Deep Learning, Artificial Intelligence, and Automation Consulting

Few technologies or developments in the last decade have had as sweeping an effect on modern business as artificial intelligence and automation. Neither of the two concepts is new. Automation and AI have been in use in businesses and organizations for at least the last sixty years. However, advancements in AI and automation have transformed the two processes into new, nearly unrecognizable products.

For much of the 21^st century, when automation and AI were used in a business context, they described the conversion of repeatable, low-skill tasks into automatic and often highly mechanized workflows that didn’t require a great deal of human interaction.

Automation and AI were used to drive the transformation of highly repeatable, low-skill tasks into automatic and often mechanized processes. One common example is automation in manufacturing and agriculture, which allowed smaller teams to take on much larger workloads. Essentially, automation and AI certainly had workplace applications, but they remained somewhat primitive in their functions. While automation could be implemented in the assembly line of an automobile manufacturer, asking for help with navigating stock trades or parsing natural human language remained far out of reach.

That’s no longer the case. Automation is now used for advanced, high-knowledge tasks in medicine, computer programming, and natural language processing. From developing algorithms for financial institutions to translating different languages in real-time, automation has moved from repeating menial tasks, to “deeper” learning that can simplify and rapidly complete entire workflows.

The missing piece of the automation puzzle was machine learning. Machine learning is a method of analysis that allows analytical models to “learn” over time. When data models are fed data sets, they can identify patterns and later make decisions based on past data. When human overseers input enough data and provide guidance on which decisions are correct and incorrect, the machine learning model learns and fine-tunes its decision-making over time. The result is an automation model that can take on increasingly complex workflows.

For example, IT teams have already used chatbots on sites to automate many parts of the customer service workflow. Chatbots can handle customer intake requests and direct users to answers on the site. Using “deep learning” can take this step forward. Advanced automation can allow chatbots to not only intake customer requests, but to then forward these on to additional automated workflows in inventory management, accounting, or a human operator for additional action. Using an advanced automation workflow, the entire customer service workflow can be automated, from customer request intakes to issue resolution and confirmation.

Another example is personalized shopping or entertainment. We’ve all seen how the internet has fostered personalization and customization when it comes to presenting choices and options. Your Netflix or Amazon dashboard does not present the same options as your friends or family’s, instead, it has been carefully curated to fit your previous choices and decisions. This is possible through deep learning models that quickly categorize all content and products to fit similar products together to provide meaningful suggestions.

The benefits of such an automation are obvious. AI-driven bots can work day or night, without any loss of accuracy or performance from fatigue, and without the need for weekends or vacations. When complicated workflows are automated from start to finish, the gains from automating each individual step are compounded through the full workflow.

Additionally, as we’ve seen in the example of customized shopping and entertainment choices, automation that employs deep learning can make cognitive decisions with accuracy and at scale. Imagine the hours that it would require a human to carefully categorize an individual’s Netflix queue based on their previous choices. A deep learning automation can make millions of similar decisions in a matter of seconds, and with a reasonably high degree of accuracy.

Not every business was suited to implement automation in the past, but every business will need to implement deep learning automation in the future. The possibilities are endless for every business and major industry. Need a better way to predict your customer trends and preferences? Looking for a way to better set meetings and agendas among your team? Automation consulting might be the answer. Automation consultants can help establish a deep learning model or a machine learning algorithm for your team or help augment your current automation capabilities.

At Affirma, our team of consultants, developers, and engineers are dedicated to using the power of technology to solve modern business problems. We’ll work with you to identify workflows in your business that are ripe for machine learning automation, and then prepare models that can bring improvements in efficiency and performance to your business processes.

The future of business will belong to those who can respond to new situations and challenges in ways that are productive and efficient. We invite you to learn more about how automation consulting can take your business into the future.