As far back as 2017, nearly three-quarters of executives believed that business advantage in the future would be driven by AI. There are companies and people who are using AI as a way to hype their businesses, as an important part of what they want to tell investors and so on. As business leaders observe this environment, there’s the nagging internal question:
Am I too late? Is it too late to even get started?
Now of course, in full disclosure, we’d have to note that as a company that sells a unique AI solution we think that right now is always a good time to get started. That said, there are some things to consider.
To start off, let’s look at this table of factors to consider as a simple TLDR.
|AI in my business, too late?|
|Reason for AI||Too late?||Get Started?|
|PR bump||Yeah||Don’t bother|
|Improve a specific thing||Nope||Yes!|
|Disrupt entire industry||Not exactly…||Start small|
|Make use of all our customer data||Nope||Yep|
|…except we don’t have any data||Probably||Better late than never|
AI in business: Why do you want it?
If you’re trying to keep up with competitors who are making AI announcements in the press or making splashy technical hires, you might be too late. There’s an awful lot of big project promises that aren’t coming to fruition because the companies doing the hires and initiating those “disruptive” projects don’t know what they’re doing. The big data scientist hire isn’t always so great. The company doing the hire often doesn’t know how to evaluate the job. Even more troubling, the company might not have the data or the culture to initiate effective AI projects.
If you’re doing AI just to get a hype bump then it’s probably too late. There isn’t much value in having an AI announcement in a crowded sea of AI announcements. You’re going to have to earn it the hard way or the smart way.
If, on the other hand, you want to make an improvement to your business it’s a great time to start integrating AI. Making improvements to business isn’t a fashion-statement or hype cycle. It’s something business leaders do.
AI, when developed and deployed right, improves business outcomes.
What kind of business outcomes do you want for AI?
The big problem with companies pursuing AI for the hype is that they so rarely consider the business outcome (or maybe the business outcome is simply the short-term PR boost, who knows?). If you don’t have a business outcome then deploying AI will only chew up your resources.
If you do have some business outcomes to improve, the next challenge is figuring out if they’re the right fit for AI as it exists today, within your company’s resources. This is important because, as we all have seen, you can spend more time and money on AI than the outcome will be worth. The faulty logic that grows from this is that we need to pursue bigger, more “disruptive” projects to justify the ROI on our AI spend.
However, the inverse is true especially for those who are getting started with AI. The AI of today that is within reach of those outside large universities or government institutions, is fantastic at incremental improvements that add up to a big win. In the same way that Olympic athletes will train and tune their systems to make gains of less than a second, a powerful contemporary AI system will help you optimize your business decisions for continuous improvement.
Instead of throwing a ton of money into a giant, multiyear project, the smart companies are delivering many smaller projects and the teams are themselves increasing their own abilities to develop and deploy AI solutions. This is the way things will go increasingly and why we’ve built our company around the idea of freeing people from the more technical tasks of AI so they can pursue the business solutions made possible by AI.
Is your company culture ready for AI?
Do you have people that can identify areas that need improvement? I don’t mean just complaining all the time, I mean do they have the resources and insights to look at the systems involved in your business and see where they might be improved? To get AI in the business right, you will need to have a company culture that is self-reflective.
If your company lacks people who can examine systems thoroughly and accurately, then it’s probably too late for AI. It’s probably too late for a lot of things, but definitely too late for AI.
However, if you do have people with these business insights and if those people can be given the time to focus on AI alongside whoever you hire, then you’re not too late. In fact, you’re right about where everyone is today.
Another thing your company culture will need is perseverance. Going after multiple “small” wins is not as flashy as pursuing so-called moonshot projects. You’ll need the perseverance to stay the course and to learn as you go. It’s an endurance event, not a sprint.
Is your data ready for AI?
Assuming your company culture is inclusive of people know how data can be useful, the next thing you’ll need is the data. Good AI in business runs on good data.
To train the models, you’ll need a pile of data and the right data that relates to your business. If your company hasn’t been gathering data or has no access to it, then you’re not ready for AI. It doesn’t mean you shouldn’t start, but you’re at the base of a very tall mountain. Take a deep breath and start in on it.
If you do have data, then the question is if you truly have access to the data. Getting it in the right formats and shape to tackle your business objectives is something you’ll need to do. And to have genuine access to the data in a usable form. You may currently have a dashboard or some other thing that makes you feel as if you have data but in reality, the data itself is so cumbersome that you effectively don’t have access. Getting that plumbing sorted out is a critical piece of getting started. Your business AI initiatives are going to need access to that data.
The good news is that sorting out the data itself is an important and useful thing for a company even if you don’t do any AI initiatives. Understanding what kind of data you have, how it’s organized, whether you have actual access to the data or not, and so on, is useful due diligence. Going through the effort of evaluating your data may identify useful areas where business AI can be deployed as well. So don’t think of this step as a lesser “cleaning” effort. The people who clean the building also know the building best.
So really, too late or not?
If the following sounds like your company, then it is in fact too late for you to begin using AI in business:
- Primary objective to generate some PR buzz
- Secondary objective to brag at the conference happy hour about a data scientist hire
- No one in the company thinks about systemic improvements
- No one in the company has time to think about systemic improvements
- Company leadership avoids thinking about systemic improvements
- Leadership and staff lack the perseverance to engage in many, small, rapid improvement initiatives
- The company does not have adequate access to the data on which it runs
If the above sounds like your company then, I’m sorry, but it’s too late for AI.
If that isn’t the company you’re leading, then now is the time. Here’s a short checklist of steps to help you get started with AI in your business:
- Identify who, outside of yourself, in your organization (anywhere in your organization) thinks clearly and comprehensively about business systems.
- Identify who, outside of yourself, in your organization clearly articulates business problems.
- Establish a small team including these individuals with the goal of articulating business problems that have clear 1-to-1 maps of data correspondence to outcomes, quick feedback loops to know whether the system works or not, and the ability to improve outcomes enough to make a difference. It’s best if these people come from diverse experiences within various parts of your company.
- Now you can start the data scientist hire and ask candidates questions to help you tackle the initiatives developed in step 3.
- We’d be remiss if we didn’t encourage you to take a Demo of our AI software which will help you and whomever you hire stay focused on business objectives and spend time on the higher order business challenges.