Artificial intelligence (AI) in business, especially in the form of machine learning (ML), is one of today’s
most disruptive technologies. Businesses across virtually every sector have at least begun to embrace it, but
some decision-makers remain hesitant. How will artificial intelligence change the future of the business?
What will it entail? Will it pay off? It’s true that jumping into AI is, in essence, jumping into the unknown.
As a result, a lot of business leaders hesitate.
A 2020 study found that the leading barriers to AI adoption include data issues, limited AI skills, an incomplete understanding of AI, ethical concerns and insufficient ROI. These issues are all worth addressing, but with the right approach - and a clear understanding of what adopting AI entails - these challenges are manageable. In fact, most of them are just part of the process.
Here’s a closer look at why these concerns shouldn’t stop you from investing in AI now.
An insufficient quantity or quality of data is one of the most common barriers to AI adoption. After all,
a data analytics algorithm is only as effective as the data you feed it.
At the same time, you may not need as much data as you think, and high-quality data isn’t always difficult to obtain.
Research indicates that in some situations, smaller datasets may be more beneficial for ML algorithms. Plus, it isn’t so much the sheer quantity of data that matters; it’s the quality of that data and how it relates to the business problems you are looking to solve. It’s also more important to have accurate, relevant data than it is to have reams of data that’s unrelated to the key issues you want to address using AI.
At AltaML, we usually recommend that people bring their data to a qualified data science team for analysis. They can help you determine whether the data you have is aligned with the problems you want to solve. If you do need to gather more data, an expert team can also help advise you about what data will be most valuable for you. Not only will this focused approach help you work with less data, but it may also produce more relevant insights.
Many business leaders are tentative about AI because they don’t have any employees experienced or skilled
in the area.
But in today’s market, you don’t have to be an AI expert to experience its benefits. In fact, if you’re just getting started, it may be better to look outward for AI-powered solutions.
As demand grows, experts expect AI to contribute $15.7 trillion to the global economy over the next decade. This is partly because building a skilled data science team is a high hurdle - one that’s likely much higher than most people realize. The depth of experience, support and multitude of skill sets in these organizations goes far beyond just hiring a data scientist or two. Building a working machine learning model isn’t just a pursuit into data science. It involves many levels of technical and business acumen as well. In other words, it takes a village!
Starting small with an AI vendor can help you cultivate AI expertise and build momentum and support around what AI can accomplish. This can be as simple as choosing a business problem and building an AI-powered solution for it.
Despite its impressive adoption rate, AI is still relatively new. As such, you may be unfamiliar with it,
making you hesitant to embrace it until later. Luckily, you don’t need a deep technical understanding of AI
to implement it. Leave the data science to the data scientists; you just need to know
how it can impact your business.
AI is a vast category, and it often brings to mind robots and self-driving cars. In many cases, however, applications in business are much simpler. Think of the problems you want to solve, then look into how AI can solve them. If you don’t know where to start here, look at how other companies in your industry have used AI.
As you start to look into the applications of AI in your specific field, you’ll begin to gain the understanding you need. You may not understand how algorithms are created or how they work but, as a business leader, what you bring to the table is unique knowledge of the industry you’re in and the needs of your organization. A qualified data science team can use this information to start coming up with ideas for workable machine learning models. Starting small and scaling your ML solutions slowly will also allow you to start to understand what applied AI has to offer, and how to leverage your own skills and expertise in the process of building a model.
You’ve likely heard some alarming stories about ethical breaches or concerns with AI. These are real issues
in the world of AI, but this is because bias and ethical concerns are real issues in the world at large.
Like other technologies, AI is just a tool, which means it is up to the humans who build and use it to
ensure it’s created and used for good.
At AltaML, being a leader in ethical AI is important to us. That’s why we’re part of the Responsible Artificial Intelligence Institute (RAI), an organization that’s committed to transforming the way disruptive technologies are built, designed and regulated. By working with RAI, we’re helping to develop resources to help address some of the ethical challenges AI presents. We are also continuously working to center our own approach to building machine learning models around ethics, and helping other organizations do the same.
Many new technologies bring forth ethical concerns. Responsible AI solutions providers engage with and help develop best practices to help address these concerns at every step of the process.
ROI is a concern for any business initiative. The technical nature of building AI solutions also means
the process can be expensive. At the same time, evidence around the competitive advantage AI can offer is
mounting. For example, AI companies regularly show higher annualized and excess returns than general tech
Plus, the likelihood of realizing a positive ROI often comes down to approach. Many companies make the mistake of making their first AI investments moonshots. They think they need to overhaul whole systems to make a real impact. In many cases, we find that solving relatively small and simple business problems can provide substantial results - and represent significantly less financial risk. By taking a smaller, more focused approach to ML deployments, organizations can begin to build significant improvements in efficiency, productivity and other measures over time. Focus and strategy are key, as 77% of AI ROI overperformers define business cases, models and plans before starting.
Analyze the specific issue you’re looking to solve and consider AI’s potential in that area. Remember that ROI doesn’t have to be a financial figure, either, but can address other objectives. AI may help you achieve diversity, environmental or other key business objectives as well.
If you start investing in AI now, you’ll have more time to gain firsthand experience as the technology advances. But starting doesn’t have to mean a huge project. In fact, we’ve found that many organizations achieve significant results by starting with a more focused approach. Applied artificial intelligence will shape the future, so if you can learn how to capitalize on it early, you could see a substantial long-term gain. Invest now and succeed sooner.
AltaML started by building and training an ML model using historical trip data found from the truck’s onboard computers. The first task was to work with the logistics managers to identify where data may be present to tell us more about good and bad driver trip behaviour.