As artificial intelligence (AI) technology advances, machine learning is being used by more and more companies of all sizes, including startups. Forty-nine percent of companies in 25 industries around the world are now using AI machine learning to improve their business processes, an Oxford Economics survey of 500 chief information officers found. CIOs say their companies are using machine learning to automate repeated tasks, make complicated data-driven decisions, identify data patterns and recognize links between events.
Machine learning is becoming such an important part of business that in the near future it will separate companies that use it from their competitors. Here’s a look at three practical ways your startup can use machine learning to get ahead of the competition and fuel your company’s success.
Automating Operations
One of the most practical ways machine learning can help startups is by automating repetitive tasks. For instance, in accounting, there are numerous monotonous tasks that machine learning can simplify. Machine learning can speed up the auditing process by learning a company’s auditing rules, automatically scanning expense claims for compliance and only forwarding claims that raise red flags for human attention. Other accounting operations that machine learning can automate include invoice categorization, bank reconciliation and generating reports for clients.
AI automation like this isn’t limited to accounting applications. Any repetitive task potentially lends itself to machine learning. Other candidates for automated repetitive tasks include marketing and sales tasks such as sorting through leads, payroll tasks such as updating schedules and time tracking, and customer service tasks such as handling frequently-asked questions.
Analyzing Data
Another useful AI application for startups is handling big data analysis. Machine learning applications can analyze large sets of data, identify patterns and predict future trends. This provides companies with not only insights into historical trends, but business intelligence that can be used to take practical advantage of past trends.
For example, Salesforce has recently introduced its AI-powered Einstein sales tool. Einstein can mine customer account information to identify purchasing patterns and predict future behavior. This allows company to identify hot prospects and personalize their sales presentations in order to appeal to individual profiles. AI can similarly be used to analyze marketing data, operational data, IT security data and other types of data to gain insights, make predictions and adjust operational procedures accordingly.
Recognizing Patterns
AI’s ability to analyze data also makes it useful for recognizing patterns. This enables machine learning to power applications that can recognize images and sounds, which can be put to many useful purposes, including computer vision, extracting objects from videos and natural speech recognition.
One practical example of how AI image recognition can be used by startups is recognizing biometric features such as facial features and voice patterns for mobile device security authentication. Using AI for image and sound recognition in this way requires a significant amount of computing power, exceeding the limits of most legacy mobile devices and requiring additional processing support from PCs or remote cloud resources. However, more recent devices powered by innovations such as the Qualcomm Artificial Intelligence platform are fast enough to handle on-device AI applications. This enables companies to use biometric authentication for employee devices, adding a stronger layer of security to traditional passwords.
Automating operations, analyzing data and recognizing patterns are three practical machine learning applications that can benefit your startup. Deploying these types of AI applications can help make your startup more efficient, more competitive and more successful.
