Conclusion

We associate many qualities such as love, emotions, creativity, imagination and values with human beings. These differentiate us from all other animals on our planet. One such quality is technology. Humans have the unique ability to understand the laws of nature and develop technologies that set us apart with additional advantages, comforts, productivities, and increase our ability to survive. We humans have developed a vast number of technologies in our history that have radically changed our lifestyles and future. It is these very changes that have allowed us to conquer fatal diseases, create new materials, program genes to create new biological products, tap into the powers of steam, electricity, atom, computing, and the Internet. These have helped in betterment of our lives and increased our life spans. However, like all good things in life, technology also has a flip side. It also creates many undesired side-effects. The new powers of technology have also eradicated huge numbers of animals from the planet, destroyed millions of acres of forest, impacted the climate and environment, killed millions of people in wars and crime, displaced people and their jobs. That is the dualistic nature of technology. AI is the next big technology with an enormous potential to transform our lifestyle for good. We cannot eliminate the negative side effects of mass job displacement, a renewed search for meaning, and the potential dangers to mankind. However, we can be more watchful and have regulatory systems in place to maximize the benefits and minimize the dangers.

We have seen how AI is a natural outcome of technologies for computing, storage, communication, and understanding the human brain, all developed in the last century. AI learning and decision making has many parallels to how the human brain operates. As a result, AI has the ability to take over a number of functions that a human brain normally performs. Decision making is our brain’s continual activity. We make decisions all the time ranging from the trivial, such as what to eat for breakfast, to the complex, such as where to invest our money. Now we can delegate a number of these decisions to AI and, in the process, simplify our lives. AI enables more seamless human interfacing to machines with almost perfect speech and gesture recognition and language recognition and translations. These alone will open up computing technology to a vast number of people who cannot use computing services today. AI can make all the decisions from driving our vehicles to operating the robots safely. And this is just the tip of the AI iceberg.

Throughout this book we have shown how it is possible for any business to harness AI to their advantage in creating new value for their customers with an AI differentiated business. We believe it is best to start right now with strategic planning on how AI can transform your business. To aid you in this journey, we have provided some tools such as the AI&U canvas and processes. Many AI platforms and services are available today to further help in the implementation of your AI based solutions.

One thing is certain. If you want to survive and thrive in the age of artificial intelligence, you must become engaged now and start building AI capabilities. We too are committed to continue our work on AI. We wish to empower individuals such as yourself and organizations like yours to seize this historic opportunity.

We have decided to open source our book and release a web version at www.ai-u.org. This online version is completely free and published under the creative commons code. We will continue to update this version with new insights and case studies as they appear.

We are here to help ignite your AI journey. We hold regular talks and workshops as well as publish many articles on the topic of AI. Sometimes, we engage in AI projects. We wish you all the best on your AI journey and would love to hear about your progress. Please share your thoughts by leaving comments on our website.

May the force be with you – and be aware of the dark side.

Sharad and Christian

Potential AI Scenarios

Scenario 1 – Productivity Gains Allow Humans to Focus on Being Human

Let’s start with a positive scenario. After all, technology provides an unprecedented opportunity to build a brighter and better future. Modern technology, especially AI and robotics, could well lead to monumental increases in productivity. This could extend to making it easier to feed the growing world population, improve health, longevity, quality of water and food. It may even enable a framework for basic income for all. However, this would require us to rethink our political and social systems and the purpose of humans, as most of the issues and developments happen on a global scale.

Scenario 2 – Inequality is on the Rise

While the advancements in artificial intelligence enable high productivity, this also translates as wealth for its creators which can cause economic inequality. Those not leading the development in AI have less chance to compete. The health and longevity of a few improve significantly, while the rest of the population falls behind and can hardly catch up again. This leads to extreme instability and suffering with social unrest.

Scenario 3 – AI Warfare and Terrorism

We are beginning to see the negative sides of technology, with automated drones and intelligent cyber warfare being exploited by people with evil or destructive intentions. As the machines become extremely powerful and super intelligent, regular defenses might not be sufficient or the good side will always lose, leading to an unstoppable downward spiral. The only way to compete is to use better AI, resulting in a worldwide arms race that consumes most resources.

Scenario 4: The Cyborg vs. Superintelligent Machine

We have evolved as cyborgs for many decades. Everything from electronic hearing aids and cardiac pacemakers to even smartwatches and smartphones have expanded our capabilities of living, problem solving, communicating significantly. As the technology becomes ever more advanced, it can greatly augment human capabilities in our favor. If scientists and companies work on solving the machine brain interface, like Elon Musk’s Neuralink is attempting, we might be able to directly tap into super intelligence. Just like the evolution of the neo cortex (the outer part of the brain) this connection would act as an additional layer of our brain, giving us super intelligence while still being human at heart. Some consider this the end of homosapiens. Elon Musk seems to think that is the only way we can keep super intelligence from destroying humans.

No one can forecast these scenarios. The future evolves one step at a time. We believe, that we are all in for an amazing ride with an unclear ending, it has always been that way. But we think that you and your organization should not become too obsessed with these unclear scenarios. You have a job to do today. AI is a promising new technology, that can help you achieve new dimensions in your offerings. It is a welcome opportunity for creating higher customer value and reducing your operational costs. So focus on delivering that today.

13. Outlook

So, where do you think all of this is going to lead your company and you as an individual? From a year ago when we first began thinking about this book up until the very completion of it, the news and buzz surrounding AI has intensified even further. So has the availability of services, the breadth of the ecosystem and the sheer amount of ongoing AI projects. This paves the road towards an accelerated future.

In this book, we have focused on what’s possible today, why companies should engage now and what are good approaches and useful areas. From this we have seen how AI technologies are destined to become or to be integrated into every part, from the software you use to managing data sources to optimizing your processes to your products and services, AI is becoming pervasive. AI will help you make better decisions and become a key ingredient in your business activities. Based on this, we are likely to see quite an increase in productivity in combination with other technologies like robotics, sensors, materials, computing and connectivity. We may even witness an exponential increase in productivity, where AI plays a key role. Soon machines create the factories. Tesla is already working on this today. The question is, what will happen then, and what does that mean for your business?

“The future is more predictable than you think” are famous words uttered by Google’s chief futurist Ray Kurzweil. And this holds true for a lot of technology. We can imagine what autonomous vehicles will be like, how voice computing will change the user experience and interfaces and how human sensors will give us more insight into our health, to name a few areas. However, the predictions for artificial intelligence to reach or surpass human level intelligence are difficult to imagine.

With this said, the impact of the technology revolution will be hard to predict. How will it change our lives? What will we do if intelligent machines can do most of the work? What will be the role of humans in a super intelligent world? Will there be states or corporations? In the early days of the Internet, it was not foreseeable where the Internet would take us in such a short period of times. The impact of technology is immensely difficult to predict and we will not even attempt to predict what might happen when super intelligence has arrived. However, we have found a few interesting points of discussion, which we would like to summarize in four scenarios to give you some inspiration of what might happen.

Organization – With a Data and AI Mindset

Machines of the industrial revolution performed repetitive mechanical tasks at a higher level of accuracy, reliability, productivity, and cost efficiency than humans, and as a result, eliminated many jobs. Similarly repetitive information jobs such as granting approval for opening a bank account, granting loans, filling out forms, providing information, transferring information from A to B, call centers etc. will be increasingly delegated to AI, making many jobs which humans perform today redundant. The new job roles in organizations will be expected to leverage data and the power of AI in order to accomplish higher level task.

An HR specialist of tomorrow will automatically have AI seek out the best candidates to hire and set up the interviews with hiring managers. The HR role will shift to a higher level ?—? salary negotiations and ensuring fast integration of the new hire. When I was first hired, mature PC and mobile skills were a basic requirement. The new hires of the future will be required to be AI-aware and AI-friendly, just as we are PC- and mobile-friendly in our current work.

There will be new AI tools used as a standard practice – such as Einstein from Salesforce.com – which will provide a sales or marketing person with much richer information. For example, analysis and prediction by AI about a customer’s intent, and for suggesting alternatives on how and when to best submit sales proposals.

Most jobs will be transformed, making Change Management one of the biggest challenges for companies seeking success in the AI age. Beyond changing individual job roles and skills, the organization of employees will also need a fresh approach. The exact nature of the transformation is not easy to predict, as it will depend on the nature of business, country of operation, and company culture.

Alex Osterwalder & Yves Pigneur, the authors of the popular books, Business Model Generation and Value Proposition Design, have made a compelling case for appointing a Chief Entrepreneur in a company to focus on the future of the company while the CEO focuses on the present. (http://blog.strategyzer.com/posts/2017/7/11/an-open-letter-to-ceos)

New Skills and Competencies

As with any project, success depends on a number of fundamental skills and efficient processes. These are areas such as strategy articulation, getting project approval, planning, project management, and customer validation. However, AI projects also require a number of new skills:

  • Understanding of new AI capabilities
  • Knowledge of available AI services
  • Working with AI platforms
  • Accessing data sources and sensors
  • Machine Learning
  • Neural Network setup, configuration and optimization
  • AI – Human interfacing

AI has the power to radically transform a company’s entire approach to products and way they are offered. This requires a very open mindset regarding the company’s business objectives and core strengths. It is vital that the company strategy and product planning team have people with excellent insights into the potential of AI, and new capabilities enabled by it. External expertise can be of big help for guidance on the maturity and applicability of new AI to company goals.

When it comes to AI basic services, it makes little sense to develop internal expertise on AI services, like speech recognition, translation, or smart mail response services, as these basic services are available as open source, or from multiple vendors. You just need to pick the service that best matches your needs. Major AI players like Google, Amazon, IBM, and Microsoft offer platforms for development of AI projects. These platforms offer many AI services, tools and consulting services, which can significantly cut down prototyping and customer validation time. AI specialist companies can be used for specific areas such as, setting up neural networks, training the AI system, and collection of data.

The design of AI systems needs a good understanding of the data needed by the AI systems for reliable operation, as data is their raw material for AI systems. Machine learning and neural networks form the core of any AI system. Machine learning needs validated data sets for training the neural network as well as reliable, up-to-date and good quality data as input for decision making. Some of the required data originate from dedicated sensors. Data scientists are the specialists in this area, and must be a part of the AI team, since they ensure that the data is appropriate, neural networks are setup and optimized, and the network is trained.

Humans have learned how to interact and communicate with humans over thousands of years. We have learned to interface with one another. AI is learning to mimic humans in these specific areas but interfacing AI with humans remains far from trivial. Even the simplest AI devices such as, the Amazon Echo or Google Assistant need excellent interfacing with humans, making the communication feel natural and human-like. I am more likely to use the Echo when my communication with it is so natural that I can hardly differentiate the experience from that of interacting with a human.

Developing Your AI Strategy

The first step in developing an AI solution is to discover and finalize a creative idea at the core of your solution. You may have devised several ideas, therefore you need to have a process for short listing the most viable idea and honing in on it. We recommend carrying out customer interviews and experiments that validate your assumptions and prevent you from delving into areas that are not relevant. We often use the method of Google Venture’s Sprints method which brings together a team of 5-8 people to find answers to big problems in just 5 days, and validates the answers with real customers. By doing this in only 5 days, it ensures the team focuses only on the important aspects, and ensures nobody becomes too attached to the solution before it is validated or discarded. If you are using a cloud-based AI service, you can also add developers to your SPRINT team, which will help you to develop a real and functioning prototype. Running these such experiments is critical to making fast progress.

Now you need to develop a strategy around the creative AI idea. Since AI is a new technology, you may find that for most companies, multiple iterations of strategic reviews and management approval are needed to get a go ahead. This is not only because AI is new in nature, but also management may not have a clear idea on the impact on the bottom line. Jeff Bezos, CEO of Amazon, says that many benefits resulting from AI are internal to the company, meaning a reduction of costs may not be immediately obvious. The value to customers can sometimes be translated in the form of significantly better personalization, higher accuracy, and ease of use.

As with any project, an AI project must start with absolute clarity on two areas defining the strategy:

  • Value Proposition (VP): What new value are you offering to your customer segments? Which customer segments are you addressing and what are their needs? Will your new value offering better satisfy customer needs?
  • Business Model (BM): How will your business benefit from the new offering? How will you monetize this value proposition? This involves a clear understanding of the sources of your revenue, costs, partners, and interactions involved.

These two areas describe the total value of your AI project to customers and to your business overall. You will find that these two areas are non-trivial and often need the highest quality of attention and time. The rest is a matter of implementation.

Fig. 12.1 Developing your AI strategy – from the creative idea to implementation

Articulating a VP for AI based projects requires an excellent understanding of the new capabilities that are enabled by AI today and in the near future. The low hanging fruits of AI capabilities are speech input, computer vision input, people and face recognition, image and object recognition and scene description. New capabilities are evolving every day. The core VP for your business will be centered around a creative AI idea that leverages one or more new capabilities of AI, in order to deliver new and unique value to your customers. The BM converts the VP into a profitable business. Development and approval of a solid VP and BM for AI solutions is the first and most important step for AI projects in most businesses. Since AI is a new technology with benefits that are often not well understood, this often takes much longer than expected. The availability of AI services and platforms help in implementation of the solution. The AI&U canvas is useful in capturing all critical elements of your solution, and allowing you and other decision makers to discuss, and form a consensus.

11. Introducing the AI&U Canvas – Your AI Strategy Blueprint

Once you have arrived at one or more business ideas for innovation with AI, it is a good practice to map out the various parameters of that idea in a structured manner. This will make it easier to examine various aspects of the idea and the correlations between them. It serves as an excellent document for objectively discussing and refining the concept with other stakeholders. There are various methodologies for doing this. Below we have developed our own chart to do this, the “AI&U Canvas,” derived from the classic Business Canvas template, by Alex Osterwalder. Once filled out with the parameters of your business model for AI, the AI&U Canvas becomes your AI strategy blueprint.

Fig. 11.1 AI&U Canvas summarizes your AI strategy

The AI&U Canvas consists of 8 fields defining the components of your AI solution. Three fields on the right (green) lists customer facing areas for the solution. Three fields on the left (blue) capture internal considerations for creating the solution. Two fields in the center (yellow) capture the value. Your AI solution fields are:

  • Customers (and stakeholders)
  • Interactions (with customers)
  • Value Proposition (for customers)
  • Data Sources and Sensors (for AI)
  • AI Services
  • Ecosystem Partners
  • AI Value Add
  • Business Benefits

These components are essential to understand any AI based solution. Here is the structure of a typical AI solution.

Fig. 11.2 Anatomy of a typical AI solution

Every AI solution converts data from the real world ?—? speech, sounds, images etc. – into value for the customer (Fig. 11.2). An AI solution has interactions with (internal and/or external, one or more) customers, delivering business value. A satisfied and happy customer is characterized as one who benefits from the solution if the value provided meets or exceeds their needs. AI solutions typically can benefit from the support of certain AI services, like speech recognition, object recognition, or face recognition. These are basic AI services typically licensed from external companies. These services make it much easier and faster to build the AI solution delivering customer value. Often there is a collaborative need from other companies, called Ecosystem Partners, to enable the entire solution to work. These are AI platform providers such as Google, Amazon, Microsoft, IBM, or specialized providers for Deep Learning. AI value add in the solution stems from the integration of AI. In the end the total solution must deliver overall business benefits for your business ?—? in terms of you winning new customers, generating higher revenues, reducing costs, or enabling expansion to other geographies.

These highlighted components (above) are identified and filled out in the AI&U Canvas fields providing an excellent documentation of the AI solution and all its major components. We recommend filling out the fields in the order shown below as objectively as possible ?—? possibly as a collaborative effort by the AI team.

You can download your copy of the canvas at the AI&U website

www.ai-u.org

  1. Customers and Stakeholders

Write down all customers (internal and external) served by the solution. You can also add their key needs.

Examples:
– Internal customers
– Stakeholders
– External customer segments
– End customers
– Channel customers

  1. Interactions

List the ways in which customer interact with the solution – especially where the AI elements come into play.

Examples:
– Presentations
– Sales process
– Writing proposals
– Customer service calls

  1. Value Propositions

Specify the value your solution offers to each of your customer segments. Keep in mind that this is not a list of your solution features.

Examples:
– Improve Ease of Use
– Higher personalization
– Proactive and responsive

  1. Data Sources & Sensors

List all sources of data used for AI. If needed for the data, name the sensors. Remember that AI transforms data into value offered to customers, hence data is the raw material for AI.

Examples:
– Medical history and clinical data for healthcare + sensors
– Income, investments, expenses, etc. for tax consultants
– Temperature, humidity, rain, soil, etc. for agriculture + sensors

  1. AI Services

List AI services needed for the solution. These services are typically licensed from third parties. If known, name the provider.

Examples:
– Speech recognition
– Object recognition
– Scene description

  1. Ecosystem Partners

List all ecosystem partners needed for creation, operation and service of the solution.

Examples:
– AI Platform – e.g. Google, IBM, Microsoft or Amazon
– AI Neural-network set up, Deep Learning training and maintenance
– Data sourcing

  1. AI Value Add

Value added to the solution due to use of AI. This is an indicator of the incremental value that AI exclusively brings to your solution.

Examples:
– Direct speech input – no typing
– Automatic recognition of critical situations
– Higher accuracy than a human expert

  1. Business Benefits

Benefits for your business as a result of the solution. These are essentially benefits to your firm, with a financial and strategic impact, by offering an AI based solution.

Examples:
– Reduction of human-power – lower cost
– Higher customer satisfaction and trust
– Higher quality of service – more competitive

Radical Path – Disrupt Existing Solutions

So here comes the fun part… for some. How do you take your business to the next level with Artificial Intelligence ?—? how do you build the next multi-billion business? While all this sounds exciting, not all enjoy this part, especially the successful multinationals that have evolved over time. They have developed mechanisms to optimize and keep the status-quo. In our work with these enterprises, we have found that there is an initial interest in Artificial Intelligence, but then there is a tendency to push it out into the future, or to find ways in arguing that this will never be really relevant, or will not work with their customers. This happens once the initial interest wears out. To overcome this natural defense of change, we have developed a simple method to help overcome the fears, doubts, and uncertainties.

We have found that in order to play to the full potential of artificial intelligence it is important to go beyond the limitations that artificial intelligence has today. It is those limitations where most people’s doubts fester and the uncertainty of costs and quality limit the execution power. In addition, it is the fear of jobs, the power of robots, the social impact that limits the drive towards leveraging AI. Our method for overcoming this is to envision ourselves ten years into the future. Let’s try this exercise now. Imagine yourself ten years in the future. What do you picture? AI will have obviously improved significantly and its combination with advancements in other areas such as robotics and taken alone will give you capabilities unthinkable of today. We chose ten years because it is just far enough to be tangible without some apocalyptic scenarios. In doing this, it offers significant chance and takes you of your current limitations. Take yourself out of your current position and time and look at the big picture. It took just ten years for the iPhone to radically change the way people interact with digital services.

We call this moment, the time of “perfect AI”. This is because regardless of how far AI will be in ten years and what other factors or technologies will play a role, it is far out enough of people’s mindset to dive head first into a space that is so fundamentally different that it forces new thinking to open up. Experts accept that perfect AI could be a reality. It is important not to be bogged down by today’s limitations and lack of imagination.

So we pose the following question:

“If there is perfect AI in ten years, what is the role of your company and what will your business model look like?”

We intentionally pose the question in two parts. We start with an opening scene. There is likely to be powerful and intelligent software and totally new interactions. Data and processing power will be ubiquitous, machines will have found a perfect way to interact with humans. Humans will have started to trust machines for some critical things in their lives. Now we will describe one of our experiences exploring this question with a firm via a workshop on the “picture of the future”. The picture of the future can contain several likely scenes that are scribbled by the team. Obviously, the quality of the scribbles does not matter. Then we enhance the picture with Post-It notes in several colors representing where AI is used (which is often hidden). This helps the team express interesting areas and gives you a visual aid where innovation is possible.

Based on this picture of the future, we worked on possible scenarios describing the role of the firm in this picture of the future. This is a vital step in understanding the scope of the change. Will you be the sole supplier, will you deliver an integrated use case, or rather just some core components? What will your relationship with other players be and how will you interact with your customers and who will those customers be? As a whole, what will the total ecosystem look like? What will be your role in the game? Again, you should focus on your strengths and potential differentiating factors.

The final step is to consider business models that monetize the value you propose. Here you can visit the areas where AI will play in the picture of the future ?—? don’t forget you are in the ultimate all perfect AI scenario, so don’t worry about any shortcomings you see today. What are the resources needed? How big is the customer value? What are potential alternative solutions and players delivering that service? We usually spend extensive time discussing these elements with companies to help them better understand their role, as well as new potential business models and threats. Do you sell the car? Are you an investment vehicle for people who buy the car to rent it out so that it will make money by delivering services for others? Are you provider of data, predicting where people will be and what services they want? Are you monetizing on the data or services? These are just some examples to illustrate how future business models based on AI might be very different.

Before you rip up your whole business model remember that this is just an exercise and you don’t have to change your vision, mission and entire organization just yet. The real intention here is to help you think far enough and to understand possible scenarios, before identifying the first step. So based on your favorite scenario, you break down the vision and think of what is the first, most logical step in making it happen. This is where you should start. Identify and describe the first step in detail. Often this first step is not too far out, and is both conceivable and achievable. It is easy to understand, discuss and test with users to ensure you are on right path.

This is best illustrated with an example. Let’s say you are an automobile manufacturer (OEM) and you are designing and producing cars. How can artificial intelligence disrupt your business? Obviously your business is influenced by three major trends in 2017. Firstly, the sharing economy where people are less inclined to purchase cars vs. renting on demand. The second impact is the move to e-mobility. The car’s system changes fundamentally from motor and transmission-based approaches to much simpler battery-powered e-motors. In addition, the move to autonomous vehicles require you to build completely new components and software into your vehicles. And here is where we see the era of artificial intelligence coming into play. Again we ask the question: “So if there were perfect AI in 10 years, what would your business model look like and what is the role of your company?”.

You can find inspiration all around you. Science fiction movies are often a good source. In a perfect world of artificial intelligence, cars might become cheaper to build. Robots might build the robots that will build cars in smart factories. Smart materials might be more flexible with built-in sensors. Smart mobility grids will allow the users and vehicles to plug-into a network of services and mobility options. Vehicles might be living spaces allowing you to play, conduct meetings and even take meals during transportation times. You might be able to seamlessly switch from one mode of transportation to another to reach your destination. For customers the biggest choice might be whether to take a self-flying drone or a ground-based vehicle. Drones might be cheaper due to simpler technology. Flying in the air can pose less complex situations than cars driving on the roads with so many distractions and obstacles. Drones might be a lot quicker, as they can take direct paths. This is an example of how to develop a picture of the future. Of course your experts, together with some external inspiration will produce a vast array of innovative scenarios to base your thinking on.

As a next step you will define the role you wish to play in this scenario. The role you will play in the future will be based on your history, what you are good at, and your area of differentiation. You might still manufacture and supply cars, if this is your key area of competence. Or will you just supply parts or designs to other players? Will you build autonomous drones? Do you offer sharing services? Will you program the network that the cars run on? Will you provide services or build an ecosystem of 3rd party service providers? It makes sense to list and discuss each of the options. Then you can discuss your business model.

Where AI will make a decisive different to you will become clear based on the discussion accumulated from these various areas. From this, you can start your engagement and experiments with artificial intelligence. You will learn to work with the technology, to build awareness and knowledge in your organization, and you will explore a journey with a bigger goal in mind.

Evolutionary Path – Six Pillars of the Evolutionary Approach

The evolutionary approach for Artificial Intelligence focuses on evolving your business by making existing products, service and business processes better. In the evolutionary approach we often observe that AI can carry out tasks cheaper than traditional means. To discover the right opportunity, it is best to start with your existing business practice and products. In our discussions with companies, we have worked on six fields to analyze the status-quo and uncover potential areas of AI improvements:

Fig. 10.2 Six pillars of evolutionary approach discovery

Let us provide some insight on tackling each area and devise the right application for your organization.

Your Company Data Streams

Big Data has shaken up a lot of organizations and created an awareness for the value of data. Although most companies we have worked with have engaged in Big Data projects, we are surprised to find that most organization are not aware of the plethora of data they operate on, where the data is stored and what costs are involved in creating, storing, and managing the data. The quality of the data is often in ‘suboptimal shape’, to put it nicely. When engaging with companies we often start by listing the data sources inside the company and the storage. To name a few: E-Mails, customer data, financial data, suppliers data, interaction data, transaction data, sales data, product lifecycle data. Thereafter we examine the data being used or generated in your products and service. Often all this data is stored in databases, data warehouses or proprietary software systems. Lastly we think of new data sets that can be created by adding additional data sources or engaging sensors.

In correspondence with this practice, we then carry out two exercises for each data set, with the goal of understanding the impact. We carry this out with a diverse group of experts from the company. If possible ?—? we recommend including a data scientist. Furthermore, do not forget to consider outside data sources that can be used to augment and enhance your company data. Here are the two exercises that you should conduct:

  • What decisions can be derived from each data set – this focuses on the primary usage of the data, but we also brainstorm how else this data could be used.
  • What efforts are needed to create, store and process each data set ?—? here we look at the total cost on how this data is being managed and used. Companies are often not aware how much effort is behind creating their own data flows.

Now let’s apply this using a tool that all organizations have at their disposal: your customers data. Think of your own organization. Are you happy with your customer data? Is it good quality and are you utilizing it effectively? Our findings conclude that for most organizations this is a huge potential to be exploited. Here are some examples of questions you might ask:

  • Who are your top customers and what are their characteristics?
  • Which customers are on their way to becoming top customers and what are the patterns that define this?
  • What is the next predicted purchase for this customer and what are the signs indicating this?
  • What customers are in the process of leaving your company for your competitor and how do you detect this?
  • What products are your customers purchasing and what products are reaching the end of their lifecycle soon?
  • Based on the communication and behavior, how happy are customers with your products and services, how loyal are they to your brand?

By crunching the numbers, understanding the patterns and predicting the trends your team of experts can figure out the answer to these questions. The data scientist can then utilize these insights and develop complex algorithms for your programming team to input into algorithms. It is even beneficial to carry this out manually as it can help you predict behavior and allows you to make better decision on product management, marketing, customer support, and many areas that consume most of your company’s budget. Most notably, this is where artificial intelligence can really make a difference. Neural networks are good at identifying complex patterns and making good predictions. AI is a great tool for helping your company understand your data streams, make sense of them, and allow you to make better decisions based on this data. We recommend running experiments on some of the data with the most leverage. By running those experiments you will learn how to engage with artificial intelligence as well as the pitfalls in quality of your data, confidence level, training the neural networks and you build up a new competence in an area, which will be a critical competitive factor in your company’s future.

Your role in all of this will be to identify the right areas where it makes sense to engage and develop your AI project. Then to find the right AI tools to help you recognize and understand the data, and finally, perhaps most importantly, your job is to find the right business model to act upon this, as we have learned in the first section of the second part of this book.

Your Software

Wherever you find data, software is not far. It is used to manage the data flows, to store the data, and to act upon it. Therefore another approach to discovering your AI potential is to review your software. You can do so by making a list of the software that is deployed in your organization. Often your software acts upon the same data, so it makes sense to also review what software is working together on data sets. Again ask yourself some question on each of the software you are using:

  • What data is your software creating or acting upon and what decisions do you take on that data?
  • What additional decisions would you like to take based on the data?
  • What efforts are needed to derive those decision and how can they be taken more intelligently?

Then rank the software and corresponding data by most impact both in terms of cost savings and also in terms of new insights and better decision to be taken.

Let’s take your HR software as an example. It lists your employees, their performance and feedback, their time spent working, vacationing, just to name a few. So what are the questions should you ask yourself?

  • Am I hiring the right people?
  • Which people are going to leave soon?
  • How is the motivation in my organization?
  • How are the managers performing?
  • What teams are creating most value and why?

Again, these are just a few examples. Your role in this will be to identify the right areas for your organization to engage, generate the data sources, enable AI to understand the patterns and find ways to engage that provide the most value to your organization. A number of software systems are being enhanced by AI from its creators or new AI based tools are available. It makes sense to do some research before you engage.

Your Processes

Most of your organization is likely to work on business processes on a daily, weekly, monthly, quarterly, or yearly basis. Make a list of your business processes, both primary and secondary. By now patterns should emerge as you dive deeper through each section.

Ask yourselves the following questions for each process:

  • What decisions are you taking based on this process?
  • How much effort is it to conduct the process, what are the total costs?
  • Could this process be automated? Could parts of the process be automated?
  • What insights could be taken on the data generated in the process?

Now rank your findings in terms of business impact. What are the biggest problems you would like to solve? Where do you expect to earn the greatest gain? Where can you significantly save costs? What is your ability to execute on these areas? There is no standard answer to these questions, we recognize that this is quite individual to each organization and this is your value in an artificial intelligence world ?— ?to translate the new capabilities into your business.

Another area is to look for modern software suites that are business process focused. Companies such as Atlassian, Salesforce, SAP, IBM, Adobe are only a few well-known players in the market. A whole new ecosystem of enterprise software has AI-capabilities built in right from the start.

Fig. 10.3 TopBots Enterprise AI landscape 2017

Your Interactions

AI is showing promising results in enhancing human and company relations, such as translating languages in real time from human to machine (bot). As well as customer service automation and bot-to-bot interactions (for business-to-business commerce). This is a prosperous area for your business to benefit from as it is expanding rapidly. List all your company’s interactions and ask yourself the following questions:

  • What decisions are being taken according to the interaction?
  • How much effort is it to engage in these interactions and what are the total costs?
  • Could part of the interaction be automated?

Again rank your findings in order of importance to your organization and identify the areas of biggest value. Note that interactions based on text, speech and image have been among the early areas of progress for AI. So, there are a lot of services and software solutions available to plug-into your enterprise to assist in these processes.

One example are chatbots or voice assistants like Amazon Echo, Apple Siri or Google Home. These AI-based conversational services allow you to automate parts of your customer interactions and thereby provide a personalized interface with a human-like interaction. Voice assistant services can be deployed through the available devices and chatbots can be integrated directly into your digital offerings or integrated into popular messenger platforms like Facebook Messenger, or WhatsApp.

The problem with this is that we often find that their experience is not perfect. Thus it often makes sense to deploy a combination of machine and human intelligence in the solution. If you take chatbots for example, the solutions can be a combination of machine and human where the machine introduces the chat and asks and responds to questions where it has a high confidence level. Questions more complex in nature are handed over to humans to carry on the interaction. This unburdening of customer support people can result in significant cost savings.

Fig. 10.4 Gradual takeover by AI from humans as the confidence level increases

Above is a model of joint deployment of human intelligence with artificial intelligence, where AI takes on more workload, as the confidence level increases.

Your Products & Services

The last area we will look at are your products and services. You can build AI into your products and service, because AI can get the job done cheaper meaning cost-savings for you, or because it can deliver new capabilities to your customers and ultimately increase the value of your products and services.

How AI can support your products and services can be based on simply adding mature AI areas, such as speech recognition, text to speech, conversation, image recognition. Look to the current set of capabilities of major AI platforms by IBM, Google, Amazon, Microsoft or look at the AI software landscape for capabilities to add to your products. You can achieve some significant results in making interfaces nice, more intelligent, adding better support capabilities and faster results for your users. Because of the proven areas, there is a lower development and integration time and it is easy to prototype and test the benefits before deploying the solution. Be aware though, that your competitors are also working on such solutions and because you are using standard AI services, it might not result in a long-term differentiation from your competitors.

As we covered in the first part of this book, an example of this might be the Netflix video service that created an Amazon Echo Skill that allows people to control their Netflix experience by simply using their voice. “Alexa, I want to watch my favorite Netflix series.” This can result in a complex multi-step service, which makes the experience so much more compelling. Amazon Echo turns the TV on, loads Netflix, tunes into the right person based on the voice profile, selects the favorite series, and launches the next episode. If you have deployed some smart-home solutions, it can also automatically lower the window blinds and turn down the lights without additional commands.

A more beneficial approach is to marry the USPs of your product and brand with AI capabilities. An excellent example of this is Amazon optimizing product suggestions based on AI or Google Translate improving their translation services based on Machine Learning or BMW offering smoother braking algorithms. Take your existing products and services and make them even better in areas that differentiate. To achieve this, you make lists in 3 areas:

  1. The use cases and values delivered
  2. The product / service features
  3. Your unique area of expertise and differentiation from the competition

For each item, list the data flows and think of how they can be made more intelligent and cost-effective. From this, you should also brainstorm what are new areas where you can deliver valuable use cases and offer new features. We recommend creating a systems map, listing on the left the stakeholders involved and on the right the end result. In order to visualize the process and data flows you draw boxes around them. Mark the areas where AI can help optimize the process. This will reveal what data is needed and how to get it?—? for example by deploying new sensors. This is not the last step though, you can deploy AI libraries in the right places to offer AI services and self-learning algorithms. You have to train these AI systems so think of training scenarios and getting training data. Then you go on extensive training phase to develop and train the artificial intelligence before deploying to your products and services.

Fig. 10.5 Example of a systems map from Google Venture SPRINT book

Reflecting again on the Salesforce CRM software. The company’s strength of offering software-as-a-service and combining many customer facing tasks in a unified platform offering was the basis for adding new value through their Artificial Intelligence offering’ Einstein.’ Traditional Salesforce CRM was good at ranking top customers based on revenue data for example, but Einstein allows you to understand why the customers are top. What are the characteristics of top customers, what other customers are on their path to become top customers? When will these customers buy again? Here Salesforce played on their differentiating strengths and they have made CRM much more intelligent and beneficial to their users. The result of this is it allows them to charge a premium. It is still early days, but we can foresee a time where companies will not buy a CRM system that is not based on artificial intelligence. It will be too costly for the companies, because business operations and decisions will not be as optimized as those where the companies deploy intelligent CRM systems.

This kind of Artificial Intelligence requires a deeper engagement by employees working on the core products and services. You also need to delve into unproven ways, which are likely to be more costly and risky. But the reward can be a higher differentiation of your products and services. Often this requires senior management commitment and a strong vision, if you wish to benefit from these capabilities early. If you are not an early mover, don’t worry?—? your competition will drive you to this space very soon, as we are entering the era of artificial intelligence.

Going through this process helps you identify areas where you can gain most benefits from AI. They are easy to identify and engage with and will kick off your AI journey. However, they are not sufficient to exploit the maximum benefits for your organization. Now we have explored this approach let’s explore an entirely different approach to identifying your AI potential.

Evolutionary Path – Enhance Existing Products

The accelerated development of Artificial Intelligence in recent years has produced a number of solid use cases. There areas have undergone massive research, testing, application in many practical areas at large scale and continue to improve with further usages, as is natural to AI capabilities. The big four platform providers IBM, Google, Amazon and Microsoft are likely to provide AI capabilities in the form of cloud platforms. Here you utilize AI capabilities as-a-service without having to download, install and configure Artificial Intelligence libraries, let alone develop your own. You can easily work with test data and run some experiments before you scale your solutions.

Let’s take IBM Watson, which offers the following areas of ready-to-use capabilities (as of June 2017) as a service:

  • Conversation – Add a natural language interface to your application to automate interactions with your end users.
  • Document conversion – The IBM Watson™ Document conversion service converts a single HTML, PDF, or Microsoft Word™ document into a normalized HTML, plain text, or a set of JSON-formatted answer units that can be used with other Watson services.
  • Language translation – Dynamically translate news, patents, or conversational documents. Instantly publish content in multiple languages.
  • Natural language classifiers – The Natural Language Classifier service applies cognitive computing techniques to return the best matching classes for a sentence or phrase.
  • Natural language understanding – Analyze text to extract meta-data from content such as concepts, entities, keywords, categories, sentiment, emotion, relations, semantic roles, using natural language understanding.
  • Personality insights – Watson Personality Insights: Personality Insights derives insights from transactional and social media data to identify psychological traits which determine purchase decisions, intent and behavioral trait.
  • Retrieve and rank – The IBM Watson Retrieve and Rank service helps users find the most relevant information for their query by using a combination of search and machine learning algorithms to detect “signals” in the data.
  • Tone analyzer – Humans exhibit a range of ‘tones’ such as joy, sadness, anger, and agreeableness, in daily communications. Tone Analyzer uses cognitive linguistic analysis to identify a variety of tones at both the sentence and document level. This insight can then be used/applied to refine and improve communications.
  • Speech to text – The Speech to Text service converts the human voice into written word. It can be used anywhere there is a need to bridge the gap between the spoken word and their written form, including voice control of embedded systems, transcription of meetings and conference calls, and dictation of email and notes.
  • Text to speech – The Text to Speech service processes text and natural language to generate synthesized audio output complete with appropriate cadence and intonation.
  • Visual recognition – Find meaning in visual content! Analyze images for scenes, objects, faces, and other content.
  • Discovery – Add a cognitive search and content analytics engine to applications to identify patterns, trends and actionable insights that drive better decision-making.

These capabilities are available to you instantly. Now back to our example of Tesla. Their built-in cameras are capable of constantly taking pictures and videos. AI can be trained to understand those pictures. What is observed in the picture? Are there any obstacles on the road? For AI to learn computer vision, it must be fed with training data. Based on the training data and feedback on correct interpretation, the AI can learn to identify objects more precisely than humans can. Take 3D simulators. These are used to train AI faster. Over time the AI-based algorithm improves and can be made available to more cars.

Let’s look at another example in the areas of customer interaction. Let’s say your website offers a support chat to assist and guide customers on finding the right information or contact. This can be based on AI capabilities. Here the training data can consist of responses from well-delivered customer interactions. These can be used to train the AI on how to communicate well. Once the AI takes over, the confidence level of a given customer interaction decides whether a human support agent jumps into the conversation, or whether the AI completes the conversation. Just try it, it is a fun experiment. And AI can speak several languages.