In the world of C-Suite managers, a majority of people are well in their fifties. This is especially true for CEOs. According to Korn Ferry’s findings, the average age in the C-level world is 55. The oldest ones, CEOs, on average are 58 years old.
Explaining Artificial Intelligence to people close to retirement is a challenge, to say the least.
But Fortune 1000 companies are feeling the pressure from disruptor, data-driven companies like Google, Amazon, Apple etc. These companies have vast amounts of data that they regularly use to extract the business value using Artificial Intelligence technologies like Machine Learning and Deep Learning.
Also, over the past few years, over 70% of companies are making significant investments in AI.
As the world moves boldly toward AI omnipresence, companies that lag behind face extinction.
So if you are a C-level manager that fears AI, or you are working for such a C-level manager, here are a few pointers on how you can sway your leaders toward acceptance of AI. Before it gets too late.
The Unavoidable Problem Of Big Data
One of the most useful applications of AI in the business sphere is business intelligence. Companies that capture a lot of unstructured data and go back to glean conclusions from this data are set to grow.
Companies that still try to run things like in the good old days are set to be overpowered by industry disruptors in their own niche.
Just to illustrate how quickly and how much this data is being generated, take a quick look at this paragraph, as published by Seeking Alpha:
“Only until recently was that much image and audio data readily available. In fact, 90% of the world’s data has been generated since 2015. That year, the digital universe, i.e., the reservoir of data created and copied, totaled less than 10 zettabytes – that would be 10, followed by 21 zeros. By 2020, it is expected to grow more than four times to 44 zettabytes. Just five years after that, it could reach 180 zettabytes.”
Let’s unpack this for a bit. If your C-level manager came into office in 2015, that year alone, humanity has generated 90% of ALL the data EVER created, in the entire history of humanity. All the books, all movies, paintings, music… ALL of that human creativity was dwarfed by the amount of data we generated as a civilization, in 2015 alone.
By the time he/she leaves office, the amount of data in the world will grow tenfold.
With so much data at our disposal, not using it to grow your company, to put it politely, is not a wise business decision.
Companies must embrace Big Data as a key source of strategic thinking.
Using AI To Deal With Big Data
When we start talking to people close to retirement, about the amount of data society creates today, things quickly get boring for them.
In fact, talking about jumping from 4.4 zettabytes to 44 zettabytes in just 5 years will get anybody bored from the conversation.
We simply cannot contextualize that amount of data. The human brain is simply not wired to manage so much data, especially unstructured data.
What is a zettabyte? It’s a gargantuan number and we can’t really make a clear comparison. At least not a direct one. But just as a rough illustration, 1 zettabyte has 1000 exabytes. And just one of these exabytes can pack 36.000 years of HD-Video. Thirty-six thousand years of HD video. That’s a lot of movie nights.
(image source: The Guardian/Cisco)
With so much data, we simply have to rely on Artificial Intelligence. Specifically, Machine Learning (ML).
The way ML handles Big Data, especially unstructured data, is very interesting. Machine Learning as a technology can take unstructured data and understand what that data is, and then assign categories, labels, tags.
With ML, an unorganized pile of data can be transformed into a usable database of names, addresses, travel routes, recent purchases etc. This organized, structured data can then be used to visualize trends that are of key importance, and even train the software to bring out key conclusions based on that data.
The use cases of all this data span pretty much all industries imaginable. From Agriculture to Zoology, using Internet of Things helps us track all kinds of physical parameters. AI can take these readouts and map optimal times to plant and harvest, optimal travel routes, optimal supply chain layout etc.
It really is up to your organization’s growth vision, and the ability of all of the decision makers to get on board and get fluent in Thinking AI, so that your organization can find all possible use cases of AI and Big Data.
Business Benefits Of Letting AI Deal With Big Data
Earlier in the article, I noted that by 2020 we’ll see a tenfold increase of the world’s data to 44 zettabytes. If you thought 40 zettabytes is a huge jump in 5 years, Seagate predicts that by 2025, the world’s data will climb up to 163 zettabytes.
A large chunk of this data will be created by internet-enabled machines used in all sorts of industries.
Since benefits will differ from industry to industry, and company to company, it’s almost impossible to put together an all-encompassing list of benefits.
That being said, we can still come to a shorter list that you can hopefully use to help your C-level senior management to understand why your organization needs AI and Big Data.
AI and Big Data in Big Pharma
A few months ago, in the Max Planck Institute of Biochemistry, the Max Planck Alumni had a few very interesting workshops discussing how AI can help the Pharmaceutical and Healthcare industries.
Several key workshops were devoted to topics such as:
- The use of AI in Personalized Medicine, where treatments will be tailor-made for individual patients based on their medical history, current health status, as well as genetic pointers of what might happen to the body if X, Y, Z happens.
- Using AI and Big Data in drug discovery, where experts discuss how pharmaceutical companies can speed up the drug discovery process and address the growing need of medication that will be a solid solution for the challenges medical staff is facing. GSK has injected a $33 Million paycheck to Exscientia to achieve key milestones.
- The use of AI in faster diagnostics and the inevitable mention of IBM’s Dr. Watson and how even small companies can rely on AI as a Service and develop some spectacular use cases.
But this is not the only group of people discussing big ideas. Unsurprisingly, Google is in the game too. There are companies using the Google DeepMind system to develop technologies like Moorfields Eye Hospital in London that is addressing aging and eyesight as a common problem. The image below shows a scan of an eye, ready to be diagnosed.
Companies working in the Healthcare industry can’t allow not using AI and Big Data. There’s so much insight from this data that C-level managers need to take the plunge.
AI and Big Data in Supply Chain Management
You’d expect that an old system like Supply Chain Management ought to be finely tuned and somewhat boring.
You’d expect that in such an established industry, there’s not much left to discover.
But Walmart decided to go with the Nielsen analytics company and dig through 200 billion rows of transaction data PER DAY, to find ways it can improve operations, and improve sales.
Their 40+ Petabyte Data Cloud solution has helped the company address simple issues like price mislabeling and stocking oversight to ensure that people never leave a Walmart without buying what they intended to buy.
AI and Big Data in Workflow Management
Workflows exist to make things work smoothly. So improving a workflow, can, in the long run, save companies millions of dollars in operational costs.
A good example is a report of a company that processes some 1200 invoices per year. So if somehow, the invoice processing workflow would somehow improve, this company could save a lot of money.
And that’s what they did. Using AI and Big Data, the company added an AI module onto its existing invoicing system. The result: time to process an invoice was reduced by 45 minutes. That meant the company saves $676.000 per year thanks to AI and Big Data.
AI and Big Data in Government Agencies
As government agencies go all-digital, people rely on web services to get the information they need. This helps citizens. A lot. But government employees end up being the ones who answer all those phone calls, emails, chat requests.
If government agencies would move these information-giving tasks to a computer system, things would be so much easier for government agencies.
The North Carolina Government Office did just that. They took an AI system, fed it Big Data, and gave it some time to read up.
The system then was able to take over 90% of the support requests, leaving employees with plenty of time to dedicate to support requests that required more human attention than “I’ve lost my password and cannot log into my profile.”
And this is a worldwide trend. Governments in Japan, Korea, Mexico, etc. are finding creative ways to free up employees from simple tasks like sifting through documents, rerouting citizens to the right point person etc. so they can focus on more complex requests that their citizens have.
A Quick Wrap-Up
With C-level management well in their fifties, clearly communicating the importance and power of AI and Big Data is no easy task.
The world’s data is in an explosive growth, and 10 years from now, we’d grow from 4.4 zettabytes to perhaps 200 zettabytes.
The enormity of data we generate every day is scary. But smart companies make huge investments in using AI to glean business decisions from their data.
Private companies working in the retail, manufacturing, transportation, pharmaceutical industry etc. are all making substantial investments in AI.
There are literally hundreds of use cases out there that you can use to help your C-level management take the plunge and accept AI as a great business growth ally.
If you have any other use cases, or questions about AI, Big Data and how you can communicate value to your C-level management, please feel free to comment below. And don’t forget to share this blog post on your Facebook/Linkedin/Twitter feed.