While AI has many uses for securing a business, it is also instrumental in a better bottom line. The convergence of artificial intelligence (AI) and the internet of things (IoT) has created a new business ecosystem that stretches from the front-lines of physical and cybersecurity to the server rooms of enterprise organizations and smaller commercial companies. The one constant among all current and emerging applications of AI is that data collected is refined and consumed in a proactive way that can protect, expedite and expand the business interests of most organizations.
It seems that almost anything that plugs into an electrical socket or represents the latest “must-have” in business or consumer technology solutions is being marketed to provide some form of AI. New gas, electric and hybrid vehicles all have AI. New white goods like dishwashers, refrigerators, washing machines and dryers have it, too. Businesses are employing AI to help enhance customer engagement, keep shelves stocked with products those customers want and guide the customers around a retail venue to find them once they are inside. The expanding world of AI and the interconnectivity of IoT has quickly moved from a novelty trend to the new reality.
Overall, I believe this is a good thing for both business and consumer products because the products will be smarter and provide better performance, utility, integration and overall value. The development of smart machines has been in the works since the middle of the past century as scientists looked to develop devices that could think like humans. The evolution continued through the mid-2000s as powerful processors and computer graphics cards brought AI to consumers in the form of smartphones and tablets. Adding the voices of AI mavens like Apple’s Siri, Microsoft’s Cortana and Amazon’s Alexa continued to expand the universe of these new “artificial neural networks,” or ANN for the sake of expediency.
ANN Can Bring Transformational Change
ANN is basically the framework for letting many different machine learning algorithms work together to process very complex operations, including what we now commonly refer to as AI. The concept behind ANN is simple: to create machine processes that mimic how humans process information. More importantly, ANN is what enables software and embedded devices to perform various tasks and operations by considering different documented behaviors as examples for comparison.
According to a report from Narrative Science and the National Business Research Institute, more than 60% of businesses implemented AI in 2018, up from only 38% in 2017. The report added that as ANN devices move deeper into the mainstream of business, it is important to remember that this is just the beginning of the migration, not the endpoint.
As businesses find more ways to integrate ANN into their operations, the impact across the enterprise will increase. The report concluded that organizations have already embedded ANN functionality into operations including business intelligence (90%), finance (87%), compliance/risk (55%), product management (68%), marketing/sales (77%) and communications (43%).
AI and machine learning are considered synonymous by many laypeople today. Systems that implement machine learning today may employ one or more of three different electronic learning techniques: supervised learning, where continuous feedback is presented to formulate decisions; unsupervised learning, where decisions are based on estimation of statistical data received by the device; and reinforcement learning, where rules are adjusted based on a combination of estimating data inputs and preestablished rules to reach the desired results. (Note that these simplistic definitions summarize much more complex operations.)
Using any of these forms of learning, ANN has been the foundation for numerous intelligent product and software solutions over the years. Examples range from handwriting recognition for law enforcement to predicting stock prices to today’s autonomous driving cars. The point is that analytics-embedded, intelligent devices have become so commonplace that soon, I believe virtually anything that requires some form of power to operate will also feature AI based on ANN. This new generation of devices provides new and unique benefits across virtually every category of business, regardless of the size or level of technical proficiency.
Where The Rubber Hits the Road
While I’ve seen through my own company’s intelligent video security offerings that the physical security and public safety sectors have realized benefits from the enhanced analytics in today’s video surveillance systems, the ability to translate AI-based applications into tangible business operations has almost unlimited potential.
The most attractive aspect of implementing an ANN solution into a midsized business is the promise of a tangible return on investment. Although many smaller business owners possess a misconception of AI being a costly solution, the track record of ANN implementation shows numerous cost and operations benefits that can aid in long-term operational cost reductions and higher employee efficiencies. For instance, if you operate a retail business and have already deployed video surveillance systems in your facilities, you can expand the system’s functionality. For example, you might use it to track customer movement in your store or monitor engagement.
If you’re a nonenterprise adopter, you must take a measured approach by ensuring you fully grasp the organization’s motives for ANN implementation and how its benefits can be leveraged, as well as the cost benefits realized.
Like any new technology tool, employees must regard it as a force-multiplier and a problem-solver, not a gimmick. Working with staff to identify problem areas that ANN solutions solve and then establishing a strategy to deploy it carefully and at a realistic pace will negate potentially costly mistakes. Remember that most ANN solutions are deployed to augment staff and employee operations, not replace them.