Tag: ai automated root cause analysis solution

Analyze

Posted on by ZIF

Have you heard of AIOps?

Artificial intelligence for IT operations (AIOps) is an umbrella term for the application of Big Data Analytics, Machine Learning (ML) and other Artificial Intelligence (AI) technologies to automate the identification and resolution of common Information Technology (IT) problems. The systems, services and applications in a large enterprise produce immense volumes of log and performance data. AIOps uses this data to monitor the assets and gain visibility into the working behaviour and dependencies between these assets.

According to a Gartner study, the adoption of AIOps by large enterprises would rise to 30% by 2023.

ZIF – The ideal AIOps platform of choice

Zero Incident FrameworkTM (ZIF) is an AIOps based TechOps platform that enables proactive detection and remediation of incidents helping organizations drive towards a Zero Incident Enterprise™

ZIF comprises of 5 modules, as outlined below.

At the heart of ZIF, lies its Analyze and Predict (A&P) modules which are powered by Artificial Intelligence and Machine Learning techniques. From the business perspective, the primary goal of A&P would be 100% availability of applications and business processes.

Come, let us understand more about the Analyze function of ZIF.

With Analyzehaving a Big Data platform under its hood, volumes of raw monitoring data, both structured and unstructured, can be ingested and grouped to build linkages and identify failure patterns.

Data Ingestion and Correlation of Diverse Data

The module processes a wide range of data from varied data sources to break siloes while providing insights, exposing anomalies and highlighting risks across the IT landscape. It increases productivity and efficiency through actionable insights.

  • 100+ connectors for leading tools, environments and devices
  • Correlation and aggregation methods uncover patterns and relationships in the data

Noise Nullification

Eliminates duplicate incidents, false positives and any alerts that are insignificant. This also helps reduce the Mean-Time-To-Resolution and event-to-incident ratio.

  • Deep learning algorithms isolate events that have the potential to become incidents along with their potential criticality
  • Correlation and Aggregation methods group alerts and incidents that are related and needs a common remediation
  • Reinforcement learning techniques are applied to find and eliminate false positives and duplicates

Event Correlation

Data from various sources are ingested real-time into ZIF either by push or pull mechanism. As the data is ingested, labelling algorithms are run to label the data based on identifiers. The labelled data is passed through the correlation engine where unsupervised algorithms are run to mine the patterns. Sub-sequence mining algorithms help in identifying unique patterns from the data.

Unique patterns identified are clustered using clustering algorithms to form cases. Every case that is generated is marked by a unique case id. As part of the clustering process, seasonality aspects are checked from historical transactions to derive higher accuracy of correlation.

Correlation is done based on pattern recognition, helping to eliminate the need for relational CMDB from the enterprise. The accuracy of the correlation increases as patterns reoccur. Algorithms also can unlearn patterns based on the feedback that can be provided by actions taken on correlation. As these are unsupervised algorithms, the patterns are learnt with zero human intervention.

Accelerated Root Cause Analysis (RCA)

Analyze module helps in identifying the root causes of incidents even when they occur in different silos. Combination of correlation algorithms with unsupervised deep learning techniques aid in accurately nailing down the root causes of incidents/problems. Learnings from historical incidents are also applied to find root causes in real-time. The platform retraces the user journeys step-by-step to identify the exact point where an error occurs.

Customer Success Story – How ZIF’s A&P transformed IT Operations of a Manufacturing Giant

  • Seamless end-to-end monitoring – OS, DB, Applications, Networks
  • Helped achieve more than 50% noise reduction in 6 months
  • Reduced P1 incidents by ~30% through dynamic and deep monitoring
  • Achieved declining trend of MTTR and an increasing trend of Availability
  • Resulted in optimizingcommand centre/operations head count by ~50%
  • Resulted in ~80% reduction in operations TCO

For more detailed information on GAVS’ Analyze, or to request a demo please visit zif.ai/products/analyze

References: www.gartner.com/smarterwithgartner/how-to-get-started-with-aiops

ABOUT THE AUTHOR

Vasudevan Gopalan


Vasu heads Engineering function for A&P. He is a Digital Transformation leader with ~20 years of IT industry experience spanning across Product Engineering, Portfolio Delivery, Large Program Management etc. Vasu has designed and delivered Open Systems, Core Banking, Web / Mobile Applications etc.

Outside of his professional role, Vasu enjoys playing badminton and focusses on fitness routines.

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A Deep Dive into Deep Learning!

Posted on by ZIF

The Nobel Prize winner & French author André Gide said, “Man cannot discover new oceans unless he has the courage to lose sight of the shore”. This rings true with enterprises that made bold investments in cutting-edge AI that are now starting to reap rich benefits. Artificial Intelligence is shredding all perceived boundaries of a machine’s cognitive abilities. Deep Learning, at the very core of Artificial Intelligence, is pushing the envelope still further into unchartered territory. According to Gartner, “Deep Learning is here to stay and expands ML by allowing intermediate representations of the data”.

What is Deep Learning?

Deep Learning is a subset of Machine Learning that is based on Artificial Neural Networks (ANN). It is an attempt to mimic the phenomenal learning mechanisms of the human brain and train AI models to perform cognitive tasks like speech recognition, image classification, face recognition, natural language processing (NLP) and the like.

The tens of billions of neurons and their connections to each other form the brain’s neural network. Although Artificial Neural Networks have been around for quite a few decades now, they are now gaining momentum due to the declining price of storage and the exponential growth of processing power. This winning combination of low-cost storage and high computational prowess is bringing back Deep Learning from the woods.

Improved machine learning algorithms and the availability of staggering amounts of diverse unstructured data such as streaming and textual data, are boosting performance of Deep Learning systems. The performance of the ANN depends heavily on how much data it is trained with and it continuously adapts and evolves its learning with time as it is exposed to more & more datasets.

Simply put, the ANN consists of an Input layer, hidden computational layers, and the Output layer. If there is more than one hidden layer between the Input & Output layers, then it is called a Deep Network.

The Neural Network

The Neuron is central to the human Neural Network. Neurons have Dendrites, which are the receivers of information and the Axon which is the transmitter. The Axon is connected to the Dendrites of other neurons, through which signal transmission takes place. The signals that are passed are called Synapses.

While the neuron by itself cannot accomplish much, it creates magic when it forms connections with the other neurons to form an interconnected neural network. In artificial neural networks, the neuron is represented by a node or a unit. There are several interconnected layers of such units, categorized as input, output and hidden, as seen in the figure. 

A Deep Dive into Deep Learning!

The input layer receives the input values and passes them onto the first hidden layer in the ANN, similar to how our senses receive inputs from the environment around us & send signals to the brain. Let’s look at what happens in one node when it receives these input values from the different nodes of the input layer. The values are standardized/normalized-so that they are all within a certain range-and then weighted. Weights are crucial to a neural network since a value’s weight is indicative its impact on the outcome. An activation function is then applied to the weighted sum of values, to help determine if this transformed value needs to be passed on within the network. Some commonly used activation functions are the Threshold, Sigmoid and Rectifier functions.

This gives a very high-level idea of the generic structure and functioning of an ANN. The actual implementation would use one of several different architectures of neural networks that define how the layers are connected together, and what functions and algorithms are used to transform the input data. To give a couple of examples, a Convolutional network uses nonlinear activation functions and is highly efficient at processing nonlinear data like speech, image and video while a Recurrent network has information flowing around recursively, is much more complicated and difficult to train, but that much more powerful. Recurrent networks are closer in representation to the human neural network and are best suited for applications like sequence generation and predicting stock prices.

Deep Learning at work

Deep Learning has been adopted by almost all industry verticals at least at some level. To give some interesting examples, the automobile industry employs it in self-driving vehicles and driver-assistance services, the entertainment industry applies it to auto-addition of audio to silent movies and social media uses deep learning for curation of content feeds in user’s timelines. Alexa, Cortana, Google Assistant and Siri have now invaded our homes to provide virtual assistance!

Deep Learning has several applications in the field of Computer Vision, which is an umbrella term for what the computer “sees”, that is, interpreting digital visual content like images, photos or videos. This includes helping the computer learn & perform tasks like Image Classification, Object Detection, Image Reconstruction, to name a few. Image classification or image recognition when localized, can be used in Healthcare for instance, to locate cancerous regions in an x-ray and highlight them.

Deep Learning applied to Face Recognition has changed the face of research in this area. Several computational layers are used for feature extraction, with the complexity and abstraction of the learnt feature increasing with each layer, making it pretty robust for applications like public surveillance or public security in buildings. But there are still many challenges like the identification of facial features across styles, ages, poses, effects of surgery that need to be tackled before FR can be reliably used in areas like watch-list surveillance, forensic tasks which demand high levels of accuracy and low alarm rates. 

Similarly, there are several applications of deep learning for Natural Language Processing. Text Classification can be used for Spam filtering, Speech recognition can be used to transcribe a speech, or create captions for a movie, and Machine translation can be used for translation of speech and text from one language to another.

Closing Thoughts

As evident, the possibilities are endless and the road ahead for Deep Learn is exciting! But, despite the tremendous progress in Deep Learning, we are still very far from human-level AI. AI models can only perform local generalizations and adapt to new situations that are similar to past data, whereas human cognition is capable of quickly acclimatizing to radically novel circumstances. Nevertheless, this arduous R&D journey has nurtured a new-found respect for nature’s engineering miracle – the infinitely complex human brain!

Is Your Investment in TRUE AI?

Posted on by ZIF

Yes, AIOps the messiah of ITOps is here to stay! The Executive decision now is on the who and how, rather than when. With a plethora of products in the market offering varying shades of AIOps capabilities, choosing the right vendor is critical, to say the least.

Exclusively AI-based Ops?

Simply put, AIOps platforms leverage Big Data & AI technologies to enhance IT operations. Gartner defines Acquire, Aggregate, Analyze & Act as the four stages of AIOps. These four fall under the purview of Monitoring tools, AIOps Platforms & Action Platforms. However, there is no Industry-recognized mandatory feature list to be supported, for a Platform to be classified as AIOps. Due to this ambiguity in what an AIOps Platform needs to Deliver, huge investments made in rosy AIOps promises can lead to sub-optimal ROI, disillusionment or even derailed projects. Some Points to Ponder…

  • Quality in, Quality out. The value delivered from an AIOps investment is heavily dependent on what data goes into the system. How sure can we be that IT Asset or Device monitoring data provided by the Customer is not outdated, inaccurate or patchy? How sure can we be that we have full visibility of the entire IT landscape? With Shadow IT becoming a tacitly approved aspect of modern Enterprises, are we seeing all devices, applications and users? Doesn’t this imply that only an AIOps Platform providing Application Discovery & Topology Mapping, Monitoring features would be able to deliver accurate insights?
  • There is a very thin line between Also AI and Purely AI. Behind the scenes, most AIOps Platforms are reliant on CMDB or similar tools, which makes Insights like Event Correlation, Noise Reduction etc., rule-based. Where is the AI here?
  • In Gartner’s Market Guide, apart from support features for the different data types, Automated Pattern Discovery is the only other Capability taken into account for the Capabilities of AIOps Vendors matrix. With Gartner being one of the most trusted Technology Research and Advisory companies, it is natural for decision makers to zero-in on one of these listed vendors. What is not immediately evident is that there is so much more to AIOps than just this, and with so much at stake, companies need to do their homework and take informed decisions before finalizing their vendor.
  • Most AIOps vendors ingest, provide access to & store heterogenous data for analysis, and provide actionable Insights and RCA; at which point the IT team takes over. This is a huge leap forward, since it helps IT work through the data clutter and significantly reduces MTTR. But, due to the absence of comprehensive Predictive, Prescriptive & Remediation features, these are not end-to-end AIOps Platforms.
  • At the bleeding edge of the Capability Spectrum is Auto-Remediation based on Predictive & Prescriptive insights. A Comprehensive end-to-end AIOps Platform would need to provide a Virtual Engineer for Auto-Remediation. But, this is a grey area not fully catered to by AIOps vendors.  

The big question now is, if an AIOps Platform requires human intervention or multiple external tools to take care of different missing aspects, can it rightfully claim to be true end-to-end AIOps?

So, what do we do?

Time for you to sit back and relax! Introducing ZIF- One Solution for all your ITOps ills!

We have you completely covered with the full suite of tools that an IT infrastructure team would need. We deliver the entire AIOps Capability spectrum and beyond.

ZIF (Zero Incident Framework™) is an AIOps based TechOps platform that enables proactive Detection and Remediation of incidents helping organizations drive towards a Zero Incident Enterprise™.

The Key Differentiator is that ZIF is a Pure-play AI Platform powered by Unsupervised Pattern-based Machine Learning Algorithms. This is what sets us a Class Apart.

  • Rightly aligns with the Gartner AIOps strategy. ZIF is based on and goes beyond the AIOps framework
  • Huge Investments in developing various patented AI Machine Learning algorithms, Auto-Discovery modules, Agent & Agentless Application Monitoring tools, Network sniffers, Process Automation, Remediation & Orchestration capabilities to form Zero Incident Framework™
  • Powered entirely by Unsupervised Pattern-based Machine Learning Algorithms, ZIF needs no further human intervention and is completely Self-Reliant
  • Unsupervised ML empowers ZIF to learn autonomously, glean Predictive & Prescriptive Intelligence and even uncover Latent Insights
  • The 5 Modules can work together cohesively or as independent stand-alone components
  • Can be Integrated with existing Monitoring and ITSM tools, as required
  • Applies LEAN IT Principle and is on an ambitious journey towards FRICTIONLESS IT.

Realizing a Zero Incident EnterpriseTM

The future of AIOps

Posted on by ZIF

AIOps or Artificial Intelligence based IT operations is the buzzword that’s capturing the CXO’s interest in organizations worldwide. Why? Because data explosion is here, and the traditional tools and processes are unable to completely handle its creation, storage, analysis and management. Likewise, humans are unable to thoroughly analyze this data to obtain any meaningful insights. IT teams also face the challenging task of providing speed, security and reliability in an increasingly mobile and connected world.

Add to this the complex, manual and siloed processes that the legacy IT solutions offer to the organizations. As a result, the productivity for IT remains low due to their inability to find the exact root cause of incidents. Plus, the business leaders don’t have a 360-degree view of all their IT and business services across the organization.

AIOps is the Future for IT Operations

AIOps platforms are the foundation on which the organizations will project their future endeavors. Advanced machine learning and analytics are the building blocks to enhance their IT operations through a proactive approach towards service desk, monitoring and automation. Using effective data collection methods that utilize real time analytic technologies, AIOps provide insights to impact business decisions.

Successful AIOps implementations depend on key parameters Index (KPIs) whose impact can be seen on performance variation, service degradation, revenue, customer satisfaction and brand image.

All these impacts the organization’s services including but not limited to supply chain, online or digital. One way in which AIOps can deliver a predictive and proactive IT is by decreasing the MTBF (Mean time between failure), MTTD (Mean time to detection), MTTR (Mean time to resolution) and MTTI (Mean time to investigate) factors.

The future of AIOps is already on the way in the below mentioned use cases. There is just the surface with scope for many more use cases to be added in the future.

Capacity planning

Enterprise workloads are moving to the cloud with providers such as AWS, Google and Azure setting up various configurations for running them. The complexity involved increases as new configurations are added by the architects involving parameters like disk types, memory, network and storage resources.

AIOps can reduce the guesswork in aligning the correct usage of the network, storage and memory resources with the right configurations of servers and VMs through recommendations.

Optimal resource utilization

Enterprises are leveraging cloud elasticity to improve their application scaling in or scaling out automatically. With AIOps, IT administrators can rely on predictive scaling to take the auto scale cloud to the next level. Based on historical data, the workload will automatically determine the resources required by monitoring itself.

Data store management

AIOps can also be utilized to monitor the network and the storage resources that will impact the applications in the operations. When performance degradation issues are seen, the admin will get notified. By using AI for both network and storage management, mundane tasks such as reconfiguring and recalibration can be automated. Through predictive analytics, storage capacity is automatically adjusted by adding new volumes proactively.

Anomaly detection

Anomaly detection is the most important application of AIOps. This can prevent potential outages and disruptions that can be faced by organizations. As anomalies can occur in any part of the technology stack, pinpointing them in real-time, using advanced analytics and machine learning is crucial. AIOps can accurately detect the actual source which can help IT teams in performing efficient root cause analysis almost in real-time.

Threat detection & analysis

Along with anomaly detection, AIOps will play a critical role in enhancing the security of IT infrastructure. Security systems can use ML algorithms and AI’s self-learning capabilities to help the IT teams detect data breached and violations. By correlating various internal sources like log files, network and event logs, with the external information on malicious IPs and domains, AI can be used to detect anomalies and risk events through analysis. Advanced machine learning algorithms can be used to identify unexpected and potentially unauthorized and malicious activity within the infrastructure.

Although still early in deployment, companies are taking advantage of AI and machine learning to improve tech support and manage infrastructure.  AIOps, the convergence of AI and IT ops, will change the face of infrastructure management.

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What chatbots will do for your enterprise?

Posted on by ZIF

Gen X, Y or any other fancy term describing the current demographics is tuned to using voice, text and natural language to complete their work. That’s why a new generation of enterprise chatbots is needed at work.

Read over the textbook definition of a chatbot and you’ll understand it’s a computer program designed to hold conversations with humans over the internet. They can understand written and spoken text and interpret its meaning as well. The bot can then look up relevant information and deliver it to the user.

While chatbots reduces time and efforts, it’s not easy to create a chatbot that customers will trust. Businesses will have to consider the overall.

  • Security
  • Team complexity
  • Brand image
  • Scalability/availability
  • Identity and access management
  • Other parameters to fully integrate chatbots in their organizational structure

If correctly implemented enterprise chatbots can perform pre-defined roles and tasks to improve the business processes and activities.

Shortlisting the right chatbot

Automating repetitive and mundane work will increase the productivity, creativity, and efficiency of the organization. Evolution of chatbots will create more business opportunities for enterprises and new companies. Both SMBs and enterprises can improve their customer satisfaction with customized chatbots that help in offloading employee workload or support the various teams in the organization.

Enterprises first need to identify the type of chatbots needed for their organization to kick start their digital transformation. Depending on their requirements, there are two types of chatbots.

  • Standalone applications
  • Built within the messengers

Usually chatbots associated with messengers have an edge over standalone apps. They can be downloaded and used instantly. They are even easy to build and upgrade, faster compared to apps and websites and also cost effective. You also don’t have to worry about memory space.

AI based or machine learning chatbots learn over time from past questions and answers, and evolve their response accordingly.

What’s in it for enterprises?

There are some universal benefits that businesses in any industry or vertical can benefit from.

Streamlining your IT processes

A variety of business processes across your departments can be streamlined using chatbots. Your employees’ mundane, repetitive but essential tasks can be taken up by the chatbots, giving more time for revenue generating activities. For instance, they can be tasked with follow ups with clients or answering the FAQs by customers.

Act as personal assistants

Chatbots are a great help for the time constrained employees to manage, schedule, or cancel their meetings, setting alarms and other tasks. Context sensitive digital assistants help in organizing their daily routine by understanding the context, behaviors and patterns and suggesting recommendations.

24/7 customer support

Customer expectation is high with them demanding instant and quick resolution for their concerns and problems. Enterprise chatbot solutions offer a cost effective 24/7 customer services for you. Advancements in AI, machine learning and natural language processing (NLP) can allow them to understand the context, usage of slangs, and human conversation to a large extent. On a cautionary note, chatbots should easily handover the conversation to humans to avoid any unnecessary customer conflicts.

Generate business insights

The data deluge faced by the enterprises is costing them through lost insights and business opportunities. Vast data generated across the organization by employees, customers and business processes cannot be completely analyzed, and it leaves data gaps. Leveraging chatbots for processing and analyzing the stored data can result in identifying potential problem areas and take preemptive actions to mitigate the risks.

Reduce Opex & Capex costs

Enterprise chatbots are one-time investments, where you pay only for the chatbot, train it and its forever yours. No monthly payrolls, or sick leaves. You have a 24/7 virtual employee managing your routine and repetitive tasks.

Increase efficiency and productivity

The end result of all the above points is increased productivity. By training your employees about the services and products, a chatbot solution helps your employees to tackle the generic queries from customers. Thus, ending the time-consuming customer facing tasks and helping in the sales funnel.

In conclusion, chatbots are changing the working dynamics of enterprises. The best way to ensure a satisfied customer experience is to build bots that act without being supervised and offer the best solutions to their problems. With new advancements like AI, NLP and Machine Learning, it’s safe to say that chatbots are the future of enterprises.

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Can enterprises gain from cognitive automation?

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What is cognitive automation (CA)?

“There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035,” stated Gray Scott. Cognitive automation is a subcategory of artificial intelligence (AI) technologies that imitates human behavior. Combined efforts of robotic process automation (RPA) and cognitive technologies such as natural language processing, image processing, pattern recognition and speech recognition has eased the automation process replacing humans. The best part of CA solutions are, they are pre-trained to automate certain business processes hence, they don’t need intervention of data scientists and specific models to operate on. Infact, a cognitive system can make more connection in a system without supervision using new structured and unstructured data.

Future of CA

There is a speedy evolution of CA with increasing investments in cognitive applications and software platforms. Market research indicates, approximately $2.5 billion has been invested in cognitive-related IT and business services. There is also an expectation of 70% rise in such investments by 2023. The focus areas where CA gained momentum are:

  • Quality checks and system recommendations
  • Diagnosis and treatment recommendations
  • Customer service automation
  • Automated threat detection and prevention
  • Fraud analysis and investigation

Difference between normal automation and CA

There is a basic difference between normal IT automation and CA technologies. Let’s try to understand it with a use case where a customer while filling an e-form to open an account in a bank, leaves few sections blank. A normal IT automation will detect it, flag it red and reject the form as incomplete. This then, will need human intervention to fix the issue. CA, in a similar situation, will auto-correct the issue without any human intervention. This will increase operational efficiency, reduce time and effort of the process and improve customer satisfaction.

Enterprises’ need for CA

As rightly mentioned by McKinsey, 45% of human intervention in IT enterprises can be replaced by automation. Tasks with high volumes of data requires more time to complete. CA can prove worthy in such situations and reshape processes in an efficient way. Businesses are becoming complex with time, and enterprises face a lot of challenges daily like; ensuring customer satisfaction, guaranteeing compliance, staying in competition, increasing efficiency and decision making. CA helps to take care of those challenges in an all-encompassing manner. CA can improve efficiency to the extent of 30 – 60% in email management and quote processing. It ensures an overall improvement in operational scalability, compliance and quality of business. It reduces TAT and error rates, thus impacting enterprises positively.

Benefits of CA in general

A collaboration between RPA and CA has multiplied the scope of enterprises to operate successfully and reap benefits to the extent that enterprises are able to achieve ROI of up to 300% in few months’ time, research reveals. The benefits enterprises can enjoy by adopting CA are:

  • It improves quality by reducing downtime and improving smart insights.
  • It improves work efficiency and enhances productivity with pattern identification and automation.
  • Cognitive computing and autonomous learning can reduce operational cost.
  • A faster processing speed can impact business performance and increases job satisfaction resulting employee retention, since it boosts employee satisfaction and engagement.
  • It increases business agility and innovation with provisioning of automation.
  • As a part of CA, Natural Language Processor (NLP) is a tool used in cognitive computing. It has the capacity to communicate more effectively and resolve critical incidents. This increases customer satisfaction to a great extent.

Enterprises using CA for their benefit:

  1. A leading IT giant combined cloud automation service with cognition to reduce 50% of server downtime in last two years. It also reduced TAT through auto resolution of more than 1500 server tickets every month. There was reduction of critical incidents by 89% within six months of cognitive collaboration.
  2. An American technology giant introduced a virtual assistant as one of their cognitive tools. It could understand twenty-two languages and could handle service requests without human intervention. It eased the process of examining insurance policies for clients, help customers open bank accounts, help employees learn company policies and guidelines.
  3. A leading train service in UK used virtual assistant starting from refund process to handling their customer queries and complaints.
  4. A software company in USA uses cognitive computing technology to provide real-time investment recommendations.
  5. Cognitive computing technology used in media and entertainment industries can extract information related to user’s age, gender, company logo, certain personalities and locate profile and additional information using Media Asset Management Systems. This helps in answering queries, adding a hint of emotion and understanding while dealing with a customer.

Conclusion

Secondary research reveals that the Cognitive Robotic Process Automation (CRPA) market will witness a CAGR of 60.9% during 2017 – 2026. The impact CA has on enterprises is remarkable and it is an important step towards the cognitive journey. CA can continuously learn and initiate optimization in a managed, secured and reliable way to leverage operational data and fetch actionable insights. Hence, we can conclude that enterprises are best poised to gain considerably from cognitive automation.

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8 Ways AI Will Impact Healthcare

Posted on by ZIF

Artificial Intelligence (AI) is still a layered subject that’s both exciting and scary to say the least. Given the new information being discovered each day, people are still nervous when it comes to letting AI handle their personal data (fears of security, privacy issues etc.). But they are comfortable with doctors and physicians using AI in healthcare for providing accurate and precise medical treatments and information.

This implies a growing acceptance of the impersonal AI in healthcare, where the physical and personal contact between the caregivers and patients is high. The myriad and increasingly mainstream applications of AI in healthcare are propelling this strong and growing acceptance.

Such openness to AI is vital for healthcare companies, as it empowers the patients and caregivers to gain valuable insights from the data collected and act on them accordingly. AI can analyze loads of medical data and identify patterns to detect any deviations in the individual patient’s behavior and suggest treatment plans / changes. It can sort through assist doctors to improve the accuracy of diagnosis and help in correct treatment.

This AI aided healthcare is not only beneficial to the patients, but also healthcare companies can save time and money performing basic, non-patient care activities (like writing chart notes and prescriptions, etc.) so that caregivers have more time to spend with people.

Research shows that amongst the largest sources of savings are robot-assisted surgery ($40 billion in savings), virtual nursing assistants ($20 billion) and administrative workflow assistance ($18 billion).

AI, Healthcare, and Interconnection.

The bridge between AI and healthcare can only function and give value if the interconnection is smooth and inter-operable. That’s because AI is highly data driven requiring a secure, instant, and low latency connectivity among the multitude data sources between the users and cloud applications.

Given the multi-tenant cloud architecture and the still existing traditional healthcare IT infrastructures, GAVS Technologies enables healthcare providers to easily migrate to the new AI enabled digital infrastructure.
Cost, transparency, and compliance with the various healthcare regulatory bodies are the biggest challenges today for healthcare institutions. With the GDPR already in effect, requiring data protection for all the collected data and its correct usage becoming mandatory, it’s vital for them to have a clear road map for their business strategies involving AI.

Here are eight ways that highlight the technologies and areas of the healthcare industry that are most likely to see a major impact from artificial intelligence.

• Brain-computer interfaces (BCI) backed by artificial intelligence can help restore the patients’ fundamental experiences of speech, movement and meaningful interaction with people and their environments, lost due to neurological diseases and trauma to the nervous system. BCI could drastically improve quality of life for patients with ALS, strokes, or locked-in syndrome, as well as the 500,000 people worldwide who experience spinal cord injuries every year.

• Artificial intelligence will enable the next generation of radiology tools that are accurate and detailed enough to replace the need for tissue samples in some cases. AI is helping to enable “virtual biopsies” and advance the innovative field of radiomics, which focuses on harnessing image-based algorithms to characterize the phenotypes and genetic properties of tumors.

• AI could help mitigate the shortages of trained healthcare providers, including ultrasound technicians and radiologists which can significantly limit access to life-saving care in developing nations around the world. This severe deficit of qualified clinical staff can be overcome by AI taking over some of the diagnostic duties typically allocated to humans.

• Electronic Health Records (EHR) have played an instrumental role in the healthcare industry’s journey towards digitalization, but this has brought along with cognitive overload, endless documentation, and user burnout. EHR developers are now using AI to create more intuitive interfaces and automate some of the routine processes that consume so much of a user’s time like clinical documentation, order entry, and sorting through their inbox mail.

• Smart devices using artificial intelligence to enhance the ability to identify patient deterioration or sense the development of complications can significantly improve outcomes and may reduce costs related to hospital-acquired condition penalties.

• Immunotherapy (using the body’s own immune system to attack malignancies) is one of best cancer treatments available now. But oncologists still do not have a precise and reliable method for identifying which patients will benefit from this option. AI and Machine learning algorithms and its ability to synthesize highly complex datasets may be able to illuminate new options for targeting therapies to an individual’s unique genetic makeup.

• AI to assimilate the health-related data generated through wearables and personal devices for better monitoring and extracting actionable insights from this large and varied data source.

• Using smartphones which have built-in AI software and hardware to collect images of eyes, skin lesions, wounds, infections, medications, or other subjects is an important supplement to clinical quality imaging especially in under-served populations or developing nations where there is a shortage of specialists while reducing the time-to-diagnosis for certain complaints. Dermatology and ophthalmology are early beneficiaries of this trend.

• Leveraging AI for clinical decision support, risk scoring, and early alerting are some of the most promising areas of development for this revolutionary approach to data analysis.

• AI allow those in training to go through naturalistic simulations in a way that simple computer-driven algorithms cannot. The advent of natural speech and the ability of an AI computer to draw instantly on a large database of scenarios, means the response to questions, decisions or advice from a trainee can be challenging and the AI training programme can learn from previous responses from the trainee.

Contact GAVS Technologies to know more about how AI will impact Healthcare here at https://www.gavstech.com/reaching-us/

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AIOps Trends in 2019

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Adoption of AIOps by organizations

Artificial Intelligence in IT operations (AIOps) is rapidly pacing up with digital transformation. Over the years, there has been a paradigm shift of enterprise application and IT infrastructure. With a mindset to enhance flexibility and agility of business processes, organizations are readily adopting cloud platforms to provision their on-premise software. Implementation of technologies like AIOps and hybrid environment has facilitated organizations to gauge the operational challenges and reduced their operational costs considerably. It helps enterprises in:

  • Resource utilization
  • Capacity planning
  • Anomaly detection
  • Threat detection
  • Storage management
  • Cognitive analysis

Infact, if we look at Gartner’s prediction, by 2022, 40% of medium and large-scale enterprises will adopt artificial intelligence (AI) to increase IT productivity.

AIOps Market forecast

According to Infoholic Research, the AIOps market is expected to reach approximately $14 billion by 2024, growing at a CAGR of 33.08% between 2018–2024. The companies that will provide AIOps solutions to enhance IT operations management in 2019 include BMC Software, IBM, GAVS Technologies, Splunk, Fix Stream, Loom System and Micro Focus. By end of 2019, US alone is expected to contribute over 30% of growth in AIOps and it will also help the global IT industry reach over $5,000 billion by the end of this year. Research conducted by Infoholic also confirmed that AIOps has been implemented by 60% of the organizations to reduce noise alerts and identify real-time root cause analysis.

Changes initiated by enterprises to adopt AIOps

2019 will be the year to reveal the true value of AIOps through its applications. By now, organizations have realized that context and efficient integrations with existing systems are essential to successfully implement AIOps.

1. Data storage

Since AIOps need to operate on a large amount of data, it is essential that enterprises absorb data from reliable and disparate sources which, then, can be contextualized for use in AI and ML applications. For this process to work seamlessly, data must be stored in modern data lakes so that it can be free from traditional silos.

2. Technology partnership

Maintaining data accuracy is a constant struggle and in order to overcome such complexity, in 2019, there will be technology partnership between companies to deal with customer demands for better application program interface (APIs).

3. Automation of menial tasks

Organizations are trying to automate menial tasks to increase agility by freeing up resources. Through automation, organizations can explore a wide range of opportunities in AIOps that will increase their efficiency.

4. Streamling of people, process and tools

Although multi-cloud solutions provide flexibility and cost-efficiency, however, without proper tools to monitor, it can be challenging to manage them. Hence, enterprises are trying to streamline their people, process and tools to create a single, siloed-free overview to benefit from AIOps.

5. Use of real-time data

Enterprises are trying to ingest and use real-time data for event correlation and immediate anomaly detection since, with the current industrial pace, old data is useless to the market.

6. Usage of self-discovery tools

Organizations are trying to induce self-discovery tools in order to overcome the challenge of lack of data scientists in the market or IT personnel with coding skills to monitor the process. The self-discovery tools can operate without human intervention.

Conclusion

Between 2018 to 2024, the global AIOps market value of real time analytics and application performance management is expected to grow at a rapid pace. Also, it is observed that currently only 5% of large IT firms have adopted AIOps platforms due to lack of knowledge and assumption about the cost-effectiveness. However, this percentage is expected to reach 40% by 2022. Companies like CA Technologies, GAVS Technologies, Loom Systems and ScienceLogic has designed tools to simplify AIOps deployment and it is anticipated that over the next three years, there will be sizable progress in the AIOps market.

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AI and its impact on app competitiveness

Posted on by ZIF

AI in mobile tech world

This is the era of the fourth industrial revolution where technology without artificial intelligence (AI) is unimaginable. With the global acceptance of AI, it has encompassed all spheres, touching human life in several ways that also includes the mobile tech world. Research indicates that AI is rapidly gaining popularity, tech giants like Baidu and Google have already spent between $20 to $30 billion on AI to improve IT operations. Segments like healthcare, education, finance and IT ops are investing heavily in AI, however the prominence of AI in mobile tech world deserves a special mention.

Importance of AI in mobile app

The focus of AI is to develop intelligent machines that think, work and learn from experiences like humans. When AI joined hands with machine learning, the ability to analyze visual inputs such as gesture, object, and facial recognition was made seamless. For example, an iPhone app powered by AI can enhance perception, apply reason and even solve problems.

Deployment of AI in mobile app

AI uses the modest process of trial and error to learn about a solution when it comes to developing mobile app. Through this method, various attempts are made to locate the appropriate solution. Then that solution is stored for future usage, considering it as a reference point for similar circumstances. Along with the solution, the mobile app developers are also focusing on drawing appropriate inferences to enhance the interaction process. This helps users reach predefined solutions addressing various device problems.

Example of AI apps

The following are the existing apps that provides an enriched user experience:

  • Replika is an advanced AI app for iPhone that covers several aspects of a user’s life. This app can have conversations with the user like a real person.
  • App Airpoly can identify three objects in a single second.
  • Cortana can assess relevant information, sort them and deliver services efficiently like scheduling meetings, sending emails, tracking events, sharing updates and reminders.
  • Personal assistant like Siri became popular with its voice interface in place. It assists in phone and text actions, can provide information about weather and currency, schedule events, set reminders and provides an engaging experience.
  • My Starbucks Barista mobile app enabled customers to place their orders by mentioning it to the app.
  • Taco Bot launched by Taco Bell recommended personalized menu considering user-specific purchase trends.

Technologies empowering apps

In order to create apps empowered with AI, developers ensure they choose an appropriate platform and install features keeping the end user preferences in mind. The technologies that improve app performance and competitiveness include:

  1. Speech to text (STT) and text to speech (TTS) engine that converts voice to text message and vice versa.
  2. Tagging helps the app analyze users’ requirement.
  3. Noise reduction engine eliminates white noise improving voice command capacity.
  4. Voice biometrics and recognition works as an authentication for refining security.

Impact of AI on app competitiveness

Innovation has led end users expect better performance from mobile apps. Retail giants like eBay and Amazon have already proved the worth of AI in mobile apps. AI-enabled apps engage its user and strategically secure the brand, enhancing productivity and helps reduce errors. The algorithms present will adjust the app and forms more meaningful and context-rich prospects to keep end-users engaged. AI-aided chatbots on mobile devices use standard messaging tools and voice-activated interfaces, this reduces data collection time and simplifies the task. Also, user specific personalization will help with mundane or repeatable tasks. It even has a great impact in healthcare industry where reliability, predictability, consistency, quality and patient safety has seen improvements with the usage of AI-enabled apps.

AI in app market based on geography

The following geographical areas indicate extensive impact of AI on mobile app:

  • North America
  • South America
  • Europe
  • Asia Pacific
  • Middle East and Africa

Conclusion

We can conclude that AI has a dramatic impact on transformation and competitiveness of mobile app. As per market research, this competition is yet to increase by 2020 since more organizations globally are investing in AI for revenue improvements and cost reductions. The deployment rates among different industry verticals have surged exponentially over the fast few years.

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Pivotal Role of AI and Machine Learning in Industry 4.0 and Manufacturing

Posted on by ZIF

Industry 4.0 is a name given to the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of things, cloud computing and cognitive computing.Industry 4.0 is commonly referred to as the fourthindustrial revolution.

Industry 4.0 is the paving the path for digitization of the manufacturing sector, where artificial intelligence (AI) and machine-learning based systems are not only changing the ways we interact with information and computers but also revolutionizing it.

Compelling reasons for most companies to shift towards Industry 4.0 and automate manufacturing include;

  • Increase productivity
  • Minimize human / manual errors
  • Optimize production costs
  • Focus human efforts on non-repetitive tasks to improve efficiency

Manufacturing is now being driven by effective data management and AI that will decide its future. The more data sets computers are fed, the more they can observe trends, learn and make decisions that benefit the manufacturing organization. This automation will help to predict failures more accurately, predict workloads, detect and anticipate problems to achieve Zero Incidence.

GAVS’ proprietary AIOps based TechOps platform – Zero Incident Framework TM (ZIF) can successfully integrate AI and machine learning into the workflow allowing manufacturers to build robust technology foundations.

To maximize the many opportunities presented by Industry 4.0, manufacturers need to build a system with the entire production process in mind as it requires collaboration across the entire supply chain cycle.

Top ways in which ZIF’s expertise in AI and ML are revolutionizing manufacturing sector:

  • Asset management, supply chain management and inventory management are the dominant areas of artificial intelligence, machine learning and IoT adoption in manufacturing today. Combining these emerging technologies, they can improve asset tracking accuracy, supply chain visibility, and inventory optimization.
  • Improve predictive maintenance through better adoption of ML techniques like analytics, Machine Intelligence driven processes and quality optimization.
  • Reduce supply chain forecasting errors and reduce lost sales to increase better product availability.
  • Real time monitoring of the operational loads on the production floor helps in providing insights into the production schedule performances.
  • Achieve significant reduction in test and calibration time via accurate prediction of calibration and test results using machine learning.
  • Combining ML and Overall Equipment Effectiveness (OEE), manufacturers can improve yield rates, preventative maintenance accuracy and workloads by the assets. OEE is a universally used metric in manufacturing as it combines availability, performance, and quality, defining production effectiveness.
  • Improving the accuracy of detecting costs of performance degradation across multiple manufacturing scenarios that reduces costs by 50% or more.

Direct benefits of Machine Learning and AI for Manufacturing

The introduction of AI and Machine Learning to industry 4.0 represents a big change for manufacturing companies that can open new business opportunities and result in advantages like efficiency improvements among others.

  • Cost reduction through Predictive Maintenance that leads to less maintenance activity, which means lower labor costs, reduced inventory and materials wastage.
  • Predicting Remaining Useful Life (RUL): Keeping tabs on the behavior of machines and equipment leads to creating conditions that improve performance while maintaining machine health. By predicting RUL, it reduces the scenarios which causes unplanned downtime.
  • Improved supply chain management through efficient inventory management and a well monitored and synchronized production flow.
  • Autonomous equipment and vehicles: Use of autonomous cranes and trucks to streamline operations as they accept containers from transport vehicles, ships, trucks etc.
  • Better Quality Control with actionable insights to constantly raise product quality.
  • Improved human-machine collaboration while improving employee safety conditions and boosting overall efficiency.
  • Consumer-focused manufacturing: Being able to respond quickly to changes in the market demand.

Touch base with GAVS AI experts here: https://www.gavstech.com/reaching-us/ and see how we can help you drive your manufacturing operation towards Industry 4.0.