In this podcast, we will review the concept of business agility, its importance, and how organizations are building capabilities to become more agile to stay competitive.
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How can an organization develop its Artificial Intelligence or AI strategy? What are the various facets of developing an organization’s AI strategy? What is the CIO’s role in developing that strategy?
In an earlier episode, I had covered the basics of AI, its related technologies and the impact that AI is having on organizations in general and touched upon certain business use cases. So, if you are looking to get a quick review on the basics of AI, then please look for the episode published before this one.
In this episode, we will take the discussion forward and go deeper and start discussing the overall strategy or strategies that CIOs and CTOs need to pursue to start building an effective AI-enabled organization. We will also discuss topics that should be of importance for any CIO or technology executive when defining an organization’s AI strategy.
So, let’s get started.
It’s no secret that CIOs and CTOs across all industries are under pressure to accelerate the adoption of AI within their organizations to give them the needed technological and operational advantages and boost over their competition. Strong AI-capabilities, similar to digital transformation efforts are becoming quite essential for survival. Not having an AI strategy and corresponding capabilities can be very risky. I mean not building the needed AI capabilities will leave organizations stuck with old systems and processes, and as those legacy systems and processes lose their relevance fast in this digital economy, it could put the organization at a great peril. On the other hand, if applied properly, we know having the right AI capabilities can provide organizations with the needed competitive advantage due to AI’s ability to deliver deep business insights from its data, automation of operations, ability to forecast business demand, and creating better products and other capabilities that can help position organizations to start competing effectively in the new AI and digitally enabled marketplace.
As AI is fairly new, CIOs and CTOs should also ensure that there is collaboration and cohesion amongst various departments of the organization wherever there may be any AI related work and experimentation going on. This collaboration will ensure that organizations as a whole can learn from each other faster in turn accelerating AI’s adoption within the enterprise. Having shadow activities throughout the organization can prevent executives from formulating a cohesive strategy, which can deliver more impactful results than standalone or siloed initiatives.
Before we get into the various dimensions of developing an AI strategy, let’s ensure that we understand a CIO’s role and responsibility in the development of such a strategy. As CIO’s are increasingly become part of the organization’s top executive suite, we should remember that CIOs are also responsible to reinvent their organization’s business models as being demanded by the external market forces. CIOs can do so more effectively by using AI and its technologies to extract relevant insights and knowledge from its operational data that can help in business executive decision making.
CIOs are also responsible to work with the business to identify various business problems and use cases where ML can benefit the enterprise and to deploy relevant ML solutions across the enterprise. In this context, CIOs are also responsible to select the right tools and technologies, architectures, and patterns along with identifying the right AI models and institute a process where deeper data analysis can yield insights from data. In a nutshell, CIOs need all this to help them accelerate the creation of customer value and increase business growth.
Keeping this in mind, we will review 7 key dimensions that contribute to the creation of an organization-wide AI strategy. They are the following:
In the rest of this episode, we will now cover each of these in greater detail.
The first dimension we will look at has to do with getting clear on the organization’s overall strategy and objectives. As always, when adopting any type of strategy, executives such as CIOs and CTOs should be focused on the overall business strategy of their organizations, and stay focused on specific business outcomes as they consider developing their AI strategy. As we will note later in the episode, implementing an AI technology infrastructure and processes can be an expensive proposition and before embarking upon any such initiatives it’s important that business executives get an idea on how AI is going to benefit their organization and their commitment on pursuing such initiatives. As we had noted in an earlier episode, implementation of AI within an organization can happen pretty much at all levels of the organization – from automation of simple processes to bringing in robotics and implementing real-time Deep Learning algorithms and processing against large volumes of disparate data collected from various sources. As a business, you can’t afford to just buy a set of tools and technologies and then hope to get the right ROI from that investment. It’s important to spend time and understand the business outcomes that you desire, discuss with your stakeholders, understand your organization’s readiness in terms of the data and the underlying platform, and then you can start to formulate strategies and invest in those capabilities.
It’s therefore essential to ensure that an organization takes a methodical and systematic approach towards AI’s adoption while ensuring that their overall approach is aligned to the organization’s overall strategy. Perhaps at this stage, an organization can also develop some sort of a maturity model to get a roadmap that shows the adoption of various AI capabilities over time within the organization.
Taking a strategic approach doesn’t necessarily mean that enterprises should stop themselves for addressing some obvious problems that they could resolve readily or to ignore the low hanging fruit, but it means that keeping the perspective of the overall organization strategy is a must. AI should, therefore, be a core component of an organization’s overall digital transformation strategy.
The second dimension of developing an organization’s AI strategy is to invest in the right research as a precursor to building larger AI capabilities within the organization. In general, we know from industry surveys that a number of organizations have been investing billions in AI research for the past 3 years. This is also confirmed by a number of research organizations including a study by Mckinsey. Depending on the potential business cases that you as a technology executive have identified in the organization, you should look to start experimenting to gain basic capabilities in ML, DL, natural language processing, speech recognition, computer vision, etc.
When investing in research and experimentation, a CIO’s focus should be to democratize AI and its capabilities in the organization. When carried out properly, eventually this can result in a change of the organization’s overall mindset. As an example, analytics is widely used in all parts of the organization. An organization can take its analytics progressively from non-AI to AI-enabled analytics to give them more insights from that data. Most organizations are used to descriptive analytics, which essentially informs end users on historical information and explains ‘what happened’. The next step up from that type of analytics is “diagnostic analytics”, which delves into the root causes of the events and provides information on ‘why did it happen’. The third step up from that touches on predicting future outcomes from existing information and data and the next level after that is for the system to recommend actions – something which is also referred to as “prescriptive analytics’. Finally, we enter the domain of advanced AI, where systems become self-healing and find solutions to various problems. Although a number of such self-healing solutions have been built-in in various computing and telecom hardware, their use is also showing up in other user applications as well. So, we see that by democratizing the principles of AI and the insights they can provide the business can enable an organization’s users to start thinking at a totally different level and augment an organization’s overall intelligence.
As part of the research, an organization should also start experimentation with building of a digital platform that can have AI applications covering scenarios across various devices, the cloud and the edge. Starting the experimentation process by embarking on various Proof of Concepts and pilots related to the various use cases based on a coherent strategy can help accelerate AI’s adoption within the enterprise.
Leading the research and experimentation process can thus trigger the needed conversations at all levels of the organization on the possibilities for the organization and help an organization rollout AI and augmented intelligence across all the organization’s business processes.
Another key input to developing an organization’s strategy is for the CIO along with other key business stakeholders to start the conversation and identify the specific business use cases where an organization can benefit from AI. This is an important point that we should reiterate and that is that a CIO should not get too bogged down in the tools and algorithms but rather in the specific business use cases that your organization can benefit from. In that context, it doesn’t hurt to discuss the use cases in the context of the available tools and technologies but the focus should be on specific business use cases and the overall business case for piloting AI solutions for those use cases. CIOs should also start the conversation by broadly including staff from IT, LOBs, industry players, competition, and other business stakeholders. As a CIO, it’s useful that you start by asking the big questions, identify the real business problems, and discuss opportunities where AI can bring the most impact to the business. As you have these conversations, you will begin to see your strategy start taking shape and as that happens ensure that the strategy stays aligned to the overall goals and objectives of the organization. As you get clarity on that, eventually you can start a deeper discussion related to the relevant AI tools and frameworks.
More commonly, CIOs start by implementing AI in their departments and usually the data center. AI is being used in the data center for the past few years and can be used in a number of cases such as forecasting hardware and computing requirements, optimizing space and energy requirements, and in dealing with infrastructure management issues such as detecting or even preventing hardware failure. We also see AI being used to the extent that a majority of data center tasks are not only being completely automated but AI solutions provide recommendations on further optimizing various aspects of a data center’s operations. AI is also extensively being used in the realm of cybersecurity to fend off online attacks. AI technologies allow not only for detection of attacks but based on historical incidents and other data across the net, can provide various prevention strategies.
Before we leave the topic of use cases, you should keep in mind that the AI platform or infrastructure that you end up building and its corresponding costs will depend largely on your business uses cases. For example, if you are implementing an AI system to provide you real-time insights related to the health of a manufacturing operations or let’s say a trading operation, you will need to invest appropriately in the right kind of storage and processor hardware solutions to make up for the latency and scalability issues associated with regular network attached storage systems. Similarly, if you are planning to implement Deep Learning based solutions, they sometimes require more processing power than the traditional CPUs. In that case, you will need GPUs or better processing capability to handle those AI workloads and services.
Building the AI-enabled technology platform
Once an organization identifies opportunities where AI can potentially help the organization, CIOs should start focusing on building the right technical infrastructure that can deliver those capabilities. Again, depending on the specific uses cases, this can have wide-ranging implications. Decisions needed in the building of the right technological infrastructure include the choice of tools, service providers, ML architectures that may be needed for the various business areas of the organization, and other such decisions.
Similar to putting together any other digital or technology solution and infrastructure, implementing an enterprise wide AI system and infrastructure requires a well thought-out and cohesive strategy. After all, implementing such a system that touches enterprise wide data elements, requires massive processing power and large and continually growing storage, numerous interfaces and pipelines that bring together data from various sources, high bandwidth networks, algorithms that are far sophisticated in logic than the other workloads that an enterprise may be running, etc. needs strategic thinking and planning and a clear implementation roadmap.
Accordingly, as you build sophisticated Al systems and applications, you will need to ensure that the surrounding systems management and processes are upgraded as well to handle the upgraded capabilities of your systems and applications.
As for building the AI applications and services, one obvious choice is to start with AI service providers, which offer various ready-made AI services. These include services from the large service providers such as Microsoft, Amazon, Google, IBM, and others.
For example, if we look at Microsoft, Microsoft’s Azure ML enables the building of ML and DL models. They also offer Pre-Built AI by providing access to finished services on the cloud, which organizations can start consuming with little effort. If your organization wants to build a chatbot, for example, they have a framework for building bots and provide tools for the development of conversational AI. In fact, Azure provides a number of solution templates, reference architectures and design patterns on their AI gallery.
Amazon’s AWS, IBM’s Watson, Google and other service providers too, provide a number of pre-built AI services as well as tools to build AI applications. Depending on your business situation, you can explore those service providers and their tools and see which one would fit your situation more than the others. Perhaps in subsequent episodes, we will have a more in-depth conversation about the various types of tools and some of their pros and cons. If you are interested, ensure that you subscribe to this podcast so you are notified of our future episodes.
Next, let’s take a look at another dimension related to developing an organization’s AI strategy and that has to do with how to AI-enable various applications within the enterprise. In this context, organizations can start using the capabilities embedded in their internal enterprise systems along with developing their own AI applications for their sophisticated business use cases. I discussed this topic briefly in an online meet-up when answering a specific question. Here is that clip.
For example, a number of enterprise systems have already started to incorporate AI capabilities augmenting the capabilities of those tools and essentially providing the users with augmented intelligence. We see AI functionality being built in systems such as SalesForce, MS Dynamics, Workday, and others. When used in these systems, AI turbo charges capabilities of those tools, providing organizations better insight into their customers, suppliers, operations, and other business processes. So, one strategy might be to use AI in that context. More specifically, let’s consider SalesForce, an established CRM tool in the market. This tool has added a layer of artificial intelligence through a product called Einstein, which brings various AI functionality to its users. Einstein in SalesForce allows users to perform sophisticated data analysis and allows users to predict business outcomes, allows an organization to build chatbots that can be trained with the organization’s CRM data making chats with customers more personable and other such capabilities. Microsoft’s SharePoint is another example in which Microsoft has packed a number of AI features, especially related to image recognition and text extraction. Users can essentially teach SharePoint a number of things such as recognizing a certain image and when the tool scans documents and detects that image, it can treat them in a certain way using the rules defined in the system. Such capabilities can help in managing contracts, invoices, and more.
So, we see that many tools have already incorporated AI under the hood and while it may not be very obvious to end users, a lot of reports and analytics that is presented to users already make use of machine learning algorithms to report on more sophisticated analytics.
Organizations can also start developing AI applications of their own by incorporating specific AI technologies that are more relevant for their business use cases and needs. For example, technologies are available from companies like Second Spectrum that use ML to watch every second of video of a basketball game, understand it, and then deliver insights about the game and potential improvements to teams and coaches. Earlier this was accomplished by interns spending hours and hours to watch games, tagging videos, and making them available for coaches to watch. Now all that can be squeezed into minutes and seconds. Organizations who see any relevance in such technology, can deploy appropriate ML models to get insights from all types of data including video, images, text, and others and use those insights to improve their business processes.
Organizations can also enable their other apps and cloud workloads with AI and ML technologies to enhance their customers’ experience. AWS, for example, provides what they call “pre-trained” AI services through AI APIs, which can provide a basic set of ready-made intelligence for use cases, which include but are not limited to personalized recommendations, increasing customer engagement, getting security recommendations, image and video analysis, advanced text analysis, and more.
The sixth dimension in developing an organization’s AI strategy has to do with data. Organizations have now realized the massive insights and intelligence that is trapped in their processes and data. CIOs, therefore, must immediately adopt a comprehensive data strategy to help them get that data organized and then to start leveraging the right tools to provide them the insights and enact processes to act on that intelligence and derived insights. A CIO has the responsibility to establish a platform that takes the data across its lifecycle from its initial creation and allow for democratization and easy consumption and analysis of that data. Considering the complexity of the process and the vast stores of enterprise’s data that’s easier said than done and thus requires a solid data strategy. As we know, data is the fuel that powers AI and its applications. The problem that we have at the enterprise level related to making data available for AI is manifold. First, we need to ensure that we prepare and organize the data assets appropriately to make it AI-ready. Second, at an enterprise level we usually are dealing with large volumes and multiple stores of data. Digital transformation initiatives are already resulting in solutions that are inundating enterprises with large volumes of data. The nature of AI system that you decide upon may further increase the volume of data that your organization may handle and process. Third, we need to ensure that the various types of data are organized for organizations to run ML and AI to extract the needed insights. Finally, the digital transformation is making enterprises generate huge amounts of data constantly and an enterprise’s AI infrastructure needs to ensure that it caters for this non-stop data streaming as well. In lots of cases, the amount of data that an organization is collecting is faster than the organization can run insights on that data.
So, with these challenges at hand necessitates that an enterprise and its senior leadership develop a relevant data strategy. Having the right data architecture can make the development of AI applications faster and easier and can also pave the way for future AI applications as they get identified depending on the organization’s business needs. So, in the next few minutes, I will touch upon various points that a CIO should consider when developing an organization’s data strategy for AI.
Another dimension of building an organization-wide AI strategy is to ensure that the organization has the right types of skills to help it build its AI capabilities. The skills can vary quite a bit depending on your organization’s needs and mostly would depend on the types of capabilities that you as a CIO decide to get within the organization. CIOs and technology executives also should not overlook the need for formal training for its key staff. This training can address the basics of AI. And once the organization has a basic strategy in place, more advanced training can be provided in line with that strategy. So, for example, if the organization plans to use AI services from an external service provider such as Amazon, then it can focus its training on that front.
The various type of skills that an organization may need include the following:
With this we come to the end of this podcast. I will have more topics on AI and how to overcome various challenges in bringing AI in organizations in a later episode. Again, please subscribe to this podcast to ensure you are notified of those episodes. This is Wasim Rajput and thanks for listening.
What are the key digital and technology trends for this year? What business outcomes can organizations expect from these technology trends and how are these technologies expected to benefit organizations?
So, with that let’s get started and review the top seven trends that I see are on CIOs agendas for this year.
There’s nothing new when we say that we live in a world where change is the only constant. What is surprising however is the rapid pace of change that industries and organizations are experiencing and going through. The impact of the change is being felt almost globally and across all organizations and industries, as organizations rush to alter their business models to minimize the impact of disruption triggered by this constant and rapid change. A number of new technologies are causing this disruption and triggering new business models. To stay ahead of the curve, organizations should get aggressive about exploring opportunities and explore the relevance of these technologies to their organizations.
Technologies that are enabling this change and our new world are many. But in today’s episode we will focus on technologies that are at the forefront and are changing the way organizations operate and compete in the new economy. Any organization that hasn’t yet identified potential use cases for the application of these technologies should do so before they are left behind by their competition.
CIOs across industries are applying these technologies to bring business benefits such as increased automation, improved user experiences, new products and services, and other such business outcomes for their organizations.
A review of these technological trends can help you learn not just some of the buzzwords but also to learn about their disruptive potential, the opportunities they offer, and what organizations need to do to start weaving them into the fabric of their organizations.
Before I jump in and cover those 7 trends, they are:
The first trend I would like to highlight today is about the advances that are occurring in the field of analytics. We can call it advanced analytics or as a number of research organizations like Gartner like to refer to it as Augmented Analytics. Essentially, advanced or augmented analytics is analytics on steroids. More specifically, its analytics coupled with Artificial Intelligence and automation.
Over the past few years, we have observed how numerous technologies in the area of AI, ML, and data science have made it possible for organizations to run sophisticated analytics on the massive amounts of data that they are generating, along with the historical data that they already have and the external data that they can get to from social media and other sources. However, accordingly the amount of preparation and other related manual work has made this process quite tedious. As the need for getting ones hands on advanced analytics and business insights goes up, it has resulted in more work related to data preparation, data discovery, ML model selection, searching and querying for data insights and more. To tackle the manual and laborious effort that accompanies such tasks, many systems and tools today are embedding a number of features that are automating a number of these steps. This then is helping businesses to focus on getting key business insights and intelligence more quickly thus helping them make the right decisions rather than struggling with the many steps of getting to that point.
So, getting back to augmented or advanced analytics – we can say that this discipline refers to a suite of technologies in the realm of analytics that brings more automation and intelligence to this overall process of data preparation, discovery, model creation to analyze and extract insights, and searching for or querying of insights through a natural language interface. Again, the idea is to help organizations spend more time on acting on the insights and intelligence that they derive from data rather than spending manual time and effort on preparing the data and getting insights from that data.
For CIOs this should especially matter if they have been investing in standalone data science, data integration, and other analytics solutions as they may find that a number of the functions and features that they may be paying for are perhaps now being addressed by their respective systems of record or ERP vendors and these functions are features are perhaps becoming more accessible part of other tools as well. For sophisticated AI applications they may still need those tools but that’s something that should be looked at more closely on a case by case basis for each business. If, for example, we look at enterprise systems related to HR, CRM, finance, procurement, customer service, and others, we will see that a number of them now incorporate a number of such capabilities to help their users with their decision making. To cite a specific example, SalesForce has introduced Einstein Analytics, which is essentially advanced analytics powered by AI. These sutie of tools on SalesForce pack a lot of functionality related to providing predictive insights and prescriptive recommendations and accordingly provide apps and functionality, which allow organizations to not only visualize their overall sales and marketing pipelines but also help in making complex forecasting decisions.
We should note that there is no one tool per se to deliver all such capabilities for an organization but rather it’s something that organizations must understand as part of their overall AI, analytics, and decision making framework so they can start making the right changes to their processes and overall strategy of acquiring and operationalizing technology related to getting more intelligent insights. By understanding the overall process and the complexities inherent in the process, organizations will start to select the right business tools which will make it easier for them to get the right insights and to automate the many tasks that are involved in the derivation of these business insights so they can make faster decisions.
We see analytics also getting advanced to a level where organizations are enabling constant streaming of data from their business processes and analyzing that data in real time to give them real time intelligence and business insights. A number of technologies come together to make this happen including those of Artificial Intelligence, ML, DL, data management, data science and others. The idea is to have an organization’s systems and processes create new intelligence constantly to help it in making instant decisions.
The next trend I would like to cover is that of digital twins. A digital twin refers to a digital representation of any physical entity, which needs to be monitored. Physical entities include but are not limited to people, process, equipment, places, and others. Creating a digital twin (or having a digital representation of the entity) allows an organization to study the behavior of the actual physical entity and to run various types of analysis to understand its behavior, improve its functioning, perform diagnostics, and more.
Due to the nature of the digital twin, it’s important in many cases to have data related to the entity constantly transmitted to the system using sensors and IoT devices. For example, to have a digital twin of an equipment such as a jet engine or large machinery, sensors on them would constantly transmit data to the main system providing engineers deep insights into the behavior of the equipment. They can then use that data to predict failures, test configurations, and perform more of such analysis.
Although the idea is not new and we have seen its applications on a number of platforms, its adoption is still not as widespread. For example, GE’s Predix platform is a digital industrial IoT platform and cloud based service that maintains digital representations or digital twins of various industrial equipment and provides its users the ability to run analytics and learn more about the devices and equipment that it monitors. This concept of digital twins is also used in managing assets in the energy sector where lifecycle of physical assets can be studied and improved. Organizations, for example, can monitor offshore oil rigs and study variables, which can further help in improving their performance without being at the physical rig itself.
IoT and sensors have further popularized the concept of digital twins and its use is expanding to pretty much all industries where there’s a need to monitor physical entities.
Moving on, the next technology that we will discuss is that of Blockchain. Blockchain is a distributed ledger technology that provides decentralized trust across a network of untrusted participants. Cryptocurrencies like Bitcoin and Ethereum were founded on these technologies. Since then, interest in Blockchain technologies and relevant investments has grown exponentially for the past few years. According to Statista, a leading provider of market and consumer data is forecasting that blockchain technology revenues will grow to more than $23 Billion by 2023. Organizations that have shown more interest in Blockchain are from the financial industry — and that’s for a good reason. It’s in this industry where organizations participate in carrying out financial transactions in the larger ecosystem and have to trust each other. Blockchain due to its distributed ledger technology solves that problem for them.
Other organizations from different industries are also experimenting and implementing blockchain in their businesses. We see this technology being applied to solve manufacturing supply chain issues, food and agriculture industries, and others.
A number of technology service providers have built platforms, which allow organizations to build and deploy blockchain platforms. IBM, Microsoft, and Amazon provide cloud based blockchain services, which many have been experimenting with for the past couple of years.
Building enterprise blockchain solutions can be more challenging as it not only requires a strong technology platform but also cooperation from various participants. It also requires extensive testing to ensure security, performance, trust, and scalability issues are appropriately addressed. It’s for this reason that sometimes it takes longer and relatively more extensive planning to roll-out these solutions.
Another technology worth mentioning again is that of cloud computing. Although, the move to cloud computing has been going on for a few years now, but we also know that not all organizations have moved to the cloud. Many who have started the process, have only moved a fraction of their workloads on the cloud. But due to the successes of this computing paradigm the trend continues to be hot and enterprises will continue to invest a sizable chunk of their investments to migrate their old and new workloads to the cloud. Although the initial move to the cloud was triggered by cost reasons and for software and applications to be used as a utility, many other advantages have come to fore over the past few years. A number of technical innovations over the past few years simply would have not been possible without cloud computing. This includes but is not limited to Artificial Intelligence applications that deal with a lot of data and need massive processing power. Technologies such as Blockchain and IoT also have gained a lot from having a cloud based backend. So, in general we can say that enterprises have plenty of reasons to move to the cloud.
Before discussing some of the trends related to cloud computing, let’s review some of its basics. Cloud computing is a computing paradigm, which allows for network access to a number of computing services made available through shared physical and virtual resources. Cloud computing services are available in different configurations. First, in a non-cloud environment, an organization manages all of the resources and services related to networking, storage, servers, virtualization, O/S, Middleware, Data, and applications. In an IaaS configuration, everything related to the applications and middleware is managed by the organization, whereas the cloud service provider manages the bottom layers of the stack namely the networking, storage, and hardware. In the PaaS configuration, with the exception of the applications and data, everything else is managed by the cloud service provider. Finally, in the SaaS model, all layers are managed by the service provider. So, which model you or your organization decide to get depends on your specific business case.
Having said that, we should mention a number of trends in that arena. First, we see that the hybrid cloud environments are becoming more popular and many organizations, especially the larger ones are settling on this paradigm. A hybrid cloud environment is one where an organization uses a mix of public and private clouds and on-premises environments for its computing needs. Although a number of enterprises have started the move to the cloud, many have come to realize that they won’t be able to (or have reasons to) migrate all their workloads to the cloud. So, a hybrid cloud configuration gives them the best of the different worlds out there and gives them the flexibility of capitalizing on cloud technologies for applications that can benefit the most from such computing. Another trend that we see within the realm of cloud computing is that of serverless computing because it provides the organizations the flexibility to get and pay for the software and computer services without worrying about the underlying infrastructure. In serverless computing although the servers are still there but customers don’t have to worry about them as they are focused on getting software and compute services from the cloud services provider. This is another step up from the orginal cloud computing model where customers have only to pay for the compute services they use rather than leasing cloud resources that they wouldn’t use. In the past couple of years, the industry has seen a rising popularity of this model, especially if there are services that organizations can use from the cloud services provider without worrying about getting the underlying infrastructure.
Another cloud computing model that has been on the rise is that of edge computing. Edge computing is an architectural construct, which refers to running certain programs near the edge without having to run everything on the cloud. This helps with latency, bandwidth, and other performance issues and compute tasks and information can be allocated across the overall architecture more intelligently. This model has become more popularized with the emergence of IoT computing, where computing can be run on or near the IoT devices near the edge without overloading the cloud.
The next trend that we will cover has to with CSPs providing Anything as a Service or XaaS as it’s usually written. Xaas or Anything as a Service is a general term that refers to various services that are available through Cloud Service Providers. Over the years with the successful adoption of cloud computing and the services that one can get from the various cloud service providers, the world has seen service providers increase the number of such services. We are all familiar with the IaaS (or Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service). In addition to these, other types of services have started to come up recently. So, here we will review some of the services that are becoming increasingly popular.
The first one I will state is the Storage as a Service. With demand for storage constantly growing, organizations are increasingly running into limitations of having their own storage irrespective of the type of cloud environment that they have. For more storage needs, organizations are therefore, turning to their CSPs for their storage needs as through them they are finding better performance, scalability, flexibility, and manageability options for their storage needs.
Another cloud service worth mentioning is that of DBaaS (or Database as a Service). As organizations’ needsrelated to databases is constantly increasing, it’s equally becoming difficult and costly for them to provision, manage, configure, consume, and operate their databases. In such cases, organizations are turning to their CSPs, who alternately provide a better, efficient, cost effective and overall agile way to perform such activities.
A third service we will mention here is that of Analytics as a Service. We know how the field of business Intelligence and Analytics have exploded over the past few years. In line with that trend, CSPs have been maturing in the services that they have been offering and we therefore see many organizations turn to CSPs for their business intelligence and analytics needs.
Finally, in this context, we will also mention AI as a Service. As we have mentioned in the previous episodes on this show, many CSPs such as Amazon, Microsoft, Google, IBM, and others offer a number of AI based services through their public cloud environments and the consumption of such services is on the rise.
Besides these services, there are many others that CSPs provide. We will cover more of these services in other episodes of the CIOtechCentral podcast shows.
The next technology I would like to focus on is the Internet of Things or IoT as it’s more popularly referred to. Although IoT and related technologies have been used in the marketplace for a number of years – may be even couple of decades, their use has skyrocketed more recently due to innovations such as cloud computing, faster Internet, ability to store data collected from those IoT devices and so on. If we look at manufacturing organizations, we will observe that they have been using these and related technologies for more than two decades where they had intelligent devices attached to certain manufacturing and factory assets collecting data on the health of those assets and then making that data available for analysis. However, the miniaturization of devices and other technologies that I just mentioned earlier has enabled IoT to take over the world by the billions and perhaps this number will go even higher. Intelligent devices are making their way in a wide number of business processes. And CIOs challenge is not so much about learning the basics of this technology because as I mentioned earlier, they have been around for a number of years but their challenge is more on how the larger acceptance and adoption of IoT will impact an organization’s technology infrastructure. So, that’s what we will look at today.
Let’s first take a quick look at the potential use cases in the market. If we look at the data from the various research organizations, it’s clear that the market for IoT is growing without bounds. Most electronic devices are now getting connected to the Internet and coming online providing more opportunities for them to communicate with each other. These devices include laptops, household appliances, automobiles, vending machines, and others.
IoT technologies are also finding their use in smart homes, wearables, industrial internet, retail, supply chain, etc. Cities across the world are deploying sensors in cameras, streetlights, and other electronics deployed across the city to track various types of activity. These sensors capture and relay all types of data including audio, video, and others. IoT is also being used in Industrial Internet settings to ensure effective operations of industrial equipment and to facilitate a safe working environment. The industry for Industrial IoT or IIoT is expected to grow exponentially until it makes its way in most industrial operations. These applications are already helping organizations control costs, increase operational efficiency, and facilitating safer industrial operations. This is impacting industries such as Oil and Gas, Healthcare, Electric and Water, Transportation, and others.
Key considerations that CIOs should be looking into have to do with ensuring implementation of the right IoT platform. This obviously is driven by the extent of the expected IoT use within the organizations. The overall IoT platform architecture usually comprises of the IoT devices communicating with some edge devices and then to some type of a gateway. The gateway connects to the cloud where most of the data collected from IoT devices is stored. Depending on the scale of the overall operation, organizations could be looking at storing massive amounts of data so scale should be considered. Some devices, for example, generate and transmit millions of pieces of information to the backend, which then necessitates considerations for data storage and processing at the backend.
Besides scalability, security should be another consideration to ensure end to end protection of all the data. Also, with all this data being generated, chances are that CIOs would want to process and store the data in a way to help them get the right analytics and insights. In that case, a number of system integration issues will also have to be considered that would ensure that all operations from data transmission from IoT devices to storage to processing at the cloud and making it available for analysis works seamlessly.
So, these are some of the issues that organizations will be grappling with this year and next related to IoT. Accordingly, they will be looking at the right solutions to ensure they can address these issues to maximize returns from this technology.
The last technology trend that we will look at today is that of Mixed Reality. Mixed Reality refers to two technologies related to Augmented Reality (or AR as it’s called) and Virtual Reality (VR). Although these technologies are still in the early stages of their development and use, according to Statista (which is a market research organization), the market for Mixed Reality is expected to hit around $4 Billion by 2025.
So, let’s review these two technologies. First, VR or Virtual Reality is a technology that uses computer simulation to provide the user with an experience of being in another location or space. Their common applications to date have been in the area of gaming and similar entertainment. Sophisticated headsets in the market that enable greater immersive experiences allow customers and organizations to explore new opportunities in the enterprise space. VR has potential applications in retail where it allows customers to experience the products or services in multiple dimensions and in greater detail and can help them decide quickly about buying those products and services. VR is also making its way in the education market where customers can go through better experiences for learning and training. The military, too, is making extensive use of VR in training their soldiers for tough terrains and situations.
AR, on the other hand differs from VR in that it provides a blend of real and virtual worlds where users while wearing special headsets can see a projection of a virtual world (such as graphics) onto the real world that they see. It essentially augments the user’s experience to allow them to experience virtual items in physical spaces. So, augmented reality enhances the real life environment and provides immersive experiences. It can be used in the area of Construction and Architecture for example, where structures can be superimposed as 3D visuals onto real space to provide an idea of how they would look like in reality. It can also be used in the field of education where supplementary information can be superimposed on the actual physical learning materials. Microsoft’s HoloLens and Google Glass are examples of products and services that provide both AR and VR capabilities. A number of other AR headsets, apps, glasses, and other devices have started to appear in the market. Their uses are becoming popular in a lot of industries such as manufacturing, retail, energy, and others. These devices can help in the performing of complex surgeries where surgeons can get an enhanced and 3D view of the area being operated on with the devices pointing out various details to help in the surgery.
Shoppers can walk into showrooms and don one of these headsets and see their choice of configurations before making a selection. For example, a shopper can visualize a car with a specific color, and other accessories before making a selection. Within a manufacturing setting, imagine an inspector walking onto a manufacturing floor wearing these devices, where these devices can point out certain processes, people, equipment and other details on the shop floor that can help in their inspection. Its use is becoming even more popular in the area of product design where designers can visualize a number of models and make changes before finalizing the design of the product. Headsets are available to allow users and tourists to visit places virtually and walk on a beach or downtown of a city and experience the city without actually going there or to get a taste of the place before buying tickets. Similarly, tourists can experience hotel rooms and other spaces before making their accommodation reservations.
As a technology leader of your organization, depending on their business of course, you can find its applications in a number of areas. You can also develop your own customized mixed reality experiences using various developer toolkits. Microsoft HoloLens among others provides that type of support. They also support an open API surface and driver model in line with open standards making it easier to build applications for your business that can provide immersive experiences. For Microsoft, they provide support for developing and deploying these applications on their Azure cloud platform where you can build cross-platform, spatially aware mixed reality experiences and connect it with other services as well.
What is Artificial Intelligence or AI? What are the related technologies of AI? And what are some of its basic use cases?
In this episode, we will review AI or Artificial Intelligence and how it’s being used across industries by organizations to boost their performance by doing things smarter, intelligently, better, faster, and cheaper. As this is a large topic, one episode won’t be able to do justice. So, we will begin this episode with the basics of AI, it’s underlying technologies, and their use cases. In later episodes on this show, we will slowly build up to cover more specialized topics such as developing an AI strategy within the organization, types of applications and overall capabilities that organizations can start building and other such topics.
So, let’s get started with the basics of the topic.
In the past few years, especially since about 2016 the interest in AI has picked up enormously as organizations for the first time have started witnessing and observing AI’s real business benefits and its direct impact on an organization’s overall performance. If we look at some of the use cases, we can easily see that a lot of organizations now have successfully deployed AI in various forms and fashions within their organizations. In fact, many industry pundits are saying that organizations, which won’t be using AI fairly soon will be left far behind resulting in the erosion of their competitive positioning. The National Science Foundation clearly states on their website and other channels that “To stay competitive, all companies will, to some extent, have to become AI companies.” So, what that essentially means is that all organizations will have to build certain AI capabilities to be able to compete effectively. For CIOs, this also means that not only will they have to start building AI capabilities within their organizations and to show its applications in real business situations but they should also have a longer-term strategy for making this happen.
So, to see what AI is able to do, consider Xiaoice (spells as xxx), a chatbot released by Microsoft, which can talk and interact with its users. In this chatbot, Microsoft has brought in their best research of all of its AI technologies related to speech recognition, natural language processing, machine and deep learning and more. This Chinese celebrity chatbot is learning so fast from its interactions that young Chinese men and women feel comfortable turning to Xiaoice to talk about their issues, heartbreaks, and daily stresses. Many send it love letters and some have even invited it to dinner in the hopes that it (or she) would show up. According to Microsoft, part of the chatbot’s popularity stems from the way she exhibits high emotional quotient (EQ) by remembering parts of a conversation and following up in later conversations. The chatbot is also a poet and has published a book of poetry and helps Chinese people write poems. To date, it has penned hundreds of millions of poems and is currently hosting a TV morning news show with viewership approaching ONE BILLION viewers. Yes, that’s a billion with a B.
In this context, we should also make it clear that AI does not refer to one system or one type of technology – such as a talking or a working robot. Rather, depending on the AI capability that your organization needs, AI refers to a number of systems and technologies that must be assembled and integrated depending on your organization’s business case. So, if you are trying to process large amounts of data and looking for specific patterns and insights in that data or to derive certain types of sentiments to gauge public opinions, you will need a different set of technologies and systems than let’s say if you were putting together a shop floor automation system involving computer vision and robotics or if you were trying to build an intelligent chatbot such as Xiaoice. Therefore, it’s important to always develop the right AI strategy before jumping in. In other episodes, I will accordingly delve deeper into the need to have an enterprise wide AI strategy that must be driven by your organization’s overall business goals, strategies, and objectives.
To get an idea of how big AI is going to be in the future and its impact on the global economy and organizations, consider the following:
So, we can see that organizations of all types are seeing many benefits in AI and it should be part of every organization’s strategy and CIOs and technology executives should have a clear roadmap for its adoption and to bring relevant benefits to their businesses. Although as we just heard, interest in AI has picked up dramatically, we are still at a stage where organizations have the opportunity to catapult themselves to great successes and use their data to bring out major transformations to their operations and overall performance. However, if they were to wait longer, it may be too late.
So, in this episode, we will answer 3 questions.
After we review the answers to these questions, we will be ready in the next episode to delve into the type of overall AI strategy that organizations need to start adopting AI related technologies in their enterprises and to boost their performance.
So, let’s get started and address the first question, which is ‘What is Artificial Intelligence?’
There are lots of definitions of AI or Artificial Intelligence, with each providing a unique dimension of what AI can do. To put it simply, an AI program is able to learn and do more things with time without being explicitly programmed. And that’s where the term ML came from. It’s the ability of machines to learn and do more things and help machines achieve Intelligence or as we would like to call it ‘Artificial Intelligence’. That’s what distinguishes AI software from regular software.
AI also refers to a set of technologies in the computer science domain that make computers and machines perform human like activities such as those related to visual perception, recognizing patterns, speech recognition, continuous learning from experience, problem solving, reasoning, making predictions, and others. In some cases, these machines end up doing those tasks even better than humans. For example, using these technologies computers can identify patterns in vast stores of knowledge and information very quickly something that a human mind isn’t able to do. But in other cases, these technologies are still quite far behind, especially when it comes to visual perception, reasoning, learning, and so on.
But regardless, when applied and used adequately, AI and related technologies can help organizations achieve a manifold improvement in their business processes and overall performance.
AI technologies work by a using combination of AI algorithms, lots of data, programming models, and hardware acceleration. Since AI works with a lot of data and the fact that it needs these sophisticated neural network algorithms working in the background executing various models, it increases the computation and processing needs, thus necessitating advanced hardware as well. So, for example, if the given AI problem involves a lot of image processing then GPUs can be used to help accelerate the processing of tasks. As we know, GPUs stand for Graphics Processing Unit and are dedicated hardware units to perform graphics only functions.
So, with the development of these human like functions in software and hardware, we see full-fledged intelligent solutions and machines surface over the past few years, which are helping organizations solve complex problems. For example, we see the implementation of virtual chatbots, which automatically and seamlessly respond to customer and user inquiries thus freeing up agents to focus on answering more difficult types of questions. We also see manufacturing plants collect a lot of data related to their ongoing operations and then using AI technologies like ML and DL to look for specific patterns in that data and to derive useful business insights that are then being used to improve the overall operations by reducing wasteful steps, optimizing processes, reducing costs, and so on.
A quick note about chatbots as their use is constantly increasing. Chatbots are software help agents that allow users to have intelligent conversation with the system, which help the users with answering questions, filling forms, taking orders, ordering food, and so on. In many cases, the person may not even know that they are talking to a machine or software. A number of organizations have installed chatbots on their websites including UPS, Macys, and others helping users with their queries and helping them carry out more advanced tasks. Chatbots can interact intelligently with the users who may ask questions such as “where can I order dinner, or where can I drop my package, where can I buy some xyz perfume, etc.
Now, we will move on to answering the second topic, which has to do with the different technologies of AI. AI is usually associated with a number of key technologies. They include but are not limited to Machine Learning (ML), Natural Language Processing (NLP), Deep Learning, machine perception and even robotics. Again, although these technologies have been available for some time, they have become more accessible and practical for use now due to the world’s new found capabilities to process large data and availability of massive computing power through the cloud platform.
Machine Learning – Let’s look at ML first. ML is one of the most important and foundational technologies underlying the field of Artificial Intelligence, and refers to the ability of a machine to behave not through specific programmed instructions (the way computers and software usually work) but through its own learning. Normally, the program starts with a generic algorithm but then builds on the data that it’s provided to build its logic. The basic algorithm is programmed based on a mathematical representation of the problem that one is trying to solve and then uses available data to train the overall logic of the program. ML usually works on structured data. Essentially, this means feeding the algorithm the data, which is structured and labeled and then it uses that data to learn and improve on its performance.
Typical applications of ML include image recognition where a program becomes better at detecting certain objects such as faces or others. It can be used in existing applications such as E-mails. AI enabled plugins exist today, for example, that allow for E-mail filtering based on certain criteria. ML is also being widely used in medical diagnosis where a program scans numerous X-rays or CT-scans and helps the physicians in the diagnosis of a number of ailments. And there are numerous such examples.
Deep Learning is another technology related to AI. DL can be considered a specialized form of ML. DL algorithms are usually used for more sophisticated and complex cases in AI. DL algorithms work by mimicking the human brain, which is made of neurons and where thinking and reasoning works across various layers of neural networks. Similar to those concepts, in DL, a set of multiple algorithms working at different layers, act as Artificial Neural Networks to interpret the data that they feed on. With DL, a software program improves itself through ANN (Artificial Neural Networks). Data is processed through each layer of artificial neurons and progresses through processing to discover more features and patterns from the data. So, in summary, DL is inspired by neural networks of the brain where the brain’s network works to represent concepts, relationships, see and understand the context, and represent the vast amount of data thrown to it.
To illustrate an example of Deep Learning, some of us may have heard how Google’s AlphaGo system, which was able to beat humans at the game called Go. Go is an abstract strategy game that is played by two players in which the idea is to capture more territory on a board. By using DL techniques, the machine was able to learn by first playing many games with many players and similar to humans kept learning from its experiences eventually beating many game masters.
Natural Language Processing is another form of AI related technology. This technology take human generated text and renders it in machine form. For example, it takes human generated text, detects nouns such as people, places, things, relationships between those entities, detects sentiments and emotions in that text, extracts certain keywords from the text, categorizes information, and so on. One can build such capabilities by either programming all the rules in software or using machine learning.
These are only some technologies related to AI. So, depending on your specific use cases, your applications may use ML, DL, along with other technologies such as image recognition, Natural Language Processing, Robotics, and others to give you a full-fledged AI system.
Next, we will address the third and final topic of this episode, which is to review the specific use cases of AI to give us an indication of how this technology can benefit organizations. So, here are some of the ways that AI can benefit organizations.
These were only some of the use cases related to AI but the point was to provide an idea on how AI can dramatically help an organization advance its goals. Deciding where to start applying is a major strategic decision. We will review those strategic decisions in another episode on CIOtechCentral’s podcast.
So, here are a key points that I want you to take away from today’s episode.
Welcome to CIOtechCentral, a podcast that focuses on digital and information technologies. This is the first and introductory episode meant to give you an idea of the topics that will be covered on this podcast.
On this podcast, we will cover the latest digital and Information Technology trends, and their applications within organizations and enterprises of all types and how CIOs and CTOs can advance their organizations’ agendas and strategies using these technologies to position themselves competitively in the marketplace.
Some of the topics that we will cover in this podcast will include the following:
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Thanks and hope to see you on the future episodes of this show.
This post discusses the six cloud migration strategies (Based on Gartner and Amazon Methodologies).
In today’s episode I will cover the 6 fundamental migration strategies that organizations have at their disposal when migrating to the cloud. These strategies are based on Gartner’s research and also on the work that Amazon has done in helping their customers to migrate to the cloud. Both Gartner and Amazon discuss these extensively on their blogs and websites as well. As a technology executive, if you are still in the early phases of your cloud migration journey, a review of these strategies can help you in developing the right mental models that in turn can guide you to develop your own digital transformation journey.
So, let’s get started.
One of the key phases that every organization goes through when considering migrating its legacy systems to the cloud is that of a discovery process. In this phase, the organization essentially takes a detailed inventory of its systems and then decides one by one on the effort and cost required to do the migration. This step is usually done by keeping the overall business case and objectives of the migration in perspective. For each of the applications and systems in its inventory, the organization may decide on a specific migration strategy or approach. We will discuss those strategies next.
Re-hosting – The first strategy is that of re-hosting. This is also referred to as lift and shift and involves migrating a system or application as is to the new cloud environment. The focus is to make as few changes to the underlying system as possible. During the discovery process of the migration planning exercise, systems that qualify for such a migration are usually considered quick-wins as they can be migrated with minimal cost and effort. However, as the application and system usually involves a simple lift and shift, such as system isn’t expected to utilize the cloud native features and thus isn’t optimized to run in a cloud environment. Thus depending on the system, it may even be more expensive to run the new migrated system on the cloud. These types of issues should be considered before categorizing a system for such type of migration.
Refactoring – Refactoring is the second migration strategy and falls on the other extreme of the migration effort because it requires a complete change and reengineering of the system or application logic to fully make use of all the cloud features. When complete, however, this application is fully optimized to utilize cloud native features. So, even though the cost and effort required for this migration can be quite high, in the long run this approach can be efficient and cost effective because the application is reengineered to make use of the cloud native features. A typical example of refactoring is changing a mainframe based monolithic application from its current form to a microservices based architecture. When categorizing an application as refactoring, the business should perform a detailed business case analysis to justify the investment of the cost, effort and a potential business impact and also to ensure that other alternatives are considered as well.
Replatforming – This type of migration is similar to re-hosting but requires few changes to the application. Amazon’s AWS team refers to this approach as lift-tinker-and shift. Even though this approach closely resembles that of re-hosting, it’s categorized differently simply because it requires some changes. For example, in doing such migrations, an organization may plug its application to a new database system that’s on the cloud or change its web server from a proprietary version such as Weblogic to Apache Tomcat, which is an open source based web server. So, for planning purposes it’s important to categorize it as such. Obviously, if a system or application is going to be changed to make even slight changes, it may need to be put through more thorough re-testing processes.
Repurchasing – This migration strategy entails essentially switching the legacy application in favor of a new but similar application on the cloud. Migrating to a SaaS based system would be an example of such a migration where an organization may decide to migrate from its legacy financial system to a SaaS based financial ERP system.
Retire – The fifth strategy is about retiring systems and applications that an organization no longer needs. During the discovery process, an organization may find applications as part of its inventory that are no longer actively used or have limited use. In such cases, those types of applications may be considered for retirement and users of those systems (if any) can be provided other alternatives.
Retain – In some cases, the organization may decide not to touch certain applications and systems and to postpone their migration for later in the future. This may be either that the applications are too critical to be touched at that point in time or require a more thorough business case analysis. Either way, it’s normal for organizations to not touch some applications and systems during their cloud migration efforts. However, in certain cases such as a data center migration, organizations may not have a choice and will have to consider one of the earlier described strategies.
To conclude, although the strategies that I have covered address most of the common cloud migration scenarios, as a technology executive you can devise other categories based on your business needs. Defining these migration categories and their criteria upfront can be a major and helpful step to aid in the migration of one’s legacy systems to the cloud.
Hope this session was useful. Again, to ensure that you don’t miss any future episodes, do subscribe to this channel.
In this podcast, we will introduce the concept of PPM (Project Portfolio Management), its benefits to the enterprise, key PPM functions, and the PPM tools used by enterprises to implement the PPM framework.
(Take online course on PMP Certification)
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