Machine Learning Cloud Provider Overview

Digital. Cloud. AI-driven. – The Magic Triangle

Digital Enterprise Evolution

The Digital Evolution – Step by Step

A Brief History of Artificial Intelligence

I recently had an interview with Los Angeles-based technology journalist Tom Samiljan on the current state of artificial intelligence (AI), in particular from a marketing professional perspective.

What types of tasks are machines best suited for vs humans? Conversely, what are humans better at? How does this translate into a marketing context?

There is an interesting contrary between humans and machines. Humans are typically better in doing things where machines struggle and vice versa. Observe how a robot moves, not very human-like. And all the things that make us human, like show feelings, being social, decide with our gut instinct etc. On the contrary, machines are better in processing things. A machine will beat a human any time when it is about to search through a lot of information or quickly identify anomalies in a manufacturing plant.

How will marketing professionals and AI/machines work together in the future? What jobs will be replaced and what jobs will be augmented/aided by AI?

AI offers marketing professionals the capabilities to get a much better understanding of their customers. By connecting various data sources, created by customers and analyzing the coherences, an AI will extract more insights and thus let marketing professionals engage even deeper with customers. Leveraging AI also means to serve the customer based on analyzing his data, location and behaviour with a better experience via a digital touchpoint at the point of sales or even better at the point of presence. Regarding jobs, not AI is replacing jobs but automation and the more unique the job skills are the less it is replaceable by automation.

What kinds of skills and insight/intelligence will marketing professionals need in the future to work with AI and machines?

Creativity and a better understanding of what types of data can be used to let it analyzed by an AI. Creativity is crucial since AI allows to engage in a more fancy way with customers. Today we still use our smartphones and typing in our commands. However, Alexa and Siri already show us a lot of potential for customer interaction (eg. enhancing user experience). And even if Jarvis (Iron Man AI) is still far away. That’s the way to go.

What sorts of tasks or solutions is AI offering marketing professionals already and what is still missing that’s needed in the future?

Chatbots via Facebook Messenger already try to enhance the user experience by giving the customer the expression that there is a call center agent 7/24/365 exclusively available. Personal virtual assistants like Amazon Echo (Alexa) or Siri are the next evolutionary step to equip a chatbot with a human-like voice. Netflix and Pandora are leveraging AI techniques by recommending movies respectively songs based on the own watching/ listing behaviour but also considers the network of friends etc.

Where do you see the role of AI in marketing in one year, 5 years, and 10 years?

AI will not only become an essential part for marketing but for any single department within an organisation. However, the more various data an AI gets and the more it is able to connect these data sources, the smarter it gets. In doing so, it is important that there is not an AI exclusively for the marketing department but a general AI that is connected to every single part of the organisation having access to any data source of a company. This is part of an AI journey organisations have to walk through to become an AI-enabled enterprise.

AI-defined Infrastructure in a Nutshell

In the first episode of “AnalystCast”, Rene Buest (Analyst POV) is speaking with Holger Mueller (Constellation Research) mainly about the differences of digitization between the United States and Europe. In doing so, they are discussing the state of digitization and what impact emerging technologies like artificial intelligence have on the strategies of organizations worldwide.

Artificial Intelligence Technology Landscape

The Hotel California Effect – Big Internet Companies Build Their Own General AIs.

The Four Pillars of Building a General AI.

The Four Pillars of Artificial Intelligence.

State-Of-The-Art IT Stack: Cloud Occupies Center Stage.

In November 2017, I was interviewed by Rosalie Bartlett for her tech podcast series Rozee. We discussed how Artificial Intelligence (AI) has a significant impact on business process automation.

In November 2017, I was interviewed by Judith Ellis from dotmagazine, which is powered by eco – Association of the Internet Industry. In this podcast we are talking about digitization, the significance of the cloud, the advantages of Artificial Intelligence (AI) in enterprise solutions and the power of digital correlation.

Podcast Transcript

DOTMAGAZINE: So looking firstly at AI for Internet infrastructure, how is Artificial Intelligence used to protect networks and Internet infrastructure from disturbances?

BUEST: The good thing, or the good use case for AI for protecting the Internet infrastructure is, since there are a lot of data that are created during the use of Internet infrastructure, you can basically use AI perfectly for analyzing the data, the log files, to see what is going on right now in the Internet or in your typical infrastructure. And you can act on that. So, if you see that something is happening which is actually not normal, so if you see anomalies, you can react there on time and start prevention on this. This is also where AI perfectly comes into the place right now, because everyone is talking about AI replacing humans, but right now it’s more about analyzing the data, making suggestions out of it, making recommendations, but mostly analyzing what is going on, and understanding basically what you can do in order to prevent things or to help people know what they can do. It’s more like a buddy approach, meaning also that all these security administrators are not necessarily going to lose their jobs, but they can get a better understanding of what is happening and then can react to this.

DOT: So what about when we look at its application in cloud computing? How is AI evolving there?

BUEST: When you’re looking at the big public cloud providers right now, like Amazon Web Services, Microsoft with Azure, or a Google Cloud platform, you see that they started very early to introduce AI services like machine learning, or analytics services, or they also have services for their virtual private assistants – the Google Assistant, or Amazon Echo, or Microsoft’s Cortana. And my opinion is that Artificial Intelligence in the cloud is actually the logical development, because the cloud is there for having good access or easy access to infrastructure when you need it, to use it on demand. And the AI application around this is more to get a better understanding of the customer based on their past interactions and also their behavior.

This means what you can see in the market right now is that these digital masterminds, I would call them – like companies who have been digital for a very long time now – embraced cloud computing a very long time ago. They are actually the pioneers when it comes to cloud computing – like Netflix, or Expedia, or Spotify – and they also started very early to engage with AI-related services. They use cloud computing for better efficiency, or to change their business model. I mean, as you know, Netflix started in 1997 shipping DVDs, and now they have the biggest market share when it comes to video streaming. But they are also leveraging Artificial Intelligence in the background by giving recommendations to their customers, based on past interactions or other viewing behavior, for example.

So this is what you see. It’s what I would call the “magic triangle” – companies who are digital are utilizing the cloud to a big extent and they also now have been using AI-related services for a very long time to give their customers a better experience, to understand their customers in a better way, and therefore they are also actually on the leading edge right now when it comes to the use of technology.

DOT: Thinking about cloud computing as being one fundamental tool for enterprises, what other applications are developing through Artificial Intelligence to support enterprises?

BUEST: What we see right now is, especially also in our customer base, that enterprises are introducing AI for autonomous process automation. This is not only about developing new products or services. This is more about driving efficiency inside their organizations – meaning that an AI is basically able to run and operate any process that exists inside an organization. Again, this is not only about setting people free. This is about, based on high automation rates, giving people more time and also saving money. And we are of the opinion that when you have high automation rates, you save money, you give your people more time – and more money plus more time leads to innovation.

What we see right now is that enterprises are introducing autonomous process-automation based on Artificial Intelligence, firstly at the IT level, in the IT operations. Because in the end, when it’s about the data you also need to analyze, where is the data ending or stored in the end? It’s in the IT environment. And if you are able to give the Artificial Intelligence technology access to the data at an early stage so that the AI technology can get access to the applications, the data stores, the databases, everything within the IT operations, it’s also possible afterwards, or easier, to introduce an AI to any business process you have inside an organization.

DOT: How do you envisage the development of innovative business models based on AI?

BUEST: So what we see is this part of the AI-driven world that we are starting to live in. And this is about a better understanding of the customer. As I said, this is based on the past interactions and their behavior, but it’s also about giving their customers a better, a fancier experience of the changing customer interaction from keyboard entry to the speech control. Because right now we are still typing when we are searching for something, or when we are starting an application on our iPhone or Android device. But over time, and it has already started, we are also speaking with our devices, we’re saying Alexa, or hey Siri, or those kind of things.

This also has a deep impact on how applications will be developed in the future. So the developers also have to understand, when they are creating an application, that the way the customer or the user is interacting with this application is completely different. And this is also about getting a deeper engagement through, for example, the smart virtual assistants enhancing the customer experience. So that you can give your customers a 24/7, basically also 365, support by having an Artificial Intelligence introduced in your customer engagement lifecycle. So, even if you, as a smaller company, are not able to have a call center that is available over the entire day, week, month or year, you can introduce an AI at least to give answers to the most asked questions. And in the end, it’s about the customer knowing, OK, there is someone I can basically talk to and get an answer. Maybe not a 100 percent qualified answer in the beginning, but at least there’s someone who’s taking care of me.

It’s all about customized products and service offerings aligned with the customer’s needs. In the beginning of this digital world, we had mass customization, and now it’s more about the customization of individuals, because we are basically providing so many data and information to our providers or to the companies we are buying goods from, and they can actually analyze everything and also deliver us products highly customized, based on our needs and what we actually really want. It’s also about prediction – by analyzing data interactions and the behavior from the past, and especially in real time. This actually comes along with these customized products and – as I already said – you also have a more conversational experience with these smart personal systems, or with the bots. And in the end, it’s about augmentation of the existing and new products, services, applications, and processes, so that existing things that are already there are becoming more intelligent by having maybe more sensors that can be analyzed, or the data that is coming out of these sensors can be analyzed. And then, in the end, this gives us a better user experience and better customer experience.

DOT: We’ve heard a lot in the media recently about the threat of AI. I get the feeling that you don’t see Artificial Intelligence as a threat. Where do you stand on the evolution of AI?

BUEST: I’m not afraid that the machines are going to kill us in the future. I mean, you don’t need Artificial Intelligence in order to build robots who run around and kill everybody. You just need a script, basically, a script which says randomly shoot around. So, I don’t think Artificial Intelligence is a threat to kill us.

It’s the same with losing jobs. You should think about this: It’s not AI, in the end, that is actually killing the jobs. It’s automation. But to be honest, the weavers are also not weaving anymore. It’s about this industrial revolution we have. Of course, certain people will lose their jobs, but jobs that are very repetitive, kind of boring jobs, where you have to do things on a daily basis, and you’re doing it every time, and you’re repeating it, repeating it, and repeating it. Meaning that the more individual your skills are, you actually would not lose your job – if you have very high skills which are very individual. And what we also see is that there are also lots of jobs where gut feeling is very important, or where empathy is very important. It’s also something where machines actually cannot replace us.

When it comes to the ethical part, I just recently got asked these questions when I was on stage: How should a car decide, if there is a human being on the street, or is there is an older man on the street and a young boy on the street? How should an AI decide on this? And actually, I don’t have an answer to this, because this is a question that the entire society should think about. And then there was an answer from the audience, who said well, if there’s still no answer for this question, no one will use this car. And I disagree 100 percent on this. People will still use it because they actually don’t think about it. And I’m not sure if anyone will ever have an answer to this. So how should the machine decide? In the very end, it’s still that we program these machines, and we can influence how these machines are also acting. So it’s still up to the human being who is developing these machines based on Artificial Intelligence.

DOT: Do you have anything else you would like to add?

BUEST: Maybe one thing. My advice is actually that one should not only think about a certain technology. It’s actually not only about cloud. It’s not only about Artificial Intelligence. It’s not only about blockchain, or IoT. It’s about digital correlation. If you think about it, everything actually is connected together. Meaning without cloud computing, for example, AI would not have this hype we have right now. And also without fog computing, IoT would also not have the success it’s going to have in the future. So it’s not only about single technologies or single concepts. It’s about the big picture and every organization, every decision maker, has to think about this.

Extracted Statements from the Podcast

• Security administrators can get a better understanding of what is happening in the network.
• The “magic triangle” – companies who are digital utilize the cloud and now also use AI-related services to give their customers a better experience & to understand their customers better.
• When you have high automation rates, you save money, you give your people more time – and more money plus more time leads to innovation.
• In the beginning of this digital world, we had mass customization, and now it’s more about the customization of individuals.
• There are lots of jobs where gut feeling is very important, or where empathy is very important – something where machines actually cannot replace us.
• AI & ethics: We program these machines, and we can influence how these machines are also acting. So it’s still up to the human being.
• If you think about it, everything actually is connected together.

The World of IT Automation.

Today, the artificial intelligence (AI) hype wouldn’t exist without cloud computing. Only the easy access to cloud-based innovative AI services (machine learning etc.) and the necessary and fast available computing power enable the developments of novel “intelligent” products, services and business models. At the same time, AI services ensure growth of public cloud providers like Amazon Web Services, Microsoft and Google. Thus, one can observe a “Cloud-AI interdependency”.

After more than 10 years, cloud computing has evolved into a fertile business for providers such as Amazon Web Services or Microsoft. However, competition is getting stronger from laggards like Google and Alibaba. And with the massive and ongoing introduction of AI-related cloud services, providers have increased the competitive pressure themselves, in order to raise attractiveness among their customers.

The Cloud Backs AI and Vice Versa

To build and operate powerful and highly-scalable AI systems is an expensive matter for companies of any size. Eventually, training algorithms and operating the corresponding analytics systems afterwards need oodles of computing power. Providing the necessary computing power in an accurate amount and on time via the own basement, server room or data center is impossible. Computing power that afterwards is not required anymore.

Looking into the spheres of Amazon, Microsoft or Google, all three providers built up an enormous amount of computing power in recent years and equally own a big stake of the 40 billion USD cloud computing industry. For all of them, expanding their portfolios with AI services is the next logical step in the cloud. On one side, developing AI applications respectively the intelligent enhancement of existing applications requires easy access to computing power, data, connectivity and additive platform services. Otherwise, it is necessary to obtain attractiveness among existing customers and to win new customers. Both are looking for accessible solutions to integrate AI into their applications and business models.

Amazon Web Services

Amazon Web Services (AWS) is not only the cloud pioneer and innovation leader, but still by far market leader of the worldwide public cloud market. Right now, AWS is the leading cloud environment for developing as well as deploying cloud and AI application, due to its scalability and comprehensive set of platform services. Among other announcements, AWS presented Amazon Cloud 9 (acquisition of Cloud9 IDE Inc. in July 2016) at the recent re:Invent summit. A cloud-based development environment that is directly integrated into AWS cloud platform to develop cloud-native applications. Moreover, AWS announced six machine learning as a service (MLaaS) services, including a video analysis service as well as a NLP service and a translation service. In addition, AWS offers MXNet, Lex, Rekognition and SageMaker, powerful services for the development of AI applications. SageMaker, in particular, attracts attention, since it helps to control the entire lifecycle of machine learning applications.

However, as with all cloud services, AWS pursues the lock-in approach with AI-related services as well. All AI services are tightly meshed with AWS’ environment to make sure that AWS remains the operating platform after the development of an AI solution.
Amazon also sticks to its yet successful strategy. After Amazon made the technologies behind its massive scalable ecommerce platform publicly available as a service via AWS, technologies behind Alexa, for example, has followed to help customers integrate own chatbots or voice assistants into their applications.

Microsoft

Microsoft has access to a broad customer base in the business environment. This along with a broad portfolio of cloud and AI services offer basically good preconditions to also establish oneself as a leading AI market player. Particularly because of the comprehensive offering of productivity and business process solutions, Microsoft could be high on the agenda of enterprise customer.

Microsoft sticks deep in the middle of digital ecosystems of companies worldwide with products like Windows, Office 365 or Dynamics 365. And that is exactly the point where the data exist respectively the dataflows happen that could be used to train machine learning algorithms and build neural networks. Microsoft Azure is the central hub where everything runs together and provides the necessary cloud-based AI services to execute a company’s AI strategy.

Google

In the cloud, Google is still behind AWS and Microsoft. However, AI could become the game changer. Comparing today’s Google AI services portfolio with AWS and Microsoft you can see that Google is the clear laggard among the innovative provider of public cloud and AI services. This is astounding if you consider that Google invested USD 3.9 billion in AI so far. Compared to the competition, Amazon has invested USD 871 million and Microsoft only USD 690 million. Google simply lacks in consistent execution.

But! Google already has over 1 million AI user (mainly through the acquisition of data science community “Kaggle”) and owns a lot of AI know-how (among others due to the acquisition of “DeepMind”). Moreover, among developers Google is considered as the most powerful AI platform with the most advanced AI tools. Furthermore, TensorFlow is the leading AI engine and for developers the most important AI platform, which serves as the foundation of numerous AI projects. In addition, Google has developed its own Tensor Processing Units (TPUs) that are specifically adapted for the use with TensorFlow. Recently, Google announced Cloud AutoML, a MLaaS that addresses unexperienced machine learning developer, to help creating deep learning models.

And if you keep in mind where Google via Android OS has its fingers in the pie (e.g. Smartphones, home appliances, smart home or cars) the potential of AI services running on the Google Cloud Platform is clearly visible. The only downer is that Google is still only able to serve developers. The tie-breaking access to enterprise customers, something that Microsoft owns, is still missing.

AI Becomes the Game Changer in the Public Cloud

The AI platform and services market is still at an early stage. But in line with the increasing demand to serve their customers with intelligent products and services, companies are going to proceed to search for the necessary technologies and support. And it’s a fact that only the easy access to cloud-based AI services as well as the necessary and fast accessible computing power is imperative for developing novel “intelligent” products, services and business models. Hence, for enterprises it doesn’t make any sense to build in-house AI systems since it is nearly impossible to operate them in a performant and scalable way. Moreover, it is important not to underestimate the access to globally distributed devices and data that has to be analyzed. Only globally scalable and well-connected cloud platforms are able to achieve this.
For providers, AI could become the game changer in the public cloud. After AWS and Microsoft started leading the pack, Google wasn’t able to significantly play catch-up. However, Google’s AI portfolio could make a difference. TensorFlow, particularly and its popularity among developers could play into Google’s hands. But AWS and Microsoft are beware of it and act together against this. “Gluon” is an open source deep learning library both companies have developed together, which looks quite similar to TensorFlow. In addition, AWS and Microsoft provide a broad range of AI engines (frameworks) rather than just TensorFlow.

It is doubtful that AI services are enough for Google to catch up with AWS. But Microsoft could quickly feel the competition. For Microsoft it is crucial, how fast the provider is able to convince its enterprise customer of its AI services portfolio. And at the same time to convey how important other Microsoft products (e.g. Azure IoT) are and to consider them for the AI strategy. AWS is going to stick to its dual strategy and focus on developers as well as enterprise customers and will still lead the public cloud market. AWS will be the home for all those who solely do not want to harness TensorFlow – in particular cloud-native AI users. And not to forget the large customer base that is innovation oriented and is aware of the benefits of AI services.

In November 2017, I met Christian Lorentz from NetApp during Cloud Expo in Frankfurt. Based on my experience, he wanted to find out if artificial intelligence (AI) is eating the data infrastructure.

Many big industry names are saying that AI is eating software. Is AI also eating the hardware?

Rene Buest: Perhaps a better way of looking at this one is asking: Is AI eating the IT admins? And the answer is yes, and no. Yes, AI is going to transform the jobs of today’s IT admins, because AI is going to automate a lot of tasks which have been done manually in the past. As an example, finding an anomaly in a multitude of log files is something that an AI systems can do better and faster than a human administrator. However, IT admins will be able to focus on tasks which are more valuable for the business. Instead of just keeping the lights on, IT professionals will be designing new solutions, looking into new trends, and bringing creative technical approaches into the business world. This is certainly a positive development.

What is the impact of AI on traditional infrastructure? How about on data?

Rene Buest: The next-generation infrastructure needs to be more autonomous, in order to support AI – I am talking about an AI-defined Infrastructure. Server-less infrastructure and containers, for example, are two of the technologies which it is imperative to make ”AI-ready”. With regards to data, AI does not only need a large quantity of data, but it does require adequate quality data. What I mean with that is, that good quality data is necessary to build a process in an AI system, as is the experience of specialists. Without the right data and expertise, AI cannot reliably learn and, ultimately, act autonomously in the most positive way.

How can enterprises and IT organizations prepare themselves to have an AI-ready data infrastructure?

Rene Buest: It is very easy. First of all, an organization needs to accept a continuous data flow as a foundation for its future strategies. This includes getting rid of data silos. To do this, organizations need to invest in open systems and open APIs. AI needs easy and secure access to data, independent of its location and format. Once this is ensured, AI then needs to be implemented to automate IT operations. Because every single piece of data that exists or is created inside an organization ends up being stored in the IT environment. Afterwards this can be expanded to other business processes by using knowledge of the organization gathered through IT automation , ultimately making more processes autonomous. Eventually, an organization could think about data-driven processes by using data, knowledge, experience and AI to generate outcome based processes like new business models, services or products. This is the execution path to an AI-enabled enterprise.

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The interview with Christian Lorentz was also published under “Is AI Eating Data Infrastructure? An Interview with Rene Buest“.

History Has Shown That Digitization Is More Evolution Than Revolution.

Some people say one should not look behind. The past is gone, and you cannot change it anymore. Well, that’s true and it is indeed the right attitude to look into the future because this is the only place where you can change things and introduce innovation. However, I am on the opinion that it is important to consider what has happened in the past. It is significant to understand why things happened and to learn from it, in order to avoid and overcome mistakes from others and thus make things better in the future.

As a technology analyst, I am frequently in touch with CEOs, CIOs and other decisions makers from organizations of any size. During advisory sessions and workshops, digitization is the hot topic to discuss for the last 5+ years. However, after a short period of time, the majority of decision makers start defending themselves, claiming their organization is already digital for quite some time. They are digital by providing their employees with software applications, desktop computers, smartphones, tablet computers or running networks of servers and so on. Well, on the one hand they are right. On the other hand, they express that they did not understand what digitization really means today. And thus, why companies like Amazon, Google, Alibaba and so on are out of their sight and far ahead of the competition. However, the success of Google, Amazon, etc., are no overnight success stories but took at least 10 years or even longer. And this is why it is so important to consider what has happened in the past. Hint: Amazon was only a web shop some years ago. Now, it has multiple business units and is still growing by experiments and just tries if something potentially works.

Considering that in my opinion the term “digital transformation” is misleading. It is a digital evolution; every organization worldwide is faced with for already a long time. Based on my ongoing experiences above, I decided to write this piece to unveil the broken mindset of the old economy, by getting a wrong understanding of digitization and underestimating the impact of information technology (IT) and digital technologies for the business.

All in all, digitization is nothing new and has changed its face over the last decades. Digitization today is completely different from digitization in the 1980s or the 1950s when the first microprocessor hit the market. However, the digital evolution has every organization over a barrel. And each company needs the necessary fundamentals in order to adapt this ongoing progress and achieve change inside the entire organization.

It’s a Digital Evolution, not a Transformation

“Digitization is changing everything.” This is a statement you might have heard sufficiently the last months and years. A statement that is absolutely wrong. It reflects a false attitude and misunderstanding of the market and its people who claim this statement. It is wrong because digitization already has changed everything!

Exactly this perception of digitization leads to the misconception by comparing digitization with the popular used but misleading term “digital transformation”. However, it might be because over the last three decades, IT departments worldwide have developed, introduced, updated and detached vast quantities of IT systems. They have digitized their organizations by introducing all kinds of IT solutions, like ERP and CRM systems, office solutions or self-developed applications. The IT department was the maintenance crew of the digital engine room, no colleague wanted to deal with respectively was able to deal with. Thus, IT solutions have simply been used by many people but obviously not considered as digital technologies.

Today, everything is believed to be completely different. Due to “consumerization of IT” and the easy access to IT resources, everyone is able to use e.g. an iPhone or get access to any kind of application via software as a service (SaaS). Hence, thanks to “consumerization of IT” digital technologies became more and more ubiquitous and came onto the radar of people who normally only have been in touch with digital technologies at work. Eventually, digital systems have been introduced and maintained over the last 30 years. Indeed, the term “digital transformation” is a little bit confusing, especially if someone is working in IT for decades and has seen all of its developments.

The term digital transformation is misleading because digitization is a continuous development process and must be considered as a progress. It is digital evolution!

The Digital Evolution, Step by Step

Digitization is hassling leadership teams by letting them challenge the existing business model and reinvent oneself to confront the competition with a digitized organization. Apart from that the leadership’s task is to develop a unique digital agenda they can leverage to highly interconnect with customers, partners and suppliers and therefore redefine all necessary processes, understanding them as an essential part of a digital enterprise. In doing so, it is important to put the customer tenaciously into focus of interest. Based on smart products and AI technologies, organizations are able to get a better understanding of their target groups and to spot the current needs and thus act proactively. These include the development of “data driven apps” to raise as much qualitative data as possible and to use it at the scene of event (digital touchpoint) in order to increase the digital user experience for customers as well as partners and suppliers.

However, talking digitization means talking about a digital evolution and not a transformation. We are within a digitization process that has started around 50 years ago when Intel introduced its “Intel 4004” processor in series and SAP, Microsoft or Apple were founded. In the meantime, you can find PCs or Macs in every office and the use of mobile devices is continuously growing. However, even after these 50 years not every process has been digitized so far and doing things for the sake of doing things is simply wrong.
Bottom line, we are already digital for 50 years. For example, without packet switching networks (introduced between 1965 – 1969), the Arpanet (introduced in 1969) and TCP/IP (both introduced in 1974), the Internet (introduced in 1990) wouldn’t exist. Without the Internet, Amazon (1994), Google (1998) and Co. wouldn’t exist. And without these companies, technologies like virtualization (introduced in 1964) or XEN (2003) and other Internet-related technologies, the cloud wouldn’t exist. And without the cloud the new hype around artificial intelligence (AI) wouldn’t exist. Because only the easy access to cloud-based innovative AI services (machine learning etc.) and the necessary and fast available computing power enable the developments of novel “intelligent” products, services and business models. However, at the same time, AI services ensure growth of public cloud providers. Thus, one can observe a “cloud-AI interdependency.”

On a final note, Netflix is one of the prime examples of the digital evolution. Netflix was founded in 1997 as a DVD shipping company (using the Internet) exclusively for US customers. But shortly after Amazon introduced its cloud business Amazon Web Services (Amazon EC2 in 2006), Netflix launched its streaming service in 2007 and thus changed its business model. This transition was a major evolutionary step. First of all, Netflix not only understood the growing power of the Internet and thus to reach a much bigger customer base by serving a global audience. But most importantly, Netflix saw the technological potential of the cloud to execute this huge business opportunity.

Evolution means to be able to adapt to ever-changing living conditions. It’s about mutability. Or in other words: eat or be eaten. This is exactly what the nature is showing us.

Netflix has changed because the environment around the company has started to change. For example, many companies have only used the Internet e.g. for hosting a website (using it to market their existing products). But no one from the pre-Internet era used the Internet early to develop new things by using the Internet. Only a few respectively only new companies like Google, Amazon, Spotify or Netflix saw the potential of the Internet and developed the Internet further by introducing new ideas, offerings and services.

Netflix is a good example showing that evolution always wins. But this includes to change the own mindset.

The Digital Enterprise Evolution

The digital enterprise has quite a long history. As a matter of fact, everything started in the 1950s – around 70 years ago. To get a better understanding of the digital enterprise evolution it’s easier to cluster this progress into four phases, starting in 1950.

Pre-Digital Enterprise

The decades from 1950 until 1969 can be described as Pre-Digital Enterprise era. These were the years of all the necessary groundwork and fundamental research and development for building the digital enterprise. In the course of this epoch the first high-level programming languages like FORTRAN, COBOL, BASIC and the compiler-compiler have been developed. The UNIVAC hit the market, which was the first commercially successful electronic computer and is regarded as the first general purpose computer. The introduction of the UNIVAC marked the ultimate beginning of the computer era. Terminal systems that were organizations-wide connected to a central computing system (mainframe) are introduced that used a timesharing concept to get equal access to computing power. The development of the UNIX operating system started, which was the foundation for Linux that powers 36.9 percent of today’s websites worldwide (as of October 23, 2017). However, one of the key milestones of this epoch was the development of ARPANET, which is the fundamental basis for what today shapes the Internet.

Digital Enterprise 1.0

The years between 1970 until 2000 shaped Digital Enterprise 1.0. This epoch signifies the introduction of information technology (IT) into the organization across all departments. And thus, giving employees access to IT providing them with personal computer (PC), other hardware like printers and software applications. In the course of this decades, Intel started producing its first microprocessor “Intel 4004” in series and today’s IT giants like SAP, Microsoft or Apple have been founded. The programming language C was developed, and Texas Instruments released the first portable electronic calculator. The programming language Prolog introduced a paradigm shift of logical programming, which is used for developing expert systems and artificial intelligence (AI) solutions.

The development of Ethernet (1980) was another major milestone, which became the common standard of connecting PCs and other hardware devices such as printers, let them interact and share data. As a matter of fact, organizations started connecting group of machines like PCs, servers etc. and thus created local area networks (LAN) and after that connecting domestic and international branch offices using wide area networks (WAN). LANs, WANs and the development of the TCP/IP protocol suite were the foundation for client-server architecture. In a client-server model, one or more central computers (servers) are acting as a resource provider e.g. a data storage device (like file server, database server) and providing client computer with software applications (e.g. application server) and other services. Client-server computing itself was the basis for the so-called enterprise computing – the professional usage of IT inside organizations rather than private users.

The most important milestone of this epoch, however, was the invention of the World Wide Web (WWW) to make documents and information accessible worldwide and across different types of computer systems. The integration of several developments such as ARPANET, TCP/IP, client-server model, networking, telecommunications, hypertext, WWW and more formed the Internet. However, the majority of companies have only used the Internet e.g. for hosting a website and to market their existing products and content, but actually no one from the pre-Internet era developed new things and services using the Internet and thus didn’t help to put progress into the development of the Internet.

In 2016, the estimated total number of Internet users was 3.431 billion (47 percent of world population (7.3 billion people)). In fact, the Internet was the driving force behind the fundamental development of search engines (e.g. Google) and helped shaping the e-commerce business (e.g. Amazon and Alibaba) as well as led to several new digital-ish business models. As a consequence, the Internet is considered as the foundational success story of the so-called platform companies (aka platform economy).

Digital Enterprise 2.0

Between 2001 and 2015 the years of Digital Enterprise 2.0 arose. This was the time when the famous buzzword digital transformation hit the media landscape and the majority of organizations worldwide finally realized that IT was more than just a supporting tool but to understand it as a central part of their business strategy. However, the significant characteristics of this epoch was the high-degree of interconnection between organizations and their customers, partners and suppliers, creating integrated ecosystems – and this systematically supported by IT solutions. By doing this, organizations started making use of technologies e.g. like mobile computing, cloud computing, web-centric architectures, software-defined environments, Internet of Things (IoT), social media and API economy and thus ensuring a better interconnection within inside as well as outside the company, across different industries and beyond borders.

Needless to say, the fast development of the IT industry helped a lot of companies, in particular, startups and digital natives building businesses upon digital disciplines. In the process, mobile computing (like RIMs BlackBerry, Apple’s iPhone and iPad, Google’s Android) served customers with information, apps and services at the scene of the event by creating a digital touchpoint without being limited of a certain place but create a mobile user experience on the go. Web-centric architectures based on software-defined environments and cloud computing supported this movement, by shifting infrastructure (IaaS), platform (PaaS) and software (SaaS) to a central place that can be accessed with an ordinary data connection via an as a service approach. In particular, the cloud helped startups avoiding heavily upfront investments in IT resources but purchase the needed resources on an on-demand base. Thanks to Amazon respectively its cloud computing subsidiary Amazon Web Services (AWS), startups like Pinterest, Spotify or Airbnb were able to gain quick success utilizing public cloud services and wouldn’t exist without it. However, the cloud helped organizations of any size getting quick access to state of the art technologies and innovative services, reach a global audience, operate scalable IT environments, but also shift their IT spending’s from capex to opex model.

The cloud is one of the most important drivers of the digital evolution. Only with the use of dynamic and global scalable infrastructure, IT organizations can face the constantly changing market conditions and thus support their business strategy with a better agility. Cloud environments provide the best preconditions to facilitate the digital strategies of organizations of any size. Start-ups benefit by starting from scratch and do not have to consider a legacy IT. They can grow slowly without heavy static investments in IT resources. As part of their digital evolution established companies in particular need speed to keep up with the ever-changing market conditions. An idea is just the beginning. Most commonly the fast go-to-market fails due to technical execution because of slow deployments of IT resources or the lack of modern tools and services that support the development process. Cloud infrastructure and platforms empower IT organizations to deliver agility and thus support the business to enhance innovation capabilities. This includes:

• Access to state-of-the-art technologies and higher-level services to develop new digital products and services
• Affordable test and development of PoCs
• Improvement of customer experience by developing new customer facing solutions to enhance the customer engagement
• Closer integration of the entire process and supply chains within the organization as well as with partners and suppliers
• Acceleration of release cycles (continuous development and deployment)

Another important fact: Organizations cannot just focus on their home market anymore. Driven by globalization that hits its climax with the digitization of our society, scalability also needs to be considered from a global angle. The cloud enables elastic infrastructure operations and supports global expansion in an easy way with:

• Flexible access to IT resources without limits
• On demand usage of infrastructure, platform, software and services
• Overcome technical challenges like avoiding high latency
• Fulfill legal requirements in the corresponding target market
• Worldwide customer reachability

As part of digital strategies, the cloud became more and more important. It served as the ideal foundation for a straightforward access to IT resources and empowered every organization to act in an agile way. As a technological foundation, it served as a means to an end and enabled the access to ongoing innovation such as AI-related services.

Along with the mobile and cloud movements, the Internet of Things (IoT) became more and more popular and arose as one of the driving mega-trends of Digital Enterprise 2.0. The IoT connects the digital with the analog world by seeking for a maximum interconnection and the highest possible information exchange. It describes the linkage of physical objects such as cars, industrial facilities, household items and many other sensors, as well as humans with digital services via a data connection. The IoT dismantles existing data silos, enables access to novel data sources and hence opens organizations entirely new business opportunities. Thanks to cloud computing, mobile solutions, wireless connections and big data analytics, the (technical) efforts and (investment) costs for necessary IT solutions dropped significantly. There is also the fact that the costs for sensors and devices are getting cheaper, which makes it more reasonable to collect and analyze data of any device at any time and location.

Organizations preferentially make use of real-time analysis to identify and proactively react to trends inside datasets by combining IoT and big data analytics. For example, to get better insights of their customers and thus to send them current and tailored service offerings directly onto their smartphone or wearable that exactly fit to the current context of their activity. Other use cases can be find in transport and logistics. Here, IoT and analytics help to optimize the arrival time of deliveries or to improve the carbon footprint based on data pattern analysis. In general, IoT application areas and capabilities are nearly infinite. These include:

• Improvement of marketing activities by a more intense monitoring of people’s behavior, of things and data based on the analysis of the time and location where objects are located and act. These include e.g. location-based advertisement and the analysis of buying behavior across a variety of stores (businesses).
• Enhancement of reactions to certain situations in real-time. This includes e.g. the control of transport routes based on distinct variables like weather, gas consumptions or soft factors like possible perils.
• Support of decision-making by sensor-based analytics based on profound analysis like the perpetual monitoring of patients for a better treatment.
• Higher automation rates and better control for the optimization of processes and a better resource utilization (e.g. smart metering) as well as for risk management systems.

During this epoch, the collection and analysis of data proliferated. Depending on the organization, this was from plain harvesting of data up to utilize data for business process optimization, evaluate new business opportunities or develop new business models. With the soaring amount, variation and velocity the data arrived, traditional infrastructure quickly reached their maximum scalability. All applications, which evolutional behavior was strictly encapsulated befell the same destiny since these were not able to dynamically keep pace with the rising requirements of the business. In order to meet the increasing challenges of IoT workloads and growing sensor and meta data new agile platforms and massive scalable infrastructure were necessary. Standardized toolkits and interfaces helped with the quick introduction of powerful IoT workloads and ensured a stable communication between applications, services, sensors, platforms, dashboards as well as the collection, analysis and preparation of data.

Another mega trend that reflects the impact of a high degree of interconnection for Digital Enterprise 2.0 is social media. The fundamental idea of social media is to bring people together via Internet-based services. Thus, when people used those Internet-based applications, they interacted with highly dynamic platforms such as blogs, microblogs, forums, social networks, business networks, video/ photo sharing and gaming sites. Using social media services through a web-browser via their desktop PCs and laptops or via native mobile applications on their smartphone and tables, they participate in a world of user-generated content by writing posts or comments, sharing photos, videos or simply share their thoughts or what they are doing at a very moment. Based on their interests and behavior social media sites connected user profiles with other individuals or groups and thus creates a social network.

Social media differs from traditional media like magazines, newspapers or TV in lots of ways. This includes quality, usability, reach, frequency and interactivity. Social media operates in a highly dialogic system, where many sources are connected to many receivers. These characteristics, however, had a substantial impact of the communication between organizations, their customers, communities and other individuals. Understanding and thus doing social media the right way, it can help to improve the connection with customers and groups of individuals and can be a powerful communication and marketing tool for businesses and organizations. With that said, social media services enable organizations to reach a broader, even global audience and engage with them in a direct and much closer way by understanding and analyzing their ways of thinking and acting and hence enhance the customer experience.

On the one hand, engaging with social media services, people get more power by praising or complaining in public to a much broader audience about a brand, product or service. However, on the other hand, individuals are becoming more transparent to the social media platforms and organizations that target these individuals, by sharing more and more personal information and behavioral pattern.

Some of the most popular social media sites are Facebook, Instagram, LinkedIn, Pinterest, Reddit, Snapchat, Tumblr, Twitter, WeChat, Weibo, WhatsApp, Wikipedia and YouTube. As of November 2017, the biggest social media sites together have more than 100,000,000 registered users. Social media plays an important role as part of the high degree of interconnection era for organizations to engage and interact with e.g. customers in a much deeper way.

In Enterprise 1.0, enterprise application integration (EAI) was the central anchor as part of client server communication to ensure business process integration within the whole value chain. The focus was on the tight interaction of a variety of applications that are distributed over several independent operated systems. The goal: the uniform and integrated mapping of all business processes in IT applications and thus to avoid data silos. However, the transition into the cloud age lead to a change in the usage behavior of on premise interfaces to a mainly consumption of web APIs. Eventually, each cloud and web services provider offer a REST or SOAP based API that enables to integrate services in the own application and thus benefit directly from external functionality. Along with the increasing consumption of cloud services and the ever-growing momentum of the Internet of Things, the importance of APIs rose significantly.

Another important trend of Digital Enterprise 2.0 was the API economy. The API economy describes the increasing economic impact of APIs. Digital natives such as Google, Amazon or Facebook used the API economy and monetized their services by offering APIs to give external users access to their services. Openness is an important part of the API economy. Google understood this from the very beginning by position itself as an open platform. Important to know, openness in the context of providing access to its own services via APIs. In his book „What Would Google Do?“ Jeff Jarvis illustrates how Google – based on its platform – enables other companies to build own business models. Not without a cause – of course. This kind of openness and the right use of the API economy quickly lead to dissemination and made Google to a cash cow – e.g. via advertising.

The API economy quickly lead to an increasing number of Mashups. In IT, a mashup describes a web application, which uses services from more than one source to build a complete new application or service. For example, Uber has integrated Google Maps into its mobile application to order a ride and set the pick-up location. Another example is Netflix that leverages Amazon Web Services (AWS) cloud services portfolio to develop and run its video streaming service on top of AWS. Electronic commerce company Zalando used the API economy externally as well as internally. Zalando builds and operates big parts of its infrastructure on top of AWS. In doing so, Zalando designed the entire environment based on a loose-coupled micro services architecture. Each micro service is developed and maintained by a single team of developers that operates its own virtual datacenter. As of 2016, Zalando runs over 60 virtual data centers. All micro services, that together assemble the e-commerce website, access each other via their public APIs.

The cloud native Internet companies reflect this trend. APIs are still a central competitive factor for players like Salesforce, Twitter, Google, Amazon and Amazon Web Services and represent the lifeline of their success. All providers have created an own API ecosystem around them, which is used by their customers and partners to develop own offerings.

Thanks to mobile, social media and cloud services, APIs are no longer exclusively popular among developers but also found their ways on the memos of CEOs and CIOs who identified the financial impact. Providers typically benefit from APIs by:

• Selling premium functions inside a free of cost service.
• Charging the sharing of content through an application or service of a partner.

CIOs benefit from the API economy by getting access to a quasi-endless choice of applications and services they can use to expand their websites, applications and systems without developing, operating or even maintaining these functionalities on their own. Furthermore, APIs enable partner, customers and communities to get an easy access to own applications, data and systems to let the CIO’s company become a part of the API economy. Everything works using pretended simple API calls. However, the devil is in the details. Integration and API management have a big significance in the API economy.

However, the vast majority of organizations worldwide are still a Digital Enterprise 2.0, trying to challenge their existing business model and reinvent oneself to confront the competition with a digitized organization. Apart from that the leadership’s task was to develop a unique digital agenda they can leverage to highly interconnect with customers, partners and suppliers and therefore redefine all necessary processes, understanding them as an essential part of a digital enterprise. In doing so, it is important to put the customer tenaciously into focus of interest. Based on smart products and artificial intelligence technologies, organizations are able to get a better understanding of their target groups and to spot the current needs and thus act proactively. This includes the development of data driven apps to raise as much qualitative data as possible and use it at the digital touchpoint in order to increase the digital user experience for customers as well as partners and suppliers.

The fact of the matter is that the entire supply chain needs to become much more agile to be ahead of competition. This is about real-time! In the best case, organizations know already one hour or even earlier what a customer is going to need. In other words: predictive analytics is becoming continuously more important. It is necessary to decrease set-up times for machines, to interconnect the entire supply chain and to scale with the help of the cloud. Because one simple fact reflects this epoch of digitization: markets are not predictable and thus the set-up times had to become faster.

Digital Enterprise 3.0

Starting in 2016 and still in being is the time of Digital Enterprise 3.0. This epoch is defined by platform-based concepts and businesses as well as a strong data-economy that is powered by AI-driven services and products. However, as of today (April 2018) only a fraction (1-2 percent) of companies worldwide can be considered as Digital Enterprise 3.0. Significant parts of this epoch are developments of end-to-end service offerings and thus to control the entire (service) value chain. Amazon is one of the few trailblazers and a prime example of a Digital Enterprise 3.0. Started as a simple web shop, right now Amazon would only need an own music label to control almost 100 percent of the world’s digital value chain of goods. Just consider Amazon Video:

• Amazon produces own movies and series (that already won Golden Globes and Oscars)
• Amazon operates its own infrastructure to run and deliver the videos (Amazon Web Services)
• Amazon has the own platform to stream the videos (Amazon Video)
• Amazon controls the distribution channels via apps (multi OS) and own devices (Kindle)

Another prime example is Vorwerk’s Thermomix. Originally introduced in 1971 the kitchen appliance was continuously advanced over the last over 40 years until 2014 when the latest version TM5 hit the market. One of the important things of this model is that it incorporates digital technologies, as it allows for “guided cooking” using proprietary memory chips to provide settings and cooking instructions and has replaced all the buttons and knobs in previous models with a touch screen. Furthermore, a mobile application allows to plan for the week (month) and communicates with the Thermomix. A new Wi-Fi module replaced the memory chips and lets the Thermomix connect to the cooking experience website Cookidoo where the Thermomix gets access to the recipes. Bottom line, Vorwerk was able to advance and even digitize a product that is over 40 years old and thus understood to align its product as part of the digital evolution.

Along with end-to-end offerings, a sophisticated interconnection based on Internet of Everything (IoE) is imperative for this epoch. Here, a very dense interconnection across people, devices, objects and locations helps to provide customers with tailor-made solutions and services at the scene of event respectively the hybrid touchpoint and thus connects the digital with the analog world even stronger. For example, even if location-based services are a trend from Digital Enterprise 2.0 that has been discussed for quite a long time. However, it actually hasn’t being used e.g. by local retailers to engage with their customers.

The sophisticated interconnection is heavily supported by a context economy based on data and knowledge. In 2017, this happened in the Internet within 60s seconds (Source: @LoriLewis & @OfficiallyChadd):

• 3.5 million searches queries on Google
• 900,000 Facebook logins
• 16 Million text messages
• 4.1 million views of YouTube videos
• 342,000 app downloads
• 46,200 post uploads on Instagram
• 452,000 tweets send on Twitter
• 156 million sent emails
• 40,000 hours listened on Spotify
• 70,017 hours watched on Netflix
• $751,522 online spending’s

With the ever-growing use of smartphones and other mobile devices, our digital society reached a point where we are actually not able to not provide data anymore. And even if we do not tweet, search or do other things actively online, we – or better our devices – are constantly sending data. Like locations or other data from us. For a digitized organization this means that based on mobile technologies as well as data, information, knowledge and opinions it is becoming much easier to get in touch with a group of target customers and thus enhance the customer engagement.

As a Digital Enterprise 3.0, organizations also participate in an AI-defined world where intelligent or smart environments make our everyday life more efficient and convenient. If you look at AI from a different angle. Consider our life as a process. Consider every day as a process that is divided into single steps. And then, consider AI as autonomous process automation that provides us with more convenience and thus makes our life easier. Imagine a virtual private assistant like Amazon Alexa or Apple Siri as personal watchdog in business. Checking your phone calls, autonomously negotiating appointments with e.g. colleagues. I am talking about a very early version of Iron Man’s AI “Jarvis”. Or what about Alexa or Siri as your very personal life assistant. Let’s say you are speaking at a conference. Your flight home leaves at 4PM. In order to reach your flight on time, your personal life assistant autonomously orders you a ride for 2:15PM since your speech ends at 1:45PM and the traffic information looks busy today. The virtual assistant just sends you the details of the pickup location which is right in front of the conference venue. The assistant simply follows the entire process you would usually go through: from taking the phone out of your pocket, opening the app, searching for the target location and then ordering the ride. So, keep your hands and mind free for more important things. Of course, you have to give the AI access to your calendar, geo location and other information. Or imagine an intelligent version of the kitchen aid “Thermomix”. The dish advisor: Based on what the kitchen helper finds in your fridge it makes recommendations what you could possibly cook. And when some ingredients are missing it could ask for permission to order them online. The health advisor: Based on your eating behavior of the last weeks it would nicely recommend you not to cook the tiramisu you were just about to prepare, because this wouldn’t be good for your consumption of calories.

However, also organizations need to understand AI as the future of any digital business. Imagine when anything that is a process can and will be run by an AI. Because, with AI anything that is a process in your company can be automated. This is an important fact on your journey to new business models since an AI-operated business empowers you to use your financial resources and talent much more effectively. AI is the next logical step after the cloud and benefits from its success. The cloud is the enabler from a technological perspective. AI is about the business value and thus leads to smarter and more intelligent applications. Based on a sophisticated data analysis, AI applications that run in the background help to refine the customer approach and tailor products even better to the current needs.

An AI runs the business operations more intelligent and hereby efficient. As part of a Digital Enterprise 3.0 strategy, AI empowers organizations to improve its operations with:

• A better understanding of customers based on past interactions and behaviors.
• A fancy experience of changing the customer interaction from keyboard entry to speech control.
• Deeper engagement with smart virtual assistance enhancing the customer experience.
• Customized products and service offerings aligned with customer needs.
• Predictive by analyzing data, interactions and behavior from the past and in real-time.
• Conversational with smart personal assistants and bots.
• Augmentation of existing and new products, services, applications and processes.

However, the potential of AI is not just simply to optimize existing processes. In the next decades, AI will become the game changer! It will accelerate innovation and lead to new business models. Today, AI applications already have the necessary maturity to increase the efficiency of single processes. However, one should not only see AI from an operational perspective to increase efficiency but also from a strategic angle in order to leverage the technological capabilities for new applications and use cases. And thus, create real values for customers, partners as well as for the own employees.

It’s about Digital Correlation

Digital technologies such as artificial intelligence, cloud computing or IoT are the foundation for innovation and new business models that will change common rules and thus lead to ongoing disruptions within and across industries. In doing so, enhancing the customer experience by addressing their needs is crucial and thus constantly raises the customer expectations. Amazon, Google, Netflix, Spotify, Uber or Airbnb are just some examples.

The next disruptors are right on your doorstep. The autonomous car will become a reality – the technological perspective is not the issue, but we have to see how everything will be handled from an ethical side. This progress will have a direct impact on the retail, hotel and logistics industry. Just think about a network of self-driving cars that autonomously deliver goods to the customer. And then think about one of these self-driving cars being your private car that you can lend to this delivery network when you are at the office – and you getting a share of the revenue. Or think about an autonomous car/ bus that functions as a hotel room as well – like an “Airbnb to Go.”

It doesn’t matter in which industry you are working right now. You should be at eye level with the common technologies and trends, even if you do not feel any impact at the moment. This includes to understand and check what a certain technology can do for your current business model or if it could become a threat that influences your company in a negative way. See the world of digital technologies this way:

• The hype around artificial intelligence today wouldn’t exist without cloud computing.
• The Internet of Things wouldn’t work without cloud computing and edge computing.
• The cloud wouldn’t work without virtualization and software-defined environments.
• Etc.

Bottom line, If the majority of existing companies would have understood the power of the Internet in the 90s and the potential of cloud and mobile in the 2000s, the market would look quite different today. So, please don’t underestimate the impact of AI!

On May 1 2018, I’ve joined Gartner Inc. – the worldwide leading analyst firm – as a member of the Managed Business & Technology Services team. In my role as a Research Director, I am covering Infrastructure Services & Digital Operations. To engage with me, feel free to schedule inquiry calls (inquiry@gartner.com), follow me on Twitter (@ReneBuest) or connect with me on LinkedIn or XING. I am looking forward to talking to you!

Please note, I am no longer updating my blog AnalystPOV and my Medium account.