If you haven’t yet implemented an Artificial Intelligence (AI) solution in your business, you may feel like you’re missing the boat. And in many ways, I agree with you. But is your business ready for artificial intelligence?
Some of the studies show that almost 99% of companies invest in AI in one way, one form or another. AI is not a “shall we, shall we not” type of technology. AI will be the de facto standard, just like an operating system or software, it will be integrated into all business technologies in the not-so-distant future.
But that doesn’t mean you should just jump on the bandwagon for fear of falling behind. There are a lot of considerations to take into account before you even dip your toes in the AI water – or to continue my first analogy, to make sure you don’t put the cart (or cart) before the horse.
Appropriate planning for the implementation of AI.
AI projects fail due to backlash due to a lack of proper planning and framing. To ensure the success of an artificial intelligence initiative, companies need thoughtful preparation.
Taking into consideration things like ensuring that AI does not exist in isolation but is integrated into larger business processes is key to success.
What questions should you ask yourself?
In addition, before you deploy an AI initiative, there are a number of important questions you should ask yourself.
- what is the business opportunity?
- Do you have the resources you need to implement the process transformation?
- Are there any security implications?
- What data do you need to solve the problem and what will you need to acquire it?
And perhaps most importantly,
- Are there any ethical implications for implementing an AI solution?
To help you clarify these questions and more, here are a few things to consider before looking for an AI solution or hiring a team of machine learning engineers to build something in-house.
Understand what artificial intelligence is good at and what it’s not.
The question may seem trivial, but many organizations we talk to don’t understand which issues are good and not good machine learning issues. Artificial intelligence isn’t the solution to everything, so make sure the problem you’re trying to solve is appropriate.
Some common tasks for which AI is ideal include forecasting, anomaly detection, object detection, pattern detection, automatic generation, enhancement, and reconstruction.
Have a well-defined problem
You must be wondering what the problem is and why you are trying to solve it. If the range is too wide, your initiative will quickly fail. For example, the pathology of a whole body offers too many variables but the concentration on one part of the body is much better and will guarantee better results.
Keep your reach close and build from there.
Identify AI performance criteria
Like any well-defined business initiative, before you start you need to identify what success looks like. Are you hoping to achieve greater accuracy than a human could achieve? Do you just want to automate a task to save time?
Good performance criteria for an AI initiative will define performance on a narrow benchmark with a given rate of accuracy.
Determine the team and technological capacity
Does your organization have the technical capacity to work with AI? Currently, there are 300,000 machine learning engineers available and several million vacant positions.
Machine learning experts can earn as much as football players. Working with AI often requires understanding obscure math and computer concepts that most software engineers simply don’t have.
Finally, do you have the right tools to build and support artificial intelligence and machine learning processes?
Understand the long-term impacts
As I mentioned, the challenge with bottom-up projects is that they often fail due to a lack of political will in organizations.
AI is simply not understood by most in the organization and even framing a business case for deploying AI is not always clear.
Obviously, a clear understanding of ROI will help, but even that is not enough because ultimately, like any other technology deployment, ROI must be compared to other non-AI alternatives.
Finally, it is likely that AI will displace individuals. At one of the companies I worked for, we developed an AI solution that reduced engineering problems by 60% for a very expensive manufacturing process.
Obviously, this would have had a significant impact on the business but in the end, after two years the solution still hasn’t gained as much strength as we would have liked as it would have resulted in the elimination of an entire team.
Training data for machine learning
Do you have the data you need to effectively train a model? In addition, are these data accessible?
Governance of artificial intelligence
Developing AI is only part of the process. Can you deploy and support AI in production, deprecate it, or determine if AI is performing to specification? Do you have a mechanism to enable large-scale deployment and management or the people to do the work required?
Few organizations have a comprehensive strategy for how AI should be used or managed by their business. For example, a simple question of whether to deploy AI in the cloud, on-premises, or deploy at the edge isn’t always clear.
Finally, is your AI solution “sustainable”? When there is a change in technology or capacity, how easily can the organization adapt?
Once you’ve gone through these questions and considerations, you’ll be ready to adopt an AI solution (AI Dynamics, Inc, Bellevue, WA) or launch an AI initiative within your organization. And that’s where the fun really begins.