Analyze

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.

READ ALSO OUR NEW UPDATES

The Chatty Bots!

Chatbots can be loosely defined as software to simulate human conversation. They are widely used as textbots or voicebots in social media, in websites to provide the initial engagement with visitors, as part of  customer service/IT operations teams to provide tier 1 support round the clock and for various other organizational needs, as we’ll see later in the blog, in integration with enterprise tools/systems. Their prevalence can be attributed to how easy it has now become to get a basic chatbot up & running quickly, using the intuitive drag-drop interfaces of chatbot build tools. There are also many cloud-based free or low-cost AI platforms for building bots using the provided APIs. Most of these platforms also come with industry-specific content, add-on tools for analytics and more.

Rule-based chatbots can hold basic conversation with scripted ‘if/then’ responses for commonly raised issues/faqs, and redirect appropriately for queries beyond their scope. They use keyword matches to get relevant information from their datastore. Culturally, as we begin to accept and trust bots to solve problems and extend support; with companies beginning to see value in these digital resources; and with heavy investments in AI technologies, chatbots are gaining traction, and becoming more sophisticated. AI-led chatbots are way more complex than their rule-based counterparts and provide dynamically tailored, contextual responses based on the conversation and interaction history. Natural Language Processing capabilities give these chatbots the human-like skill to comprehend nuances of language and gauge the intent behind what is explicitly stated.    

The Artificial Neural Network(ANN) for Natural Language Processing(NLP) 

An ANN is an attempt at a tech equivalent of the human brain! You can find our blog on ANNs and Deep Learning here.

Traditional AI models are incapable of handling highly cognitive tasks like image recognition, image classification, natural language processing, speech recognition, text-speech conversion, tone analysis and the like. There has been a lot of success with Deep Learning approaches for such cerebral use cases. For NLP, handling the inherent complexities of language such as sentiment, ambiguity or insinuation, necessitates deeper networks and a lot of training with enormous amounts of data. Each computational layer of the network progressively extracts finer and more abstract details from the inputs, essentially adding value to the learnings from the previous layers. With each training iteration, the network adapts, auto-corrects and finetunes its weights using optimization algorithms, until it reaches a maturity level where it is almost always correct in spite of input vagaries. The USP of a deep network is that, armed with this knowledge gained from training, it is able to extract correlations & meaning from even unlabeled and unstructured data.

Different types of neural networks are particularly suited for different use cases. Recurrent Neural Networks(RNNs) are good for sequential data like text documents, audio and natural language. RNNs have a feedback mechanism where each neuron’s output is fed back as weighted input, along with other inputs. This gives them ‘memory’ implying they remember their earlier inputs, but with time the inputs get diluted by the presence of new data. A variant of the RNN helps solve this problem. Long Short Term Memory (LSTM) models have neurons(nodes) with gated cells that can regulate whether to ‘remember’ or ‘forget’ their previous inputs, thereby giving more control over what needs to be remembered for a long time versus what can be forgotten. For e.g.: it would help to ‘remember’ when parsing through a text document because the words and sentences are most likely related, but ‘forgetting’ would be better during the move from one text document to the next, since they are most likely unrelated.

The Chatbot Evolution

In the 2019 Gartner CIO Survey, CIOs identified chatbots as the main AI-based application used in their enterprises. “There has been a more than 160% increase in client interest around implementing chatbots and associated technologies in 2018 from previous years”, says Van Baker, VP Analyst at Gartner.

Personal & Business communication morphs into the quickest, easiest and most convenient mode of the time. From handwritten letters to emails to phone calls to SMSs to mere status updates on social media is how we now choose to interact. Mr. Baker goes on to say that with the increase of millennials in the workplace, and their  demand for instant, digital connections, they will have a large impact on how quickly organizations adopt the technology.

Due to these evolutionary trends, more organizations than we think, have taken a leap of faith and added these bots to their workforce. It is actually quite interesting to see how chatbots are being put to innovative use, either stand-alone or integrated with other enterprise systems.

Chatbots in the Enterprise

Customer service & IT service management(ITSM) are use cases through which chatbots gained entry into the enterprise. Proactive personalized user engagement, consistency and ease of interaction, round-the-clock availability & timely address of issues have lent themselves to operational efficiency, cost effectiveness and enhanced user experience. Chatbots integrated into ITSM help streamline service, automate workflow management, reduce MTTR, and provide always-on services. They also make it easier to scale during peak usage times since they reduce the need for customers to speak with human staff, and the need to augment human resources to handle the extra load. ChatOps is the use of chatbots within a group collaboration tool where they run between the tool and the user’s applications and automate tasks like providing relevant data/reports, scheduling meetings, emailing, and ease the collaborative process between siloed teams and processes, like in a DevOps environment where they double up as the monitoring and diagnostic tool for the IT landscape.

In E-commerce, chatbots can boost sales by taking the customer through a linear shopping experience from item search through purchase. The bot can make purchase suggestions based on customer preferences gleaned from product search patterns and order history.

In Healthcare, they seamlessly connect healthcare providers, consumers and information and ease access to each other. These bot assistants come in different forms catering to specific needs like personal health coach, companion bot to provide the much-needed conversational support for patients with Alzheimer’s, confidant and therapist for those suffering from depression, symptom-checker to provide initial diagnosis based on symptoms and enable remote text or video consultation with a doctor as required and so on.

Analytics provide insights but often not fast enough for the CXO. Decision-making becomes quicker when executives can query a chatbot to get answers, rather than drilling through a dashboard. Imagine getting immediate responses to requests like Which region in the US has had the most sales during Thanksgiving? Send out a congratulatory note to the leadership in that region. Which region has had the poorest sales? Schedule a meeting with the team there. Email me other related reports of this region. As can be seen here, chatbots work in tandem with other enterprise tools like analytics tools, calendar and email to make such fascinating forays possible.

Chatbots can handle the mundane tasks of Employee Onboarding, such as verification of mandatory documents, getting required forms filled, directing them to online new-hire training and ensuring completion.

When integrated with IoT devices, they can help in Inventory Management by sending out notifications when it’s time to restock a product, tracking shipment of new orders and alerting on arrival.

Chatbots can offer Financial Advice by recommending investment options based on transactional history, current investments or amounts idling in savings accounts, alerting customer to market impact on current portfolio and so much more.

As is evident now, the possibilities of such domain-specific chatbots are endless, and what we have seen is just a sampling of their use cases!

Choosing the Right Solution

The chatbot vendor market is crowded, making it hard for buyers to fathom where to even begin. The first step is an in-depth evaluation of the company’s unique needs, constraints, main use cases and enterprise readiness. The next big step is to decide between off-the shelf or in-house solutions. An in-house build will be an exact fit to needs, but it might be difficult to get long-term management buy-in to invest in related AI technologies, compute power, storage, ongoing maintenance and a capable data science team. Off-the-shelf solutions need a lot of scrutiny to gauge if the providers are specialists who can deliver enterprise-grade chatbots. Some important considerations:

The solution should (be);

Platform & Device Agnostic so it can be built once and deployed anywhere

Have good Integration Capabilities with tools, applications and systems in the enterprise

Robust with solid security and compliance features

Versatile to handle varied use cases

Adaptable to support future scaling

Extensible to enable additional capabilities as the solution matures, and to leverage innovation to provide advanced features such as multi-language support, face recognition, integration with VR, Blockchains, IoT devices

Have a Personality! Bots with a personality add a human-touch that can be quite a differentiator. Incorporation of soft features such as natural conversational style, tone, emotion, and a dash of humor can give an edge over the competition.

About the Author:

Priya is part of the Marketing team at GAVS. She is passionate about Technology, Indian Classical Arts, Travel and Yoga. She aspires to become a Yoga Instructor some day!

A Deep Dive into Deep Learning!

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?

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

AIOps Demystified

IT Infrastructure has been on an incredibly fascinating journey from the days of mainframes housed in big rooms just a few decades ago, to mini computers, personal computers, client-servers, enterprise & mobile networks, virtual machines and the cloud! While mobile technologies have made computing omnipresent, the cloud coupled with technologies like virtual computing and containers has changed the traditional IT industry in unimaginable ways and has fuelled the rise of service-oriented architectures where everything is offered as a service and on-demand. Infrastructure as a Service (IaaS), Platform as a Service (PaaS), DBaaS, MBaaS, SaaS and so on.

As companies try to grapple with this technology explosion, it is very clear that the first step has to be optimization of the IT infrastructure & operations. Efficient ITOps has become the foundation not just to aid transformational business initiatives, but even for basic survival in this competitive world.

The term AIOps was first coined by Gartner based on their research on Algorithmic IT Operations. Now, it refers to the use of Artificial Intelligence(AI) for IT Operations(Ops), which is the use of Big Data Analytics and AI technologies to optimize, automate and supercharge all aspects of IT Operations.

Why AI in IT operations?

The promise behind bringing AI into the picture has been to do what humans have been doing, but better, faster and at a much larger scale. Let’s delve into the different aspects of IT operations and see how AI can make a difference.

Visibility

The first step to effectively managing the IT landscape is to get complete visibility into it. Why is that so difficult? The sheer variety and volume of applications, users and environments make it extremely challenging to get a full 360 degree view of the landscape. Most organizations use applications that are web-based, virtually delivered, vendor-built, custom-made, synchronous/asynchronous/batch processing, written using different programming languages and/or for different operating systems, SaaS, running in public/private/hybrid cloud environments, multi-tenant, multiple instances of the same applications, multi-tiered, legacy, running in silos! Adding to this complexity is the rampant issue of shadow IT, which is the use of applications outside the purview of IT, triggered by the easy availability of and access to applications and storage on the cloud. And, that’s not all! After all the applications have been discovered, they need to be mapped to the topology, their performances need to be baselined and tracked, all users in the system have to be found and their user experiences captured.

The enormity of this challenge is now evident. AI powers auto-discovery of all applications, topology mapping, baselining response times and tracking all users of all these applications. Machine Learning algorithms aid in self-learning, unlearning and auto-correction to provide a highly accurate view of the IT landscape.

Monitoring

When the IT landscape has been completely discovered, the next step is to monitor the infrastructure and application stacks. Monitoring tools provide real-time data on their availability and performance based on relevant metrics.

The problem is two-fold here. Typically, IT organizations need to rely on several monitoring tools that cater to the different environments/domains in the landscape. Since these tools work in silos, they give a very fractured view of the entire system, necessitating data correlation before it can be gainfully used for Root Cause Analysis(RCA) or actionable insights.

Pattern recognition-based learning from current and historical data helps correlate these seemingly independent events, and therefore to recognize & alert deviations, performance degradations or capacity utilization bottlenecks in real-time and consequently enable effective Root Cause Analysis(RCA) and reduce an important KPI, Mean Time to Identify(MTTI).

Secondly, there is colossal amounts of data in the form of logs, events, metrics pouring in at high velocity from all these monitoring tools, creating alert fatigue. This makes it almost impossible for the IT support team to check each event, correlate with the other events, tag and prioritize them and plan remedial action.

Inherently, machines handle volume with ease and when programmed with ML algorithms learn to sift through all the noise and zero-in on what is relevant. Noise nullification is achieved by the use of Deep Learning algorithms that isolate events that have the potential to become incidents and Reinforcement Learning algorithms that find and eliminate duplicates and false positives. These capabilities help organizations bring dramatic improvements to another critical ITOps metric, Mean Time to Resolution(MTTR).

Other areas of ITOps where AI brings a lot of value are in Advanced Analytics- Predictive & Prescriptive- and Remediation.

Advanced Analytics

Unplanned IT Outages result in huge financial losses for companies and even worse, a sharp dip in customer confidence. One of the biggest value-adds of AI for ITOps then, is in driving proactive operations that deliver superior user experiences with predictable uptime. Advanced Analytics on historical incident data identifies patterns, causes and situations in the entire stack(infrastructure, networks, services and applications) that lead to an outage. Multivariate predictive algorithms drive predictions of incident and service request volumes, spikes and lulls way in advance. AIOps tools forecast usage patterns and capacity requirements to enable planning, just-in-time procurement and staffing to optimize resource utilization. Reactive purchases after the fact, can be very disruptive & expensive.

Remediation

AI-powered remediation automates remedial workflows & service actions, saving a lot of manual effort and reducing errors, incidents and cost of operations. Use of chatbots provides round-the-clock customer support, guiding users to troubleshoot standard problems, and auto-assigns tickets to appropriate IT staff. Dynamic capacity orchestration based on predicted usage patterns and capacity needs induces elasticity and eliminates performance degradation caused by inefficient capacity planning.

Conclusion

The beauty of AIOps is that it gets better with age as the learning matures on exposure to more and more data. While AIOps is definitely a blessing for IT Ops teams, it is only meant to augment the human workforce and not to replace them entirely. And importantly, it is not a one-size-fits-all approach to AIOps. Understanding current pain points and future goals and finding an AIOps vendor with relevant offerings is the cornerstone of a successful implementation.

GAVS’ Zero Incident Framework TM (ZIF) is an AIOps-based TechOps Platform that enables organizations to trend towards a Zero Incident Enterprise TM. ZIF comes with an end-to-end suite of tools for ITOps needs. It is a pure-play AI Platform powered entirely by Unsupervised Pattern-based Machine Learning! You can learn more about ZIF or request a demo here.

READ ALSO OUR NEW UPDATES

What chatbots will do for your enterprise?

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.

READ ALSO OUR NEW UPDATES

Can enterprises gain from cognitive automation?

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.

READ ALSO OUR NEW UPDATES

8 Ways AI Will Impact Healthcare

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/

READ ALSO OUR NEW UPDATES

AI and its impact on app competitiveness

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.

READ ALSO OUR NEW UPDATES

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

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.