devopsy.kinsta.cloud

Category: AI

  • Putting Customer Communication at the Core of New Apps

    Putting Customer Communication at the Core of New Apps

    As developers and entrepreneurs create new products and services, it is critical for a powerful communication solution to sit at the heart of the solution and support all customer engagement. Reason being, when customers begin to engage with the application, they’ll expect easy access to troubleshooting tools like live support chat. 

    Neglecting to offer such support could make or break the overall customer experience. In fact, 78% of customers have reportedly backed out of a purchase due to a poor customer experience, and at least 54% of respondents said they would prefer to speak to a chatbot instead of a human customer service representative if it saved them 10 minutes. 

    However, doing so is no easy feat when it comes to automated communications services. Human-level support is effective, but often creates unwanted lag and is not easy or cost friendly for companies to scale. Automated customer service technologies and solutions are a viable choice but need to be able to quickly recognize the intent and context of what a human is saying in order to solve their problem effectively. 

    Not only that, bots need to be able to maintain a live dialogue with the customer and keep the conversation flowing in the same way a human customer service representative would. This ultimately creates an even bigger challenge for the developer, outside of the initial task of developing an effective and engaging application.   

    How Have Developers Traditionally Approached Customer Communications? 

    We often see companies looking to implement things like text message, video or voice calling into their customer communication strategy. While it may seem like a small task from afar, most in-house or self-built solutions require intensive upkeep and are often difficult to scale. 

    Not only that, once a company decides the type of communication it wants to deploy, the developer often struggles to find the right overall architecture and means for implementation. Once these are finally in place and working properly, expanding that functionality down the road makes for yet another grueling process. Because of this, many developers often decide to outsource all together.  

    Most enterprises and contact centers will have communications systems, such as Private Branch Exchange (PBX), that are based on software as opposed to being delivered as a service. This software may be completely run within an office or partly inside the enterprise’s cloud environment. This software was not designed for the modem cloud era, and the more advanced software option generally won’t offer any flexibility to alter or customize communications flows.

    What Are Some Best Practices for Integrating Customer Communications into New Applications?

    There are a few things for developers to keep in mind when building out their customer communications capabilities. First, you’ll need to decide what type of communications methods are required for optimal customer experience, whether it be text message, voice, video or a combination of the three. 

    From there, you’ll need to determine the communication logic that leads to the best customer experience. For example, there is no longer a need to follow the traditional model where a customer calls, navigates through an Interactive Voice Response (IVR), waits on hold and then finally talks to someone. Communications flows can be centered around what provides the best experience for the customer, not what the communications system can handle. If a customer service call results in scheduling an appointment, why not text details of the appointment for easy reference by the customer? 

    Proactive reminders as the appointment approaches are even better. Rather than subjecting customers to annoying IVRs with many simple prompts, they can leverage the technologies that drive Alexa and Google Assistant to help direct customers. Instead of the customer struggling to accurately describe what they are seeing, why not escalate to a video call so a technician can see what the agent is seeing? We also see this with options like automated call backs instead of having the customer waiting on hold to prolonged periods of time. 

    Next, you’ll need to find the appropriate technology stack that aligns with those standards. This often means using a CPaaS (communications platform as a service) platform that is compatible with your needs. Once you’ve found a solution that can cater to your needs appropriately, you can start building and integrating your communications system.

    Choosing the Right Solution

    There are a few things to consider when choosing the right solution. 

    • The solution you’re choosing needs an API function that suits your needs. Most of the apps and services need to connect your account with a consistent user ID that offers context into a user’s information and history. The most common practice is using a phone number as the ID because SMS is the fastest and easiest way to reach the customer, but you’ll need robust API capabilities in order to do that in a way that’s quick and catered to the individual’s account. 
    • If scaling your call-handling staff is a concern, you’ll need a solution that automates parts of the interaction to offload human callers. When implemented correctly, this will increase the efficiency of customer communications by solving customer problems faster. Done right, you can increase call volumes without adding to call hold times.
    • Unfortunately, scale is useless if you’re compromising quality. Automated customer service technologies and solutions need to be able to quickly recognize the intent and context of what a human is saying. Solid Artificial Intelligence (AI) capabilities can maintain a live dialogue with the customer and keep the conversation flowing in the same way a human customer service representative would. It doesn’t matter if the robot sounds and listens like a human if it doesn’t know how to respond to questions and statements in a human-like manner. 

    Customer communications should be a priority when it comes to building out any new application, but that doesn’t mean it has to be a hassle to implement. By keeping the best practices above in mind, you can deploy quality customer support in a way that’s approachable, scalable and simple to augment as needed.

    — Alexey Aylarov

  • AIoT and Intelligence on the Edge

    AIoT and Intelligence on the Edge

    Recently, one of the biggest convergences is between the fields of AI/ML and IoT, which enables the rise of Artificial Intelligence of Things (AIoT). AIoT combines data collected by sensors and other devices with the intelligence offered by AI/ML to enable real-time, intelligence on the edge. By converging AI/ML and IoT capabilities, AIoT solves two problems by merging them together into one solution. The goal of AIoT is to enable more efficient IoT operations, improve human-machine interactions and enhance how data is managed and analyzed. 

    Edge intelligence allows a high level of data to be processed and analyzed, and for decisions to be made locally, without being sent to the cloud. Take for example a self-navigating drone, instead of relying on a service hosted on the cloud to tell the drone where to go next, the drone itself is now able to decide its own path in the field, even when connections to cloud hosted services are not reliable.

    The evolution of IoT into AIoT progressed in three distinct stages: 

    1. Enabling core capabilities on the edge, including basic sensor development, integration with available devices, etc.
    2. Collecting the data generated from these sensors and storing them in a structured form on a central data store, typically on the cloud.
    3. Realizing the synergy between AI/ML and IoT and combining them together into AIoT.

    In other words, while IoT provided access to a large base of information, AI/ML has brought in intelligence and decision making.

    While great advancements toward AIoT have occurred, there are three main challenges in enabling intelligence on the edge to consider:

    1. Latency: When decision making occurred on the cloud, the only available option for an IoT device was to send the data (say, a captured image from a camera) to a service running on the cloud and get a selected decision from the service. This meant large volumes of data were transferred over slow or unreliable networks, resulting in delays–in many scenarios these delays could be extremely critical.
    2. Cost of computing and data transfer: Apart from the delay itself, the sheer volume of requests would greatly increase the cost of both transferring data back and forth and the cost of computing on the cloud–decreasing the feasibility of these implementations.
    3. Design divergence: While initial AI/ML solutions leveraged the cloud for vast amounts of computing power for training models, advances in the ability to move around or run a trained model (to calculate a decision) caused a divergence between the design of the solution and the hardware needed for the training and deciding components.

    In parallel, AI/ML learning methods also evolved–these became a lot more efficient with regards to computing power usage, and the type of hardware needed for a machine to learn or to decide. Combining AI and IoT was the next logical stage in this evolution.

    Design of an AIoT Solution

    The physical components of an AIoT solution are not that different from a standard IoT implementation; the design of the AI/ML backend also does not change drastically. The changes are to a few specific parts of the core devices on the edge–that are now more powerful–and enable a huge range of flexibility with the scenarios that can be handled, and how solutions are designed using these devices. 

    Impact on Enterprises

    Enterprises looking to benefit from AIoT may need to re-align roadmaps or make a few incremental changes based on their current state of adoption and based on the nature of their solutions.

    For CXOs

    There are four suggested, broad strategies for CXOs based on their level of investment today in the areas of IoT and AI/ML that are depicted in the following graphic.

    Strategies to follow for AIoT adoption depends on current levels of investment.
    • Group 1: Current level of investment in AI/ML and IoT are both high. The best option for these enterprises would be to evaluate the current roadmaps to identify incremental additions/upgrades to edge devices. This would leverage the existing investment in AI/ML and at the same time start demonstrating benefits early without increasing risk.
    • Group 2: Level of investment is higher in IoT compared to AI/ML. These enterprises stand to gain the most by shifting the ratio of investment toward AI/ML while making small changes to the device sets on the edge. They may also need to re-evaluate their current process to consider moving the decision part of the step to the edge.
    • Group 3: Level of investment in AI/ML is higher than investments in IoT. These enterprises would probably need minimal re-alignment with their roadmaps–a quick look at the implementations of the steps that train the ML models would provide the list of actions to take–epics/stories that needed intelligence on the edge can move higher up in the product backlog.
    • Group 4: These enterprises have the maximum freedom with the roadmap their implementations can take. Any product or platform roadmap planning can easily include AIoT epics and stories, with minimal impact to tasks already underway.

    For Architects/Program Managers

    For architects and program leads working on such initiatives within the company, it’s mainly a mindset change in regards to how the solution is designed, including capabilities of the devices on the edge and where the decision-making step in a process happens. Feasibility for scenarios such as the drone automatically calculating its own path instead of relying on a cloud-hosted service are now better than before, and a few demos or proof-of -concept attempts could now move many of these stories from the backlog and bring implementation dates forward.

    Realistic Predictions for AIoT

    While AIoT in its re-imagined, converged form may be new, the two original fields (AI and IoT) that merged to create AIoT are both mature and well into mainstream adoption. The benefits offered by AIoT for enterprises today, combined with the low risk involved with the small incremental changes outlined above and the cost savings that result in moving the decision making to the edge, create a lethal combination that positions AIoT to revolutionize business processes.

    IDC has predicted that the global spend on IoT will reach $1.1 trillion in 2023, up from $726 billion in 2019. Moreover, IDC predicts there will be around 40 billion IoT devices by 2025, resulting in a significant influx of data that will make it increasingly challenging for enterprises to drive operational insights and optimize their analysis of the large sums of data available. AIoT can help solve this problem by bringing increased intelligence to data management and thorough analysis. 

    If it isn’t already, now is an ideal time for AIoT to be a top priority for C-suite IT leaders.

    — Rajamani Saravanan

  • Threading the Needle on Kubernetes Complexity with AI-Powered Observability

    Threading the Needle on Kubernetes Complexity with AI-Powered Observability

    If you run down the laundry list of major industries–transportation, retail, healthcare, automotive, financial services–you’ll see that the major players in each have essentially become software companies. What they do and how they serve their customers rests entirely on their software working perfectly. Underpinning this is a massive digital transformation that has shifted organizations away from the old, static ways of enterprise IT monoliths. Instead, today’s businesses are working in more dynamic IT environments–agile, multi-cloud and, especially for new digital services, based on containers and microservices. All of which means embracing more IT complexity than ever before.

    Kubernetes Brings More Data to the Table, but that Impacts Observability

    A key part of this more complex environment is the soaring adoption rate of Kubernetes over the last few years. In a survey of 5,000 enterprises, 40% said they were currently running Kubernetes in production and over 80% reported that they’d adopted Kubernetes over any other container management system. Another recent survey revealed that 68% of CIOs are already using containers and a total 86% expect to deploy them over the next year. Enterprise IT is increasingly gravitating to containers and especially to Kubernetes for orchestrating their containerized applications.

    What drives organizations toward containers and Kubernetes is the flexibility given to development teams to release services, features or apps faster. But faster doesn’t necessarily mean better or higher quality, unless you also apply cloud-native architectural patterns, include quality gates into your delivery pipeline, embrace new deployment models such as canary or feature toggling and provide full visibility and short feedback loops from production back to engineering.

    Kubernetes itself provides great visibility into node or pod health. Kubernetes even ensures the health of your pods through its built-in health check mechanisms. Thanks to components such as service meshes, you also get live information about dependencies between services running in your pods. This data can, in theory, provide organizations with the necessary insights into their environments to understand whether their organization is releasing better value faster to their end users and unlocking new business value.

    The reason I said these are theoretical benefits is because, in practice, all that extra data doesn’t necessarily mean more visibility. Often it can mean less visibility–and with that, less insight into how the different elements of your environment may be impacting speed of production deployments, overall system performance and the end-user experiences. I’ve seen Kubernetes environments that are packed with literally billions of interdependencies between applications, containers, microservices and clouds. Asking human beings to keep track of that level of data manually is an impossible task now. And relying on traditional, manual approaches to manage and monitor IT environments shortchanges the value that organizations should be getting out of Kubernetes.

    Kubernetes Environments Need Full Stack Observability—that Means Needing Deterministic AI

    Getting the full potential out of Kubernetes means having observability across the full technology stack and those billions of interdependencies. But as more organizations are discovering, that can’t happen without AI. Specifically, a deterministic AI engine. In fact, in a 2019 survey, 88% of CIOs said they were looking to AI as a critical solution for overcoming IT complexity. As long as Kubernetes is fueling complexity, overcoming that complexity means making deterministic AI an integral part of any Kubernetes environment.

    A deterministic AI applies specific algorithms to detect system anomalies and apply its collected dependency information toward detecting the root causes behind those anomalies. When that deterministic AI engine ingests critical Kubernetes events, such as state changes and workload changes, it provides visibility into the Kubernetes stack’s interdependencies. That AI-powered, full stack observability matters because it gives IT new capabilities for their environments, including instrumenting and mapping containers, managing and allocating container resources and drilling down into container runtimes at work. All of that is possible due to the level of observability that you can only get with deterministic AI; traditional metrics and dashboards just can’t compete.

    By providing full stack visibility and interdependency mapping across a Kubernetes environment, deterministic AI is empowering IT teams to identify, in real time, the root cause of degradations in business service availability–from reliability and resource usage to system performance and user experience–faster than they ever could manually. That level of automated speed and visibility also makes it possible to immediately resolve those issues at the source before they can impact the end user. The automation provided by deterministic AI also works its way into DevOps’ production pipelines, accelerating the release and feedback cycles of new deployments and allowing teams to more quickly incorporate user feedback into the next iteration for even better experiences.

    In short, a deterministic AI engine makes life better for both IT and the customers they’re aiming to serve. Deterministic AI takes work that used to be time-consuming, tedious and counterintuitive when done manually, and performs it fast and automatically. That then frees up the teams that used to do that to focus on work that is more satisfying and drives new business value.

    AI-Powered Observability Relies on Quality Data and Quality Monitoring

    All that said, a deterministic AI’s ability to provide reliable, precise full stack observability in a Kubernetes environment relies on it being given consistently quality data it can process. The algorithms are dependent on the data that’s ingested by the AI engine. If there’s no standard monitoring capability being used across the board, then you invite a scenario where different teams bring their own preferred monitoring tools into the fold. The bigger a Kubernetes cluster gets, the more diverse the set of monitoring tools can be. And the greater the diversity of monitoring tools, the more inconsistent the data being fed into the deterministic AI engine.

    The way out of this predicament is to leverage a single monitoring tool as a self-service capability into Kubernetes clusters, one that can support the diversity of technologies that are introduced into the stack and provide a baseline standard of system and event data that developers, IT administrators and the deterministic AI all lean on. In other words, when you feed consistent, quality data into the AI, you make it smarter. And a smarter deterministic AI engine provides the full stack Kubernetes observability that organizations need. 

    Building a Positive Feedback Loop Between Kubernetes, Deterministic AI and Observability

    Kubernetes and deterministic AI are a natural fit. Kubernetes provides a platform for managing and instrumenting containerized applications. The deterministic AI provides a tool for mapping out the Kubernetes stack, offering insights into interdependencies and degradations where they arise and resolving them before they can affect end users. The data from Kubernetes makes AI better, and a smarter deterministic AI engine means more precise observability across your technology stack.

    It’s a self-reinforcing feedback loop that eliminates the usual pitfalls of container-induced IT complexity. Instead, it creates new standards of visible, consistent and quality data that organizations can leverage for faster and better deployments–and in turn, better user experiences and greater business value.

    To learn more about containerized infrastructure and cloud native technologies, consider coming to KubeCon + CloudNativeCon EU, in Amsterdam. The CNCF has made the decision to postpone the event (originally set for March 30 to April 2, 2020) to instead be held in July or August 2020.

    — Andreas Grabner

  • Enterprise AI Books to Read This Spring

    Enterprise AI Books to Read This Spring

    If you are anything like me, and curious about how new enterprise tools will transform our relationship with work and business-technology, then you are probably reading a lot about artificial intelligence. There’s likely no other topic that’s been written about in more depth in recent years, yet much of it is unrealistic or poorly reasoned hype.

    This is why I’ve been reading more books from trusted authors on the subject. In previous years, I’ve read books such as Erik Brynjolfsson’s and Andrew McAfee’s Race Against the Machine, and Martin Ford’s book Rise of the Robots: Technology and the Threat of a Jobless Future. Both were great books that brilliantly framed AI. Last year, I wasn’t as fortunate.

    Last year, my reading included The Master Algorithm by Pedro Domingos. It was an interesting look at machine learning algorithms, and specifically the quest for the algorithm—the master algorithm—that would create all future algorithms without the need for humans. This book proved a disappointment because the author seemed as convinced, almost dogmatically so, that general AI success was as close around the next corner as the AI pioneers of the 70s and 80s believed success for them was around the next corner.

    The next book up for me was Life 3.0 by Max Tegmark. While this was a good read, it proved to be too much of an analysis of what has been already written than a book that significantly pushed the subject of AI forward in a new or unique way.

    Hopefully, my next batch of AI reads will prove better. I realize I’m playing catch-up here, as most of these aren’t new books. But they do look to be important books on the subject.

    Here’s what I currently have lined up.

    AI Super Powers

    Author: Kai-fu Lee.

    Following the defeat of the world’s top player of the game Go, to Google’s AlphaGo AI, the government of China set ambitious plans to become the global AI hub by 2030. In his book, Kai-fu Lee contends that China has quickly caught up to the U.S. and that dramatic changes from AI developments are happening much more quickly than many expect.

    In his book, Lee examines universal basic income, and examines what jobs may be enhanced with AI the possible solutions to the biggest changes AI promises to bring to us all.

    Artificial Intelligence: A Modern Approach 4th Edition

    Authors: Peter Norvig and Stuart Russell.

    At 1152 pages, Artificial Intelligence: A Modern Approach (due out this April) isn’t a light read. The latest edition of this book looks across the entire field of AI, and a deep dive into machine learning, deep learning, transfer learning, multi-agent systems, robotics, natural language processing, causality, probabilistic programming and more.

    I have high hopes for this book, as it’s treatment appears to be exactly what I’m looking forward to reading: an overview of the state of AI without going too light on the treatment of each topic.

    Applied Artificial Intelligence: A Handbook For Business Leaders

    Authors: Mariya Yao, Adelyn Zhou and Marlene Jia.

    This book is made the list because it’s AI as it can be applied to business-technology. It promises to be a roadmap on how to use  data, technology, design and staff to solve enterprise business problems.

    “We teach you how to lead successful AI initiatives by prioritizing the right opportunities, building a diverse team of experts, conducting strategic experiments and consciously designing your solutions to benefit both your organization and society as a whole.”

    That’s exactly what enterprises need to do to succeed at integrating AI into their companies in order to get value from this growing enterprise technology.

    Machine Platform Crowd

    Authors: Andrew McAfee and Erik Brynjolfsson.

    I learned and enjoyed both Race Against the Machine and The Second Machine Age. The latest book from MIT Principal Research Scientist Andrew McAfee and Director of the MIT Center for Digital Business Eric Brynjolfsson, looked at the impact of machine intelligence and big data. In this book, the authors look at another form of augmented intelligence: our collective intelligence, and what it means for transportation, medical research, financial services and more.

    That’s it for the reading list for now. I’d appreciate hearing what AI-related business books you are reading this spring and summer.

    — George V. Hulme

  • A Deep Dive Into AI for IT Operations

    A Deep Dive Into AI for IT Operations

    Traditional IT administration and management techniques are reforming. By employing artificial intelligence (AI), IT operations are taking an interesting turn in the field of advancements. The term “AIOps” stands for Artificial Intelligence for the IT Operations. AIOps uses AI techniques and algorithms to monitor the data as well as reduce the blackout times. The dominance of digital businesses is introducing technological changes in their operations to streamline them and eliminate the overhead of traditional operational management. 

    Digital companies are generating abundant and information-rich data whose analysis and monitoring do not demand delays. From data collection to storage and data processing to analysis, all operations need to be frictionless to deal with them in an efficient manner. In the last couple of years, the AIOps market has exploded which is ultimately achieving the goal of reducing delays in problem-solving and identification.

    Paving the Road to AIOps

    The story starts with the need of AIOps and digital transformations. Cloud adoption and the shift toward other digital technologies are revamping the IT industry. It also includes the adoption of innovative solutions in the IT operations to make them frictionless. The technologies that are widely used in IT operations vary from AI to machine learning, big data to Internet of Things (IoT) and Application Programming Interfaces (APIs) for various use-cases to edge computing. All these innovations are penetrating into IT operations making them more smooth and sleek.

     AIOps platforms enhance and automate IT operations, using the following different approaches:

    • Using machine learning algorithms and neural networks for better analysis of gathered data. The data is gathered using IoT devices and tools connected with the source and sending data to the cloud for storage. 
    • The system automatically interprets if something is wrong in the operations. The errors are detected and solved immediately without introducing delays in the operations that are dependent on each other to complete.
    • Reducing human power by automating the processes that include the effort of individuals. 

    Components of AIOps

    The two basic components of AIOps that accelerate innovation in IT operations are machine learning and big data. Instead of spending human effort and time into cleaning, processing and analysis of the raw data collected from different platforms, IT operations are able to do all the tasks by employing machine learning algorithms and techniques to judge the nature of data and deduce necessary information from it.

    Factors Boosting the Emergence of AIOps

    AI is a promising technology that holds the confidence of building applications that can help the IT community. AI provides options for reliability, scalability and generic tools that can be deployed to promote the IT operations and boost them without utilizing human power.

    Factors boosting digital transformation in the IT industry:

    • Complex IT Infrastructure: Its operations face many challenges regarding data management and infrastructure. Manual IT operations are becoming harder to handle such as data collection, processing, cloud management, database efficiency, third-party management tools and SaaS integrations. Complexity management is a task that needs proper attention and the environment to deal in a better way. 
    • Handling Abundant Data: IoT technology is penetrating rapidly at an industrial level. IoT devices produce an abundant amount of data. Handling this much data is not the job of individuals. Therefore, by using AI-based algorithms the data is interpreted and analyzed. The performance is monitored using efficient algorithms. 
    • Increased Computing Power: The networking infrastructure has become robust. Therefore the IT industry is in dire need to enhance the processing speed also. By deploying IT solutions that harness AI and machine learning technologies, the computational power of the system can be increased. 

    Enterprises Using AIOps

    The IT companies that are complex and large to which human power is not enough for overall operations are using AIOps to automate the tasks.

    The adoption of technology has a huge impact on the success of IT companies:

    • Development Teams: Companies that have a DevOps model and struggle to manage the roles between the organization are using AI and other innovative technologies to smoothen their processes. With this, developers increase the responsiveness, agility and clarity in the IT operations.
    • Cloud Computing: Cloud computing has a big future. The companies store their huge data in the cloud and all the operations related to cloud data management need to streamline, as it has its own challenges. Therefore, to make the processes flow without errors and monitor the access, there needs to be automation.
    • Digital Transformation: Companies around the world are all active in adopting technology solutions. Whether it is a fintech, travel, healthcare or e-commerce company, all businesses are automating their processing by first digitizing them and providing the user’s facility of utilizing applications based on newly emerged technologies. Information handling can be done by making operations handled by robotics and blockchain technology. AIOps removes friction from the IT operations.

    — James Efron

  • OpsRamp Adds Recommendation Engine to AIOps Platform

    OpsRamp Adds Recommendation Engine to AIOps Platform

    OpsRamp added a recommendation engine to its artificial intelligence for IT operations (AIOps) platform that makes use of predictive analytics to suggest potential actions to either improve performance or avert downtime.

    The OpsRamp Winter 2020 Release also adds 19 cloud monitoring integrations for Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP), along with dynamic topology maps for Azure and GCP. OpsRamp currently provides integrations with more than 120 different cloud services.

    Finally, OpsRamp has added an ability to detect seasonality patterns that would otherwise generate alerts, an Alert Stats widget that makes it easier to keep track of raw alerts, synthetic monitoring and agentless discovery and monitoring of Windows Server instances.

    Bhanu Singh, senior vice president of engineering and DevOps at OpsRamp, said as IT continues to become more complex resistance to AI within the ranks of IT staffs is declining. Organizations realize that IT specialists are an expensive resource, so they need to maximize the capabilities of IT generalists as much as possible, he said. OpsQ Recommend Mode adds a predictive analytics module to the company software-as-a-service (SaaS) platform for managing IT that provides explainable and transparent analytical recommendations to help the average IT administrator head off issues that would require a specialist to solve later, said Singh.

    That visibility is critical because it still leaves the decision about what to automate in the hands of the IT administrator. However, as OpsRamp continues to learn the nuances of the IT environment, the more confidence IT administrators will have in the recommendations being surfaced, he said.

    It takes about 24 to 48 hours for the OpsRamp platform to learn enough about an IT environment to start surfacing recommendations, Singh noted, adding because OpsRamp is a SaaS platform, setting up the platform requires minimal effort.

    To some degree an increased reliance on AI among IT professionals is inevitable. AI may never eliminate the need for IT personnel, but many of the mind-numbing rote tasks that IT administrators perform today soon will be automated. The challenge that creates, however, is the IT staff needs to be able to manage IT at a higher level, which means many entry-level jobs in the IT sector may no longer be required. The challenge then becomes finding IT professionals who have the right level of skills, when many of the entry-level jobs for which they have been trained no longer exist.

    In the meantime, a race is clearly on to infuse IT management platforms with machine learning algorithms. Startups such as OpsRamp are looking to supplant legacy IT platforms that were designed for a previous era of IT. However, the providers of those platforms are clearly investing heavily in AI as part of their ongoing modernization efforts. Each IT organization will need to decide for themselves to what degree the rise of AI warrants switching management platforms.

    Regardless of the platform selected, however, the one thing IT teams can count on is that AI platforms are only going to get smarter as they continue to evolve.

    — Mike Vizard

  • Three Keys to Successful AI Adoption

    Three Keys to Successful AI Adoption

    Over the past several years, we have begun to see the emergence of artificial intelligence (AI) in businesses. According to a study for the AI Index 2019 Annual Report, more than half of respondents report their companies are using AI in at least one function or business unit. Thirty percent report they have AI embedded across multiple areas of their business. As businesses continue to develop their understanding of what is possible with AI, we can expect to see a continued increase in AI adoption.

    People used to say “software is eating the world.” Moving forward, people will be saying “AI is eating the software world.” You can expect to see standard enterprise and cloud software programs become indistinguishable from AI. Currently, the success rates of AI projects fall well short of what we see in enterprise software success rates. A 2019 study by Pactera found that 85% of AI projects fail to deliver on their intended promises. As software and AI bind together, expect to see a dramatic increase in the success rate of these AI projects. AI adoption will begin to align more with traditional success rates associated with enterprise software as a category.

    As software and AI begin to merge together and enterprises look to invest in AI, there are a few keys to success. 

    Increase Your Organizational IQ with AI 

    Human factors are public enemy number one as it relates to AI adoption. For companies wanting to invest in AI, it starts with education. Companies need to become better educated on the options available to them through AI. We need to move away from viewing AI as a black box where magic happens. Through better education, the average business consumer will be able to understand the various disciplines of AI, such as natural language processing, vision, unsupervised learning and machine learning. As companies increase their IQ relative to AI, businesses will begin to understand the difficulty of AI projects. While this will create short term challenges, it ultimately will help the success of AI adoption in the future. 

    Commit to a Journey with Frequent Recalibration

    Enterprises must move beyond merely being educated about AI in order to remain competitive. Business leaders will have to step past the exploratory and proof of concept phases and begin delivering real value to the business. This will mean the continuous delivery of intelligent solutions across several areas of the business. AI is not like traditional software projects where you measure twice and cut once, deploy and then observe for several months. It is a continuous journey of discovery that will require consistent adjustments. AI is different and requires you to make changes to existing predictive models based on your real-time findings. 

    If the Insight Is Not Actionable, It Doesn’t Matter 

    We have already seen AI become more and more embedded into analytics solutions and this is only going to continue. The convergence of AI and analytics is crucial to the success of AI projects. Over the past 20 years, analytical reporting has been provided to all users. With the addition of AI, machine learning models become analysis engines providing prescriptive actions to business users rather than the business uses having to interpret the analytics themselves. Again, this is a huge component in the success of AI adoption. It will not be enough for businesses to gain insights from their AI investment, success will be measured on what they do with those insights. Leveraging the insights from AI starts by providing actions directly to the users that can act on them within their existing workflow.

    The power of AI is still largely untapped. It is already creating a massive shift in how we work and that is only the beginning. For those who have worked in IT and software, you need to adopt a whole new approach to AI projects. In the AI world, data is the driving force rather than a necessary evil. The emerging technology will require professionals to expand their skill sets beyond the traditional technical skills and into the disciplines of math and statistics. By doing so, we can expect to see a massive increase in successful AI adoption.

    — Chris Heineken

  • How Integration Can Keep Up with Digital Demands

    How Integration Can Keep Up with Digital Demands

    Integration holds the key to accelerating innovation, but in many organizations, business systems and application integrations haven’t kept up with the demands of digital. Data still exists in a siloed blend of on-premises systems and unconnected cloud applications, and IoT has added another layer of complexity into the mix.

    Understanding the three trends below, and how to leverage them, will help organizations continue to drive innovation for years to come.

    The Rise of the Integration of Things

    It’s no secret that the adoption of IoT is increasing, generating massive amounts of data from devices and sensors. As the number of devices increases in response to the explosive growth of IoT, the amount of data increases exponentially. This explosion of devices–and its associated data–is driving the need to leverage that data for the benefit of the business, such as improved customer experience, cost and downtime reductions or efficiency and productivity gains.

    Integration is the key piece that will enable businesses to get control of this data and put it to work. Without the right integration tools, it’s only possible to leverage IoT at specific levels. Essentially, integration provides the ability to connect the data from IoT devices and enterprise applications, orchestrate workflow and enrich IoT data with contextual information from other systems for better decision-making.

    Organizations can even use integration to link IoT platforms together for a comprehensive view of data, analytics and actions–making it easier and quicker to analyze, draw conclusions from and determine how to use the data. On top of this, they can also create complex and sophisticated responses when something goes wrong by leveraging automated integration workflows.

    As we continue to connect our devices, the Integration of Things will be as important as the Internet of Things.

    Blurred Lines Between Application Development and Integration

    For cloud applications to survive and thrive, they require built-in integration with other apps and data sources. Otherwise, users are constantly switching contexts from one tool to another when trying to complete a task, and content is scattered across the organization in multiple applications and departments. Because of this, the lines between application development and integration will blur as apps are compelled to have integration embedded under the covers.

    There will be many benefits to this. First, organizations will easily create and deploy integrations directly within their products, fully baked into the user experience. These in-app integrations to third-party sources will unlock additional capabilities for users, empowering them to do more.

    Second, building integrations without the right tools and specialized integration know-how requires a lot of work. Embedded integration eases the pain of monitoring API changes and documentations so that maintenance is smoother and cheaper.

    Finally, business services can involve complex interactions with cloud apps and data that require ordering, have dependencies or may even need to be reversible on demand. Organizations can handle these sophisticated jobs by leveraging advanced integration orchestration to manage app interaction–meaning that instead of using manual code for complex data mapping, they can create a visual drag-and-drop data flow. 

    AI Powers Smarter APIs

    Businesses have already been leveraging intelligent automation to improve operations, drive innovation and personalize the customer experience. As we head into the new decade, API management will harness the power of AI with innovative ways of protecting and controlling APIs and the applications they serve.

    Service mesh, an increasingly popular way of discovering and observing services in distributed networks, will evolve into app mesh, an application-aware model of monitoring, managing and securing your APIs. API policies will be able to reconfigure dynamically based on traffic, security threats and identified patterns. Additional layers of defense will come from bots deployed in an organization’s network to constantly monitor behavior and these bots will use machine learning to improve automated threat responses for APIs.

    The integration landscape is changing, but understanding how to harness these trends will ensure organizations are well-equipped to take advantage of evolving technologies for innovation.

    — Ann Marie Bond

  • Five Technology Trends to Shape Future Investments

    Five Technology Trends to Shape Future Investments

    Humans invented technology to introduce innovative products in the market. Technology’s relentless cycle of innovation has now touched humanity, redefining what it means to be a human, a meaningful change that is impacting business priorities and is expected to do so in the future.    

    The most important tech investment for C-level executives relates to digital transformation, artificial intelligence (AI), Internet of Things (IoT) and cybersecurity. With 71% of the world expected to use mobile phones by 2024 and the successes of mobile banking in developing countries, experts predict that mobile and blockchain technologies will also join to bring about more financial inclusions.

    Here are five things that C-suite business people should invest in.

    Digital Transformation: Everything Online

    As one of the weapons in the arsenal of business innovation, digital transformation has become such a buzz word that it turns people on. According to a report by Forrester, 90% of the executives believe digital transformation is a necessity for their business, and around 60% believe they lag behind in digital transformation. By the end of 2020, a company would either have transformed or will be on its way to adapt to digital transformation.

    IoT: A Thousand Points of Entry?

    Business implementation of IoT and blockchain are very popular these days. However, if we look to enterprises it’s a different story. Costs, security and current political climate are the major hurdles for the enterprise-level implementation of IoT. To get the most out of this innovative technology, businesses need to determine what stands in their way and then work on strategies to sort things out. So until the issues are dealt with, IoT will be seen as a promising technology that’s not ready for prime time.

    AI: A Real Game-Changer

    Every so often a technology comes along that has the potential to completely change everything around it. The last such thing was cloud computing. Like cloud computing, AI has been around for a long time. AI, machine learning and deep learning are catching up with the initial promises and it will become more evident in the coming years.

    Most common usage of AI and machine learning will be seen in business intelligence, automating the repeated tasks, and making future prediction using the previous data. Previously, AI has been used for decision making using data analytics and this is expected to increase in the coming years with more businesses investing in the AI-driven decision-making system. Another use of AI that will be more common is digital identity verification for online businesses.

    Ultimately, AI/machine learning and deep learning will be seen more frequently in the years ahead. However, the promises it brings are far fetched and it’s worth waiting to see the true potential of AI.

    Quantum Computing: The Next Big Thing

    Currently, in R&D, quantum computing will open new ways by solving problems that require a lot of computation. Google’s investment in quantum R&D is expected to grow and more researchers will shift their focus from AI to quantum. With the emergence of 5G, the quantum research industry will see a boom.

    DevOps: For a Customer-Centric Approach

    According to the Forrester report, firms will embrace a customer-centric approach as the DevOps speeds flatter. DevOps continues to impress the heart and minds. However, with a slow-release speed, engineers are experiencing a stress point. DevOps teams must see this as an opportunity to approach DevOps differently. Within the next six to 12 months, DevOps adoption will change and the team will implement new strategies. Testing will revolve around previous radical methods and new technology will help developers to learn new skills.

    — James Efron

  • Predictions 2020: Key Trends that Will Shape the Autonomous Digital Enterprise

    Predictions 2020: Key Trends that Will Shape the Autonomous Digital Enterprise

    As 2019 comes to an end, and we look toward trends driving innovation in 2020 and beyond, technology advancements in hyper automation, AIOps, edge computing and the convergence of IT service management and IT operations management will be at the forefront. 

    Autonomous Digital Enterprise

    In order to thrive in the 2025 futurescape, organizations will need to evolve to Autonomous Digital Enterprises (ADE), a state where intelligent, integrated, value-creating functions can operate with minimal (ideally zero) human involvement or interference, across every facet of the organization, and its ecosystem of partners.

    This concept is based on the notion that every company today is being disrupted by technology and that’s fundamentally changing their business model. The notion of every company being a technology company means that software becomes more than just a system of record to be the system of action, the system of insights and the system of engagement.

    Edge Computing and IoT

    Over the next couple of years, partnerships will prove to be the pathway to success in realizing value from edge computing and IoT solutions. By using seamlessly integrated, multi-vendor, commercial off-the-shelf solutions, enterprises will experience more success than they would with single-vendor niche products. At the end of the day, what we need is an ecosystem of integrated solutions as opposed to a single silver bullet. This ecosystem will be able to harness data and look at it in meaningful ways and translate information into insight that then translates into actions that businesses can take to optimize the use of IoT assets.

    Convergence of IT Service Management and IT Operations Management

    As IT service management and IT operations continue to converge in 2020, CIOs will rely on AI. More than ever, CIOs will need to address this convergence and work to break down silos between these departments. To obtain a comprehensive view of the IT landscape, CIOs will begin to adopt AI, cognitive and robotic process automation. This will bring end-user support and managed back-end infrastructure more closely together. 

    If we look beyond traditional IT and service management, in the near future we will have the ability to go to market with fully integrated solutions that enable boundary-less operations across AppDev, SecOps, ITOps and service management. This will further enable the use of DevOps in organizations using the power of AI to improve existing capabilities and create new capabilities. This doesn’t mean companies have to start from the ground up on AI efforts, rather they need to look for software solutions that can offer prebuilt AI/ML solutions that drive change.

    AIOps

    The demand for AIOps in the enterprise will continue to rise as AI and machine learning have taken the industry by the jugular. Due to an expansion in the number of workloads—both in public cloud and on-premises—and an increase in application complexity, investment in AIOps will increase and ultimately lead to better business outcomes. Today’s challenges place a premium on differentiated vendor solutions powered by AI/ML and big data analytics techniques that can help modern IT operations evolve from traditional monitoring to observability to actionability.

    All of these trends open realms of possibilities for what businesses can achieve in 2020—as long as they can harness the massive amounts of data enterprises have access to and the technology that will allow them to unlock those insights.

    Want to learn more about what to expect in 2020? Join us Jan. 23 for our Predict 2020 Virtual Summit  featuring discussions from some of the industry’s best and brightest offering up their visions for the future. Sign up today for this free daylong virtual event.

    — Ram Chakravarti