary of this yearly obligation. According to a report, a company with 10000 employees spends around $35 million per review to conduct reviews. The substantial time and effort invested in carrying out performance reviews yield minimal benefits in terms of enhancing employee performance and, consequently, business performance. But what’s the solution to these challenges? In this blog, we will explore employee performance management and how automation can accelerate this process.

Why Automate Performance Management?

In the context of a forward-looking approach, organizations are shifting from merely analyzing past employee performance trends to a more proactive focus on enhancing employee performance in alignment with the organization’s overarching objectives. Also, with a manual performance management system, the HR team struggles with-

1. Efficiency in gaining data insights
2. Fair Evaluation of employees
3. Easy Accessibility of Information
4. Higher employee retention

To address the struggle and create efficient performance management, businesses are adopting HR process automation to handle HR processes efficiently. By implementing a robust performance management system, HR professionals can efficiently provide high-quality, precise, and uniform employee assessments while expending minimal time and effort. This system, in turn, facilitates more robust support and commitment from both managers and employees.

Let’s look at the benefits of using automation in performance management-

1. Instant Feedback

   Around 74% of employees undergo performance evaluations annually or even less frequently. However, only a meager 15% feel motivated to enhance their performance due to these assessments. This data underscores the perception that conventional performance management practices are considered outdated and may have a detrimental rather than a beneficial impact on organizations. Through the automation of the employee performance data collection and analysis process, HR teams can achieve the following:

   1. Deliver consistent and ongoing employee feedback throughout the year, eliminating the need for scheduled formal reviews.
   2. Facilitate the seamless exchange of feedback across different departments, reinforcing positive behaviors and ensuring that all organization members remain aligned with critical objectives.
   3. Enhance their ability to monitor an employee’s development progress over time and pinpoint areas where they may benefit from additional training or guidance.

2. Improve Transparency

   As per the report, less than 30% of employees perceive their performance review as fair. And only 25% consider them to be precise.
   Through the automation of the performance management process, organizations can elevate transparency and accountability among all stakeholders:

   1. Managers and HR departments gain the capability to collect performance data and continuously monitor employee progress in real-time.
   2. This empowers HR professionals to furnish each team member with specific feedback derived from objective metrics instead of subjective evaluations.
   3. Instead of periodic reports, a performance appraisal grounded in objective, real-time data empowers employees to take responsibility for their development and bolsters trust within HR.

3. Increase Training & Skills Quality

   According to a report, more than 70% of employees express a likelihood to depart from their current employer due to the lack of desired training and development opportunities.

   Dependence on manual systems for monitoring employee progress and ensuring uniform instruction can be a laborious and ineffective process. Implementing an automated performance management system can enable organizations to:

   1. Utilize real-time performance data for the assessment of both employee and departmental strengths and weaknesses.
   2. Establish personalized development goals for individual employees and customize training and skill transfer plans based on their advancement and requirements.
   3. Monitor and assess the effectiveness of different training approaches, enabling companies to make informed comparisons.

For IT agents, doing repetitive tasks can be boring and take up a lot of time, regardless of whether they are entry-level or experienced. These tasks often follow a routine, and that’s where IT support automation comes into play. It makes the service desk processes more organized and saves your company time and effort.

However, it’s not a good idea to automate everything without thinking. You have to be selective about what to automate. That’s why we’ve got some service desk automation ideas to help you enhance your service and reduce mistakes.

In this article, we’re going to give you a big list of these ideas and show you how to use InvGate Service Desk to make them even better.

So, without any more delay, let’s get started!

What is Service Desk Automation?

Service desk systems were created to make life easier and help businesses provide excellent customer service.

Unfortunately, you will hear many customer service agents say, “We’re still stuck dealing with a huge pile of support requests, and simply getting around the system takes up a lot of our time.”

Here, service desk automation can fine-tune the ticketing process and reduce the manual effort. In a nutshell, service desk automation means using technology and methods to make IT support services work better. This usually includes using smart ticket systems, self-service websites, and tools that do tasks automatically. With these IT automation technologies, you can sort and organize support requests, answer common questions automatically, offer self-help options, and manage different tasks and approvals more efficiently.

Top 7 Service Desk Automation Ideas

Now, let’s have a look at some of the service desk automation use cases that we can automate using RPA.

1. Ticket Routing

   Is your business struggling with the manual distribution of support tickets among agents? Do you often find that support agents end up with tickets that should have gone to someone else?

   If these scenarios sound all too familiar, it’s time to consider an automated service desk. With automated ticket routing, support tickets are directed to the agents best equipped to handle them.

   Automated ticket assignment ensures that each support ticket is in capable hands. For example, technical support issues are assigned to agents with the technical expertise to address them. On the other hand, billing or returns-related tickets are routed to agents who are knowledgeable about return policies and procedures.

2. Email Management

   Email management can be challenging for IT service desk agents when it comes to filtering, routing, and offering responses to each customer. Here, automation can play a role in filtering and sorting mail into categories. Once these emails are categorized, using RPA bot service desk agents can offer immediate responses to customers. Also, the RPA bot can prioritize mail and schedule its delivery for a specific time.

3. Alerts and Notifications

   In the quest to automate support services and deliver seamless customer experiences, effective communication is paramount. This is where notifications come into play. Notifications are predefined automated alerts, messages, or responses that are triggered when specific conditions are met. These notifications serve a dual purpose, keeping both support agents and customers well-informed. They are typically dispatched via email or text messages, offering a means to track the progress of a support ticket.

   IT service desk agents can use automation to provide multiple conditions under which they can send and receive email notifications. RPA bots can easily create auto responses on ticket creation, assignment, and response to users.