On the Path to Getting Your Business AI Ready



Senior editor

Parul Saxena

Chief editor

Last updated: May 2, 2021

Looking at the digital landscape today, Artificial Intelligence has gone from a staple of sci-fi novels to something that has infiltrated many areas of our lives. If you’ve ever talked to Siri, asked a chatbot a question or viewed an advert on Amazon, you’ve been touched by an AI system. But despite all the progress that has been made with AI and machine learning, we’ve only begun to scrape the surface of the opportunities it will provide.

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In 2017, Fortune Magazine conducted a survey of Fortune 500 CEOS. When asked what technologies they considered to be important, respondents listed AI/Machine Learning as more important than the Internet of Things, virtual reality, and nanotechnology. The changes that AI is set to bring our ever growing, and if you’re a business manager, you can no longer afford to ignore the opportunities AI represents and should get to work right away to keep your business AI ready.

AI will soon have a number of practical applications for businesses of all types, depending on your organizational needs and the business intelligence insights derived from the data you collect. But if you do decide to get on the AI ready, implementing it won’t be something you can decide on one day and start the next. Your business and staff will need to make specific preparations to ensure you’re ready to use AI effectively.

In this article, we discuss 4 things that your business needs to be doing to ensure you’re AI ready to take advantage of AI, even if the AI-based applications you end up using are still a few years away from being developed.

4 Must-do things to make your business AI Ready:

1. Educate yourself about what AI ready can do and what you need it to do

As a business owner you cannot afford not to understand the capabilities of AI, so make sure you take the time to become familiar with what modern AI can do. Take advantage of the online resources that are available to you and familiarize yourself with the basic concepts of AI. Online courses are also available through institutions as prestigious as Stanford University and the Columbia Business School provide online courses that help you navigate the basics of how AI systems work and can be used.

Once you’ve got to grips with what AI is able to do, you need to determine what you need AI to do for you. Start by thinking about your current processes, products and services and how the capabilities of AI can be applied. Talk to different departments within your company to find out which processes can use automation to save time and money. Automating time-consuming and laborious tasks can free your employees to focus on higher-value responsibilities instead.

2. Plug your skill gaps

Using AI doesn’t always require a major investment in systems and staff. Most businesses are likely to have enough people scattered across the organization who have the skills and aptitude to learn how to implement and work with an AI system. They just need to be given the right start. So it’s important that you provide opportunities and encourage further training wherever possible to ensure they are able to start off and keep up with AI.

As well as increasing the chances of a successful AI adoption, investing in staff training and development helps your company stay competitive in the short-term and cultivate talented employees to become forward-thinking leaders in the future.

There’s a large number of online courses you could subsidize, as well as university classes and advanced degree programs if your staff requires more expertise. You can also find on-site training sessions and workshops available if you want to get many people involved in training. When planning what courses to send people on, you need to make sure that all training and learning sessions are tailored to meet the needs of your company. You’ll need to consider your industry, company size, and data needs, so consulting with an educational expert about choosing a course first may prove beneficial in the long run.

3. Manage your data

Data forms the backbone of what an AI or Machine Learning system is capable of. The output that AI can provide for your company is limited to the quality and quantity of data you put into it. If you have bad data going in, you will get a badly performing AI system coming out. It simply isn’t possible to progress with AI until your data issues are resolved, and all companies have data issues that need resolving.

In order to better understand how suitable your data is, ask yourself these three questions.

  • Is your data prepared?

However your company implements AI, you’ll need data that is consistently formatted and labeled. Start putting the right structure in place now to avoid huge problems with data cleansing later. Having a data management tool to connect, collect and unifying your business data will leave you with clean, labeled, and formatted data that your AI can use. As Garth Laird, CEO of ZAP, an automated data management solution, says,

“It is vital that corporations first invest in solutions that align their data to achieve a trusted data store.”

The more clean, classified and meaningful your data is, the more you’ll get out of your AI system in the future.

  • Is your data contextually relevant?

Most AI systems are good at determining correlations, but they don’t understand the contextual data. So that there is no misunderstanding on the AI’s part, you should provide both the data and its context. This will help the system to understand the facts surrounding the data and thus present relevant solutions.

  • Is your data enough?

Making predictions based on a small set of data is unlikely to yield reliable results. If you’re not collecting enough data, even the best AI technology won’t be of much use to use to you. You’ll need to invest in ways to gather more, relevant data that can then be supplied to the AI. Relevancy is key as adding superfluous data will only return useless results. If you’re looking to collect customer data, you could utilize more forms on your website, employ call recording or run reward programs. The more relevant data you can collect, the better the AI system will work.

4. Start small

When your employees are trained up and your data is clean and plentiful, it’s time to start building and integrating your AI system. If your first step is to build an all-powerful system that is going to solve your every business problem, you’re almost certain to fail.
Be realistic and start small. Start with specific and discrete functions, and bear in mind the capabilities of your company at that time. Targeting low hanging fruit is a good place to start. For example, refer back to the research you did at the start of this process and look for ways to automate laborious processes in order to free up time for your employees.

Once you’ve achieved this, look for problems that can scale. Build on the data science techniques that others have used, some of which you may have covered in your training, and see how these can be modified to suit your own needs. As you tackle these problems, you’ll be building up knowledge that will help you solve similar, larger, issues in the future.

An excellent example of this foundational knowledge being used for larger issues comes from IBM’s work. IBM, working with the TSA, took simple object recognition technology and applied it to object detection for baggage screening. Once this had been perfected, this new ‘visual’ recognition system could then be developed further to answer more sophisticated questions about behavior. For example, “What does preparation for a terrorist attack look like?” And it all started with an AI that could tell the difference between an apple and an orange.

Bottom Line

Artificial Intelligence has the power to change your company, and by utilizing these four steps you may be surprised at how quickly you see results. More importantly, you’ll be creating an opportunity to grow your company into a stronger, more productive organization, both now and well into the future. Integrating AI isn’t an easy thing to do, but it’s also not something that any innovative organization can ignore for long.

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