In this article we analyze how small and medium businesses can use AI to automate core processes such as Marketing, HR, Finance, Operations and Product Development.

As seen in a previous article, one of the main applications of AI in an organizational environment is to enable software-driven machines to automate many cognitive tasks performed by humans. These machines can “learn without being explicitly programmed”, a field called machine learning.

These “machines” learn through a validation mechanism that tell them what is right and wrong. These mechanisms can be disguised by tasks that humans engage in without knowing they’re training the algorithm.What this means in practical terms is that a machine can keep improving itself without humans having to explain exactly how to accomplish all its tasks. These machines identify patterns in data and make decisions with minimal human intervention, making processes frictionless and invisible and ultimately liberating your employees from repetitive low-value tasks.

Although Machine Learning can be applied to any business process, if you’re a small business owner, you’re most likely focused on five main areas: Marketing, Human Resources, Finance & Accounting, Operations and Product Development. Let’s understand how small and medium businesses can use AI and machine learning to improve these core internal processes.

AI applied to Marketing

In marketing, machine learning is mostly used to automate marketing workflows, deliver highly targeted content and gain a deeper understanding about your customers. As a marketer, you can use Machine Learning to:

  • Marketing automation: Create marketing automations that go beyond rule-based workflows (abandoned cart emails, anyone?) and evolve over time with users’ interactions.
  • Recommendation systems: Create increasingly accurate product and content recommendations based on users’ interactions with your offer and content.
  • Customer understanding: Develop a deep understanding about your customers’ behavior with advanced analytics, dynamic segmentation and tailored profiles of each prospect or customer.

AI applied to HR

If you’re a HR manager, know that Machine Learning can make your life a lot easier by reducing the burdens of information collection by HR teams and data access by employees. The use of artificial intelligence can reduce the bureaucracy involved in HR processes, from hiring to retaining talent.

  • Recruitment: Use Natural Language Processing (or NLP) or algorithms to filter and identify potential hires over thousands of candidate resumes.
  • HR service: You can also use NLP together with chatbots (automated conversational agents) to reduce the need for human intervention when answering recurring employee questions.
  • Retention: Use internal data such as salary, performance, career evolution and personal situation to predict which factors predispose an employee’s departure.

AI applied to Finance & Accounting

In Finance, Machine Learning can be used to identify opportunities, mitigate risks and automate routine procedures and low-value compliance activities. Here are a few examples of how artificial intelligence can help you run your Finance Department efficiently:

  • Financial Planning: Implement advanced analytics to process historical financial information in order to uncover opportunities for structural and financial optimization.
  • Automation of Routine Procedures: Keep your controllers, accountants or financial planners attentive and engaged by automating low-value data extraction and preparation activities.
  • Risk Analysis: Use algorithms that iterate over your data, analyze patterns and deviations to detect fraud or potential risks.

AI applied to Operations

In Operations, Machine Learning is usually used in operational planning, predictive maintenance and quality control. Here are a few examples of how AI can help you streamline your operations.

  • Operational Planning: Use internal and external data to determine expected demand for specific products and services and ensure that your inventory reflects demand spikes and drops, reducing the time to market of your supply chain.
  • Predictive Maintenance: Use algorithms that predict failure of machines and systems and recommend proactive maintenance actions.
  • Quality Control: Detect defects and quality issues during production using various data sources and data types.

AI applied to Product Development

Artificial Intelligence isn’t only applied to processes, it can also help you create better products and provide your users and customers with unparalleled, custom experiences.

  • Improving Customer Journeys: By placing data capture at the heart of your products, you are able to track and capture data on every step of your customer journeys.
  • Business Intelligence and Experimentation: With enough data flowing, you’ll be able to set up dashboards to visualize your metrics and KPIs through charts, graphs, funnels and cohorts. You will also be able to experiment and A/B test new product features and make precise product optimization decisions.
  • Automated Functionalities: Based on the data captured and your product objectives, you can **build automations **based into the core functionalities of your product that will drive user behaviour.

Now that you’ve seen what artificial intelligence can do for your company, you’re probably overwhelmed by the thoughts of an automated organization and asking “how do I get started? In the next article you learn the practical steps to implement AI in your company.