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Why future applications must be cognitive-first

The explosive growth of data has led to a state where humans alone can't manage it - even with an army of data scientists

Sophia, a robot integrating the latest technologies and artificial intelligence developed by Hanson Robotics, is pictured during a presentation at the "AI for Good" Global Summit at the International Telecommunication Union (ITU) in Geneva, Switzerland, earlier this year.

THE March 2017 Gartner Report, "Top 10 Strategic Technology Trends for 2017: Intelligent Apps," states that, "By 2018, 90 per cent of the world's 200 largest companies will exploit intelligent apps and use the full toolkit of Big Data and analytical tools to improve their customer experience."

It's clear that new technologies are rapidly and fundamentally changing what is possible for businesses and organisations of all types. For business leaders, the opportunity is enormous.

For application development teams, the challenge of harnessing today's complex set of technologies, interface types, data sources and more to deliver on the promise of intelligent apps can be daunting. These factors - and the ability to integrate predictive results into business applications, have led to the evolution of the next generation of mission-critical apps - cognitive-first.

Businesses operate at a different cadence in today's world - they need to be fast, flexible, agile, reliable and secure - and the mission-critical applications those businesses run on need to be the same. The time to embrace innovation is now.


Today's business applications must engage the user on any device or interface type based on their digital preference, which may change throughout the user journey.

That requires moving beyond a "mobile first" or "multi-channel" approach. From graphical user interface (GUI) experiences across device types and platforms, to increasingly new forms of interaction that don't involve a GUI at all like voice, chat and augmented reality (AR), the application and user experience must be completely immersive, acting on behalf of the user or engaging the user on the device or interface type of their choice.


The explosive growth of data has led to a state where humans alone can't manage it - even with an army of data scientists.

To harness this data, applications must be intelligent, or cognitive-first. The mission-critical application of tomorrow needs to have built-in machine learning capabilities to use data to predict what will happen in the future, and the algorithms powering these intelligent systems must have self-learning capabilities.

This can only be achieved by automating the complex data science lifecycle so that highly accurate analytical models can be created, deployed and continuously improved without a large, expensive data scientist presence. The end goal is to turn data into actionable insights and automatically take preemptive actions to drive outcomes.


With the number and types of sources of data that need to be integrated growing exponentially, it is critical to determine the right way to integrate and harness all that data and information regardless of where it lives - on different clouds, on-premise, in different data centres, in systems of record, data lakes, Internet of Things (IoT ) devices and more.

Even more important is the need to move beyond tactical application integration so that we can overcome obstacles that impact people's ability to use data, analytics and gain the experiences they need.

Applications must be built for Internet-scale. While the requirement to dynamically scale for transaction and data volume is not new, there are now new ways to accomplish it.

Today's definition of scale also means the ability to support different types of application and UX workloads - like IoT, or event-based processing. This requires a modern architecture that goes beyond the concept of "infrastructure as code" to "infrastructure as microservices", combining the advantages of managing infrastructure with code, along with the development agility and deployment flexibility of microservices.


Mission-critical business applications have always had to be secure, reliant and compliant. But today, that is only the beginning. They need capabilities not usually associated with mission-critical. They need to be flexible and agile, offer rapid time to market and be easy to iterate and change - all without sacrificing those traits of security, reliability and compliance


Immersive experience: Creating an immersive experience requires building applications or experiences that support all appropriate digital touchpoints in a way that creates a true connection with the user.

Cognitive cloud: The immersive experience must be supported by a cognitive cloud with a modern set of application services that include new requirements like intelligence as a service and modern backend as a service.

Connected data: The ability to connect and integrate to any data or application - regardless of location - in the cloud or on-premise with optimal performance and security.

  • This report was first published by Progress in an October 2017 Thought Leadership Series titled Emerging Technologies in Cognitive Computing and Machine Learning.