It seems like the whole world is talking about artificial intelligence right now, and there’s a good reason for that. We are seeing its revolutionary impact in almost every industry:
· In health care, where it is used to track pandemics and develop vaccines.
· In banking and finance, where it detects fraudulent transactions and allows more precise assessments of credit risks.
· In security, where it prevents cyberattacks and data breaches.
· In biotechnology, where it promotes advances in fields such as gene editing, promising to help eradicate diseases and end food shortages.
· In retail, where you predict what customers are likely to buy and put them in front of them the moment they’re ready to pull the trigger.
I strongly believe that the true value of AI, estimated at $13 trillion to the global economy in 2030, will be realized because it will be accessible to businesses of all shapes and sizes, not just multinational corporations. A vast and eclectic ecosystem of cloud-based platforms as a service 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-augmented marketing tools or implement machine learning and real-time data analytics from top to bottom in your organization, there are a few important points to consider first. The cost of implementing AI may have dropped dramatically over the last decade, but it still requires an investment of time and money, and doing it half-heartedly, simply because it seems like everyone else is doing it and you’re afraid of missing out – can be a recipe for an expensive disaster.
The first principle is to start with a strategy. In a nutshell, this means understanding what you are trying to accomplish. AI technologies are tools that are tactically deployed to achieve strategic goals. Your strategy should be in line with your business objectives: are you looking for growth? Improve customer retention or lifetime value? Or to reduce overhead costs related to design, manufacturing, distribution, or after-sales service? Once you know what you want to achieve, you can start looking at AI technologies, such as 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 in order to achieve its goals. Who wants to buy our products or services, or how can we improve the value customers get from dealing with us? Remember, always fit the technology to a problem, rather than the problems to the technology!
What data do I need?
Once you know what your problems are, start thinking about the information you need to answer questions and solve them. The data can be internal, such as transaction records and customer interactions, or external, such as demographic trend information, social media behavioral data, or publicly available government data. Data can also be structured: clean and tidy data that fits in spreadsheets, such as statistical data or website clickstream data, or unstructured: messy but potentially very valuable data, such as images, videos, voice recordings or written text. The most advanced AI projects often work with real-time data transmission. This gives us up-to-the-minute information that can be acted upon immediately.
What infrastructure do I need?
Building an AI infrastructure does not necessarily mean creating algorithms from scratch, big data storage solutions, and a complicated systems architecture process. Cloud providers give businesses of any size access to pay-per-use storage and AI computing solutions, as well as 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 give you everything you need? In particular, if you’re interested in working with highly 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 entails both legal and moral and ethical obligations. Legislation is tightening 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 strong legal framework or face harsh 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 start. This means that you must be able to demonstrate that everything you do is governed by a strong code of ethics.
What skills will I need?
There is no escape from that; we are in the midst of an AI skills crisis. What that means is that the industry is generating ideas for using AI faster than colleges and universities can produce graduates with the skills to bring these ideas to life. Talented people in AI engineering are attractive property in the job market, and their salaries reflect this. But the AI doesn’t build itself (quietly) yet, so it will need human skills. They can be acquired by hiring them (which, as mentioned, can be expensive) or by upskilling the existing workforce. Another option is to partner with outside agencies, such as consultants. Which approach you choose will largely depend on the scale of your AI ambitions and the resources you have available.
Do you have a data-driven culture?
To some extent, this all has to do with 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 boardroom to the shop floor understands the benefits that can be achieved by putting data at the center of operations and decision-making. This is certainly not true for all organizations. Some not-exactly-helpful attitudes still prevalent in business include “We’re not ready to be an AI company,” “AI is too expensive or too complicated,” “We know our business better than a machine,” or “Our customers aren’t interested.” interested in us becoming an AI company.” There may be good reasons for all of these attitudes, but all too often they are based on fear of the unknown or an unwillingness to walk away from a methodology that has been successful in the past, even when it is clearly becoming less successful like the past. world. it becomes increasingly digitized. The fact is, you can never know enough about your customers. You can never stop looking for ways to drive efficiency across your operations. And you can never stop making your products smarter and more useful. For almost any 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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