#AI for ITOps Management Service

[jcmarchi]

Gou Rao, CEO & Co-Founder of NeuBird – Interview Series

New Post has been published on https://thedigitalinsider.com/gou-rao-ceo-co-founder-of-neubird-interview-series/

Gou Rao, CEO & Co-Founder of NeuBird – Interview Series

Goutham (Gou) Rao is the CEO and co-founder of NeuBird, the creators of Hawkeye, the world’s first generative AI-powered ITOps engineer, designed to help IT teams diagnose and resolve technical issues instantly, enabling seamless collaboration between human teams and AI.

A serial entrepreneur with a proven track record, Rao has co-founded and successfully exited multiple companies. He co-founded Portworx, acquired by Pure Storage; Ocarina Networks, acquired by Dell; and Net6, acquired by Citrix. He is also an accomplished inventor with over 50 issued patents spanning computer networking, storage, and security.

NeuBird is developing generative AI solutions for IT operations to help address the shortage of skilled professionals needed to manage modern, complex technology stacks. The company focuses on simplifying data analysis and providing real-time actionable insights, aiming to enhance efficiency and support innovation in IT management.

What inspired you to launch NeuBird, and how did you identify the need for AI-driven IT operations automation?

NeuBird was born out of the growing complexity of enterprise IT stacks and the shortage of skilled IT professionals. Traditional tools weren’t keeping up, forcing IT teams to spend 30% of their budgets navigating siloed data sources instead of driving innovation. We saw an opportunity to create an AI-powered ITOps engineer—Hawkeye—that could instantly pinpoint IT issues, reduce time-to-resolution from days to minutes, and enable enterprises to scale IT operations without being bottlenecked by labor constraints.

How is NeuBird pioneering AI-powered digital teammates, and what sets Hawkeye apart from traditional IT automation tools?

Unlike static, rule-based IT automation tools, our AI-powered digital teammate, Hawkeye, dynamically processes vast telemetry data and diagnoses issues instantly. It eliminates the bias of pre-programmed observability tools by pulling insights from diverse enterprise data sources—including Slack, cloud services, databases, and custom applications—giving IT teams a holistic, contextualized view of their infrastructure.

Hawkeye doesn’t just surface alerts; it actively collaborates with engineers through a conversational interface, diagnosing root causes and proposing fixes to complex IT issues. This fundamentally changes how IT operations work, helping them minimize downtime and respond to IT incidents with unprecedented speed.

Enterprises often struggle with data overload in IT operations. How does Hawkeye filter through massive data sets to provide actionable insights?

Traditional IT tools struggle to process the flood of telemetry data—logs, system metrics, and cloud performance indicators—leading to alert fatigue and slow incident resolution.

Hawkeye cuts through the noise by continuously analyzing real-time data, and detecting patterns that signal performance issues or failures. It complements existing observability and monitoring tools by going beyond passive monitoring to take proactive action. Acting as an engineer on your team, it interprets IT telemetry and system data from your current tools, diving into issues and resolving them as they arise.

It delivers clear, actionable insights in natural language, reducing response times from days to minutes.

Hawkeye’s unique approach leverages the power of LLMs to guide incident analysis without ever sharing customer data with LLMs, ensuring a thoughtful and secure approach.

Security and trust are major concerns for AI adoption in IT. How is NeuBird addressing these challenges?

Hawkeye’s unique approach leverages the power of LLMs to guide incident analysis without ever sharing customer data with LLMs, ensuring a thoughtful and secure approach.

Hawkeye operates within an enterprise’s security perimeter, using only internal data sources to generate insights—eliminating hallucinations that plague generic LLM-based systems. It also ensures transparency by providing traceable recommendations, so IT teams maintain full control over decision-making. This approach makes it a reliable and secure AI teammate rather than a black-box solution.

How does Hawkeye integrate with existing IT infrastructure, and what does the onboarding process look like for enterprises?

Hawkeye seamlessly integrates with enterprise IT environments by connecting to existing observability, monitoring and incident response tools, e.g. AWS CloudWatch, Azure Monitor, Datadog, and PagerDuty. It works alongside IT, DevOps, and SRE teams without requiring major infrastructure changes.

Here’s how it works:

Deployment: Hawkeye is deployed within your environment, connecting to existing tools and data sources.

Learning & Adaptation: It analyzes historical incidents and real-time telemetry to understand normal system operations and identify patterns.

Customization: The platform adapts to enterprise-specific workflows, tailoring responses and recommendations to operational needs.

Collaboration: Through a chat-based interface, teams receive real-time diagnostics, solutions, and automated resolutions where applicable.

This streamlined integration process accelerates incident resolution, reduces MTTR, and enhances system reliability—allowing enterprises to scale IT operations efficiently without adding headcount.

 What role do human engineers play alongside AI teammates like Hawkeye? How do you see this collaboration evolving?

 Hawkeye supplements, rather than replaces, human IT professionals. IT teams still drive strategic decisions, but instead of manually troubleshooting every issue, they work alongside Hawkeye to diagnose and resolve problems faster. As AI teammates become more advanced, IT professionals will shift toward higher-value tasks—optimizing architectures, improving security, and accelerating new technology adoption.

Hawkeye claims to reduce mean time to resolution (MTTR) by 90%. Can you share any real-world examples or case studies that demonstrate this impact?

 A national grocery retailer integrated Hawkeye to handle the growing complexity of its e-commerce platform. Their SRE team was overwhelmed by massive telemetry data and slow manual investigations, especially during peak shopping periods.

With Hawkeye as a GenAI-powered teammate, they saw:

~90% MTTR reduction – Instant data correlation across AWS CloudWatch, AWS MSK, and PagerDuty.

24/7 real-time analysis – Eliminated after-hours escalations.

Automated incident resolution – Pre-approved fixes deployed autonomously.

During their holiday shopping surge, Hawkeye optimized capacity, detected early issues, and made real-time scaling adjustments, ensuring near 100% uptime—a game-changer for their IT operations.

What is your vision for the evolution of AI agents from passive assistants to active problem-solvers in enterprise operations, and what key advancements are driving this shift?

 AI is shifting from passive observability to active problem-solving. Hawkeye already provides root-cause analysis and resolutions, but the next phase is full autonomy—where AI proactively optimizes IT operations, and self-heals infrastructure in real time. This evolution, driven by advancements in GenAI and cognitive decision-making models, will redefine enterprise IT.

Where do you see AI-driven enterprise automation in the next five years, and what major challenges or breakthroughs do you anticipate along the way?

 AI will shift from assisting engineers to fully autonomous IT operations, predicting and resolving issues before they escalate. Multi-agent AI workflows will enable seamless collaboration across IT, security, and DevOps, breaking down silos between departments. The biggest breakthroughs will include self-healing infrastructure, AI-driven cross-functional collaboration, and stronger human-AI trust, allowing AI teammates to take on more complex decisions. The main challenges will be ensuring AI transparency and adapting the workforce to work alongside AI, balancing automation with human oversight.

Having led multiple startups to success, what advice would you give to entrepreneurs building AI-driven companies today?

 Entrepreneurs should focus on solving real, high-value problems rather than chasing AI hype. AI must be built with enterprise trust in mind, ensuring transparency and control for businesses adopting it. Adaptability is key—AI systems must evolve with business needs instead of being rigid, one-size-fits-all solutions. Rather than replacing human expertise, AI should be positioned as a teammate that enhances decision-making and operational efficiency. Finally, enterprise AI adoption takes time, so companies that prioritize scalability and long-term impact over short-term trends will ultimately emerge as leaders in the space.

Thank you for the great interview, readers who wish to learn more should visit NeuBird. 

[devotedlywingedtheorist]

North America AIOps Platform Market Historical Analysis, Opportunities, Latest Innovations, Top Players Forecast 2028

The North America AIOps platform market is expected to grow from US$ 1,238.17 million in 2021 to US$ 8,810.61 million by 2028; it is estimated to grow at a CAGR of 32.4% from 2021 to 2028.

In dynamic, elastic contexts, traditional ways of controlling IT complexity—offline, manual activities requiring human intervention—do not even operate. It is no longer possible to track and manage this complexity by manual, human monitoring. For years, ITOps has exceeded human scale, and the situation is only getting worse. Organizations want their critical applications to be available and operate well.

📚 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐒𝐚𝐦𝐩𝐥𝐞 𝐏𝐃𝐅 𝐂𝐨𝐩𝐲@ https://www.businessmarketinsights.com/sample/BMIRE00025397

They are also seeking a highly automated setup, that makes it easier to make clear decisions about new product development by leveraging classified data. Hence, the introduction of the AIOps platform has catered to these demands. AIOps platforms consolidate all applications and infrastructure operations into a single management portal with a dashboard view. Studies claim that AIOps can automatically perform 90% of the operative tasks, and human interaction is required only for 10% of tasks. Hence, the growing digital data, coupled with premium support offered by the AIOps platform, is driving the AIOps platform market. AIOps are beneficial for any company wishing to modernize to a digital platform that incorporates cutting-edge automation, analytics, artificial intelligence, and machine learning technologies. AIOps systems decrease the flood of warnings and can perform everyday tasks such as backups, server restarts, and low-risk maintenance. AIOps are expected to become widely used and mainstream soon, which will drive the market in the coming years.

📚𝐅𝐮𝐥𝐥 𝐑𝐞𝐩𝐨𝐫𝐭 𝐋𝐢𝐧𝐤 @ https://www.businessmarketinsights.com/reports/north-america-aiops-platform-market

𝐓𝐡𝐞 𝐋𝐢𝐬𝐭 𝐨𝐟 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬

AppDynamics

BMC Software, Inc.

Broadcom Inc.

Dynatrace LLC

HCL Technologies

IBM Corporation

Micro Focus

Moogsoft Inc.

Resolve Systems, LLC

Splunk, Inc.

Regional Distinctions and Considerations:

North America exhibits significant regional variations that impact the adoption and implementation of AIOps platforms. Here's a breakdown of key considerations:

Silicon Valley and the West Coast:

Characteristics: A hub of technological innovation, with a high concentration of tech companies and early adopters of AIOps.

Opportunities: Strong demand for cutting-edge AIOps solutions, including AI-driven automation and predictive analytics.

Considerations: Highly competitive market, requiring continuous innovation and differentiation.

The Northeast (New York, Boston, etc.):

Characteristics: A major financial and healthcare hub, with stringent regulatory requirements.

Opportunities: Strong demand for AIOps solutions that enhance security, compliance, and risk management.

Considerations: High emphasis on data privacy and security, requiring robust compliance measures.

𝐀𝐛𝐨𝐮𝐭 𝐔𝐬: Business Market Insights is a market research platform that provides subscription service for industry and company reports. Our research team has extensive professional expertise in domains such as Electronics & Semiconductor; Aerospace & Defense; Automotive & Transportation; Energy & Power; Healthcare; Manufacturing & Construction; Food & Beverages; Chemicals & Materials; and Technology, Media, & Telecommunications

𝐀𝐮𝐭𝐡𝐨𝐫’𝐬 𝐁𝐢𝐨: 𝐒𝐡𝐫𝐞𝐲𝐚 𝐏𝐚𝐰𝐚𝐫 𝐒𝐞𝐧𝐢𝐨𝐫 𝐌𝐚𝐫𝐤𝐞𝐭 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐄𝐱𝐩𝐞𝐫𝐭

[sassygardenerstudent]

North America AIOps Platform Market In-Depth Analysis of the Current Development Stage, Effective Counter Strategies, Size, Status and Forecast 2022-2028

The North America AIOps platform market is expected to grow from US$ 1,238.17 million in 2021 to US$ 8,810.61 million by 2028; it is estimated to grow at a CAGR of 32.4% from 2021 to 2028.

In dynamic, elastic contexts, traditional ways of controlling IT complexity—offline, manual activities requiring human intervention—do not even operate. It is no longer possible to track and manage this complexity by manual, human monitoring. For years, ITOps has exceeded human scale, and the situation is only getting worse. Organizations want their critical applications to be available and operate well.

📚 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐒𝐚𝐦𝐩𝐥𝐞 𝐏𝐃𝐅 𝐂𝐨𝐩𝐲@ https://www.businessmarketinsights.com/sample/BMIRE00025397

They are also seeking a highly automated setup, that makes it easier to make clear decisions about new product development by leveraging classified data. Hence, the introduction of the AIOps platform has catered to these demands. AIOps platforms consolidate all applications and infrastructure operations into a single management portal with a dashboard view. Studies claim that AIOps can automatically perform 90% of the operative tasks, and human interaction is required only for 10% of tasks. Hence, the growing digital data, coupled with premium support offered by the AIOps platform, is driving the AIOps platform market. AIOps are beneficial for any company wishing to modernize to a digital platform that incorporates cutting-edge automation, analytics, artificial intelligence, and machine learning technologies. AIOps systems decrease the flood of warnings and can perform everyday tasks such as backups, server restarts, and low-risk maintenance. AIOps are expected to become widely used and mainstream soon, which will drive the market in the coming years.

📚𝐅𝐮𝐥𝐥 𝐑𝐞𝐩𝐨𝐫𝐭 𝐋𝐢𝐧𝐤 @ https://www.businessmarketinsights.com/reports/north-america-aiops-platform-market

𝐓𝐡𝐞 𝐋𝐢𝐬𝐭 𝐨𝐟 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬

AppDynamics

BMC Software, Inc.

Broadcom Inc.

Dynatrace LLC

HCL Technologies

IBM Corporation

Micro Focus

Moogsoft Inc.

Resolve Systems, LLC

Splunk, Inc.

Actionable Recommendations for Stakeholders:

Identify Untapped Segments: Explore niche markets within the North American AIOps landscape, such as edge computing AIOps or AIOps for specific industry verticals.

Develop Unique Value Propositions: Differentiate your offerings by focusing on specific capabilities, such as advanced AI-driven automation, security integration, or seamless cloud-native integration.

Leverage Data Analytics: Utilize data analytics to gain a deeper understanding of customer needs and market trends. This will enable you to develop targeted marketing campaigns and tailor your product offerings.

Build Strategic Partnerships: Collaborate with other technology vendors and service providers to create comprehensive AIOps solutions.

Invest in Talent Development: Address the talent shortage by investing in training and development programs to equip IT professionals with the skills needed to implement and manage AIOps platforms.

𝐀𝐛𝐨𝐮𝐭 𝐔𝐬: Business Market Insights is a market research platform that provides subscription service for industry and company reports. Our research team has extensive professional expertise in domains such as Electronics & Semiconductor; Aerospace & Defense; Automotive & Transportation; Energy & Power; Healthcare; Manufacturing & Construction; Food & Beverages; Chemicals & Materials; and Technology, Media, & Telecommunications

𝐀𝐮𝐭𝐡𝐨𝐫’𝐬 𝐁𝐢𝐨: 𝐏𝐫𝐚𝐠𝐚𝐭𝐢 𝐏𝐚𝐭𝐢𝐥 𝐒𝐞𝐧𝐢𝐨𝐫 𝐌𝐚𝐫𝐤𝐞𝐭 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐄𝐱𝐩𝐞𝐫𝐭

[generativeinai]

AIOps Platform Development: Integrating AI and Machine Learning for Smarter IT

As organizations expand their IT infrastructure, the complexity of managing networks, applications, and services increases exponentially. Traditional IT operations (ITOps) struggle to keep up with the sheer volume of data and the rapid response required to maintain efficiency. Enter AIOps (Artificial Intelligence for IT Operations)—a revolutionary approach that leverages AI and Machine Learning (ML) to enhance IT operations through automation, predictive analytics, and anomaly detection.

This blog explores the key components of AIOps platform development, the integration of AI/ML technologies, and the benefits of adopting AIOps for smarter IT management.

What is AIOps?

AIOps is a multi-layered technology platform that automates and enhances IT operations using big data, AI, and ML. It integrates various IT functions such as performance monitoring, event correlation, anomaly detection, and root cause analysis. AIOps platforms help organizations shift from reactive troubleshooting to proactive incident prevention.

Key Functions of AIOps

Data Collection & Aggregation – Collects data from logs, monitoring tools, and IT service management (ITSM) systems.

Event Correlation & Analysis – Uses ML algorithms to identify patterns and link related events.

Anomaly Detection – Detects unusual activities and potential issues before they escalate.

Automated Remediation – Implements self-healing actions or suggests solutions to IT teams.

Predictive Analytics – Forecasts future issues based on historical data.

Core Components of an AIOps Platform

Developing an AIOps platform requires integrating several critical components:

1. Data Ingestion & Management

AIOps relies on a massive amount of structured and unstructured data. This includes logs, metrics, alerts, and events from various IT systems. The platform must efficiently aggregate and normalize this data for analysis.

2. Artificial Intelligence & Machine Learning

ML models power AIOps by identifying patterns, detecting anomalies, and automating repetitive tasks. Some key AI/ML techniques used include:

Supervised Learning for event classification.

Unsupervised Learning for anomaly detection.

Natural Language Processing (NLP) for analyzing IT service tickets and logs.

Deep Learning for advanced predictive insights.

3. Event Correlation & Root Cause Analysis

AI-driven event correlation reduces noise and groups related incidents. This speeds up root cause analysis, helping IT teams diagnose and resolve issues faster.

4. Automation & Orchestration

AIOps automates incident response using playbooks and workflows. It can trigger scripts, restart services, or escalate critical issues to human operators.

5. Visualization & Reporting

Dashboards and reports provide IT teams with insights into system performance, trends, and potential risks. Interactive visualization tools help simplify complex data.

Steps to Build an AIOps Platform

Step 1: Define Use Cases & Goals

Start by identifying key IT challenges and how AIOps can help. Common use cases include:

Reducing MTTR (Mean Time to Resolve) incidents.

Automating root cause analysis.

Enhancing IT security monitoring.

Step 2: Integrate Data Sources

Connect the AIOps platform with various monitoring tools, logs, and ITSM solutions. A well-structured data pipeline ensures efficient processing.

Step 3: Implement AI & ML Models

Use machine learning algorithms to process and analyze data. Employ historical and real-time data to train models for anomaly detection and prediction.

Step 4: Enable Automation

Develop playbooks and workflows for automated responses. Define the level of automation, from suggestive alerts to full self-healing capabilities.

Step 5: Deploy & Monitor

Implement the AIOps platform in a phased manner. Continuously monitor and refine ML models based on new data and feedback.

Benefits of AIOps for IT Operations

1. Proactive Incident Management

AIOps detects anomalies before they impact end users, allowing for preventive actions.

2. Reduced Alert Fatigue

Traditional monitoring tools generate excessive alerts. AIOps filters out noise and prioritizes critical issues.

3. Faster Root Cause Analysis

By correlating events, AIOps quickly identifies the underlying causes of incidents.

4. Automated Remediation

AIOps reduces manual intervention through self-healing automation.

5. Improved IT Efficiency & Cost Savings

AI-driven insights and automation reduce operational costs and downtime, improving overall efficiency.

Future of AIOps

The future of AIOps will see greater integration with cloud computing, edge computing, and DevOps. Advancements in Generative AI and Large Language Models (LLMs) will further enhance IT automation and intelligence.

Conclusion

AIOps is transforming IT operations by integrating AI and ML to enhance efficiency, automation, and predictive analytics. By implementing an AIOps platform, organizations can move towards a self-healing IT environment, reducing downtime and improving service reliability. As technology advances, AIOps will continue to evolve, making IT operations smarter, faster, and more resilient.

[strategictech]

6 Key Trends Shaping ITSM and ITOps in 2025

From realistic AI expectations to growing IT job opportunities, subtle but impactful changes are set to redefine IT service management and operations in 2025.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[globalkeyinfosolution]

How Automation is Shaping the Future of IT

In today’s rapidly evolving digital landscape, automation is playing a pivotal role in reshaping the Information Technology (IT) industry. As businesses strive for efficiency, scalability, and competitiveness, the adoption of automation technologies has become essential. From streamlining routine tasks to enabling advanced decision-making processes, automation is transforming IT operations, development, and management in unprecedented ways. This article explores the profound impact of automation on the future of IT, highlighting key areas where automation is driving significant change.

1. Automation in IT Operations (ITOps)

One of the most significant areas where automation is making a difference is in IT operations (ITOps). Traditionally, IT teams have been responsible for managing and maintaining the infrastructure, ensuring uptime, and troubleshooting issues. These tasks, while critical, are often time-consuming and prone to human error.

Automation tools, such as Robotic Process Automation (RPA) and AI-driven platforms, are now capable of handling these routine tasks with greater accuracy and speed. For instance, automated monitoring systems can detect and respond to potential issues in real-time, reducing the need for manual intervention. This not only improves efficiency but also minimizes downtime, which is crucial for business continuity.

Moreover, automation allows IT teams to shift their focus from mundane tasks to more strategic initiatives, such as innovation and digital transformation. By leveraging automation, IT departments can operate more efficiently, reduce operational costs, and enhance service delivery.

2. DevOps and Continuous Integration/Continuous Deployment (CI/CD)

DevOps practices have revolutionized software development by promoting collaboration between development and operations teams. Automation is at the heart of DevOps, particularly in the context of Continuous Integration/Continuous Deployment (CI/CD) pipelines.

Automation in CI/CD enables the rapid and consistent deployment of code changes, ensuring that new features, bug fixes, and updates can be delivered to end-users quickly and reliably. Automated testing tools are integrated into these pipelines to identify issues early in the development process, reducing the risk of defects in production.

By automating the build, test, and deployment processes, organizations can achieve faster release cycles, improve software quality, and respond more quickly to market demands. This agility is essential in today’s competitive environment, where the ability to innovate and deliver new solutions rapidly can make or break a business.

3. AI and Machine Learning in IT Automation

Artificial Intelligence (AI) and Machine Learning (ML) are powerful technologies that are driving the next wave of automation in IT. AI-powered tools can analyze vast amounts of data, identify patterns, and make intelligent decisions based on those insights.

For example, AI can be used to automate incident management by predicting potential system failures before they occur. By analyzing historical data and current conditions, AI algorithms can trigger proactive measures, such as reallocating resources or adjusting configurations, to prevent disruptions.

Machine Learning models can also be employed to optimize IT resource allocation. By analyzing usage patterns and forecasting future demands, these models can automate the scaling of resources up or down as needed, ensuring that IT infrastructure is always aligned with business requirements.

The integration of AI and ML into IT automation not only enhances operational efficiency but also enables predictive and prescriptive analytics, allowing organizations to make more informed decisions and stay ahead of potential challenges.

4. Automation in Cybersecurity

As cyber threats become more sophisticated, the need for robust cybersecurity measures has never been greater. Automation is playing a crucial role in enhancing cybersecurity by enabling faster detection and response to threats.

Automated security tools can monitor networks, endpoints, and applications in real-time, identifying and mitigating threats before they cause harm. For example, Security Information and Event Management (SIEM) systems use automation to collect and analyze security data from various sources, correlating events to detect potential threats.

In addition to threat detection, automation is also being used in vulnerability management. Automated scanning tools can continuously assess systems for vulnerabilities, prioritize them based on risk, and even deploy patches automatically. This reduces the time between vulnerability discovery and remediation, minimizing the window of opportunity for attackers.

Furthermore, automation in cybersecurity extends to compliance and auditing. Automated tools can ensure that systems adhere to security policies and regulations, generating reports and alerts when deviations occur. This not only simplifies compliance management but also reduces the risk of non-compliance penalties.

5. The Role of Automation in IT Service Management (ITSM)

IT Service Management (ITSM) encompasses the processes and activities involved in managing IT services throughout their lifecycle. Automation is transforming ITSM by streamlining service delivery and improving the user experience.

One of the key areas where automation is making an impact is in the management of service requests and incidents. Automated workflows can route requests to the appropriate teams, prioritize them based on urgency, and even resolve common issues without human intervention. For instance, chatbots and virtual assistants can handle routine queries, freeing up IT staff to focus on more complex tasks.

Automation also plays a role in change management, where it can automate the deployment of changes in a controlled and consistent manner. This reduces the risk of errors during changes and ensures that services remain stable and reliable.

Overall, the integration of automation into ITSM processes leads to faster service resolution times, improved customer satisfaction, and more efficient use of IT resources.

6. Challenges and Considerations in IT Automation

While automation offers numerous benefits, it also presents challenges that organizations must address. One of the primary concerns is the potential for job displacement as automation takes over routine tasks. However, rather than eliminating jobs, automation is likely to shift the focus of IT roles toward more strategic and value-added activities.

Organizations must also consider the complexity of integrating automation into existing IT environments. Implementing automation requires careful planning, as well as the right tools and expertise. Additionally, there is a need to ensure that automation is aligned with business objectives and does not introduce new risks or inefficiencies.

Another challenge is the need for continuous monitoring and optimization of automated systems. As IT environments evolve, automation processes must be regularly updated to remain effective. This requires ongoing investment in skills development and technology upgrades.

7. The Future of IT with Automation

The future of IT is undeniably intertwined with automation. As automation technologies continue to advance, they will become even more integral to IT operations, development, and management. The next frontier in IT automation may involve greater integration of AI and ML, leading to more autonomous IT systems that can self-manage, self-heal, and even self-optimize.

Organizations that embrace automation will be better positioned to achieve operational efficiency, scalability, and innovation. However, success in this journey will depend on a thoughtful approach to automation that balances the benefits with the challenges and ensures that human expertise remains at the core of IT strategy.

As automation reshapes the IT landscape, the role of IT professionals will evolve. The focus will shift from managing individual systems and tasks to overseeing automated processes, driving innovation, and ensuring that technology aligns with business goals. In this new era, the ability to adapt, learn, and collaborate will be key to thriving in the automated IT world.

About Global Key Info Solutions

Global Key Info Solutions (GKIS) Pvt. Ltd. is at the forefront of delivering cutting-edge IT services like web development that embrace the power of automation. By leveraging the latest automation technologies, GKIS helps businesses enhance their IT operations, streamline processes, and achieve greater efficiency. With a commitment to innovation and customer satisfaction, GKIS empowers organizations to navigate the complexities of the digital age with confidence and success.

[govindhtech]

HPE Private Cloud AI With NVIDIA AI Computing Solutions

HPE Private Cloud AI NVIDIA AI Computing by HPE, a portfolio of jointly created AI solutions and go-to-market integrations that help businesses embrace generative AI more quickly, was unveiled today by Hewlett Packard Enterprise and NVIDIA.

One of the portfolio’s standout products is HPE Private Cloud AI, a first-of-its-kind offering that combines HPE’s AI storage, compute, and the HPE GreenLake cloud with the most extensive integration of NVIDIA AI processing, networking, and software to date. With the help of this solution, businesses of all sizes can create and implement generative AI applications in a sustainable manner while also saving energy and gaining flexibility. HPE Private Cloud AI comes with a self-service cloud experience with complete lifecycle management and is available in four right-sized configurations to handle a wide range of AI workloads and use cases. It is powered by the new OpsRamp AI copilot, which helps IT operations optimise workload and IT efficiency.

Through a combined go-to-market strategy that includes sales teams, channel partners, training, and a global network of system integrator that can assist businesses in a range of industries in managing complex AI workloads, such as Deloitte, HCLTech, Infosys, TCS, and Wipro, all NVIDIA AI Computing by HPE offerings and services will be made available.

NVIDIA founder and CEO Jensen Huang joined HPE President and CEO Antonio Neri in announcing NVIDIA AI Computing by HPE during the HPE Discover keynote. This announcement signifies the growth of a multi-decade collaboration and underscores the significant effort and resource commitment from both organisations.

“Fragmented AI technology provides too many dangers and impediments to large-scale industry adoption, yet generative AI has great promise for enterprise transformation and potentially threaten a company’s most valuable asset. its proprietary data,” Neri stated. “HPE and NVIDIA co-developed a turnkey private cloud for AI to unleash the immense potential of generative AI in the enterprise. This will enable enterprises to focus their resources on developing new AI use cases that can boost productivity and unlock new revenue streams.”

According to Huang, “as every industry rushes to join the industrial revolution, generative AI and accelerated computing are fueling a fundamental transformation.” “Together with HPE’s private cloud technology, NVIDIA and HPE have never before so thoroughly integrated Nvidia technologies, giving enterprise clients and AI professionals access to the most cutting-edge computing infrastructure and services to push the boundaries of AI.”

A Private Cloud AI portfolio co-developed by HPE and NVIDIA With HPE Private Cloud AI, enterprise risk from AI is managed while innovation and return on investment are accelerated through a unique cloud-based experience. The resolution provides:

Assistance with RAG AI workloads that use private data, inference, and fine-tuning. Enterprise control for requirements related to data security, privacy, and governance. Proven cloud computing background with ITOps and AIOps capabilities to boost output. Quick route to flexible consumption to take advantage of upcoming AI growth and opportunities. Data software stack and curated AI in HPE Private Cloud AI The NVIDIA AI Enterprise software platform, which includes NVIDIA NIM inference microservices, is the starting point for the AI and data software stack.

Production-grade copilot and other GenAI application development and deployment are streamlined and accelerated by NVIDIA AI Enterprise. Easy-to-use microservices for optimised AI model inferencing are provided by NVIDIA NIM, which is included with NVIDIA AI Enterprise. This allows for a seamless transition from prototype to safe deployment of AI models in a range of use cases.

With a unified control plane that offers adaptable solutions, continuous enterprise support, and trusted AI services like data and model compliance and extensible features that guarantee AI pipelines are in compliance, explicable, and reproducible throughout the AI lifecycle, HPE AI Essentials software complements NVIDIA AI Enterprise and NVIDIA NIM.

HPE Private Cloud AI provides a fully integrated AI infrastructure stack that includes NVIDIA Spectrum-X Ethernet networking, HPE GreenLake for file storage, and HPE ProLiant servers with support for NVIDIA L40S, NVIDIA H100 NVL Tensor Core GPUs, and the NVIDIA GH200 NVL2 platform in order to deliver optimal performance for the AI and data software stack.

HEP GreenLake Private cloud enables a cloud experience Thanks to HPE GreenLake cloud, HPE Private Cloud AI provides a self-service cloud experience. HPE Greenlake cloud services offer manageability and observability to automate, orchestrate, and manage endpoints, workloads, and data across hybrid environments via a single, platform-based control plane. Workload and endpoint sustainability measurements are part of this.

Observability of the OpsRamp AI infrastructure, HPE GreenLake cloud, and copilot assistance Observability and AIOps are provided to all HPE products and services through the integration of OpsRamp’s IT operations with HPE GreenLake cloud. The whole NVIDIA accelerated computing stack, comprising NVIDIA NIM and AI software, NVIDIA Tensor Core GPUs and AI clusters, NVIDIA Quantum InfiniBand and NVIDIA Spectrum Ethernet switches, is now observable with OpsRamp. IT managers may monitor their workloads and AI infrastructure in hybrid and multi-cloud settings by gaining insights to spot irregularities.

With a conversational assistant, the new OpsRamp operations copilot analyses massive datasets for insights using NVIDIA’s accelerated computing platform, increasing operations management productivity. In order to provide customers with a single service map view of endpoint security across their whole infrastructure and applications, OpsRamp will also interface with CrowdStrike APIs.

Use AI to speed up time to value and increase cooperation with international system integrators As part of their strategic AI solutions and services, Deloitte, HCLTech, Infosys, TCS, and Wipro announced their support of the NVIDIA AI Computing by HPE portfolio and HPE Private Cloud AI, with the goal of accelerating the time to value for enterprises in developing industry-focused AI solutions and use cases with evident business benefits.

Support for NVIDIA’s most recent GPUs, CPUs, and Superchips is added by HPE Server Hewlett packard enterprise The HPE Cray XD670 is perfect for LLM builders and supports eight NVIDIA H200 NVL Tensor Core GPUs. For larger models or RAG users, the HPE ProLiant DL384 Gen12 server with NVIDIA GH200 NVL2 is the best option. For LLM users seeking flexibility in scaling their GenAI workloads, the HPE ProLiant DL380a Gen12 server, which supports up to eight NVIDIA H200 NVL Tensor Core GPUs, is a great option. HPE will be ready to support the new NVIDIA Blackwell, NVIDIA Rubin, and NVIDIA Vera architectures in addition to the NVIDIA GB200 NVL72 / NVL2. Certified for NVIDIA DGX BasePOD and NVIDIA OVX systems, high-density file storage With its NVIDIA OVX storage validation and NVIDIA DGX BasePOD certification, HPE GreenLake for File Storage offers customers a dependable enterprise file storage solution for scaling up AI, GenAI, and GPU-intensive workloads. Regarding future NVIDIA reference architecture storage certification programmes, HPE will be a time-to-market partner.

Read more on Govindhtech.com

[long80llc]

A leading healthcare performance management services provider in the USA, Long 80’s suite of solutions successfully delivered 30% improvement in patientcare, 50% improvement in Time to Market, and more for a major hospital in Bronx, NY. Read the entire case study here.

[my-interesting-things]

New post out.

Especially useful for Managed Service Providers looking after cloud accounts for multilple clients.

aws #azure #gcp #cloudsecurity #cloudcomputing #security #devops #DevSecOps #developer #serverless #lambda #awscloud #engineer #cloud #amazonwebservices #amazonweb #googlecloud #microsoft #bigdata #technology #automation #devopsengineer #diagrams #PaaS #SaaS #FaaS #cloudtechnology #cloudsolutions #hybridcloud #multicloud #publiccloud #cloudcompliance #cybersecurity #cloudmonitoring #cloudautomation #cloudgovernance #edgecomputing #containerization #docker #kubernetes #itops #CTO #CFO #IaaS #AI #ML #DataScience #CloudNative #CloudMigration #CloudManagement #DigitalTransformation #Microservices #CloudServices #CloudStrategy #IoT #ServerlessArchitecture #CloudStorage #OpenStack #DeepLearning #DataCente #ITManagement #Scalability #HighAvailability #CostOptimization #CloudCostManagement

[releaseteam]

via Twitter https://twitter.com/releaseteam

[enterpriseitgeek]

What’s driving AIOps?

AIOps is the evolution of IT operational analytics (ITOA). It grows out of several trends and needs affecting ITOps, including:

IT environments exceeding human scale. Traditional approaches to managing IT complexity—offline, manual efforts that require human intervention—don’t work in dynamic, elastic environments. Tracking and managing this complexity through manual, human oversight is no longer possible. ITOps has been exceeding human scale for years and it continues to get worse.

The amount of data that ITOps needs to retain is exponentially increasing. Performance monitoring is generating exponentially larger numbers of events and alerts. Service ticket volumes experience step-function increases with the introduction of IoT devices, APIs, mobile applications, and digital or machine users. Again, it is simply becoming too complex for manual reporting and analysis.

Infrastructure problems must be addressed at ever-increasing speeds. As organizations digitize their business, IT becomes the business. The “consumerization” of technology has changed user expectations for all industries. Reactions to IT events—whether real or perceived—need to occur immediately, particularly when an issue impacts user experience.

More computing power is moving to the edges of the network. The ease with which cloud infrastructure and third-party services can be adopted has empowered line of business (LOB) functions to build their own IT solutions and applications. Control and budget have shifted from the core of IT to the edge. And more computing power (that can be leveraged) is being added from outside core IT.

Get to know why AIOps should be a part of your IT operations and how the blend of AI and IT operations helps IT Ops and DevOps teams work smarter and faster.

[paradisetechsoftsolutions]

What Is AIOps — And Why You Should Care?

What is AIOps?

The term AIOps which is considered as Artificial Intelligence for IT Operations was coined by a research company refers to the integration of analytics and machine learning for scaling and enhancing the various IT operations. Originally coined by Gartner in 2017, the term refers to the way data and information from an IT environment are managed by an IT team–in this case, using AI.

AIOps (Artificial Intelligence for IT Operations ) is simply positioned as continuous integration and continuous deployment for core IT functions and holds two main components- colossal data and machine learning (ML). It represents a shift from isolated data to a more compelling business environment which will be beneficial for digital transformation. Moreover, the primary explanation of AIOps is that it subsumes executing AI and ML (artificial intelligence & machine learning) to maintain all primary IT operations. The main objective is to turn the data all caused by IT systems platforms into significant insights. You may also see some modifications to this extensive-term. That's because technology is spontaneously emerging and is comparatively new.  

AIOps (artificial intelligence for IT operations) also develops automation, by permitting workflows to be triggered with or without human interference. The capacities of ChatOps make existing automation and orchestration functionality available as an integral part of the normal collaborative diagnostic & remediation method or techniques. As ML (machine learning) systems become more reliable than usual. Moreover, it becomes more possible for routine and well-conceivably resolving issues all before the users are influenced and affected plus even aware of any problem.

How do AIOps works?

Artificial Intelligence for IT operations, software platforms use cutting-edge computing technologies like ML (machine learning) and advanced analytics to support IT operations in three major areas:

Monitoring

Automation

Service Desk  

AIOps (artificial intelligence for IT operations) software helps promote IT infrastructure monitoring by congregating and aggregating data from the central network. Data sources subsume event log files from the servers, applications, and other network endpoints. Obtaining data from multiple sources that were previously siloed and integrating them into a single database forge it easier for ML (machine learning) algorithms to assess network characteristics and performance in real-time.

Artificial Intelligence for IT operations works with existing data sources, subsuming many traditional IT monitoring, log events, application and network performance anomalies, and many more. The complete data from these sources systems are mainly processed by a mathematical model that is capable to identify essential events automatically, without requiring any laborious manual pre-filtering.

Moreover, the second layer of algorithms analyzes these events to identify the clusters of relatable events that are all the symptoms of the exact underlying issues. Plus, if we discuss this algorithmic filtering colossally lessens up the commotion level of the operations of IT teams or organizations, would otherwise have to deal with, and also bypasses the replication of work that can happen when the unnecessary tickets are routed to distinct teams.

Besides, virtual organizations can be compiled on the fly, allowing discrete specialists to "swarm" around an issue that spans all across the technological or organizational boundaries. Existing ticketing and incident management systems can take advantage of Artificial Intelligence for IT Operations (AIOps) capacities directly into an existing process. Artificial Intelligence for IT Operations also enhances the automation, by permitting workflows to be triggered with or without any human intervention. ChatOps capacities make existing automation and the functionality of orchestration available as a fundamental component of the normal collaborative characteristic & remediation method.

As ML (machine learning) systems become more and more precise and stable, it becomes feasible for routine & well-understood actions to be triggered without any human intervention or even aware of any concern.

Business Benefits of AIOps

Now, if we talk about the benefits of AIOps is that it usually sets the operations of IT up to and perform with the level of speed and coordination that end the users' expectation and requirement. On some model-based processes, mushrooming the specialization into disengaged silos, and above all, quite much monotonous manual activity, forged it difficult for IT Ops to maintain up with the ever-increasing speed and volume of demands on their experience.

Cohesive Coordination- The data is usually scattered all across business verticals; Artificial Intelligence for IT Operations i.e. AIOps helps to build a cohesive relationship between such verticals through algorithms based on Machine Learning whilst staying coordination. Collecting and processing this scattered data necessitates almost zero manual effort, as automated algorithms will do their due thoroughness. In other words, Artificial Intelligence for IT Operations builds meaningful connections from siloed data to deliver intelligent and actionable business insights. In this way, the business teams can always work at their speed, whilst staying connected.

Faster Digital Information- Here, digital transformation is all about discovery all via new technologies artificial intelligence for IT operations complements that change. Consideration of AIOps, advanced algorithms aid in detecting and, more impressively, reacting to the events in actual-time, by providing firms with greater control over their business applications and infrastructure of IT. ITOps teams can bid goodbye to that late-night emergency calls or queries because AIOps has got IT covered.

Eliminating the skills gap- Eliminating the skills gap is quite easier to access data with built-in intelligence permits, current specialists, to spend more span all on the key judgments and other streamlines the learning process for newer members of the team.

Better prioritization of urgent, high-impact concerns-  As Artificial Intelligence for IT Operations has produced, solutions are supporting to point the mission-critical concerns. Assume a situation where there's an obtrusive blunder - a broken drive, for example- on a little-used archival system at the same moment there is an emerging concern with a key application server. AIOps can simply support the direct attention of the teams to the latter, where quick actions could prevent costly downtime.  

By applying artificial intelligence to IT operations (AIOps) in the performance and capacity discipline, problems are easier to understand, resolve, predict, and prevent. Better intelligence; better availability; better results. Here, by employing artificial intelligence to IT operations (AIOps) in the performance and capability control, concerns are easier to grasp, resolve, assume, and secure. Superior intelligence; superior availability, and superior outcomes.

Role of AIOps in Digital Transformation

The "Digital Transformation" is the paradigm shift from legacy IT infrastructure to more dynamic frameworks that permit continuous improvement, agility, instant communication, and data-backed decision making.

All of this resolve around 3 key areas:

1.Customer Experience

2. Operational Process

3. Business Model

AIOps at its core is a process to streamline inputs from all of the above sections and generates insights from the collective data. Therefore, it will work as the best way to make all of this practically possible for large enterprises. By coupling "Artificial Intelligence" and colossal data, the system saves the firms from decaying the time on repetitive tasks & makes them more responsive to change.  

Final Words

Now, we hope these sources outline helpful and most suitable practices and strategies you can take away to instantly obtain value from machine learning-driven association and insights.

[blackfeline-wastaken]

Digital Transformation Ideas to Stay Relevant

Can Today's Challenges Be a Catalyst for Positive Change?

The COVID-19 pandemic has had a devastating impact on our world, affecting our day-to-day lives in ways we would never have thought even a couple of brief months ago.

For many organizations, like Singapore top app developer, these changes have prompted a fundamental move by they way we characterize business as usual, causing many companies to reassess and reprioritize their business and IT goals and budgets in dramatic and surprising ways.

During this environment of phenomenal change and uncertainty, it may be enticing for certain companies to require digital transformation initiatives to be postponed and instead center around progressively familiar and less disruptive endeavors. Be that as it may, this is already a period of disruption, and many aspects of digital transformation are exactly what's expected to enable your organization to push ahead, regardless of whether you look to convey more value to customers, seek after new opportunities for growth, improve efficiency—or all of the above.

As you contemplate what's next for your organization and industry, consider how the following four IT initiatives can help advance your digital transformation goals and better position your organization to develop and succeed today—and all through all the better days ahead.

Break Through Silos With Agile Practices

With laborers physically separated, many are looking for cunning ways to encourage collaboration, teamwork, and community. From digital water cooler chat hours to new platforms and technologies, the ebb and flow remote work scenario has in many instances prompted new connections with departments or individuals collaborating that have never cooperated.

As these connections are manufactured, you have a one of a kind opportunity to encourage DevOps style practices all through IT operations. Self-service help desks, team integrations, and feedback loops that span traditionally siloed arenas, for example, ITOps, development, security, and backing are a portion of the ways you can encourage innovation by dissolving departmental barriers. Gaining C-level purchase in is a critical success factor to turning out agile initiatives with clear and regular communication about who is included, what requirements to happen, and why these changes make sense given company goals.

Make "Everything as a Service" a Reality

The industry has been kicking around the expression "XaaS" for a considerable length of time as marketing teams added "aaS" to the end of pretty much any sort of web-based, on-demand service. Presently like never before, the ideal opportunity for XaaS is here. One example is large scale occasions going digital-only with "Occasions as a Service."

The current environment is ideal for scaling a cloud and - as-a-Service strategy because it allows companies to be progressively agile amid fluctuating market conditions. As monthly consumption for IT services develops, companies will require IT staff to manage subscription spend. As leadership develops increasingly comfortable with operational costs versus fixed cost spending on physical hardware, it can lay out a plan to continue scaling services into what's to come.

The key is figuring out which platforms and applications are a solid match to change to a consumption-based model, as going wholesale into the cloud may lead to greater expenses for certain situations.

Concentrate on Customer Experience

Despite the devastation caused by the pandemic environment, it has also drawn out the best in many of us. Like never before, individuals and organizations are paying special mind to our neighbors, families, small businesses, and communities at large. And although it's easy to excuse pandemic-situated services as astute, the general attitude of customer-centricity is one many companies are hoping to adopt pushing ahead.

The customer experience is what ultimately drives revenue or demonstrates its value services (and the individuals giving them). One of the initial steps to digital transformation is changing the main impetus from products to the customer experience.

While considering this move, ask: "How does our technology make things easier for the customer or the end-user?" Soliciting and implementing feedback from users into technology conveyance, and doing so rapidly enough with the goal that they take notice, will lead to amazing experiences that help your organization develop and retain customers.

Identify Opportunities to Automate

On the off chance that you've required certain projects to be postponed, Singapore top app developer can consider transitioning that IT staff to different initiatives aimed at gaining efficiencies and decreasing expense through automation.

Consider testing via automating basic and routine tasks utilizing mature technology like business rules motors, mobile app platforms, and native cloud automation tools. Companies that are ready to go further can investigate low-code, AI, and machine learning.

Many different changes to IT operations may happen, either as an immediate aftereffect of the pandemic or as a related symptom. In any case, the utilization of automation, agile practices, customer-centric services, and "- an a-S" and cloud solutions will play a job in different technologies and procedures the company actualizes.

While it can be overpowering to toss too much change at your organization at once, it's beneficial to utilize this chance to prioritize your transformation endeavors and map out a plan. So when we do overcome this troublesome time, we're all prepared and better positioned to push ahead together.

[prithvisinha19]

AIOps Platform Market New Business Opportunities & Investment Research Report 2024

The Global AIOps Platform Market is estimated to reach USD 11.07 Billion by 2025 at a CAGR of 32%, observes forencis research (FSR). AIOps (Artificial intelligence for IT operations) platforms are software systems that combine big data, artificial intelligence and machine learning to enhance IT operations. However, with the use of AIOps, the desired need of the customer which is continuous insights to yield continuous improvements through automation is fulfilled. These platforms enable the use of multiple sources of data, data collection methods, and analytical and presentation technologies. To use these technologies, AIOps platforms contain multiple layers that address different functionalities, such as analytical tools, visualization, data collection and storage, and integrations with other applications and services. 

 Ask for The Report Sample PDF Here: https://www.forencisresearch.com/aiops-platform-market-sample-pdf/

AIOps Platform Market: Drivers & Challenges

 Market Drivers:

·         Increasing Adoption for Cloud Platform

Increasing adoption for the cloud service platform boosts the growth of the Artificial intelligence for IT operations (AIOps) platform market. Cloud-based AI platform is used for storage management and to enhance the ability of customer services and performance. However, many enterprises have already adopted Cloud-based technologies to work more efficiently and to increase performance. This technology allows the organization to use intelligent software automation.

Hence, the increasing adoption of cloud platform is expected to surge the AIOps Platform market during the forecast period.

·         Increasing Data Retention Requirements

Increasing data retention requirements are excepted to grow as the performance and monitoring data are growing at an exponential rate. Furthermore, the growing number of APIs, IoT devices, mobile applications, and digital users are driving the service. However, AIOps uses this data to monitor assets and gain visibility into dependencies. Also, protection and retention are important aspects of AIOps that diversify the availability of data sources as well as proper data storage.

Therefore, the increasing demand of artificial intelligence in IT operations is expected to drive the AIOps Platform market during the forecast period.

Market Challenges:

·         Increasing Changes in IT Operations Platform

With the ongoing changes in information technology operations (ITOps) of the emerging technologies, impact the performance of the operation platforms. However, AIOps impacts the profile allocation of ITOps employees as the new emerging technologies are combined with general workload.

Hence, the increasing changes in the IT operations platform hinder the growth of the AIOps platform.

  AIOps Platform Market: Key Segments

·         Based on Deployment Mode: Cloud, and On-premises.

·         By Solution: Application Performance Management, Networks, and Digital Business Automation.

·         Based on Services: Managed Services, Education Services, and

·         By Component: Data collection, Data analytics, Machine Learning and Artificial Intelligence (AI).

·         Based on Application: Prediction, Alarm management, Intelligent remediation, Anomaly detection, Causal analysis, and Real-Time Analytics.

·         Based on End-User: Banking, Financial Services, and Insurance, Government, Manufacturing, Media and Entertainment, Retail and Consumer Goods and

·         Key Regions Covered: North America, South America, Europe, Asia-Pacific, Middle East & Africa and South America, with individual country-level analysis.

Request Report Methodology On Competition On Global Market Here: https://www.forencisresearch.com/aiops-platform-market-request-methodology/

 AIOps Platform Market: Report Segmentation

For the scope of report, In-depth segmentation is offered by Forencis Research

AIOps Platform Market, by Deployment Mode

·         Cloud

·         On-premises

 AIOps Platform Market, by Solution

·         Application Performance Management

·         Networks

·         Digital Business Automation

 AIOps Platform Market, by Service

·         Managed Services

·         Education Services

·         Consultancy

 AIOps Platform Market, by Component

·         Data collection

·         Data analytics

·         Machine Learning

·         Artificial Intelligence (AI)

 AIOps Platform Market, by Application

·         Prediction

·         Alarm management

·         Intelligent remediation

·         Anomaly detection

·         Causal analysis

·         Real-Time Analytics

 AIOps Platform Market, by End-Users

·         Banking, Financial Services, and Insurance

·         Government

·         Manufacturing

·         Media and Entertainment

·         Retail and Consumer Goods

·         Others 

 AIOps Platform Market, by Region

·         Asia-Pacific (China, India, Japan, South Korea, Australia, Rest of Asia-Pacific)

·         North America (US, Canada, Mexico)

·         Europe (Germany, UK, France, Italy, Spain, Rest of Europe)

·         Middle East & Africa (Saudi Arabia, UAE, Rest of Middle East & Africa)

·         South America (Brazil, Argentina, Rest of South America)

 Purchase this Premium Industry Research Report with Analysis @ https://www.forencisresearch.com/aiops-platform-market-purchase-now/

About Forencis Research

Forencis Research is a B2B market research, intelligence and advisory firm engaging in market research and consulting services across leading industries, globally. Our robust and meticulous research team provides high growth and niche syndicated reports, customized reports and consulting reports to the diverse global fortune clientele and intellectual institutions. Forencis Research database is a constantly evolving pool of reports and white paper studies which helps companies to foster accelerated revenue growth in global and regional markets. Forencis Research delivers market research and consulting reports on high growth markets to help companies dominate their competition and set themselves apart by attaining increased revenue growth. To enable exclusive insights around the target market, Forencis Research employs robust research Methodology & Design which includes data acquisition, data synthesis and data correlation, through Primary and Secondary Research. Through the obtained data, Top-down and bottom-up methods are exercised to attain and verify data sanity within the entire market. This market data is yet again correlated with Forencis Research’s internal database before presenting it in any of our final publications. These methods of data correlation and amalgamation benefit us to put forward accurate market estimates enabling our clients to transform their business, markets and most importantly their “REVENUES”.

Contact Us

FORENCIS RESEARCH

Phone: +1 (720) 306 9020

Email: info@forencisresearch.com

For more market research insights, please visit : https://www.forencisresearch.com

[long80llc]

The Next Frontier in Telehealth

https://long-80.com/The Next Frontier in Telehealth

Telehealth is the next big frontier. With people living further and further away from each other, medical professionals are finding it more difficult to provide care in person. Telehealth makes it possible for patients to get care from doctors at hospitals even if they are miles away.

Telehealth is an area of health that is growing quickly because with the advent of new technologies, doctors can now treat their patients over video conference call. This is a great supplement for traditional medical care, but not a replacement. Doctors still need to be on site when a patient needs immediate attention.

Telehealth and other applications of AI in hospitals are on the rise because of its convenience. Patients can access care when they need it, without having to travel and wait in line. It allows for a more personalized approach to care where physicians can see the patient’s symptoms and provide feedback in real time. Patients are able to obtain their medical records, check the status of test results, get prescriptions refilled and order lab work without needing to go into a physical office. They are also able to communicate with physicians through phone calls or video chats – which makes follow-up appointments easier and less stressful for both parties.

Key Components of Telehealth

Teleconsultation: Teleconsultation is a form of medicine that is provided through the use of technology. It is used to provide services such as diagnosing a condition, providing treatment, and prescribing medications. Teleconsultation has been around for a while but it’s only recently that it’s become more popular and there are many companies who offer this type of service.

Telemonitoring: Telemonitoring is a cost-effective way to monitor patients' health and can help doctors make better decisions. It is also a good way for elderly people to stay active and connected with the world.

Telemonitoring has been around since the 1990s, but it's only recently that we have seen it adopted in hospitals. This is because of its many advantages: it's cheaper than the more traditional method of monitoring, it can monitor patients at home (which means less time in hospitals), and doctors can receive alerts when something goes wrong.

On the other hand, telemonitoring does have some downsides - one is that patients may not follow instructions or they might be too embarrassed to speak to someone on the phone.

Role of EHR Integration

EHR integration is a concept that many hospitals and clinics would like to implement but there are many challenges that they face when trying. Hospitals may not even have the resources necessary for this type of investment. One of the biggest challenges that hospitals face when trying to integrate their EHRs with other systems is the lack of information on patient outcomes before and after implementation.

Technology can make it easier for clinicians to access data, but there are still barriers that need to be addressed before widespread adoption can occur.

Some of the major advantages of Telehealth EHR Integration are mentioned below:

Automates the data entry process and relieve the medical staff of time-consuming efforts

Gets the complete patient history along with insurance information during the teleconsultation and ensures that critical details are not missed out on

Provides a seamless experience for patients and doesn’t require patients to keep track of all their past information and documentation

Telehealth Architectures

Telehealth architectures are designed to provide the highest level of care for patients who need it but cannot be in a traditional hospital. They are also designed to reduce the cost of health care for providers and patients alike.

Telehealth architectures consist of a variety of different types of interfaces that offer all types of communication between providers, patients, and the outside world. One example is remote patient monitoring systems which allow medical professionals to monitor the health status of a patient from any location with internet access.

Telehealth architectures can potentially save billions in cost because they reduce demand on overbooked hospitals by increasing the availability of care in places where doctors would not normally set up a practice.

The Role of Telehealth Kiosks

The telehealth kiosks are a new way for patients to communicate with their providers without having to physically go into the clinic.

One of the main benefits of these kiosks is that they allow for patients to have a more convenient way of providing feedback and getting updates from their doctors.

With telehealth kiosks, health care providers will be able to build relationships with their patients and provide more in-depth care. Telehealth kiosks increase face-to-face interaction with a doctor, by allowing patients to speak with a doctor on camera or over the phone.

The patient can use the kiosk to order lab tests and prescriptions while they wait for a nurse or physician to come over and meet them in person. The telehealth kiosk will also help reduce waiting times for doctors as they provide care remotely.

The following are some of the typical peripherals that could be fit in a telehealth kiosk –

Otoscope

Pulse Oximeter

Blood Pressure Cuff

Stethoscope

Dermatoscope

Thermometer

Advantages of Telehealth Kiosks

Healthcare access to employees working in remote manufacturing locations or onshore locations like oil drilling, etc.

Community outreach especially in rural areas

Help for those individuals who cannot setup their own telehealth monitoring solutions at home

Relieving the healthcare workers from over working and concentrating more on patient care

Know more about the use of artificial intelligence in the healthcare industry with Long80 - https://long-80.com/