What is digital transformation?

The digital transformation framework has four main components, which are customer relations, processes & systems, products & services, and organization.

  • Digitizing Customer Experience: In order to maximize customer satisfaction and life-time revenue from a customer, companies are collecting data of customers and providing personalized products to individuals. Customers expect to engage with the company 24/7 from all available channels; omnichannel interaction and services are the key for successful digital transformation. 
  • Digitizing Products & Services: Companies need to focus on selling to customers a journey rather than the product. This can only happen if companies insert a digital tag that enables them to analyze customer behavior and lets to interact with customers. For example, a digitized wallet which counts the cash in the wallet then, if necessary, alerts individuals’ phone if they want to go ATM.
  • Digitizing Operations: Automating operational processes make organizations cost-efficient and more agile. Agility is an essential characteristic for a firm because increasing competitiveness in the market obligates organizations to act quicker.
  • Digitizing Organization: Companies need to achieve a new organizational model which involves human and machine together. Labor force should transfer into processes that are about designing, auditing, and innovating rather than operating processes. A culture that is open to change and prioritize improvement is an essential driver to evolve through digital. Organizations should encourage innovations so that new technical capabilities can be implemented to help employees adopt the digital world and gain the skills and knowledge they require to transform.
  • Read more


    State of Quantum Computing in 2019 for Business Leaders

    Spare yourself the trouble and delay learning anything about quantum computing until 2020 eoy unless you are working on:

    • a problem that is not solvable in reasonable time with current computers (e.g. building deep artificial neural networks with millions of layers or simulating molecular interactions). Such problems are common and almost all Fortune 500 companies could benefit from quantum computers
    • Cryptography, or at an intelligence agency or need to transmit nation or mega corporation level secrets
    • quantum computing (sorry had to be MECE)

    If you are in one of these fields, quantum computing has the possibility to transform your field in a few years. If not, check back in 2020 eoy, technology may have progressed to the point that others may also need to learn about quantum computing.

    As non-technical corporate leader, what should I do about quantum computing?

    If you are working on cryptography, or at an intelligence agency or need to transmit nation or mega corporation level secrets, stop relying on cryptographic methods that rely on factoring large integers. There are already quantum-ready alternatives as we discuss in the use cases section. Read more


    Data-driven decision making in 2019: Step-by-step guide

    Most successful organizations treat data-driven decision as a primary objective and pursue it with religious zeal. However, data-driven decision making, the steps leading to it and how AI is changing it are not well-defined.

    In an AI-first company, data-driven decision making means

    • Strategic decisions are made by a diverse group including executives that rely on a sufficiently comprehensive set of information. By definition, a bad strategic decision should be important enough to lead to failure of the company.
    • Most operational decisions are handled by continuously learning machine learning models which produce explainable decisions. Operational decisions are frequent (once a week or more frequent) and not critical (a single mistake is unlikely to lead to failure of the company).
    • Operational decisions which can not be automated with good accuracy are delegated to humans.
      • If data is lacking, opinion based decisions are made.
      • If data exists and has been analyzed, decision maker relies on analysis.
      • If data exists but is not analyzed yet, cost of analysis determines whether an opinion or data based decision will be made.

      Before settling on this framework for decision making for modern corporations, we need to identify how we can evaluate different decision making models. However, if you like you can directly skip to the sections that interest you: Read more


    Affective Computing: In-Depth Guide to Emotion AI [2019]

    A digital heart

    Affective computing systems automatically identify emotions. Affective computing is also called emotion detection, emotion AI, artificial emotional intelligence or affective AI. Understand affective computing in detail:

    What is affective computing?

    Affective computing systems auto-recognize emotions. See a longer definition below:

    Affective computing  is the development of systems that can recognize, interpret, process, and simulate human feelings and emotions.


    It may seem strange that machines can do something that is so inherently human. However, there is growing research supporting the point that human emotions are recognizable using facial and verbal clues. Read more


    Machine Learning Accuracy: Learn the Metric to Assess ML Models [’19]

    Demonstrates the 4 possible categories of results of a model: True positive, false positive, true negative, false negative

    There are various theoretical approaches to measuring accuracy* of competing machine learning models however, in most commercial applications, you simply need to assign a business value to 4 types of results: true positives, true negatives, false positives and false negatives. By multiplying number of results in each bucket with the associated business values, you will ensure that you use the best model available.

    Further complicating this situation is the confidence vales provided by the model. Almost all machine learning models can be built to provide a level of confidence for their answer. A high level approach to using this value in accuracy* measurement is to multiply it with the results, essentially rewarding the model for providing high confidence values for its correct assessments. Read more