Winning with AI is a state of mind. You can't do it by simply deploying technology, and you can't do it if your company isn't ready to give up control. The key to winning with AI is having an ecosystem that allows people and technology to work together, creating an environment where data is available for everyone in the organization. When that happens, there are no gaps between what the data says and how people make decisions based on it—and that's when you start winning.
There are four key elements to putting AI to work in any organization.
AI is a tool, not a solution. If you think of AI as a way to solve all your problems, you will be disappointed. It's not going to happen overnight and it won't happen without effort on your part. AI is not a silver bullet. There are many things that AI cannot do; there are also many things that humans can do better than machines for some time to come (though AI is rapidly catching up). So don't expect an AI system to take over the day-to-day management of your business or organization—at least not yet! AI is a way of thinking about problems (which includes finding solutions), rather than just another technical solution in itself. You need to understand how the technology works before you can use it effectively in practice—and there's no substitute for good old fashioned common sense."
It's the framework where people and technology work together, inside and outside the company.
The framework is the business processes that are in place to enable the people and technology to work together. In other words, it's the complete set of steps needed to do something. In this instance, it's a system that allows salespeople and engineers to coordinate with each other so they can achieve optimal results through AI
AI is less about technology and more about protocol.
AI is not a technology. It's a protocol. That's right, we said it: AI is not a technology! The reason you don't see many "AI technologies" or even "AI products" on the market today is that most companies are still focused on building products (which requires building an infrastructure to support those products), rather than focusing on the protocol—the framework for how all these technologies will work together to achieve success in your business. AI isn't about building one piece of software, it's about understanding how all these pieces fit together and what processes need to be put in place so they can talk to each other effectively. Our advice? Start with understanding what your goals are for using AI and then focus on creating those processes first before worrying about which machine learning model should solve which problem for you next year
Deploying AI-first principles requires a deeper dive into data.
Deploying AI-first principles requires a deeper dive into data. After all, AI is just a tool—and like any other tool in your arsenal, it's useless without the right materials to work with. In this case, that material comes in the form of data: lots and lots of it. And while you could certainly build an AI system without having a strong understanding of how your organization collects its information (or if they even have any collected at all), what happens when you do? The answer is simple: You'll get better results faster because your team will be able to turn information into insights in no time flat—instead of spending weeks or months mining through terabytes upon terabytes of raw data just trying to figure out where they should start looking. But how exactly does one go about ensuring there's enough high-quality information available for an effective AI deployment strategy? First off, it's important not only for everyone on your team who works with data— whether software developers or business analysts—to understand exactly how their company manages that information...
It is an ecosystem of data, people and processes.
The next time you hear someone say that AI is a state of mind, ask them to define the three components of an AI ecosystem they believe are necessary for success. If they say “data”, ask how they know that data is the fuel that powers AI. If they answer “people” and “processes”, ask how these two things relate to each other as well as to data. In short: what makes up your company's artificial intelligence ecosystem?
Protocol is data-driven decision making.
● Protocol is data-driven decision making.
● Data-driven decisions are the basis for protocols that govern how data is used.
● Protocols are the key to achieving AI-enabled state of mind and achieving winning outcomes through implementation of these protocols
Data has to be available to everyone in the organization.
To truly win with AI, data needs to be available to everyone in the organization. The value of a data-driven approach extends beyond making better decisions: it also allows you to create new business models and revenue streams, integrate emerging technologies into existing operations and build lasting competitive advantages. In short, you’re giving your company a competitive edge by turning information into knowledge that can improve decision making, increase efficiency and reduce costs. But while most companies understand this conceptually, many don’t realize how important access is when implementing an AI strategy. Sharing data across the enterprise requires each department or division to have access to all relevant information—not just what they need for their operations but also what others may have collected on them or their customers as well as shared externally with vendors and third parties (such as customers).
Data should be viewed with respect to both privacy and security.
The importance of data protection and security cannot be overstated. Data is a strategic asset, and must be treated as such. This means that data should always be protected from internal and external threats, with measures such as encryption, anonymization, securing the location where it's stored and other methods of implementing privacy by design.
People have to be able to ask questions of that data without fear of reprisal or negative consequences if they are wrong.
When you're trying to use AI in your business, you need to be able to ask questions of that data without fear of reprisal or negative consequences if you are wrong. This is where most people get into trouble with data science. They don't know how to ask any questions at all, and so they get frustrated when the answers aren't clear-cut and obvious. This leads them to abandon their projects entirely because they "can't figure it out." If this sounds like something that could happen in your organization, then stop what you're doing for a moment and ask yourself: "Why don't my employees feel comfortable asking me questions about AI?"
Algorithms have a role, but they don't have all the answers.
Algorithms have a role, but they don't have all the answers. Data is only as good as the algorithms used to analyze it. Algorithms can be biased and manipulated by those with access to their source code or data sets, so transparency is essential if organizations want to use AI responsibly and ethically. Sometimes, companies make decisions based on data that doesn't reflect reality—a phenomenon known as "garbage in, garbage out." For example: if you feed an algorithm a disproportionate number of resumes from white males for certain jobs, it will recommend hiring more white males for those jobs; but when presented with a diverse applicant pool, it should be able to find qualified candidates regardless of gender or race. That's why organizations need diversity and inclusion leaders who understand how algorithms work (and fail).
Your top four takeaways.
AI is a tool, not a solution. AI is not the key to success, people and technology have to work together. Data has to be available for everyone in your organization. People need to ask questions of that data without fear of reprisal or negative consequences if they are wrong.
You can win with AI by taking time to think through how you use it in your business.
You can win with AI by taking time to think through how you use it in your business. In some cases, the data you have may not be as good as you think or the algorithms will not perform as well as expected. The technology is still relatively new and there are many unknowns about how it will perform in practice. It's important to understand what AI can do for your organization, but also understand its limitations and when not to use it at all (i.e., if the problem doesn't lend itself well to machine learning).
And that's just the start. You can use AI to improve your marketing campaigns and identify new customers, but you'll need to think through how you do it. AI will only help if people are trained properly, they have access to the right data and they know what questions they should be asking. So, if you're ready for this challenge—and we hope you are—then let's get started!