What you need to know before starting to work with artificial intelligence

It seems like everyone is talking about artificial intelligence right now, and there’s a good reason for that. We are seeing its revolutionary impact in virtually every industry:

· In healthcare, where it is used to track pandemics and develop vaccines.

· In the banking and financial sector, where it detects fraudulent transactions and allows for more accurate assessments of credit risks.

· In security, where it prevents cyberattacks and data breaches.

· In biotechnology, where it extends advances in areas such as gene editing, promising to help eradicate disease and end food shortages.

· In retail, where it predicts what customers are likely to buy and puts them in front of them when they’re ready to pull the trigger.

I firmly believe that the true value of AI – estimated at $13 trillion to the global economy by 2030 – will be achieved by being accessible to companies of all shapes and sizes, not just multinational corporations. A vast and eclectic ecosystem of cloud-based “as a service” platforms reduces the need for costly infrastructure investments and also means that niche solutions exist to help automate solutions across industries.

But whether you’re simply looking to use AI-enhanced marketing tools or implement top-down, real-time machine learning and data analytics in your organization, there are a few key points to consider first. The cost of deploying AI may have dropped dramatically over the past decade, but it still requires an investment of time and money, and getting in half-triggered – simply because it seems like everyone else is doing it and you’re afraid of missing out – can be a challenge. recipe for an expensive disaster.

Strategy first

The first principle is to start with a strategy. Simply put, this means understanding what you are trying to achieve. AI technologies are tactically deployed tools to achieve strategic goals. Your strategy must align with your business goals – are you aiming for growth? Improve customer retention or lifetime value? Or to reduce the overheads involved with design, manufacturing, distribution or after-sales service? Once you know what you want to achieve, you can start looking for AI technologies — like machine learning, computer vision, or natural language processing — that can help you get the job done. I like to start by thinking about the key questions a company needs to answer to achieve its goals. Who wants to buy our products or services, or how can we improve the value customers get from doing business with us? Remember, always tune technology to a problem, rather than problems to technology!

What data do I need?

Once you know what your problems are, start thinking about the information you need to answer the questions and solve them. Data can be internal, such as records of transactions and customer interactions, or external, such as information on demographic trends, social media behavioral data, or publicly available government data. Data can also be structured – neat and tidy data that fits into spreadsheets, such as statistical data or website clickstream data, or unstructured – messy but potentially highly valuable data such as images, videos, speech recordings or written text. The most advanced AI projects often work with real-time streaming data. This gives us up-to-date insights that can be acted upon immediately.

What infrastructure do I need?

Building an AI infrastructure doesn’t necessarily mean building algorithms from scratch, big data storage solutions, and a complicated systems architecture process. Cloud providers give businesses of any size access to pay-as-you-go AI computing and storage solutions, plus the consulting expertise to get them up and running. However, it is still important to understand the range of services and solutions available in your market. Will a public cloud provider provide everything you need? Especially if you are interested in working with very sensitive personal data, you may need to consider on-premises or hybrid infrastructure at some level, which gives you more direct control over your information.

What governance issues will I face?

Working with data brings legal, moral and ethical obligations. Legislation is becoming stricter around companies involved in collecting and processing personal information from their customers or the general public, a good example of this is the European Union’s GDPR, introduced in 2018. The law (and similar ones, such as the California Consumer Privacy Act) force companies that collect personal data to operate within a robust legal framework or face severe financial penalties. Governance also encompasses the ethical and moral issues that need to be addressed when applying technology in ways that can affect people’s lives. In the information age, trust is essential – if customers don’t trust you with their data, your plans are thwarted before you even get started. This means that you need to demonstrate that everything you are doing is governed by a strong code of ethics.

What skills will I need?

There’s no getting around it; we are in the midst of an AI skills crisis. What this means is that the industry is coming up with ideas for using AI faster than colleges and universities can produce graduates with the skills to bring those ideas to life. People with AI engineering talents are a hot property in the job market, and their salaries reflect that. But AI still doesn’t build itself (silent), so you’ll need human skills. They can be acquired by hiring them (which, as mentioned, can be expensive) or by upgrading existing workforces. Another option is to partner with external agencies, such as consultants. The approach you choose will largely depend on the scale of your AI ambitions and available resources.

Do you have a data-driven culture?

To some extent, this is all a matter of attitude. What is the attitude, at all levels, towards technology, data, and AI-driven innovation in your organization? In a data-driven business culture, everyone from the board to the shop floor understands the benefits that can be achieved by putting data at the heart of operations and decision making. This is certainly not true for all organizations. Some not exactly useful attitudes that still prevail in business include “We’re not ready to be an AI company”, “AI is too expensive or complicated”, “We know our business better than a machine ever will” or “Our customers don’t are interested in us becoming an AI company.” There may be good reasons for all of these attitudes, but often they are based on fear of the unknown or an unwillingness to move away from a methodology that has been successful in the past – even when it is clearly becoming less successful as that the world becomes increasingly digitized. The fact is, you can never know enough about your customers. You can never stop looking for ways to increase efficiency in your operations. And you can never stop making your products smarter and more useful. For almost every business, AI is the key to making these things happen.

Of course, this article only scratches the surface of what you need to know before you start working with AI. But all of these topics (and many more) are covered in depth in the new edition of my book, Data Strategy: How to Profit From A World of Big Data, Analytics And Artificial Intelligence..

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