Technology-driven advancements will revolutionize how global industry players respond to changing consumer and co-worker behaviour and needs, develop and foster alliances throug strategic partnerships, and drive transformational entries in newly identified target segments and industries for growth.


Refers to the process by which access to technology and platforms fastly continues and evolves to become more accessilbe offering greater ease-of-use and more best-fit-for-purpose for anyone, and everyone.

New tech and improved UX have empowered those outside the technical IT industry to access and use any technological product and service as they see fit for their purpose and their benefit by no-code, Do-it-Yourself platforms with just-click, no-code interfaces for advanced product innovation and development with high speed velocity and time-to-market, and time-to-value in the create of value.

It democratizes, institutionalizes and empowers AI and IT to the non-technical people, to the masses, to the business users and strategic business areas, to analysts for data insights, and for their predictive analytics & reporting, reliable, real-time and secure, without any dependency nor on restricted by any developers anymore, anytime, and anywhere.

The democratization of AI and IT is seen as the final stage in the evolution of information technology and services. It's genesis and origin is the 1st, digitization, then deception and as 3rd the digital transformation and disruption and each stage it moves more exponentially to the next, and amplifies it.


Refers to an IT-based organizational model that supports customer service, repairs & returns, co-worker motivation, and deployment of products/services for the create of value from anywhere, regardless of physical and/or digitized location. It depends and relies heavily on pre-packaged IT capabilities widely available and needed for this, being.

Integration for the unlocking and sharing of all data easily from anywhere, and to anywhere, both digitized and in the physical form as well as distributed cloud for always access easily to all data, from any where, and at any time.

It also needs legacy modernization to also get that data out of the legacy and/or (heavy) customized systems, whenever one chooses, wherever, and at any time to reduce any technical debt and any vendor lock-in as it should be digital strategy wise.

These enable the hybrid workforce these day as a global response to the pandemic.


Refers to the use of advanced technologies, like artificial intelligence (AI), machine learning (ML), and moving by just rule-based robotic process automation (RPA), to automate tasks that were once completed by humans.

Hyperautomation not only refers to the tasks and processes that can be automated, but also the level of automation. It is often referred to as the next major phase of digital transformation.

The ability to include humans in the digitization process is a key component of hyperautomation. The first wave of automation technologies largely relied on robotic process automation (RPA). RPA involves the use of bots to mimic repetitive human tasks. These processes are rule-based and utilize structured data - information - to complete actions.

Unlike artificial intelligence which seeks to simulate and stimulate the human intellect, RPA focuses solely on human actions, and not on intent. With hyperautomation, advanced digital agents such as virtual mediation, rating billing agents, digital agents for AI/ML/DL, VR/AR/AX virtual agents. All of these machines and the other onse operate alongside humans to deliver unmatched efficiency in the creation of value, and organizations will automate anything that can be automated.

Another major attribute for hyperautomation is integration. To achieve scalability in operations, various automation technologies must work together seamlessly, and all date needs to be easiliy shared to customers and for co-worker purposes and work, and their tasks to be done daily. The data also needs to exchanged with all partners in ones ecosystem, real-time and unstructured in raw formats.


Refers to the combination of IOT, digital manufacturing and machine learning that usher us into the 4th industrial revolt with the factories of the future made up by them. The 4th revolt marks the transition of human labor into intelligent machines, autonomous robotics and EVs that run by themselves in the manufacturing process, and the logstics of raw materials and produced goods.

The history of manufacturing can be traced back to the 1st industrial revolt during the 19th century, where raw materials were converted into finished goods. The period marked the transition from human labor technology into machinery and chemical manufacturing processes, turning artisans into wage laborers.

It will turn the factory wage laborers on payrolls back to creativity, to science and art.

Surprisingly, a big part of this augmented industry 4.0 genesis and origin is not Sillicon Valley, but Berlin.

That is because the German people have been creating physical things for a long while now. The cars, toys, turbines, trains, powerplants, etc. they all make, and by which the German mass production has become distinctively efficient and effictive. Silicon Valley has always been evovling around just digitizing stuff, and did not originate from manufacturing, but from print marketing, and before that the military in the US. It's furthermore a horizontal one, not a vertical industry like automotive.

No other industry in this is undergoing as rapip technological growth as the automotive industry, and it will be dominated and owned by Berlin. The automotive industry comprises a wide range of companies and organizations involved in the design, development, manufacturing, marketing, and selling of motor vehicles. It is one of the world's largest industries by revenue.

4 Disruptions are said to drive the next revolt:

- the spectacular rise in elastic cloud compute, cloud storage, cloud connect, and cloud power and energy neutral energy creation and distribution by demand and supply grids made by a coin in common - crypto, the blockchain ledgers;

- the emergence of predictive intelligent analytics and the use of vast amounts of data anywhere, anyplace, anytime and realtime in a reliable and secure manner by system integration for the unlocking and the easily sharing, and exchanging data with partners across value chains;

- new forms of human-machine interaction, such as the metaverse, AX, VR, AI;

- Game changing breakthroughs in transferring digital instructions and digitized data into the physical world, such as advanced autonomous cognitive robotics - the machines - and other autonomous things.

It uses and utilizes autonomous manufacturing concept and technology set that is used by today's manufacturers in order to achieve greater productivity, profitability and efficiency no matter what demands they face. Factories that employ "lights-out manufacturing" are fully automated and require no human presence on-site. These factories are considered to be able to run "with the lights off."

Many factories are capable of lights-out production, but few run exclusively lights-out.


Refers to the distribution of public cloud services to different physical locations, while the operation, governance, updates and evolution of the physical and self-services are the responsibility of the originating public cloud provider.

A cloud enablement provider offers and executes professional services indepentent of the original cloud vendor and on a global scale. Cloud enablement partners work independently and one is not tied to a single original cloud provider, but has the right to choose based on the requirements, best-fit-for-use, best-cost, easy-of-use, etc., and based on individual use cases for hyperscale compute, connect, storage.

Distributed can be run and operaate as multicloud, intercloud and hybrid cloud with on-prem infra, and/or any or all of this combined for best fit.


Refers to applying large numbers of resources, usually large amounts of processing capacity or data storage, to a single task, by applying resources from more than one system. A grid is a collection of resources that’s coordinated to enable the resources to solve a common problem. A computing grid harnesses multiple computers from several owners to run one very large application problem.

Data fabric computing is a set of computing, storage, memory and I/O components joined through a fabric interconnect, and the software to configure and manage them, and applying mass automation to it.


AI engineering is an emergent discipline & doctrine focused on developing people & culture, advanced product innovation, tools, robotics, EVs, machines, systems, and combined digital and physical processes to enable the application of artificial intelligence in real-world contexts.

In contrast to the prevalent rush to develop IT capabilities and progress as single, individual and siloed tools, AI Engineering uses the holistic view and approach in its development and delivery. The rise in availability and ease of accessibility of distributed cloud compute, cloud storage and cloud connect and massive datasets through the unlocking and easy sharing of all data by integration have led to the creation of new AI made up of thousands of variables, and able of making rapid and impactful cognitive decisions.

AI engineering provides, roadmaps, a framework patterns, centres of enablement and co-creation (CoEs and no-code, do-it-yourself tools for this to proactively design and deploy AI systems to function in environments characterized by high degrees of complexity, ambiguity, and dynamism. The discipline of AI engineering aims to equip practitioners to develop systems across the corporate enterprise-to-edge spectrum, their entire value chain and ecosystems in the creation of value, and to anticipate requirements in changing operational environments and conditions

AI engineering ensures and guarantees business and human needs are translated into understandable, ethical, and thus trustworthy AI and its machines.

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Through 2023, at least 50% of IT leaders will struggle to move their AI predictive projects past POCs to a mature production stable state.

Gartner Strategic Assumption

They fail in their time-to-market, time-to-value, and their velocity in their attempts to create value.