People think talking about AI makes them, well, “cool.” Or, worse yet, turns them into data scientists. (I don’t think so, folks.)
Unfortunately, slinging artificial intelligence lingo around without understanding what you are talking about is “artificial.”
Ultimately, ubiquitous utilization of artificial intelligence (AI) in business, manufacturing, finance, healthcare – you name it – represents the hallmark of the Internet of Things ecosystem. Consider how the field – and exponential complexity – of AI describes interactions and outcomes generated by the intersection of machines, software and people.
As a result, the term AI carries a lot of weight, breadth, depth and significance, depending on whom you speak with (including a machine!).
Consequently, when you work with, or sell to, skeptical technical decision makers in this field, look before you take that AI buzz-word slinging leap. Otherwise, your professional credibility is jeopardized.
Are you willing to disrupt yourself professionally and learn new ways of thinking about the same-old, same-old?
Ask yourself these three questions:
- How, why, when and where is AI impacting your own organization?
- Are you overwhelmed? Do you turn off and tune out?
- Is it time to self-educate, just like those machines with AI are doing?
Ultimately, AI defines a new global economic paradigm, that of Convergence (itself yet another buzz word).
To begin with, Artificial Intelligence is a broad topic. Consequently, one sentence definitions do not even begin to boil the AI ocean. However, you have to start somewhere, so here we go. Mark Torr’s article provides a solid synopsis of the various terms and concepts falling under the AI umbrella.
Overall, AI encompasses technologies which allow machines not only to communicate (machine-to-machine, M2M) with each other. Also, M2M technologies permit machines to learn from their experiences executing various protocols.
For example, specific applications of artificial intelligence allow computers, as well as robotics applications, to efficiently, expediently and exactly perform the same, high-volume, low-value tasks as humans. In addition, AI technologies allow specialized machines to perform hazardous tasks, rather than endangering humans.
Cognitive computing is artificial intelligence becomes “interesting.”
Machines, like IBM’s Watson, represent the provocative potential of what happens when machines, software and humanity converge. First of all, greater compute power is harnessed. Then, the machine is trained to separate and classify data. As a result, when new data presents itself, the machine “learns” how to classify it. This capability is referred to as deep learning.
At some point, the machine, itself, “learns” to model. Then, it classifies, separates, interprets and re-models data based on its experiences. This facet of deep learning, the ability of the machine to learn on its own, allows the network to alter and upgrade itself as more and more data enter its network.
A sobering thought.
Ultimately, the full potential of AI focuses on its ability to scale in the (theoretical / actual) absence of human interaction. Then the question becomes: will the interface between “what is machine” and “what is human” become indistinguishable? After all, many of us already interact with machines and are unaware that we are doing so.
The father of artificial intelligence and theoretical computer science, Alan Turing, pondered these implications in his brilliant research.
New initiatives explore the long-term economic impact of AI.
Recent initiatives launched by Accenture and PwC explore consumer / user perceptions about the impact of AI on economic growth and the viability of their own industries. I’ll be writing about specific examples in manufacturing and healthcare this month. Stay tuned.
However, for now, let’s think about just what AI looks like when your own organization “wears” it.
For starters, adoption of AI disrupts how we think about what we do, professionally. Then, as the technology evolves, our ability to collaborate is called into question. There’s far more involved than digital transformation of the workplace.
For example, when machines assume low-value, high-volume tasks, the workforce is freed up to focus on performing non-repetitive tasks involving critical thinking and empathy. However, what happens when these high-value, low-volume tasks do not incorporate the functional skill sets of the current workforce?
How will organizations create the learning cultures necessary to incorporate the impact of the IoT – and AI – on how they best serve internal and external customers?
Is it possible to successfully retrofit AI onto an existing workforce? Furthermore, what is the impact of that “smart” workplace, powered by AI technologies, on the education system preparing future workforces?
Now, let’s get personal. What is the impact of AI on your own personal and professional digital transformation? A little philosophy for you today. Go ahead. Think about it. Give yourself permission to move forward from what’s holding you back.
Depending on where we sit around the business table, we all see, hear, think and speak about the same things, differently. My advice: stop buzzing about AI. Instead, start learning how to collaborate with each other and with machines.
Is it time to demystify the IoT and AI? How will you harness technology for your own organization’s growth, expansion and stability? Then, let’s have that discussion. Today.
Babette Ten Haken is a STEM-trained catalyst, corporate strategist, storyteller and facilitator. Her focus? How collaboration revolutionizes and humanizes the industrial Internet of Things (IIoT) value chain. The results? Increased customer loyalty, customer success and customer retention. Babette’s One Millimeter Mindset™ programs draw from her background as a scientist, sales professional, enterprise-level facilitator, Six Sigma Green Belt and certified DFSS Voice of the Customer practitioner. Babette’s playbook of IIoT team collaboration hacks, Do YOU Mean Business? is available on Amazon. Contact Babette here.