When we talk about cognitive computing, we are referring to a set of technologies designed to mimic the way the human brain works. They work a little differently from “narrow” AI. Put simply, (narrow) AI can solve problems that humans set for it, but the system has no inherent “intelligence”. This is not to diminishes the impact of AI, which has unquestionably been put to use in great ways. Take for example the AI algorithms that are increasingly diagnosing illnesses more effectively and faster than human doctors can. The same system that can diagnose cancer, can’t do other tasks. By contrast, the technologies behind cognitive computing are known as artificial general intelligence. The goal of these algorithms is to be able to make sense of complex situations, to interpret vague, incomplete or conflicting data, to learn from past experience and to apply that understanding to future scenarios.
Cognitive services are a big growth area. Valued at USD 4.1 billion in 2019, the market is predicted to grow at a CAGR of around 36 percent (Mordor Intelligence). Enterprises are using cognitive services to improve insights and user experience and to increase operational efficiencies through process optimization. Cognitive services are set to be a significant competitive differentiator. They will enable those organizations that successfully leverage cognitive to be streets ahead of the competition when it comes to understanding and improving customer experience.
Cognitive computing is a compute-intensive undertaking, which is of course why cognitive services are so closely linked to cloud-based environments. The leading providers in this field – Amazon AWS, Microsoft Azure, Google Cloud, and IBM Watson – are all cloud computing heavyweights. They offer highly modular services with capabilities such as image analysis, video analysis, natural language processing, translation, text analysis, and many other search and voice-based capabilities. By offering the building blocks to create powerful cognitive systems, these players are democratizing the whole field of cognitive computing. Competition is fierce. They each have their strengths and weaknesses, of course, but broadly speaking they cover similar capabilities. Given there are dozens of new applications and start-ups emerging across almost all the sectors all the time, defining an approach to cognitive services for your organization can be baffling.
It does not need to be. In our fifteen years designing and implementing proven strategies that put digital to work, we always start with (and return to) the underlying business case. Asking enterprises to cut through the hype is never easy, nor should you expect it to be with cognitive computing, a discipline made up of many constituent technologies.
Do you need a strategy for cognitive? Absolutely. Cognitive computing is less than two years away from creating a palpable difference between brands and wreaking digital disruption. The companies that are investing now in tooling up to take advantage of the wide range of cognitive technologies, will be significantly better placed to combine these cognitive skills to build radically better user experiences in six to twelve months. That is why cognitive services should join the arsenal of tools that can help advance an organization’s digital transformation. That is also why enterprises should look to build a business case for cognitive computing.
The next step is to start looking for the right approach to get enterprises to their end goal with cognitive services. While the big cloud service providers offer great building blocks with which to create cognitive-enabled digital products, there are plenty more options out there. A little research will be required to understand fully the capabilities and pricing options for the combination of services your organization requires. Working with a digital engineering services specialist like Apexon will ease and accelerate the journey. We base our counsel on our deep expertise in the fields of AI and ML, and on knowing the start-ups to watch. Added to that is our cloud engineering experience and the fact we are accredited Microsoft Azure Development Partners and Amazon AWS Consulting Partners.
Your path to building and operationalizing cognitive services is highly dependent on your starting point. Cloud-native cognitive services require a degree of digital maturity. For a company well used to leveraging cloud, and comfortable designing, building and deploying in a cloud-native environment, the transition to cognitive will necessarily be quicker. If your organization is still grappling with, say, automation or is fairly new to the DevOps approach, the possibilities inherent in cloud-based resources are still open to you. Apexon has a long track record of helping organizations accelerate digital, whether it’s helping them transition from monolithic to microservices architectures, implement Agile DevOps, deploy intelligent automation or create a continuous innovation pipeline.
Priming your product delivery environment for cloud-based cognitive services is one part of the equation. A robust, efficient test environment is also needed when it comes to deploying predictive analytics in real-time. As previously mentioned, a highly automated system is important since a team relying on high levels of manual intervention generally will not have the bandwidth to take advantage of what cognitive services have to offer. Apexon’s own intelligent testing suite relies on bots and other AI technologies to optimize every aspect of your testing lifecycle – improving test quality, speeding up the process and prioritizing actions that really need attention.
If you have plans for cognitive computing this year and have some questions along the way, we would like to hear them. Whatever your digital technology challenge, get in touch using the form below.