Like any other industry, the IT industry has seen its share of fads. The thing about fads, though, is that they often make no sense. I mean, who can explain the pet rock, inflatable furniture, or planking? As much fun as it may be to look back and laugh about some of the “what were we thinking” fads of the moment, there are some things that start out as fads, but eventually evolve into something useful.
I tend to think of Big Data analytics as being just such an item. Somewhere around 2012 or so, it seemed that Big Data analytics became something of an overnight sensation. At the time, I was doing a year-long, international speaking tour, and had the chance to talk to a whole lot of other IT pros. The thing that I found interesting is that while everyone that I talked to had heard about the so-called Big Data revolution, I didn’t run into anyone who could explain it in any sort of depth. At that point, the Big Data revolution, it seemed, was little more than marketing hype. Eventually, of course, the Big Data trend began to prove its worth and today data analytics are used for a variety of purposes.
AI in the business world: Still just a fad?
In some ways, I kind of think of artificial intelligence in the same way that I think of Big Data. AI in the business world has become a fad. Even so, it’s a useful fad. When properly implemented, AI has a huge potential to improve countless business processes.
In spite of its potential, AI poses at least one major hurdle for those who wish to implement it. Like so many other things in the world of IT, the hurdle is funding. In some ways, it might actually be a little bit more difficult to get funding for an AI project than for some other IT initiative. The reason for this is that because AI has become so popular, those who control the purse strings may view it as a wasteful expenditure (or perhaps even as a toy), rather than as a legitimate business tool. This is not to say of course, that the higher-ups in your company are completely oblivious to the benefits of AI, but rather that there are still at least some people who view the technology with great skepticism.
Getting your AI project approved
The key to getting your AI project funded and approved is to build a compelling business case for the technology. If your company is going to invest a large sum of money into an AI initiative, it will expect to see some sort of return on that investment. It’s your job to figure out what that return on investment will be.
The first step in creating a solid business case for an AI project is to avoid the common IT trap of creating a solution in search of a problem. New technologies can be exciting. I get it. But as much as an IT pro may want to bring AI into their organization, it makes little sense to do AI just for the sake of AI.
The most compelling way to create a business case for AI is to look for inefficiencies within the organization that could conceivably be addressed by AI. Remember that whether or not your project gets approved will ultimately come down to dollars. This means that whatever inefficiency you happen to identify, it needs to be something that is currently costing the organization a significant amount of money (even if in an abstract way). The AI solution that you’re proposing must be able to either eliminate or bring significant savings to that business process.
Like any other IT resource, there are certain costs associated with AI. These costs may be CapEx, OpEx, or some combination of the two. Your AI proposal must clearly illustrate that the cost savings delivered by your AI solution will be sufficient to completely offset the solution’s total cost of ownership (the acquisition costs and the ongoing operational costs). Ideally, the AI solution should do more than just offset its own costs. Its use should result in significant net savings for the company.
Another important aspect of getting your AI project approved is risk management. Although the primary objective of any IT project is to make the organization more profitable by either lowering cost or creating new business opportunities, risk management is almost as important. Management needs to be assured that any new processes or technologies that are adopted will not create risks that could potentially cause the organization to suffer a catastrophic loss.
From an IT perspective, the concept of AI-related risk management might seem a little bit silly. After all, AI is a “safe” technology that is nothing like the way that it is so often portrayed in the movies. Doing a bit of risk management upfront, however, can make it easier to get your project approved. Risk management may set the leadership team’s collective minds at ease, and if nothing else it will show that you have taken the time to really think through the project.
This, of course, raises the question of what types of risks you need to manage. The first thing that must be considered is the fact that your AI project is presumably going to be replacing another process that is already in place (possibly a manual process). The important thing to remember about this is that the current process has probably been in place for quite a while. Even if that process isn’t perfect, it has withstood the test of time and has been proven to work.
By introducing AI, you are essentially replacing a known good process with a process that has not yet been proven to work. You will, therefore, need to convince the higher-ups in your company that your proposed AI project will indeed work, and that you have a contingency plan in place for keeping the organization running in the event that your project does not work as planned.
On a side note, the way in which you present this contingency plan is critical to your success. If presented in the wrong way, your contingency plan can be perceived as a lack of confidence rather than attention to detail.
Road to AI project success
Ultimately, there is no secret formula for getting your AI project approved. Every organization is different, as is every manager. The best advice that I can give you is to put yourself in your manager’s shoes and think of all of the objections that they might have to your project. Once you compile a list of those objections, you can begin to come up with a way of presenting your idea in a way that directly addresses those concerns.
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