I think that we can all agree that AI is coming. In fact, it’s probably already here. AI lives in our mobile phones, in our homes in smart speakers, and if it’s not already there, it’s almost in our workplaces. What this means for CIOs is that we are going to have to adjust to a new world order. We are going to have to become AI experts. We will have to understand what AI can and cannot do. The big question that we are being faced with is just exactly how can we go about doing this?
CIOs Need To Prepare For The Arrival Of AI
CIOs can remember not all that long ago that AI was the exclusive domain of data scientists. However, now, industries as diverse as retailing, manufacturing, finance and insurance are taking advantage of new products that make it much easier for businesses to create AI tools specific to their needs. All people have to do is to plug data into standardized AI templates. By doing this analysts, auditors and actuaries who lack specialized AI training are able to identify sales prospects, spot risks and fraud, and boost organizational efficiency. These templates still require tech-savvy users to be used and can take weeks of model training, testing and validation. The enabling of “citizen data scientists” – an industry term for professionals who have developed competence in AI using automated tools but who aren’t trained specialists in statistics or analytics – also translates to huge cost savings for businesses that can dispense with data scientists who would otherwise spend several weeks coding a custom program.
The growing adoption of these products follows a classic pattern in the tech evolution, where technology turns every employee into a programmer. An example of this are tasks once viewed as too complicated for the layperson, such as sending an attachment or cropping a photo, have become effortless as digital-productivity tools kept improving and getting easier to use. Thanks to their easy-to-use interfaces, programs for these AI templates which are known as automated machine learning, or automated ML are even being used by data scientists themselves. The person with the CIO job needs to understand that this is the future. Automated ML can be used to ease the pain of data science. Studies are predicting that by next year, citizen data scientists will surpass data-science pros in handling the majority of automated ML tasks in business and industry. In three years automated ML may become so commonplace that the adoption of data science and machine learning will no longer be held back by the chronic shortage of data scientists.
The person in the CIO position needs to keep in mind that AI involves vast amounts of sensitive data and computing power. Because of this some experts fear that use of automated ML by less-well-trained users could lead to errors and bias that could prove embarrassing and costly for the company. Skeptics caution that automated ML may require careful supervision by CIOs and guidance from a data scientist, AI ethicist or other third party. Those who use the technology are mostly data engineers, software engineers and business analysts. Success in automated ML requires not only basic competence with data but a deep understanding of the business problems that AI is being harnessed to solve. AI tool customers are discouraged from operating their programs with complete independence. Some tool companies’ software licenses require clients to work with a dedicated field engineer and a data scientist in the first year, when the automated ML functions are being set up and the algorithms are being deployed.
Next Steps For Citizen Data Scientists
Different companies will use these new AI tools in different ways. One situation that these tools might be used in is when a company is interested in using AI to segment business clients according to sales potential to prioritize marketing and retention efforts. What CIOs need to understand is that the new tools are not pure magic CIOs need to spend some time with them. Once they start to use the AI tools the technology can discover intriguing patterns that may have escaped human detection. An example of such a pattern would be that a customer who is a heavy spender on services may have a tendency to schedule the first services of the week for a Monday event; what’s more, these businesses tend to place their Monday orders days ahead, not one day before the event.
CIOs can set up teams that can train, test and operate the automated ML platform. A company’s goal needs to be to tap into customer data to identity opportunities to prevent unnecessary costs and to maximize cost savings. In the health field, an automated ML program can make startling discoveries such as patients who get regular teeth cleanings and dental checkups have markedly better cardiac health in the following year. If something like this is discovered, then a CIO has to make sure the connection wasn’t a fluke. This could require analyzing several years of patient data, and looking at patients covered by different health plans.
What All Of This Means For You
CIOs have seen artificial intelligence (AI) coming for some time now. It turns out that it may have arrived, but perhaps not in the way that CIOs were expecting. Traditionally, AI tools have required the company to employ data scientists who are able to use their knowledge of data manipulation and statistics to implement complex AI tools. Something has happened and this anticipated future may not come about.
New tools allow untrained users to plug data into standardized AI templates. The result of this is that citizen data scientists are now able to apply AI tools to the company’s data and extract insights that have not been available in the past. These new tools are called automated machine learning. It is predicted that the number of citizen data scientists will soon exceed the number of data scientists. These new tools have to be used carefully because of the sensitive information they are processing. These easy-to-use AI tools can allow a company to partition their customers. When these AI tools are used, new patterns in customer data can be detected.
The future that CIOs had been anticipating in which AI would enter the company and allow new discoveries to be gleamed from company data may not happen. Instead, easy to use AI tools may be used by untrained staff in order to process company data. CIOs need to understand that this is now possible and they have to create boundaries to make sure that these simple but powerful tools are used correctly. Good things can happen, but CIOs are going to have to spend the time to keep things under control.
Question For You: Should CIOs control who has access to company data in order to prevent incorrect use of AI tools?
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What We’ll Be Talking About Next Time
As the company’s CIO, you have a responsibility to make sure that the company’s employees are equipped to be as productive as possible. A key part of this productivity is to make sure that employees can communicate with each other. No matter if they are working on the next customer proposal or if they are just trying to determine where they want to go out to lunch today, tools that help them to exchange information are critical. There are a lot of different ways to go about doing this, what is the best way?