The Three Laws of Robotics were introduced to the viewing public in 1940 by Issac Asimov in his short story; "Runaround".
1. A robot may not injure a human being or, through inaction allow a human to come to harm
2. A robot must obey the orders given it by human beings, unless such orders would conflict with the first law.
3. A robot must protect its own existence, as long as such protection does not conflict with the first or second law.
What AI Isn't Good At
These days, artificial intelligence (AI) seems to be everywhere. With the number of tools and services on the market claiming to leverage AI, some might fear we're heading toward a future where intelligent machines manage every aspect of business operations,
and human workers become completely obsolete.
While it's true that AI systems have been increasing in complexity and capabilities in recent years, AI still falls short in several critical areas. Rather than acting like a synthetic human, as you might see in movies, today's AI is predominantly structured
and rule-based, or heavily dependent on what is called "machine learning" (ML), which enables powerful data analytics and pattern recognition but nothing approximating creative human intelligence. Still, businesses have a lot to gain from harnessing the capabilities
Three Ways Businesses Use AI And ML
Robotic Process Automation. While the term "robot" probably brings to mind a humanoid machine, Robotic Process Automation (RPA) is actually more software than hardware. With RPA, digital programs, or "bots," handle repetitive, rules-based tasks
with great speed and accuracy. This is a popular option, as it's relatively inexpensive to implement and typically brings about a very quick return on investment. It's also the least "smart" of the common smart technologies, as these bots aren't programmed
to learn and improve as they go.
Analysis and insight. It's debatable whether computers are actually smarter than human beings, but one thing's for certain: They are much better at pattern recognition, especially in massive datasets. Advanced, AI-powered data analytics tools are
often trained on a specific set or type of data, using this as a baseline to identify patterns humans might otherwise miss. Unlike the bots used in RPA, these prediction or categorization models are designed to improve as they process more and more data. Businesses
employ this type of technology to improve performance on high-frequency tasks better suited to computers, meaning they're more of a productivity tool than a potential replacement for human workers.
Engagement. In today's fast-paced world of instant gratification, customers expect information to be available at a moment's notice. Luckily, chatbots have come a long way since the early days of instant messaging, and businesses can leverage AI programs
drawing on historical customer data to provide an enhanced and optimized customer service experience without dramatically increasing headcount. Natural language processing and machine learning help these "intelligent" agents interact with and assist humans,
while continually improving their performance over time.
Where AI Falls Short
There's no question that AI is becoming a powerful and useful tool for businesses of all shapes and sizes. However, it's become such a common buzzword in the business technology space that there are many misconceptions about it. As it turns out, AI isn't
yet very effective on its own. Even the best AI-powered tools require good data — and lots of it — as well as training on datasets that must first be organized to be useful.
Similarly, without a specific task that is defined by human beings, AI can't function. Very advanced programs may be able to write their own algorithms, which are often so complex they're beyond our understanding, but they can only do so in pursuit of a
goal determined by programmers.
Perhaps the most significant limitation of current AI systems is that they lack what we might call "common sense." In other words, they can't apply learnings from one domain to another situation or problem. Given enough data, an AI system can make pretty
good predictions or accurately categorize items. But even minor changes in the assigned task can mean the system needs to be entirely retrained.
Through incredibly fast trial and error — a process often referred to as reinforcement learning — an AI program may learn which moves are more strongly associated with victory in a game of chess, but in order to play a game of checkers, it would need to
start training again from the ground up. Similarly, AI can only make predictions based on variables it has already seen. This severely limits AI's ability to handle "what if" scenarios and make accurate predictions about new or novel proposals.
How To Effectively Leverage AI In Your Business
1. Identify opportunities.
While AI can be a very useful tool, it's not a one-size-fits-all solution. The best way to lay the groundwork for a successful integration of AI technology is to identify key business areas where it can help. Try to find areas where your organization is
facing processing challenges, bottlenecks or a lack of data insight — even better if you can single out areas where business rules can be applied, so that an AI solution can be tailored to fit the problem.
2. Understand the tools.
Once you have a clear idea of the specific issues within your organization that AI solutions may be able to address, it's important to get a firm grasp of the solutions available to you and which is best suited for your company.
If you are just getting started with AI, you may want to start small with RPA solutions that assist with data handling and "mechanical" or repetitive tasks. If you are a bit further along and work in an industry with appropriate use cases, something more
sophisticated, like a cognitive automation platform that assists with the categorization and summarization of large information sets, may be more valuable.
3. Invest in quality data and quality partners.
Data is the fuel that makes smarter AI go. If you want your AI to interpret and respond to a customer's question, for example, you will need to train it on good data, and a lot of it. So, make sure you are capturing and organizing good data to make the most
of your AI tools. Once you have that, you're ready to work with an AI partner who can set you on the path to utilizing AI technology in ways that will most benefit your company, improve your product or service and generate a solid return on your investment.