Created by two researchers in the US, the fastest robot can solve a Rubik’s cube in 0.38 seconds. Using similar technology, artificial intelligence, or AI, can work wonders for your organization by saving you money and time by automating tasks ranging from simple to complex and tedious.
However, many business leaders hit significant obstacles when implementing artificial intelligence. Often, they find they don't know how to start using AI, which tasks to use AI for, or that the advantages of AI are not well understood within their organizations. This article is an initial guide on how to start using artificial intelligence in your organization. Included are tips for you as a leader on breaking down, analyzing, and solving a problem using artificial intelligence and/or machine learning.
Adopting Machine Learning
If you want to adopt machine learning, use the following list of five important considerations to evaluate the preparedness of your company for an AI-based solution:
1. The Problem
The problem that you want to solve with machine learning needs to be precise, explicit, and easily measurable. It should also be able to be represented as a question with a “yes/no” or numeric value as its answer. Before beginning your implementation project, task yourself with clearly defining the uncertain event, value, or metric you wish to predict.
2. The Culture
Think about your business and your employee and customer culture. Make sure before you start that your coworkers and/or customers will accept automated decision-making in the areas you wish to apply AI. Securing their trust up front is important because they won’t be involved in the machine learning process beyond the model testing stage.
Make sure you also have enough people and money available to enable predictive models within your existing business processes. Successfully implementing artificial intelligence into a business requires time, patience, technical expertise, and constant communication. You shouldn’t start this process without ensuring the appropriate staff and funding are readily accessible to create a data-driven culture. To learn more about creating a data-driven organization, check out my article here.
3. The Implementation
From the beginning of your project, planning out a detailed strategy is essential. First, decide on the system or process you’ll use to put your new model into practice. Then, consider the performance metrics that will evaluate the success of your model. And most importantly, think about how decisions made based on machine learning algorithms might be implemented by the relevant business functions within your organization.
4. The Data
Ensure that your organization has sufficient data to build and maintain a machine learning solution. Consider the quality of your data. Does it come from reliable sources? Is it well-organized and consistent? Do you expect your data-gathering format to change in the near future, or will it remain stable long term? An ideal dataset for machine learning is high-quality, organized, and stable for the foreseeable future.
5. The Analytical Capability
Thoroughly evaluate yourself and your company and be realistic about the necessary software tools and technical expertise required to analyze data, apply machine learning, and build a successful predictive model. If you believe you have the capabilities within your staff, you’re in a good starting place. If you could use some technical assistance, consider hiring outside help, such as a data science consultant.
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How to Plan Your Implementation
If you’ve reviewed the five important considerations for adopting machine learning and you’re ready to move forward, the following list is an AI strategy framework to help you decide which problems to tackle first with artificial intelligence:
1. Understand Your Project’s Strategic Rationale
You need to understand how your artificial intelligence opportunity fits into your company’s overall goals and strategic plan. Do you view your AI project as a measure to increase revenue or a measure to cut costs? Be prepared for your products and services to possibly change in response to insights brought about by AI implementation. Examine possible new business opportunities for the near and long term that may appear due to your artificial intelligence project.
2. Consider the Opportunity
The problem you’re considering needs to warrant an advanced artificial intelligence solution. Before embarking on a complex machine learning project, ask your employees if they think they can solve the same problem using older technology, and if so, how much investment that would require. If you do find that your employees or existing technology can offer a more viable method for solving your problem, take the opportunity to consider if you could leverage artificial intelligence in the future by planning ahead for similar tasks.
3. Consider the Required Level of Investment
Estimate the time and money that need to be allocated toward your problem, including internal costs. Even if you opt for an external vendor, consider that you’ll still have to sustain internal management costs.
4. Estimate a “Break-Even” Point and the Likelihood of Success
Calculate your break-even threshold for your artificial intelligence project, or the point at which the value gained by your project exceeds the capital spent in implementing it. Include any internal costs for project management. Evaluate the likelihood of your project reaching your pre-defined level of “success” to reach your break-even threshold.
5. Consider the Risk
What is the probability that your artificial intelligence project will be successful and deliver the expected return on investment? Be realistic about whether your project is a sure win or a long shot. When considering implementing AI, also consider the risks associated with opportunity costs. If your organization doesn’t take this step, your competitors may adopt AI before you do. Consider how that might affect your competitive advantage.
6. Consider the Timeline
Don’t expect to see immediate results from implementing machine learning. Most artificial intelligence projects require at least a few months of investment before you can see and measure results. Have patience and work to continually optimize and test your AI solution. If your project takes longer than expected to implement, set interim milestones to measure your progress.
7. Consider the Stakeholders
Have your business’s stakeholders bought into an artificial intelligence solution? Ensure that they do so before starting your project, as you’ll likely need their help for data collection, approvals, and the adoption of your solution.
The Prerequisites of Machine Learning
Developers are constantly creating new artificial intelligence-based tools to help businesses incorporate the technology into their existing processes. You can find more information in detail about some of the newest and most popular AI and machine learning tools here. But before purchasing a tool, consider the following list of questions to ensure you have the prerequisites in place for successful machine learning implementation:
1. Can Machine Learning Solve Your Problem?
Consider the optimal process to solve your problem. How is it currently solved? Break down your current solution and define its inputs, outputs, and contingencies. Check how long the solution takes to perform, how often each step is taken, and how many people do the same task. Performing this analysis can help you realize whether or not you can solve your problem with machine learning.
2. Is Machine Learning a Suitable Solution For Your Problem?
As long as a task has clear and measurable answers, machine learning can—in most cases—make decisions with the same accuracy as humans, or better. When considering your problem, can you picture a clear answer immediately or does it require prolonged deliberation? When several people try to answer the same question, do they all come up with the same answer? If you and others can picture a clear answer to your problem, it is likely an appropriate application for machine learning.
3. Do You Have Available Data?
You must have enough relevant and easily accessible data available from which an artificial intelligence algorithm can learn. If you don’t, think about how long it would take to convert the data you do have into a usable format. Moreover, you’ll need an excellent technical team to manage and analyze this data. Learn more about what it takes to build a successful AI team here.
You should now have a clearer idea of what to consider before leveraging artificial intelligence, as well as how to plan and organize your project so that you’re ready from a technical point of view. Ready to get started? Before you set out to build your artificial intelligence or machine learning team, visit my guide to understanding the different roles in a data science team.