Without any doubt, the Blockchain and AI are the two extreme sides of the technology spectrum: one fostering centralized intelligence on close data platforms, the other promoting decentralized applications in an open-data environment. However, if we find a smart way to make them work together, the total positive externalities could be magnified in a blink. The future for businesses that apply this combination of technologies with well-managed neural networks, with accurate data-points which optimize the Blockchain networks will set the absolute standard for all business of the future.

Artificial Intelligence, specifically the neural networks of learned data-points will be able to manage and monitor the Blockchain with the utmost efficiency. Smart business strategies, on the other hand, will build optimum margins.

The Blockchain is a pretty complex technology and its development and maintenance require powerful hardware and software. Nevertheless, AI provides plenty of new possibilities on this matter. As we know, one of the main advantages of the Blockchain is its security of data storing and processing. AI can assist in collecting smart insights about target audience going as far as analyzing the smallest details of customer behavior and using this data to improve the system’s performance with machine learning algorithms. We have started exploring such possibilities only recently but we are already fascinated by the fact how many possibilities it hides. In marketing automation, for example, we can collect insights in real time and send them to secure decentralized database. AI will later use this information for customized smart campaigns.

On top of that, modern AI systems feed on data. In order for algorithms to learn, they need to collect and process information, and through that action get new possibilities. This, however, gives us reasons to be concerned with possible privacy issues. Right now we’ve seen that companies who want to implement AI algorithms in their interactions with clients are frequently questioned about the security of their measures.

The Blockchain helps to make AI more transparent, and therefore, trustworthy. Since there is no single storage that could be targeted by hackers, it significantly increases the system’s safety. As I mentioned before, the Blockchain, by its nature, is a complex innovation which requires not only high programming skills but also powerful tools to handle all the processes. AI computers, along with self-learning assistants, can lend a hand with writing a code and implementing it.

Here are the examples of use cases that point out what the Blockchain and AI can do together.

Similarly, it can monitor the migration of people, groups, and the percentage of terrorist health issues depending on those movements. As AI gets this information, predictions become faster that help government agencies make better decisions regarding immigration policies and health concerns.

Besides, I will highlight a few more domains where one can track down a great combination of AI and the Blockchain. I believe we will face the further developments and emerging benefits.


Surely, digitalization has introduced complicated digital rights to the IP management spectrum, but when intelligent AI finds out the rules of the game it can point out the players who break international copyright law. Mentioning IP contract management, the Blockchain technology triggers immediate payment methods to artists and authors.


Although global organizations like NATO and the UN won’t disappear, the Blockchain technology and AI could both contribute to the development of direct democracy. The Blockchain and AI can transfer big hordes of data globally, tracing e-voting procedures and displaying them publicly so that citizens can engage in real-time.


Smart contracts could also take center stage where transparent information is essential in financial services. Financial transactions may no longer rely on a human “clearing agent” as they’d become automatized, performing more efficiently and faster. However, since confidence in transactions remains dependent on people, AI can step in to monitor human emotions and predict the most optimal trading environment.


Moreover, green-friendly AI and the Blockchain help reduce energy waste and optimize energy trade. For example, an AI system governing a building can predict energy use by taking into consideration such factors as the presence and number of residents, seasons, and even traffic details.


Mentioning that the role of AI in Healthcare is dramatic will be an underestimation. AI and Machine Learning can introduce those changes that have an essential impact on healthcare processes and administration. While there is a lot we have to overcome to reach the stage of AI-reliant healthcare, there is sufficient potential in the technology today to push governments, healthcare institutions, and providers to invest in AI-powered solutions.

So far, Artificial Intelligence and Machine Learning are supposed to make the work of healthcare providers more logical and streamlined than repetitive. The technology is helping reshape personalized healthcare services decreasing the time to search for information that is critical to decision making and triggering better care for patients. Artificial Intelligence in Healthcare has immense potential to improve costs, the quality of services, and access to them.

Automated Image Diagnosis with AI/ML

Medical image diagnosis is another AI use case in Healthcare. Besides, one of the most essential trouble spots that medical practitioners have to encounter is handling with the volume of information available to them, thanks to EMRs and EHRs. This data also includes imaging data from procedure reports, pathology reports, downloaded data, etc. Moreover, in the future, patients will send even more data through their remote portals, including images of the wound site to check if there is a need for an in-person checkup after a treatment period.

These images can now be potentially scanned and assessed by an AI-powered system. X-rays are only one piece of the puzzle when it comes to medical imaging. We also have MRIs, CT scans, and ultrasounds.

Oncology and Pathology

Whenever we talk about Artificial Intelligence in Healthcare, we shouldn’t leave   deep learning aside. Researchers are using deep learning to train machines to identify cancerous tissues with the precision comparable to a trained physicist. Deep learning presents unique value in detecting cancer as it can help aim at higher diagnostic accuracy in comparison to domain experts.

Machine learning in Healthcare can help reshape the efforts in pathology often traditionally left to pathologists as they often have to work on multiple images in order to reach a diagnosis after finding any trace of deviations. With assistance from machine learning and deep learning, pathologists’ efforts can be streamlined, and the accuracy in decision making can be increased.

While these networks and AI-powered solutions can assist pathologists, we need to emphasize that artificial intelligence is not replacing physicians in this domain any sooner. Deep learning networks can only become so nifty when they get experience and learning over a period, just as physicians do.

AI in Healthcare, specifically in pathology, can help substitute the need for physical samples of tissues by improving upon the available radiology tools – making them more accurate and detailed.

Errors and frauds scar the landscape of healthcare. Therefore, one of the most critical of using AI in Healthcare is providing the security of data and possible solutions. Fraud and breach detection traditionally depended on reviewing systems manually. However, using Artificial Intelligence in Healthcare for monitoring and detecting security anomalies can create trust as the foundation for more digital disruption in the healthcare area.

Managing Data

AI approaches employing machines to sense and comprehend data like humans has opened up previously unavailable or unrecognized opportunities for clinical practitioners and health service organizations. Some examples include utilising AI approaches to analyse unstructured data such as photos, videos, physician notes to enable clinical decision making; use of intelligence interfaces to enhance patient engagement and compliance with treatment; and predictive modelling to manage patient flow and hospital capacity/resource allocation. With modern Medicine facing a significant challenge of acquiring, analysing and applying structured and unstructured data to treat or manage diseases, AI systems with their data-mining and pattern recognition capabilities come in handy. While leaving the communication of serious matters and final decision making to human clinicians, AI systems can take responsibility for routine and less risky diagnostic and treatment processes. The intention here is not to replace human clinicians but enable a streamlined high-quality healthcare delivery process. Healthcare delivery has over years become complex and challenging. A large part of the complexity in delivering healthcare is because of the voluminous data that is generated in the process of healthcare, which has to be interpreted in an intelligent fashion. AI systems with their problem solving approach can address this need. Their intelligent architecture, which incorporates learning and reasoning and ability to act autonomously without requiring constant human attention, is alluring. Thus the medical domain has provided a fertile ground for AI researchers to test their techniques and in many instances; AI applications have successfully solved problems with outcomes comparable to that of human clinicians. As healthcare delivery becomes more expensive, stakeholders will increasingly look to solutions that can replace the expensive elements in patient care and AI solutions will be sought after in these situations.

The role of Artificial Intelligence in Healthcare is not constrained by the mentioned above examples. As trends come up and physicians look for more innovative ways to improve healthcare services and experiences for patients, we will have novel concepts turning into reality. Despite the fact that the healthcare space is the fertile soil for changes, it will be a while before these systems can be made affordable and available to all healthcare institutions.


It is well-known that outsourcing has always been ‘threatened’ by forces–only for it to achieve new levels of scale and innovation. For instance, with the increasing use of cloud computing, companies will very reluctantly prefer having their own set of IT assets and infrastructure. A simple “lease and use” model is something which helps them to be independent from maintaining and handling the issues related to their own IT infrastructure and at the very same time, it is cost-effective too.

Considering the fact that revenues for a majority of outsourcing service providers came from application development, application maintenance and implementation, and the fact that there was no software to install, maintain or develop in the cloud model – the cloud was seen as a threat to the outsourcing model. Today, outsourcing service providers have converted the cloud threat to an advantage by offering services to organizations who want to build their own private clouds, or those who want to migrate from traditional on-premise solutions to the cloud. In summary, outsourcing will remain, and will continue to remain a formidable force – though the scale and the type of services to be offered in the future will significantly differ.

Having said this, how would the future of outsourcing really look like? Given the speed with which we see a shift in trends and technologies in IT industry, it is really tough to take a stance and comment about where is IT Outsourcing heading to? Still few macro trends are there that we can see on the horizon, and which can form the core components of outsourcing business models in the near future. Below are the few trends, which we are bound to play a very important role in shaping the future of IT outsourcing industry.

Started with “Software-as-a-service”, ”Infrastructure-as-a-service” to “Platform-as-a-service”, thanks to the cloud, we have achieved the state where the model has been changed to “Everything-as-a-service”. Today, almost every service can be offered in a virtual way. Consumers, hence, will not worry where the service is coming from. The word ‘offshore outsourcing’ may lose its relevance and a more appropriate word such as ‘virtual sourcing’ may eventually take its place. Services will be consumed via a self-service model, and will require minimum intervention from the service provider. While today we already have Storage-as-a-service, Communications-as-a-service, Network-as-a-service and Monitoring-as-a-service, the future will represent a catalogue driven model, where customers will pick and choose services off the shelf with defined expiry dates.

Most IT services are now commoditized, and this trend is likely to continue in the future. What will define outsourcing service providers is the ability to offer automated self-service platforms which will allow customers to use the platforms of the service providers for performing tasks such as testing or development. Organizations will have the option of creating their own tasks on these self-service platforms, and monitor them using sophisticated tools provided by service providers. For example, a customer may use a regular platform of a service provider to centralize processes, and apply common standards and rules to ensure consistent practices across global locations. Using steps and processes clearly defined in the platform, global organizations can apply automated technologies to enforce enterprise policies.

In the future, we may see organizations take an investor like approach and fund joint initiatives with outsourcing service providers, where both sides invest to create a favorable risk-reward ratio. The co-creation can be in the form of products or service-led innovation, where the client organization looks to make investments to boost the service provider’s capability to develop products or innovative services and reduce time to market. This will not be similar to marketing alliances, but will involve significant amount of research and time to improve a process or system. The same product or service may then be offered to other clients of the service providers, with the revenues and profits shared equally between the service provider and the client. For example, a large airline can use/offer its knowledge and expertise to a cloud hosting company in building a core solution for other small airlines with built-in frameworks, in association with a technology service provider.

While crowd sourcing refers to the art of using the power of the crowd to solve problems or tasks, it has not been harnessed to its full potential in the world of outsourcing. Going beyond the usual tasks of crowd sourcing, outsourcers will search for the help of the community in developing an algorithm, code a new program or create a new method to build a better IT architecture. To do this, outsourcers may set up a managed crowd sourcing model with the help of service providers, who may define the way the process must work through a well defined workflow or template. Crowd sourcing has the potential to dramatically alter the way outsourcing is done today, as it allows outsourcers to tap individual talent located in different corners of the world. A good example of crowd sourcing is Silicon Valley start-up,  Kaggle. This company has the world’s largest community of data scientists, and they compete with each other to solve complex problems. Prizes are given to whoever offers the best solution. Crowd sourcing can also help outsourcers develop quality solutions, as the community can rank the best solution for a particular issue or problem.

With the growing adoption of cloud computing and the popularity of the “pay for what you use’ approach, one can expect outsourcing agreements to be defined by outcome-based models where the service provider gets a huge opportunity to demonstrate its capability based on the level of efficiencies achieved or the market share gained. This is a win-win situation for both the service provider and the client, as it focuses on the value generated by the deal. However, as customer value is not a static asset, service providers will need to continuously reinvent themselves as markets evolve, technologies change, economies rise or fall and most importantly change based on what customers want and expect. If they succeed in doing so, they can easily convert small projects into engagements and engagements into long term relationships.