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Three components of AI

There are a number of technology trends that are being embedded into many customer and employee-facing solutions where AI is significantly accelerating available use cases and impact. To better understand AI, it's helpful to understand three aspects where it is making a difference:

  • Proximity Solutions. This technology is in high demand by retailers and business owners wanting to connect with consumers in nearby geographic locations. In order to accomplish this, a company needs solid network discovery in place as well as application integration.
  • Interactive Intelligence (II). II is an integral part of AI. Companies are thirsty for knowledge to better understand how humans relate with technology. This is where II comes into play. II professionals have an understanding of how technology and people interact and how to leverage this interaction and the information and communication derived from it. II includes the use of predictive analytics and chatbots.
  • Analytic-driven Decisions. Analytic-driven decision-making continues to emerge throughout the enterprise to help business users make faster, smarter decisions based on corporate data. Using data analytics and context, companies are able to leverage fact-based decision-making to automate repetitive processes, improve user experiences, and more quickly react to market demands.

AI in the infrastructure

Although AI can transform an organization, it's important to note that AI in a vacuum offers very little. To produce new applications and achieve new experiences from AI, significant technical events have to occur.

It first starts with a strategy and a vision for innovation. As part of that planning, we must understand that AI rides within the network, runs on the infrastructure, and is integrated in a way that connects with people and systems. In other words, AI must be weaved into the fabric of our systems to influence how we work, live, and interact with our customers, markets, and future opportunities.

Once we've integrated AI with our architecture and accounted for integration, UX design, and analytics, our environment has been set to enable AI to go to work.

AI at work

Let's consider a simple scenario of how AI can work, power management within the home. Using voice-to-text technology, AI could reactively adjust our thermostat based on how we are feeling at the moment. By learning the characteristics of our house and our preferences, it could make further adjustments on its own.

To do this, we could integrate third-party applications for AI to correlate with and manage. We would incorporate various data captured by the thermostat (e.g. how long it takes to increase temperature based on the efficiency of our equipment) with other data such as outside temperature, temperature in different rooms within the house, humidity and other factors.

While AI can perform these actions to increase our comfort through intelligently adjusting the thermostat, there are other potential downfalls that would cause us to be uncomfortable, such as our air conditioning unit breaking down on the hottest day of the year right before our dinner party (isn't that what always happens). To address this, we would need analytics in place to proactively monitor our HVAC system, anticipating failure and optimizing performance through intelligent sensor integration. This isn't the future, it is out there today.

AI in the enterprise

As a real-world example of AI working in industry, a large financial organization recently reached out to WWT asking if we would design, build and efficiently operate a resilient, agile intelligent and automated infrastructure to help them prevent and survive cyber events.

To do so, our project team built an AI environment based on an integrated enterprise architecture. The results were nothing short of spectacular. The organization believes that through AI and machine learning, it's well on its way to substantially lowering operational costs.

The financial institution was able to collapse workstreams through automation, reduce time between occurrence of errors and correction of the systems (i.e. reduction in time to fix, as well as reducing impact of outages), and eliminate many manual tasks previously done by people that are now automatically completed by the system. These tasks include quarantining penetrated systems and creating new virtual machines, including launching of related applications to replace services provided by penetrated systems. This allows people to spend time analyzing and understanding vs remediating issues.

A main factor for the success of the project for the financial institution was our ability to replicate their environment in the Advanced Technology Center (ATC). The ATC is a collaborative ecosystem to design, build, educate, demonstrate and deploy innovative technology products and integrated architectural solutions for WWT customers, partners and employees. The ATC allowed us to accomplish development, testing and deployment in a matter of weeks, and in many cases, days, that would typically take months or years to accomplish on its own.

Business and AI

Desires for business growth and greater efficiencies are driving AI. Companies are looking for new ways to streamline processes, automate repetitive tasks, reduce costs, enhance user experiences, and deliver innovative products and solutions. These events can all occur by leveraging AI, but make no mistake, the future of AI is now upon us, and many companies are currently reaping the benefits that AI can deliver.

Rather than viewing AI as a separate initiative within IT, leading organizations are integrating these technologies into the fabric of how they deliver technology solutions. Through iterative implementation, learning that occurs through integrated pilots and focusing on business value, AI is accelerating it impact on our lives and the success of our companies.