It seems like everyone is talking about artificial intelligence right now, and there’s a good reason for that. We see its revolutionary impact in almost every industry:
In banking and finance, where it detects fraudulent transactions and enables more accurate credit risk assessments.
Safe, where it prevents cyberattacks and data breaches.
In biotechnology, where it augments advances in areas such as gene editingpromising to help eradicate disease and end food shortages.
In the retail trade, where it predicted what customers are likely to buy, and puts it in front of them when they’re ready to pull the trigger.
I strongly believe that the true value of AI – estimated at worth 13 trillion dollars to the global economy by 2030 – will be achieved because it is accessible to businesses of all shapes and sizes, not just multinationals. A large 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 in every industry.
But whether you’re just looking to use AI-enhanced marketing tools or implement machine learning and real-time data analytics from top to bottom of your organization, there are a few important things to consider in 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 going halfway – just because it seems that everyone else is doing and you’re afraid you’ll miss out – can be a recipe for costly disaster.
The first principle is to start with a strategy. Simply put, it means understanding what you are trying to accomplish. AI technologies are tools that are deployed tactically to achieve strategic goals. Your strategy should be in line with your business goals – are you aiming for growth? Improve customer retention or lifetime value? Or to reduce design, manufacturing, distribution or after-sales service overhead? Once you know what you want to accomplish, you can start researching 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 business 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 doing business with us? Remember, always adapt 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 what information you need to answer questions and solve them. Data can be internal, such as records of customer transactions and interactions, or external, such as demographic trend information, social media behavioral data, or publicly available government data. Data can also be structured – neat, tidy data that fits into 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 streaming data. This gives us up-to-date information that we can act on immediately.
What infrastructure do I need?
Building an AI infrastructure does not necessarily mean creating algorithms from scratch, large data storage solutions and a complicated system architecture process. Cloud providers give businesses of all sizes access to paid AI storage and compute solutions, as well as consulting expertise to get them up and running. Nevertheless, 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 want to work 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 involves legal as well as moral and ethical obligations. Legislation is tightening around companies involved in the collection and processing of personal information from their customers or the general public, a good example of which is the European Union’s GDPR, introduced in 2018. The law (and others similar , such as the California Consumer Privacy Act ) require companies that collect personal data to operate within a strong legal framework or face severe financial penalties. Governance also encompasses the ethical and moral issues that need to be addressed when applying technology in a way that could affect people’s lives. In the information age, trust is key – if customers don’t trust you with their data, your plans are thwarted before you even get started. This means you must be able to demonstrate that everything you do is governed by a strong code of ethics.
What skills will I need?
There’s no getting away from it; we are in the midst of an AI skills crisis. This means 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 skills are sought after assets in the job market, and their salaries reflect that. But The AI is not building yet (silently), so you’re going to need people skills. They can be acquired either by hiring them (which, as mentioned, can be expensive) or by upgrading the skills of the existing workforce. Another option is to partner with outside agencies, such as consultants. The 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, it’s a matter of attitude. What is the attitude, at all levels, towards technology, data and AI-based innovation in your organization? In a data-driven corporate culture, everyone from the boardroom 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 the case for all organizations. Some not exactly useful attitudes that are 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 does ever will,” or “Our customers don’t want us to become an AI company.” There may be good reasons for all of these attitudes, but too often they are based on a fear of the unknown or a reluctance to stray from a methodology that has worked in the past – even when it becomes clearly less effective as the world becomes increasingly digitized. The fact is, you can never know enough about your customers. You can never stop looking for ways to improve the efficiency of 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 take advantage of a world of big data, analytics and artificial intelligence.