The Next Wave of Artificial Intelligence Applications
We are often asked if robotic process automation, and specifically artificial intelligence (AI), can solve a specific business problem and the answer is typically, “Yes!” But where is AI headed? How can machine learning, natural language technology and structured analytics change and improve business operations?
Let’s take a closer look where these technologies are now and how they can be applied in the near future.
Artificial Intelligence is, increasingly, everywhere. AI allows Uber to disrupt the personal transportation market across the globe by learning trends and matching routes with drivers and riders. AI enables Facebook to automatically photo-tag a post you see in your newsfeed and helps serve the ads you see. AI powers the voice assistant on your phone and the shopping recommendations on nearly every major e-commerce site. AI is also used by Google to improve search results, operate its self-driving cars, and beat the world Go master.
Perhaps more prosaically — but nonetheless with far-reaching effect — AI, in the form of smart robotic process automation, is being implemented in financial services, insurance companies and much more with productivity yields reportedly of over 1,000%.
According to an analysis by McKinsey, AI technologies that exist right now could automate 45% of the activities people are paid to perform; and that about 60% of all occupations could see 30% or more of their constituent activities automated.
AI is a broad field and covers many applications, but the areas of perhaps most intense focus today include:
- Machine Learning (ML) is best thought of as a subset of the AI field. It means basically what it sounds like: software (machines) written in such a way that it can learn from experience (via data) to make improvements in function without operator or programmer input.
- Deep Machine Learning (aka Deep Learning) — in a hierarchy where machine learning is a subset of AI, deep ML is a subset of Machine Learning. Deep ML is the most advanced form of machine learning, and it works by leveraging Neural Nets.
- Neural Net — a Neural Net is a software architecture that mimics the function of the human brain for learning and processing. In a Neural Net, software nodes mimic neurons, and perform generally one task. In turn, these nodes pass the output from the task they perform to another node, and so on, allowing a layered approach to processing… and in deep ML, this architecture is what enables the ability to learn from the data that has been processed.
- Analytics — both structured and unstructured. Structured analytics refers to data analysis on well-organized datasets; unstructured analytics — which is much harder to do — refers to working with data that may be “dirty,” poorly organized, and/or coming from disparate sources. With respect to AI, effectiveness of any type of artificial intelligence will depend substantially on the quality of the data that is being analyzed.
- Natural Language Processing (NLP) is another important area of AI. NLP is also exactly what it sounds like: developing software-based algorithms that can better understand human speech. When combined with Machine Learning, the NLP is able to improve over time (and with more data).
So, what are the implications, especially for business-to-business mid-market companies? Here are a few examples of where AI is headed:
- Human-Intensive Processes — do you have human-intensive processes that are not highly variable, such as accounting and AR/AP, customer service, data entry, data processing, predictable physical work? All of this — and more — can be automated leveraging AI techniques, in many cases with simple machine learning. In other words, you won’t need IBM’s Watson to automate much of this labor-intensive work… The algorithms to do so are fairly simple and inexpensive to build, and the technology to do so exists right now.
- In-Shore Versus Off-Shore — have you relied on off-shoring to reduce sourcing and labor costs? AI-driven automation may fundamentally change that equation, and you may find that it is more efficient, cost-effective and yields better results to bring services and production back to your home country.
- Core Business Processes — AI and, especially, machine learning will create new and more cost-effective ways of thinking about diagnostic processes, logistics, service/product implementation, and much more. Basically, if something can be optimized, AI and machine learning tools will make them better, more predictable, faster, and cost-effective.
We are on the cusp of sweeping change in the business world, with entire industries being fundamentally altered and disrupted. Artificial Intelligence and its derivatives are driving this change. It is incumbent upon every business leader to figure out how to best deploy this incredible powerful set of tools to thrive in this brave new world.
If you need any help, we welcome the opportunity to arrange a call to discuss how AI can impact your business.