Artificial Intelligence Within Industry and Business

When we talk about AI, there seems to be a wide range of understanding as there are types of AI technologies available. But right now, let’s see if we can find some common ground on why we are, where we are. Let’s discuss some of the common constraints or roadblocks – whatever challenges that have prevented some of us from adopting artificial intelligence, and why we might not be there yet!

Sometimes we may simply have difficulty embracing changes to our regular routines. Or after embracing the plan to change, maybe we didn’t spend enough time on the requirements, the baseline or the expected benefits. Overall, sometimes we should be careful about what we ask for. Just like the guy in the Dilbert cartoon here:

Perhaps your business doesn’t have the amount of data required or the structure of data required to easily adopted artificial intelligence. You might not want to underestimate or trivialize these tasks, as they are very very important to the adoption success. In fact, you may have experienced that AI talent might be hard to find. Especially if your competitors or big business corporations have harvested a big portion of the so-called ‘experts’ in the AI resource marketplace. As we see in this graph, the demand for talent is rising fast, but the supply hasn’t moved up as quickly:

Even if you managed to acquire the so-called experts, maybe they aren’t the corporate fit that you were hoping for. Yet, if your ‘expert’ is OK and seems to settle in fine, are they playing well with others? Are you experiencing any issues outside of the expected forming, storming, norming, performing team dynamics? Can your new AI employee grow to be productive across your business enterprise, beyond your immediate project needs? These are very important things to consider. We have many years of successful IT delivery in custom software, integration, project management and more.

Does AI really have to be complex, difficult, unattainable? Of course not! We all know from our own personal experience that AI is very achievable. Just look at examples all around us. So Artificial Intelligence does look achievable, right? Let’s take a look at some of the most common and/or popular AI use-cases for business. Here are three that at least some of us can relate to:

In Supply Chain, for example, machine learning can use your data to help pinpoint the most influential factors to your supply networks’ success. Key factors are ⁽¹⁾ influencing inventory levels, ⁽²⁾ supplier quality, ⁽³⁾ demand forecasting, ⁽⁴⁾ procure-to-pay, ⁽⁵⁾ order-to-cash, ⁽⁶⁾ production planning, ⁽⁷⁾ transportation management and more are becoming known for the first time.

In Human Resources we can use AI to:

1. help customize the onboarding of new employees.  Such as managing FAQ lists, allocating space, allocating new laptop, etc.
2. Automate scheduling interviews, provide ongoing feedback to candidates and answer their questions in real time.
3. Identify and remove unconscious language bias patterns to welcome diverse applicants.
4. Recognize employees that may be heading for the exit door, aka ‘flight risk’.

All this contributes to keeping our employees happy and dynamically engaged.

Another great example, this time with ‘human in the loop’, rather than having AI totally replace the human in their job function: AI tools can train the CSR in real-time with emotional intelligence tips. When the caller’s voice increases in pitch, volume and/or speed, the software assistant may recommend strategies to the CSR to reduce the caller’s stress to help the caller find their happy place. This would ⁽¹⁾increase call efficiency, ⁽²⁾boost satisfaction, ⁽³⁾gain unrivaled insight, and ⁽⁴⁾ continues improvement and learning will lead to better results. A happy caller makes a happy employee, and vice versa, leading to better client satisfaction, better client retention, and gain loyal customers.

In the banking and payments industry, there are many many opportunities for AI to automate tedious, time-consuming, error-prone human activities.  Reduce costs, reduce errors, increase reliability, increase client satisfaction. Here is an infographic sourced from Forbes in 2017. Which they borrowed from Foresters Research.

As we started in 2005, located in Toronto, our team of experts has an extensive background in many disciplines of IT and is ready to compliment your existing IT team. We have partnered with many businesses in Toronto where our team has successfully delivered IT solutions. We also have been awarded Vendor of Record by the Ontario Government.

For hundreds of years, philosophers speculated that one-day human thought would be mimicked by machines.  During WW2, Allan Turing and the “Bletchley Park” team created arguably the very first AI, used to defeat the German’s Enigma Machine. Soon after, in the 1950s, the word Artificial Intelligence was branded in  Dartmouth college, and new A games such as checkers, chess, and GO started to become the first AI programs that attempted to solve complex problems. Not long after AI was introduced in robotics, reasoning and search engines, speech and language recognition and more. However, in the 1970s, an AI winter shut down all fields actively progressing in developing and maturing it. Yet, Japan started other attempts to bring back AI and progress the evolution of intelligence in modern hardware and software technologies.

One of the earlier examples of AI in business software was Microsoft’s Clippy, which was supposed to be helpful, though it was proven unuseful. In other case search engines, another AI tool such as Ask.com, Google, and Yahoo was starting to mature and help people to solve problems such as “how to disable Clippy and get on with our jobs.”