AI and machine learning are experiencing a watershed moment. Recent technological breakthroughs have made these tools more practical than ever — they can point out operational inefficiencies, discover untapped markets, set effective price points, and much more. Yet for all these benefits, machine learning has its limits. To act on its insights, businesses require a robust supporting infrastructure and comprehensive data collection tools. Without them, leaders shouldn’t expect to see much of an ROI.
So how can decision makers tell when it’s the right time to adopt a machine learning platform? Here are a few key considerations that will maximize ML’s impact on your business:
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Would implementation meet a specific need?
Before you start shopping for a machine learning system, you need to establish clear goals for what it will accomplish. This is a crucial step that influences how to deploy the system and provides a benchmark for measuring its success. A lack of clear business goals for machine learning means it will have little use for your organization.
On the whole, machine learning technologies resolve specific and repetitive data-oriented tasks. Activities that are more variable in nature will likely require human decision making. One common business goal is to leverage machine learning’s advanced decision-making capabilities. This requires a company to identify the departments and teams that would benefit most from the new system. For example, a sales team could improve performance by determining the most effective prices for annual promotions — a data-centered task where machine learning excels.
Alternatively, businesses might turn to machine learning to locate and optimize any operational inefficiencies. Teams and departments that rely on repetitive tasks can often save time with machine learning by clearing up bottlenecks from work processes. Manual data entry is a perfect example of an inefficient, error-prone task that can be improved by supplementing existing systems with machine learning tools.
Machine learning has many practical uses for today’s businesses. It’s proven remarkably effective at detecting fraud and reducing supply chain waste. But to maximize its effectiveness, your business should have clear goals from the outset.
Do you have enough data for actionable insights?
Perhaps the biggest misconception about machine learning is that it intelligently serves up business decisions the moment it’s switched on. In reality, machine learning is not the Oracle of Delphi. Humans must input generous volumes of relevant data — often with thousands of instances — before it can generate actionable insights. If you don’t have any data to work with, machine learning won’t help your organization at all.
Hype notwithstanding, machine learning systems are simply advanced pattern recognition platforms. Their function is to detect correlations and trends within large datasets, separating the signal from the noise at a scale humans often can’t observe directly. When machine learning generates false predictions or errors, it’s not usually due to inaccuracy. Rather, it’s because there wasn’t enough reliable data to generate useful insights in the first place.
For businesses, the practical question is whether you have the data to make investing in machine learning worthwhile. To obtain the most precise insights, data sources must be relevant, comprehensive, and reliable. Each data element should also be properly labeled and categorized — information scraped from random internet sources typically has limited use.
Most importantly, your data collection efforts must be ongoing. Machine learning is not a “one and done” solution — its recommendations are as subject to change as any human insight. You’ll need to constantly update your data so machine learning systems can detect new trends and revise their insights accordingly.
Is your organization agile enough to respond to machine learning insights?
Too often, businesses get caught up in hot industry trends without considering whether they’re suitable for the organization. Machine learning is no exception. For all its strengths, machine learning isn’t capable of running a company and cannot make its supporting infrastructure more effective on its own.
Machine learning capabilities are like the adage of leading a horse to water — it can generate actionable insights about your business objectives, but it can’t make you take advantage of them. Any pre-existing structural inefficiencies or organizational roadblocks won’t disappear after deploying a machine learning system. If your business isn’t agile enough to act on these insights, you’re not going to see a high ROI from machine learning systems.
ML is an immensely powerful tool for today’s businesses, but it is still just a tool. Its systems require clearly defined objectives, high volumes of data, and agile supporting infrastructures to be truly valuable for today’s businesses. Any organization that doesn’t have these elements would be better served investing in them over machine learning. But if your company has each of these traits, it might be time to consider exploring the benefits of a machine learning platform.