Machine Learning or artificial intelligence is one of the hottest trends and the pioneer developments in the enterprise software space. What exactly is machine learning and artificial intelligence?
As organizations look to technology to drive their digital transformations, artificial intelligence and machine learning are two trends and technologies that many organizations turn to. It's something that's been around for a while and has been emerging for some time now. But the question we often get from clients is what exactly is machine learning? What exactly is artificial intelligence?
Here is everything you need to know.
Machine learning and AI, or artificial intelligence, have been around for a long time, they're technically not the same thing, but they're often used interchangeably. Machine learning is generally viewed as a subset of artificial intelligence, and probably the simplest example of artificial intelligence is on your phone if you use Siri or Alexa as a voice recognition type of command approach. That is often viewed as machine learning because it's learning your voice.
This AI understands the diction of the way you talk. It also knows how to pick up on what it is you're looking for, and over time, the technology learns from its mistakes. It discovers how to optimize and continuously improve how it recognizes your voice and how it addresses your commands.
This is a real basic consumer technology example of how machine learning and AI work. When we look at enterprises in general, in business technology, machine learning, and artificial intelligence are being incorporated into those strategies as well.
What this technology does is it takes data, and it takes business processes throughout your organization. It learns from the data, understands the patterns, and ultimately knows what's happening in the end. AI realizes what is transpiring with some of the transactions, workflows, and data sets you have throughout your organization. A predecessor to machine learning and AI was once known as predictive analytics.
Predictive analytics would try to predict the future based on the past and looking at the processes and the data sets that your organization has. Machine learning and AI is an extension or an evolution of predictive analytics. It's more proactively learning to optimize and make better sense and use of data to help you and your organization make decisions.
I mentioned the real simple example of AI and machine learning with your phone and the voice recognition. Within organizations, machine learning and AI is being built into enterprise technologies, such as ERP systems, HCM systems, etc.
Some examples of where we're starting to see machine learning and AI be used would be in situations like accounts payable processing. If you're an accounts payable clerk right now, you may be manually approving, and releasing payment for different invoices that you receive. Machine learning would enable you to automate that process to where it recognizes the normal transactions and normal processes. It's smart enough to know and to learn where the anomalies are and where the potential red flags are.
In other words, instead of focusing on approving every invoice for payment, machine learning would automatically approve invoices that fall within a certain threshold. Machine Learning would learn to flag those invoices or those transactions that look out of line with history.
Artificial Intelligence learns what normal means and what the thresholds and the different parameters are within your accounts payable processes, so that it can learn when to approve and when to reject those invoices and to require human intervention. This is one example.
A second example is in supply chain management. ERP systems and supply chain management technologies will take data from throughout the supply chain to try and anticipate potential bottlenecks, or where you might need to shift your production to other vendors / locations. It can start to recognize peaks in demand to where it might put strains on your supply chain.
In the end, humans learn to anticipate based on past history or experience – just like machine learning. The longer you use these technology, the more likely they are to optimize your supply chain.
The use case of how machine learning and AI are applied in many organizations to help forecast demand and analyze different data points or fluctuations in supply / demand trends. They're going to drive demand up in what might cause demand to be softer than expected.
This is a way for technology to learn and anticipate how your demand will look for your customers, which then also feeds into the supply chain management and the accounts payable process that I mentioned earlier.
These are just three real basic simple examples. It's not simple technology, but they're basic examples that might be relatable to you as an organization of how machine learning and AI can help enable some of your business processes via this new technology.
Now, a caveat and a prerequisite to effective machine learning in AI is going to be to have good data. If you don't have good data within your organization if your data is corrupt. If the data is not accurate, then your machine learning and your AI is going to be inaccurate as well. Data is always important to any sort of technology initiative. It is especially important if you're truly going to take advantage of these machine learning and AI capabilities.
This goes for not only the initial migration of your data into the system in the first place, but it also involves and requires you as an organization to ensure that your employees are maintaining that data well and keeping the data clean. In other words, you don't want to just move the data over to your new system.
If your end-users and employees are corrupting the data in which case the machine learning and AI is going to be reliant on your dirty data. So, it's really important to not only have clean data brought over to the new technology but also to make sure that over time, you're basing your machine learning and AI functions on data that's being maintained and preserved and ideally optimized over time.
Having an effective data strategy is a very important predecessor and prerequisite to taking full advantage of machine learning and artificial intelligence. Now while data is very important in ensuring that you take full advantage of machine learning and AI, what's even more important; perhaps the biggest requirement in order to take advantage of machine learning and AI is organizational change management.
It is pivotal to ensure that you're helping people, meaning the employees within your organization, navigate their way through this post machine learning and AI implementation. What I mean by that is - it's one thing to say that we've got technology that can help automate some processes and help create this machine learning environment. But it's another thing to actually change your processes and change your human relationships with that technology.
We see a lot of organizations that have the technical capabilities to take advantage of machine learning and artificial intelligence, but people resist the change.
If you think about the source of resistance, it's because people are afraid. They are fearful of the fact that now technology can do what many people have spent their entire careers doing. In many cases, machine learning and AI is creating a learning environment, and an optimized environment that's even more effective than what humans can do and that can be very scary.
If you've built an entire career or your whole sense of self-worth is based on doing those processes yourself. It's very important to clearly redefine and communicate what people's roles are. It's not enough just to say we're going to put in machine learning and AI, and we'll figure out the rest later. We need to identify what people are going to do in parallel with this machine learning and AI world.
It may be a matter of assigning new responsibilities, sourcing feedback. or managing the exceptions from the machine learning and focusing on strategic business processes. This, on the other hand, can be very exciting for people when they realize that the company vision and let a machine handle some of the more mundane processes.
An organization needs to clearly say, “we're going to let you (the human or the employee) manage these new tasks and responsibilities and here is what your role is going to evolve into with the support of these new tools that are available to you.”
Just to build on the example I talked about before, with accounts payable processing, and the machine learning, they're focused on kicking back exceptions to the humans rather than the massive volumes of day-to-day approvals.
Now, we have to figure out what that accounts payable clerk is going to do with their time. In addition to managing exceptions, maybe there's more strategic focus on procurement processes or payment terms are things that can help the company from a strategic perspective.
Those are the sorts of things we have to figure out prior to the implementation. What are people's jobs going to look like in the future and how can we help migrate people to this future state in a way that's not going to create too much fear and anxiety around using these new technologies? So, the more we can focus on organizational change management, the more likely it is that we're going to be able to take full advantage of technologies like machine learning, and AI.
I hope this has given you an overview of what machine learning is and what AI is and how it can be used to enable improved business processes within your organization. Hopefully, this is part of your bigger picture quest to understand digital transformation. Please also review our 2021 Digital Transformation Report, which provides best practices on how to drive a successful digital transformation.
I'd love to hear any feedback and experience that you have in using machine learning and AI, both positive or negative. Feel free to reach out to me directly with any questions or if you’d like more customized feedback on machine learning and AI best practices for your specific organization. I am happy to be an informal sounding board for your project and throughout your business transformation journey.