In the age of the internet of things, automation is not limited to robots performing repetitive processes. robotic process automation (RPA) combined with artificial intelligence and machine learning capabilities allows for communication and collaboration with different bots, application program interfaces (APIs), or devices just like humans collaborate. Each inter-connected program can interact with each other and complete the required tasks without any human intervention.

However, as the business grows in scale, the business problems grow more complex and require solutions that are as reliable as human experts and also have self-learning capabilities.

Similar to the human experts, the intelligent automation solutions that imitate a human expert must base its decision on its knowledge base. For example, automation programs with natural language processing (NLP) capabilities can make sense of the consumer opinion expressed on different platforms and decode large amounts of unstructured consumer data. This acts as a knowledge base on which an enterprise can automate and personalize ads. This consumer opinion data is also crucial to predict consumer’s future buying behavior and automate sales communications to promote products that are aligned with the consumers buying behavior.



Man and machine communication:

Intelligent automation is playing a pivotal role in marketing. As developers connect RPA programs with wider data pool such as search history and browsing behavior, intelligent automation is becoming even more effective through being able to manage big data and turn it into insights that marketing teams can use for personalized ads, product recommendations or improved customer experiences.

This gives a glimpse of the future of intelligent automation where automation can work alongside humans, improving the customer experience whilst freeing up the existing teams to become more strategic and add more value to the business. Processes in sales, HR, finance, or procurement across multiple sectors, which require manual repetitive tasks can be automated using RPA and when combined with AI delivers an improved experience for the end-user. Over the last few years, intelligent automation has been adopted for training and development, customer self-service chatbots and customer journey mapping.

Technology disruption and global competition are reshaping the way the world does business. Recent innovations in virtual reality (VR) and augmented reality (AR) are providing new ways for training and development of teams and as the modern-age organizations step into this new time, enterprises must focus on training employees to work alongside bots and elevate their departments to become more strategic.

Proven Consult as your transformation partner, delivers AI and ML-enabled RPA solutions to accelerate your business process. We create customized VR and AR programs that suit your workforce needs with engaging training experience.

To learn more about how we can help you contact us on or +966 11 411 1127.



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Strategizing Data as a Service

Importance of Big Data is evident in every industry and in every scale of business. The need for greater storage capacity, faster data services, and agile solutions has increased substantially over the past couple of decades. These are the needs of the new-age enterprises to sustain and survive in a competitive market. These needs have inspired enterprises to go beyond an internal IT team and look for a holistic data solution, even if it required outsourcing. Data as a Service (DaaS) is a data management strategy where companies outsource data service. Be it—data storage, processing, analytics, or monetization, these services are offered via a cloud network where data is stored in servers and transmitted through the internet.

Traditionally cloud technology was only meant for hosting an app or basic data storage. Although, the recent innovation in computing power and internet speed has enabled cloud computing services to go beyond its legacy approach. This has enabled enterprises to strategize how they can use integrate data at all the points of sale and touchpoints to know and serve their customers better. Along with this, cloud computing is responsible for analytics which requires higher computing power, therefore, greater cost.

As managing cost is one of the biggest challenges in Data as a Service, it is clearer that monetizing data is even more essential. Therefore, businesses partner with data service enterprises that offer a holistic solution and flexible data services as per their business’s needs.

However, some DaaS firms limit the number of tools that a business can use, to offer services at a low cost. Users can work with only those tools that are hosted on or compatible with their DaaS platform, as opposed to using tools of their choice. Hence, business leaders must prioritize agility and flexibility while building their DaaS strategy.

Although, depending on the location and the region you are operating your business in, this strategy can change. As countries have different data and privacy regulations, it can affect your DaaS strategy. For instance, few countries may have privacy laws that require Data of a consumer in a specific country, be stored in local servers. As the data and privacy laws differ from one country to another dealing with different government compliances is one of the roadblocks in DaaS. This also may lead to an addition in latency and cost of accessing data globally.

In the times where companies focus on rapid and disruptive growth, an increased latency time is challenging for companies’ growth. Accessing data of a consumer in Sweden from the US can be difficult because of the transmitting time between servers in two different countries. As a result, companies store these data in multiple locations for faster delivery of data.

With growing consciousness about data privacy and security, the DaaS service must comply with all the regulations and be transparent with consumers. In the times where consumers are more aware of the usage of their data, focusing on building security features and trust is crucial. Encrypting data, building a firewall system, and a clear privacy policy helps build a trustworthy relationship between a brand and a consumer.

Be it flexibility, agility, security or latency, these challenges require a holistic solution that guarantees quality standards.